research objectives of air pollution

REVIEW article

Environmental and health impacts of air pollution: a review.

• 1 Delphis S.A., Kifisia, Greece
• 2 Laboratory of Hygiene and Environmental Protection, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
• 3 Centre Hospitalier Universitaire Vaudois (CHUV), Service de Médicine Interne, Lausanne, Switzerland
• 4 School of Social and Political Sciences, University of Glasgow, Glasgow, United Kingdom

One of our era's greatest scourges is air pollution, on account not only of its impact on climate change but also its impact on public and individual health due to increasing morbidity and mortality. There are many pollutants that are major factors in disease in humans. Among them, Particulate Matter (PM), particles of variable but very small diameter, penetrate the respiratory system via inhalation, causing respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer. Despite the fact that ozone in the stratosphere plays a protective role against ultraviolet irradiation, it is harmful when in high concentration at ground level, also affecting the respiratory and cardiovascular system. Furthermore, nitrogen oxide, sulfur dioxide, Volatile Organic Compounds (VOCs), dioxins, and polycyclic aromatic hydrocarbons (PAHs) are all considered air pollutants that are harmful to humans. Carbon monoxide can even provoke direct poisoning when breathed in at high levels. Heavy metals such as lead, when absorbed into the human body, can lead to direct poisoning or chronic intoxication, depending on exposure. Diseases occurring from the aforementioned substances include principally respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD), asthma, bronchiolitis, and also lung cancer, cardiovascular events, central nervous system dysfunctions, and cutaneous diseases. Last but not least, climate change resulting from environmental pollution affects the geographical distribution of many infectious diseases, as do natural disasters. The only way to tackle this problem is through public awareness coupled with a multidisciplinary approach by scientific experts; national and international organizations must address the emergence of this threat and propose sustainable solutions.

Approach to the Problem

The interactions between humans and their physical surroundings have been extensively studied, as multiple human activities influence the environment. The environment is a coupling of the biotic (living organisms and microorganisms) and the abiotic (hydrosphere, lithosphere, and atmosphere).

Pollution is defined as the introduction into the environment of substances harmful to humans and other living organisms. Pollutants are harmful solids, liquids, or gases produced in higher than usual concentrations that reduce the quality of our environment.

Human activities have an adverse effect on the environment by polluting the water we drink, the air we breathe, and the soil in which plants grow. Although the industrial revolution was a great success in terms of technology, society, and the provision of multiple services, it also introduced the production of huge quantities of pollutants emitted into the air that are harmful to human health. Without any doubt, the global environmental pollution is considered an international public health issue with multiple facets. Social, economic, and legislative concerns and lifestyle habits are related to this major problem. Clearly, urbanization and industrialization are reaching unprecedented and upsetting proportions worldwide in our era. Anthropogenic air pollution is one of the biggest public health hazards worldwide, given that it accounts for about 9 million deaths per year ( 1 ).

Without a doubt, all of the aforementioned are closely associated with climate change, and in the event of danger, the consequences can be severe for mankind ( 2 ). Climate changes and the effects of global planetary warming seriously affect multiple ecosystems, causing problems such as food safety issues, ice and iceberg melting, animal extinction, and damage to plants ( 3 , 4 ).

Air pollution has various health effects. The health of susceptible and sensitive individuals can be impacted even on low air pollution days. Short-term exposure to air pollutants is closely related to COPD (Chronic Obstructive Pulmonary Disease), cough, shortness of breath, wheezing, asthma, respiratory disease, and high rates of hospitalization (a measurement of morbidity).

The long-term effects associated with air pollution are chronic asthma, pulmonary insufficiency, cardiovascular diseases, and cardiovascular mortality. According to a Swedish cohort study, diabetes seems to be induced after long-term air pollution exposure ( 5 ). Moreover, air pollution seems to have various malign health effects in early human life, such as respiratory, cardiovascular, mental, and perinatal disorders ( 3 ), leading to infant mortality or chronic disease in adult age ( 6 ).

National reports have mentioned the increased risk of morbidity and mortality ( 1 ). These studies were conducted in many places around the world and show a correlation between daily ranges of particulate matter (PM) concentration and daily mortality. Climate shifts and global planetary warming ( 3 ) could aggravate the situation. Besides, increased hospitalization (an index of morbidity) has been registered among the elderly and susceptible individuals for specific reasons. Fine and ultrafine particulate matter seems to be associated with more serious illnesses ( 6 ), as it can invade the deepest parts of the airways and more easily reach the bloodstream.

Air pollution mainly affects those living in large urban areas, where road emissions contribute the most to the degradation of air quality. There is also a danger of industrial accidents, where the spread of a toxic fog can be fatal to the populations of the surrounding areas. The dispersion of pollutants is determined by many parameters, most notably atmospheric stability and wind ( 6 ).

In developing countries ( 7 ), the problem is more serious due to overpopulation and uncontrolled urbanization along with the development of industrialization. This leads to poor air quality, especially in countries with social disparities and a lack of information on sustainable management of the environment. The use of fuels such as wood fuel or solid fuel for domestic needs due to low incomes exposes people to bad-quality, polluted air at home. It is of note that three billion people around the world are using the above sources of energy for their daily heating and cooking needs ( 8 ). In developing countries, the women of the household seem to carry the highest risk for disease development due to their longer duration exposure to the indoor air pollution ( 8 , 9 ). Due to its fast industrial development and overpopulation, China is one of the Asian countries confronting serious air pollution problems ( 10 , 11 ). The lung cancer mortality observed in China is associated with fine particles ( 12 ). As stated already, long-term exposure is associated with deleterious effects on the cardiovascular system ( 3 , 5 ). However, it is interesting to note that cardiovascular diseases have mostly been observed in developed and high-income countries rather than in the developing low-income countries exposed highly to air pollution ( 13 ). Extreme air pollution is recorded in India, where the air quality reaches hazardous levels. New Delhi is one of the more polluted cities in India. Flights in and out of New Delhi International Airport are often canceled due to the reduced visibility associated with air pollution. Pollution is occurring both in urban and rural areas in India due to the fast industrialization, urbanization, and rise in use of motorcycle transportation. Nevertheless, biomass combustion associated with heating and cooking needs and practices is a major source of household air pollution in India and in Nepal ( 14 , 15 ). There is spatial heterogeneity in India, as areas with diverse climatological conditions and population and education levels generate different indoor air qualities, with higher PM 2.5 observed in North Indian states (557–601 μg/m 3 ) compared to the Southern States (183–214 μg/m 3 ) ( 16 , 17 ). The cold climate of the North Indian areas may be the main reason for this, as longer periods at home and more heating are necessary compared to in the tropical climate of Southern India. Household air pollution in India is associated with major health effects, especially in women and young children, who stay indoors for longer periods. Chronic obstructive respiratory disease (CORD) and lung cancer are mostly observed in women, while acute lower respiratory disease is seen in young children under 5 years of age ( 18 ).

Accumulation of air pollution, especially sulfur dioxide and smoke, reaching 1,500 mg/m3, resulted in an increase in the number of deaths (4,000 deaths) in December 1952 in London and in 1963 in New York City (400 deaths) ( 19 ). An association of pollution with mortality was reported on the basis of monitoring of outdoor pollution in six US metropolitan cities ( 20 ). In every case, it seems that mortality was closely related to the levels of fine, inhalable, and sulfate particles more than with the levels of total particulate pollution, aerosol acidity, sulfur dioxide, or nitrogen dioxide ( 20 ).

Furthermore, extremely high levels of pollution are reported in Mexico City and Rio de Janeiro, followed by Milan, Ankara, Melbourne, Tokyo, and Moscow ( 19 ).

Based on the magnitude of the public health impact, it is certain that different kinds of interventions should be taken into account. Success and effectiveness in controlling air pollution, specifically at the local level, have been reported. Adequate technological means are applied considering the source and the nature of the emission as well as its impact on health and the environment. The importance of point sources and non-point sources of air pollution control is reported by Schwela and Köth-Jahr ( 21 ). Without a doubt, a detailed emission inventory must record all sources in a given area. Beyond considering the above sources and their nature, topography and meteorology should also be considered, as stated previously. Assessment of the control policies and methods is often extrapolated from the local to the regional and then to the global scale. Air pollution may be dispersed and transported from one region to another area located far away. Air pollution management means the reduction to acceptable levels or possible elimination of air pollutants whose presence in the air affects our health or the environmental ecosystem. Private and governmental entities and authorities implement actions to ensure the air quality ( 22 ). Air quality standards and guidelines were adopted for the different pollutants by the WHO and EPA as a tool for the management of air quality ( 1 , 23 ). These standards have to be compared to the emissions inventory standards by causal analysis and dispersion modeling in order to reveal the problematic areas ( 24 ). Inventories are generally based on a combination of direct measurements and emissions modeling ( 24 ).

As an example, we state here the control measures at the source through the use of catalytic converters in cars. These are devices that turn the pollutants and toxic gases produced from combustion engines into less-toxic pollutants by catalysis through redox reactions ( 25 ). In Greece, the use of private cars was restricted by tracking their license plates in order to reduce traffic congestion during rush hour ( 25 ).

Concerning industrial emissions, collectors and closed systems can keep the air pollution to the minimal standards imposed by legislation ( 26 ).

Current strategies to improve air quality require an estimation of the economic value of the benefits gained from proposed programs. These proposed programs by public authorities, and directives are issued with guidelines to be respected.

In Europe, air quality limit values AQLVs (Air Quality Limit Values) are issued for setting off planning claims ( 27 ). In the USA, the NAAQS (National Ambient Air Quality Standards) establish the national air quality limit values ( 27 ). While both standards and directives are based on different mechanisms, significant success has been achieved in the reduction of overall emissions and associated health and environmental effects ( 27 ). The European Directive identifies geographical areas of risk exposure as monitoring/assessment zones to record the emission sources and levels of air pollution ( 27 ), whereas the USA establishes global geographical air quality criteria according to the severity of their air quality problem and records all sources of the pollutants and their precursors ( 27 ).

In this vein, funds have been financing, directly or indirectly, projects related to air quality along with the technical infrastructure to maintain good air quality. These plans focus on an inventory of databases from air quality environmental planning awareness campaigns. Moreover, pollution measures of air emissions may be taken for vehicles, machines, and industries in urban areas.

Technological innovation can only be successful if it is able to meet the needs of society. In this sense, technology must reflect the decision-making practices and procedures of those involved in risk assessment and evaluation and act as a facilitator in providing information and assessments to enable decision makers to make the best decisions possible. Summarizing the aforementioned in order to design an effective air quality control strategy, several aspects must be considered: environmental factors and ambient air quality conditions, engineering factors and air pollutant characteristics, and finally, economic operating costs for technological improvement and administrative and legal costs. Considering the economic factor, competitiveness through neoliberal concepts is offering a solution to environmental problems ( 22 ).

The development of environmental governance, along with technological progress, has initiated the deployment of a dialogue. Environmental politics has created objections and points of opposition between different political parties, scientists, media, and governmental and non-governmental organizations ( 22 ). Radical environmental activism actions and movements have been created ( 22 ). The rise of the new information and communication technologies (ICTs) are many times examined as to whether and in which way they have influenced means of communication and social movements such as activism ( 28 ). Since the 1990s, the term “digital activism” has been used increasingly and in many different disciplines ( 29 ). Nowadays, multiple digital technologies can be used to produce a digital activism outcome on environmental issues. More specifically, devices with online capabilities such as computers or mobile phones are being used as a way to pursue change in political and social affairs ( 30 ).

In the present paper, we focus on the sources of environmental pollution in relation to public health and propose some solutions and interventions that may be of interest to environmental legislators and decision makers.

Sources of Exposure

It is known that the majority of environmental pollutants are emitted through large-scale human activities such as the use of industrial machinery, power-producing stations, combustion engines, and cars. Because these activities are performed at such a large scale, they are by far the major contributors to air pollution, with cars estimated to be responsible for approximately 80% of today's pollution ( 31 ). Some other human activities are also influencing our environment to a lesser extent, such as field cultivation techniques, gas stations, fuel tanks heaters, and cleaning procedures ( 32 ), as well as several natural sources, such as volcanic and soil eruptions and forest fires.

The classification of air pollutants is based mainly on the sources producing pollution. Therefore, it is worth mentioning the four main sources, following the classification system: Major sources, Area sources, Mobile sources, and Natural sources.

Major sources include the emission of pollutants from power stations, refineries, and petrochemicals, the chemical and fertilizer industries, metallurgical and other industrial plants, and, finally, municipal incineration.

Indoor area sources include domestic cleaning activities, dry cleaners, printing shops, and petrol stations.

Mobile sources include automobiles, cars, railways, airways, and other types of vehicles.

Finally, natural sources include, as stated previously, physical disasters ( 33 ) such as forest fire, volcanic erosion, dust storms, and agricultural burning.

However, many classification systems have been proposed. Another type of classification is a grouping according to the recipient of the pollution, as follows:

Air pollution is determined as the presence of pollutants in the air in large quantities for long periods. Air pollutants are dispersed particles, hydrocarbons, CO, CO 2 , NO, NO 2 , SO 3 , etc.

Water pollution is organic and inorganic charge and biological charge ( 10 ) at high levels that affect the water quality ( 34 , 35 ).

Soil pollution occurs through the release of chemicals or the disposal of wastes, such as heavy metals, hydrocarbons, and pesticides.

Air pollution can influence the quality of soil and water bodies by polluting precipitation, falling into water and soil environments ( 34 , 36 ). Notably, the chemistry of the soil can be amended due to acid precipitation by affecting plants, cultures, and water quality ( 37 ). Moreover, movement of heavy metals is favored by soil acidity, and metals are so then moving into the watery environment. It is known that heavy metals such as aluminum are noxious to wildlife and fishes. Soil quality seems to be of importance, as soils with low calcium carbonate levels are at increased jeopardy from acid rain. Over and above rain, snow and particulate matter drip into watery ' bodies ( 36 , 38 ).

Lastly, pollution is classified following type of origin:

Radioactive and nuclear pollution , releasing radioactive and nuclear pollutants into water, air, and soil during nuclear explosions and accidents, from nuclear weapons, and through handling or disposal of radioactive sewage.

Radioactive materials can contaminate surface water bodies and, being noxious to the environment, plants, animals, and humans. It is known that several radioactive substances such as radium and uranium concentrate in the bones and can cause cancers ( 38 , 39 ).

Noise pollution is produced by machines, vehicles, traffic noises, and musical installations that are harmful to our hearing.

The World Health Organization introduced the term DALYs. The DALYs for a disease or health condition is defined as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences ( 39 ). In Europe, air pollution is the main cause of disability-adjusted life years lost (DALYs), followed by noise pollution. The potential relationships of noise and air pollution with health have been studied ( 40 ). The study found that DALYs related to noise were more important than those related to air pollution, as the effects of environmental noise on cardiovascular disease were independent of air pollution ( 40 ). Environmental noise should be counted as an independent public health risk ( 40 ).

Environmental pollution occurs when changes in the physical, chemical, or biological constituents of the environment (air masses, temperature, climate, etc.) are produced.

Pollutants harm our environment either by increasing levels above normal or by introducing harmful toxic substances. Primary pollutants are directly produced from the above sources, and secondary pollutants are emitted as by-products of the primary ones. Pollutants can be biodegradable or non-biodegradable and of natural origin or anthropogenic, as stated previously. Moreover, their origin can be a unique source (point-source) or dispersed sources.

Pollutants have differences in physical and chemical properties, explaining the discrepancy in their capacity for producing toxic effects. As an example, we state here that aerosol compounds ( 41 – 43 ) have a greater toxicity than gaseous compounds due to their tiny size (solid or liquid) in the atmosphere; they have a greater penetration capacity. Gaseous compounds are eliminated more easily by our respiratory system ( 41 ). These particles are able to damage lungs and can even enter the bloodstream ( 41 ), leading to the premature deaths of millions of people yearly. Moreover, the aerosol acidity ([H+]) seems to considerably enhance the production of secondary organic aerosols (SOA), but this last aspect is not supported by other scientific teams ( 38 ).

Climate and Pollution

Air pollution and climate change are closely related. Climate is the other side of the same coin that reduces the quality of our Earth ( 44 ). Pollutants such as black carbon, methane, tropospheric ozone, and aerosols affect the amount of incoming sunlight. As a result, the temperature of the Earth is increasing, resulting in the melting of ice, icebergs, and glaciers.

In this vein, climatic changes will affect the incidence and prevalence of both residual and imported infections in Europe. Climate and weather affect the duration, timing, and intensity of outbreaks strongly and change the map of infectious diseases in the globe ( 45 ). Mosquito-transmitted parasitic or viral diseases are extremely climate-sensitive, as warming firstly shortens the pathogen incubation period and secondly shifts the geographic map of the vector. Similarly, water-warming following climate changes leads to a high incidence of waterborne infections. Recently, in Europe, eradicated diseases seem to be emerging due to the migration of population, for example, cholera, poliomyelitis, tick-borne encephalitis, and malaria ( 46 ).

The spread of epidemics is associated with natural climate disasters and storms, which seem to occur more frequently nowadays ( 47 ). Malnutrition and disequilibration of the immune system are also associated with the emerging infections affecting public health ( 48 ).

The Chikungunya virus “took the airplane” from the Indian Ocean to Europe, as outbreaks of the disease were registered in Italy ( 49 ) as well as autochthonous cases in France ( 50 ).

An increase in cryptosporidiosis in the United Kingdom and in the Czech Republic seems to have occurred following flooding ( 36 , 51 ).

As stated previously, aerosols compounds are tiny in size and considerably affect the climate. They are able to dissipate sunlight (the albedo phenomenon) by dispersing a quarter of the sun's rays back to space and have cooled the global temperature over the last 30 years ( 52 ).

Air Pollutants

The World Health Organization (WHO) reports on six major air pollutants, namely particle pollution, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. Air pollution can have a disastrous effect on all components of the environment, including groundwater, soil, and air. Additionally, it poses a serious threat to living organisms. In this vein, our interest is mainly to focus on these pollutants, as they are related to more extensive and severe problems in human health and environmental impact. Acid rain, global warming, the greenhouse effect, and climate changes have an important ecological impact on air pollution ( 53 ).

Particulate Matter (PM) and Health

Studies have shown a relationship between particulate matter (PM) and adverse health effects, focusing on either short-term (acute) or long-term (chronic) PM exposure.

Particulate matter (PM) is usually formed in the atmosphere as a result of chemical reactions between the different pollutants. The penetration of particles is closely dependent on their size ( 53 ). Particulate Matter (PM) was defined as a term for particles by the United States Environmental Protection Agency ( 54 ). Particulate matter (PM) pollution includes particles with diameters of 10 micrometers (μm) or smaller, called PM 10 , and extremely fine particles with diameters that are generally 2.5 micrometers (μm) and smaller.

Particulate matter contains tiny liquid or solid droplets that can be inhaled and cause serious health effects ( 55 ). Particles <10 μm in diameter (PM 10 ) after inhalation can invade the lungs and even reach the bloodstream. Fine particles, PM 2.5 , pose a greater risk to health ( 6 , 56 ) ( Table 1 ).

Table 1 . Penetrability according to particle size.

Multiple epidemiological studies have been performed on the health effects of PM. A positive relation was shown between both short-term and long-term exposures of PM 2.5 and acute nasopharyngitis ( 56 ). In addition, long-term exposure to PM for years was found to be related to cardiovascular diseases and infant mortality.

Those studies depend on PM 2.5 monitors and are restricted in terms of study area or city area due to a lack of spatially resolved daily PM 2.5 concentration data and, in this way, are not representative of the entire population. Following a recent epidemiological study by the Department of Environmental Health at Harvard School of Public Health (Boston, MA) ( 57 ), it was reported that, as PM 2.5 concentrations vary spatially, an exposure error (Berkson error) seems to be produced, and the relative magnitudes of the short- and long-term effects are not yet completely elucidated. The team developed a PM 2.5 exposure model based on remote sensing data for assessing short- and long-term human exposures ( 57 ). This model permits spatial resolution in short-term effects plus the assessment of long-term effects in the whole population.

Moreover, respiratory diseases and affection of the immune system are registered as long-term chronic effects ( 58 ). It is worth noting that people with asthma, pneumonia, diabetes, and respiratory and cardiovascular diseases are especially susceptible and vulnerable to the effects of PM. PM 2.5 , followed by PM 10 , are strongly associated with diverse respiratory system diseases ( 59 ), as their size permits them to pierce interior spaces ( 60 ). The particles produce toxic effects according to their chemical and physical properties. The components of PM 10 and PM 2.5 can be organic (polycyclic aromatic hydrocarbons, dioxins, benzene, 1-3 butadiene) or inorganic (carbon, chlorides, nitrates, sulfates, metals) in nature ( 55 ).

Particulate Matter (PM) is divided into four main categories according to type and size ( 61 ) ( Table 2 ).

Table 2 . Types and sizes of particulate Matter (PM).

Gas contaminants include PM in aerial masses.

Particulate contaminants include contaminants such as smog, soot, tobacco smoke, oil smoke, fly ash, and cement dust.

Biological Contaminants are microorganisms (bacteria, viruses, fungi, mold, and bacterial spores), cat allergens, house dust and allergens, and pollen.

Types of Dust include suspended atmospheric dust, settling dust, and heavy dust.

Finally, another fact is that the half-lives of PM 10 and PM 2.5 particles in the atmosphere is extended due to their tiny dimensions; this permits their long-lasting suspension in the atmosphere and even their transfer and spread to distant destinations where people and the environment may be exposed to the same magnitude of pollution ( 53 ). They are able to change the nutrient balance in watery ecosystems, damage forests and crops, and acidify water bodies.

As stated, PM 2.5 , due to their tiny size, are causing more serious health effects. These aforementioned fine particles are the main cause of the “haze” formation in different metropolitan areas ( 12 , 13 , 61 ).

Ozone Impact in the Atmosphere

Ozone (O 3 ) is a gas formed from oxygen under high voltage electric discharge ( 62 ). It is a strong oxidant, 52% stronger than chlorine. It arises in the stratosphere, but it could also arise following chain reactions of photochemical smog in the troposphere ( 63 ).

Ozone can travel to distant areas from its initial source, moving with air masses ( 64 ). It is surprising that ozone levels over cities are low in contrast to the increased amounts occuring in urban areas, which could become harmful for cultures, forests, and vegetation ( 65 ) as it is reducing carbon assimilation ( 66 ). Ozone reduces growth and yield ( 47 , 48 ) and affects the plant microflora due to its antimicrobial capacity ( 67 , 68 ). In this regard, ozone acts upon other natural ecosystems, with microflora ( 69 , 70 ) and animal species changing their species composition ( 71 ). Ozone increases DNA damage in epidermal keratinocytes and leads to impaired cellular function ( 72 ).

Ground-level ozone (GLO) is generated through a chemical reaction between oxides of nitrogen and VOCs emitted from natural sources and/or following anthropogenic activities.

Ozone uptake usually occurs by inhalation. Ozone affects the upper layers of the skin and the tear ducts ( 73 ). A study of short-term exposure of mice to high levels of ozone showed malondialdehyde formation in the upper skin (epidermis) but also depletion in vitamins C and E. It is likely that ozone levels are not interfering with the skin barrier function and integrity to predispose to skin disease ( 74 ).

Due to the low water-solubility of ozone, inhaled ozone has the capacity to penetrate deeply into the lungs ( 75 ).

Toxic effects induced by ozone are registered in urban areas all over the world, causing biochemical, morphologic, functional, and immunological disorders ( 76 ).

The European project (APHEA2) focuses on the acute effects of ambient ozone concentrations on mortality ( 77 ). Daily ozone concentrations compared to the daily number of deaths were reported from different European cities for a 3-year period. During the warm period of the year, an observed increase in ozone concentration was associated with an increase in the daily number of deaths (0.33%), in the number of respiratory deaths (1.13%), and in the number of cardiovascular deaths (0.45%). No effect was observed during wintertime.

Carbon Monoxide (CO)

Carbon monoxide is produced by fossil fuel when combustion is incomplete. The symptoms of poisoning due to inhaling carbon monoxide include headache, dizziness, weakness, nausea, vomiting, and, finally, loss of consciousness.

The affinity of carbon monoxide to hemoglobin is much greater than that of oxygen. In this vein, serious poisoning may occur in people exposed to high levels of carbon monoxide for a long period of time. Due to the loss of oxygen as a result of the competitive binding of carbon monoxide, hypoxia, ischemia, and cardiovascular disease are observed.

Carbon monoxide affects the greenhouses gases that are tightly connected to global warming and climate. This should lead to an increase in soil and water temperatures, and extreme weather conditions or storms may occur ( 68 ).

However, in laboratory and field experiments, it has been seen to produce increased plant growth ( 78 ).

Nitrogen Oxide (NO 2 )

Nitrogen oxide is a traffic-related pollutant, as it is emitted from automobile motor engines ( 79 , 80 ). It is an irritant of the respiratory system as it penetrates deep in the lung, inducing respiratory diseases, coughing, wheezing, dyspnea, bronchospasm, and even pulmonary edema when inhaled at high levels. It seems that concentrations over 0.2 ppm produce these adverse effects in humans, while concentrations higher than 2.0 ppm affect T-lymphocytes, particularly the CD8+ cells and NK cells that produce our immune response ( 81 ).It is reported that long-term exposure to high levels of nitrogen dioxide can be responsible for chronic lung disease. Long-term exposure to NO 2 can impair the sense of smell ( 81 ).

However, systems other than respiratory ones can be involved, as symptoms such as eye, throat, and nose irritation have been registered ( 81 ).

High levels of nitrogen dioxide are deleterious to crops and vegetation, as they have been observed to reduce crop yield and plant growth efficiency. Moreover, NO 2 can reduce visibility and discolor fabrics ( 81 ).

Sulfur Dioxide (SO 2 )

Sulfur dioxide is a harmful gas that is emitted mainly from fossil fuel consumption or industrial activities. The annual standard for SO 2 is 0.03 ppm ( 82 ). It affects human, animal, and plant life. Susceptible people as those with lung disease, old people, and children, who present a higher risk of damage. The major health problems associated with sulfur dioxide emissions in industrialized areas are respiratory irritation, bronchitis, mucus production, and bronchospasm, as it is a sensory irritant and penetrates deep into the lung converted into bisulfite and interacting with sensory receptors, causing bronchoconstriction. Moreover, skin redness, damage to the eyes (lacrimation and corneal opacity) and mucous membranes, and worsening of pre-existing cardiovascular disease have been observed ( 81 ).

Environmental adverse effects, such as acidification of soil and acid rain, seem to be associated with sulfur dioxide emissions ( 83 ).

Lead is a heavy metal used in different industrial plants and emitted from some petrol motor engines, batteries, radiators, waste incinerators, and waste waters ( 84 ).

Moreover, major sources of lead pollution in the air are metals, ore, and piston-engine aircraft. Lead poisoning is a threat to public health due to its deleterious effects upon humans, animals, and the environment, especially in the developing countries.

Exposure to lead can occur through inhalation, ingestion, and dermal absorption. Trans- placental transport of lead was also reported, as lead passes through the placenta unencumbered ( 85 ). The younger the fetus is, the more harmful the toxic effects. Lead toxicity affects the fetal nervous system; edema or swelling of the brain is observed ( 86 ). Lead, when inhaled, accumulates in the blood, soft tissue, liver, lung, bones, and cardiovascular, nervous, and reproductive systems. Moreover, loss of concentration and memory, as well as muscle and joint pain, were observed in adults ( 85 , 86 ).

Children and newborns ( 87 ) are extremely susceptible even to minimal doses of lead, as it is a neurotoxicant and causes learning disabilities, impairment of memory, hyperactivity, and even mental retardation.

Elevated amounts of lead in the environment are harmful to plants and crop growth. Neurological effects are observed in vertebrates and animals in association with high lead levels ( 88 ).

Polycyclic Aromatic Hydrocarbons(PAHs)

The distribution of PAHs is ubiquitous in the environment, as the atmosphere is the most important means of their dispersal. They are found in coal and in tar sediments. Moreover, they are generated through incomplete combustion of organic matter as in the cases of forest fires, incineration, and engines ( 89 ). PAH compounds, such as benzopyrene, acenaphthylene, anthracene, and fluoranthene are recognized as toxic, mutagenic, and carcinogenic substances. They are an important risk factor for lung cancer ( 89 ).

Volatile Organic Compounds(VOCs)

Volatile organic compounds (VOCs), such as toluene, benzene, ethylbenzene, and xylene ( 90 ), have been found to be associated with cancer in humans ( 91 ). The use of new products and materials has actually resulted in increased concentrations of VOCs. VOCs pollute indoor air ( 90 ) and may have adverse effects on human health ( 91 ). Short-term and long-term adverse effects on human health are observed. VOCs are responsible for indoor air smells. Short-term exposure is found to cause irritation of eyes, nose, throat, and mucosal membranes, while those of long duration exposure include toxic reactions ( 92 ). Predictable assessment of the toxic effects of complex VOC mixtures is difficult to estimate, as these pollutants can have synergic, antagonistic, or indifferent effects ( 91 , 93 ).

Dioxins originate from industrial processes but also come from natural processes, such as forest fires and volcanic eruptions. They accumulate in foods such as meat and dairy products, fish and shellfish, and especially in the fatty tissue of animals ( 94 ).

Short-period exhibition to high dioxin concentrations may result in dark spots and lesions on the skin ( 94 ). Long-term exposure to dioxins can cause developmental problems, impairment of the immune, endocrine and nervous systems, reproductive infertility, and cancer ( 94 ).

Without any doubt, fossil fuel consumption is responsible for a sizeable part of air contamination. This contamination may be anthropogenic, as in agricultural and industrial processes or transportation, while contamination from natural sources is also possible. Interestingly, it is of note that the air quality standards established through the European Air Quality Directive are somewhat looser than the WHO guidelines, which are stricter ( 95 ).

Effect of Air Pollution on Health

The most common air pollutants are ground-level ozone and Particulates Matter (PM). Air pollution is distinguished into two main types:

Outdoor pollution is the ambient air pollution.

Indoor pollution is the pollution generated by household combustion of fuels.

People exposed to high concentrations of air pollutants experience disease symptoms and states of greater and lesser seriousness. These effects are grouped into short- and long-term effects affecting health.

Susceptible populations that need to be aware of health protection measures include old people, children, and people with diabetes and predisposing heart or lung disease, especially asthma.

As extensively stated previously, according to a recent epidemiological study from Harvard School of Public Health, the relative magnitudes of the short- and long-term effects have not been completely clarified ( 57 ) due to the different epidemiological methodologies and to the exposure errors. New models are proposed for assessing short- and long-term human exposure data more successfully ( 57 ). Thus, in the present section, we report the more common short- and long-term health effects but also general concerns for both types of effects, as these effects are often dependent on environmental conditions, dose, and individual susceptibility.

Short-term effects are temporary and range from simple discomfort, such as irritation of the eyes, nose, skin, throat, wheezing, coughing and chest tightness, and breathing difficulties, to more serious states, such as asthma, pneumonia, bronchitis, and lung and heart problems. Short-term exposure to air pollution can also cause headaches, nausea, and dizziness.

These problems can be aggravated by extended long-term exposure to the pollutants, which is harmful to the neurological, reproductive, and respiratory systems and causes cancer and even, rarely, deaths.

The long-term effects are chronic, lasting for years or the whole life and can even lead to death. Furthermore, the toxicity of several air pollutants may also induce a variety of cancers in the long term ( 96 ).

As stated already, respiratory disorders are closely associated with the inhalation of air pollutants. These pollutants will invade through the airways and will accumulate at the cells. Damage to target cells should be related to the pollutant component involved and its source and dose. Health effects are also closely dependent on country, area, season, and time. An extended exposure duration to the pollutant should incline to long-term health effects in relation also to the above factors.

Particulate Matter (PMs), dust, benzene, and O 3 cause serious damage to the respiratory system ( 97 ). Moreover, there is a supplementary risk in case of existing respiratory disease such as asthma ( 98 ). Long-term effects are more frequent in people with a predisposing disease state. When the trachea is contaminated by pollutants, voice alterations may be remarked after acute exposure. Chronic obstructive pulmonary disease (COPD) may be induced following air pollution, increasing morbidity and mortality ( 99 ). Long-term effects from traffic, industrial air pollution, and combustion of fuels are the major factors for COPD risk ( 99 ).

Multiple cardiovascular effects have been observed after exposure to air pollutants ( 100 ). Changes occurred in blood cells after long-term exposure may affect cardiac functionality. Coronary arteriosclerosis was reported following long-term exposure to traffic emissions ( 101 ), while short-term exposure is related to hypertension, stroke, myocardial infracts, and heart insufficiency. Ventricle hypertrophy is reported to occur in humans after long-time exposure to nitrogen oxide (NO 2 ) ( 102 , 103 ).

Neurological effects have been observed in adults and children after extended-term exposure to air pollutants.

Psychological complications, autism, retinopathy, fetal growth, and low birth weight seem to be related to long-term air pollution ( 83 ). The etiologic agent of the neurodegenerative diseases (Alzheimer's and Parkinson's) is not yet known, although it is believed that extended exposure to air pollution seems to be a factor. Specifically, pesticides and metals are cited as etiological factors, together with diet. The mechanisms in the development of neurodegenerative disease include oxidative stress, protein aggregation, inflammation, and mitochondrial impairment in neurons ( 104 ) ( Figure 1 ).

Figure 1 . Impact of air pollutants on the brain.

Brain inflammation was observed in dogs living in a highly polluted area in Mexico for a long period ( 105 ). In human adults, markers of systemic inflammation (IL-6 and fibrinogen) were found to be increased as an immediate response to PNC on the IL-6 level, possibly leading to the production of acute-phase proteins ( 106 ). The progression of atherosclerosis and oxidative stress seem to be the mechanisms involved in the neurological disturbances caused by long-term air pollution. Inflammation comes secondary to the oxidative stress and seems to be involved in the impairment of developmental maturation, affecting multiple organs ( 105 , 107 ). Similarly, other factors seem to be involved in the developmental maturation, which define the vulnerability to long-term air pollution. These include birthweight, maternal smoking, genetic background and socioeconomic environment, as well as education level.

However, diet, starting from breast-feeding, is another determinant factor. Diet is the main source of antioxidants, which play a key role in our protection against air pollutants ( 108 ). Antioxidants are free radical scavengers and limit the interaction of free radicals in the brain ( 108 ). Similarly, genetic background may result in a differential susceptibility toward the oxidative stress pathway ( 60 ). For example, antioxidant supplementation with vitamins C and E appears to modulate the effect of ozone in asthmatic children homozygous for the GSTM1 null allele ( 61 ). Inflammatory cytokines released in the periphery (e.g., respiratory epithelia) upregulate the innate immune Toll-like receptor 2. Such activation and the subsequent events leading to neurodegeneration have recently been observed in lung lavage in mice exposed to ambient Los Angeles (CA, USA) particulate matter ( 61 ). In children, neurodevelopmental morbidities were observed after lead exposure. These children developed aggressive and delinquent behavior, reduced intelligence, learning difficulties, and hyperactivity ( 109 ). No level of lead exposure seems to be “safe,” and the scientific community has asked the Centers for Disease Control and Prevention (CDC) to reduce the current screening guideline of 10 μg/dl ( 109 ).

It is important to state that impact on the immune system, causing dysfunction and neuroinflammation ( 104 ), is related to poor air quality. Yet, increases in serum levels of immunoglobulins (IgA, IgM) and the complement component C3 are observed ( 106 ). Another issue is that antigen presentation is affected by air pollutants, as there is an upregulation of costimulatory molecules such as CD80 and CD86 on macrophages ( 110 ).

As is known, skin is our shield against ultraviolet radiation (UVR) and other pollutants, as it is the most exterior layer of our body. Traffic-related pollutants, such as PAHs, VOCs, oxides, and PM, may cause pigmented spots on our skin ( 111 ). On the one hand, as already stated, when pollutants penetrate through the skin or are inhaled, damage to the organs is observed, as some of these pollutants are mutagenic and carcinogenic, and, specifically, they affect the liver and lung. On the other hand, air pollutants (and those in the troposphere) reduce the adverse effects of ultraviolet radiation UVR in polluted urban areas ( 111 ). Air pollutants absorbed by the human skin may contribute to skin aging, psoriasis, acne, urticaria, eczema, and atopic dermatitis ( 111 ), usually caused by exposure to oxides and photochemical smoke ( 111 ). Exposure to PM and cigarette smoking act as skin-aging agents, causing spots, dyschromia, and wrinkles. Lastly, pollutants have been associated with skin cancer ( 111 ).

Higher morbidity is reported to fetuses and children when exposed to the above dangers. Impairment in fetal growth, low birth weight, and autism have been reported ( 112 ).

Another exterior organ that may be affected is the eye. Contamination usually comes from suspended pollutants and may result in asymptomatic eye outcomes, irritation ( 112 ), retinopathy, or dry eye syndrome ( 113 , 114 ).

Environmental Impact of Air Pollution

Air pollution is harming not only human health but also the environment ( 115 ) in which we live. The most important environmental effects are as follows.

Acid rain is wet (rain, fog, snow) or dry (particulates and gas) precipitation containing toxic amounts of nitric and sulfuric acids. They are able to acidify the water and soil environments, damage trees and plantations, and even damage buildings and outdoor sculptures, constructions, and statues.

Haze is produced when fine particles are dispersed in the air and reduce the transparency of the atmosphere. It is caused by gas emissions in the air coming from industrial facilities, power plants, automobiles, and trucks.

Ozone , as discussed previously, occurs both at ground level and in the upper level (stratosphere) of the Earth's atmosphere. Stratospheric ozone is protecting us from the Sun's harmful ultraviolet (UV) rays. In contrast, ground-level ozone is harmful to human health and is a pollutant. Unfortunately, stratospheric ozone is gradually damaged by ozone-depleting substances (i.e., chemicals, pesticides, and aerosols). If this protecting stratospheric ozone layer is thinned, then UV radiation can reach our Earth, with harmful effects for human life (skin cancer) ( 116 ) and crops ( 117 ). In plants, ozone penetrates through the stomata, inducing them to close, which blocks CO 2 transfer and induces a reduction in photosynthesis ( 118 ).

Global climate change is an important issue that concerns mankind. As is known, the “greenhouse effect” keeps the Earth's temperature stable. Unhappily, anthropogenic activities have destroyed this protecting temperature effect by producing large amounts of greenhouse gases, and global warming is mounting, with harmful effects on human health, animals, forests, wildlife, agriculture, and the water environment. A report states that global warming is adding to the health risks of poor people ( 119 ).

People living in poorly constructed buildings in warm-climate countries are at high risk for heat-related health problems as temperatures mount ( 119 ).

Wildlife is burdened by toxic pollutants coming from the air, soil, or the water ecosystem and, in this way, animals can develop health problems when exposed to high levels of pollutants. Reproductive failure and birth effects have been reported.

Eutrophication is occurring when elevated concentrations of nutrients (especially nitrogen) stimulate the blooming of aquatic algae, which can cause a disequilibration in the diversity of fish and their deaths.

Without a doubt, there is a critical concentration of pollution that an ecosystem can tolerate without being destroyed, which is associated with the ecosystem's capacity to neutralize acidity. The Canada Acid Rain Program established this load at 20 kg/ha/yr ( 120 ).

Hence, air pollution has deleterious effects on both soil and water ( 121 ). Concerning PM as an air pollutant, its impact on crop yield and food productivity has been reported. Its impact on watery bodies is associated with the survival of living organisms and fishes and their productivity potential ( 121 ).

An impairment in photosynthetic rhythm and metabolism is observed in plants exposed to the effects of ozone ( 121 ).

Sulfur and nitrogen oxides are involved in the formation of acid rain and are harmful to plants and marine organisms.

Last but not least, as mentioned above, the toxicity associated with lead and other metals is the main threat to our ecosystems (air, water, and soil) and living creatures ( 121 ).

In 2018, during the first WHO Global Conference on Air Pollution and Health, the WHO's General Director, Dr. Tedros Adhanom Ghebreyesus, called air pollution a “silent public health emergency” and “the new tobacco” ( 122 ).

Undoubtedly, children are particularly vulnerable to air pollution, especially during their development. Air pollution has adverse effects on our lives in many different respects.

Diseases associated with air pollution have not only an important economic impact but also a societal impact due to absences from productive work and school.

Despite the difficulty of eradicating the problem of anthropogenic environmental pollution, a successful solution could be envisaged as a tight collaboration of authorities, bodies, and doctors to regularize the situation. Governments should spread sufficient information and educate people and should involve professionals in these issues so as to control the emergence of the problem successfully.

Technologies to reduce air pollution at the source must be established and should be used in all industries and power plants. The Kyoto Protocol of 1997 set as a major target the reduction of GHG emissions to below 5% by 2012 ( 123 ). This was followed by the Copenhagen summit, 2009 ( 124 ), and then the Durban summit of 2011 ( 125 ), where it was decided to keep to the same line of action. The Kyoto protocol and the subsequent ones were ratified by many countries. Among the pioneers who adopted this important protocol for the world's environmental and climate “health” was China ( 3 ). As is known, China is a fast-developing economy and its GDP (Gross Domestic Product) is expected to be very high by 2050, which is defined as the year of dissolution of the protocol for the decrease in gas emissions.

A more recent international agreement of crucial importance for climate change is the Paris Agreement of 2015, issued by the UNFCCC (United Nations Climate Change Committee). This latest agreement was ratified by a plethora of UN (United Nations) countries as well as the countries of the European Union ( 126 ). In this vein, parties should promote actions and measures to enhance numerous aspects around the subject. Boosting education, training, public awareness, and public participation are some of the relevant actions for maximizing the opportunities to achieve the targets and goals on the crucial matter of climate change and environmental pollution ( 126 ). Without any doubt, technological improvements makes our world easier and it seems difficult to reduce the harmful impact caused by gas emissions, we could limit its use by seeking reliable approaches.

Synopsizing, a global prevention policy should be designed in order to combat anthropogenic air pollution as a complement to the correct handling of the adverse health effects associated with air pollution. Sustainable development practices should be applied, together with information coming from research in order to handle the problem effectively.

At this point, international cooperation in terms of research, development, administration policy, monitoring, and politics is vital for effective pollution control. Legislation concerning air pollution must be aligned and updated, and policy makers should propose the design of a powerful tool of environmental and health protection. As a result, the main proposal of this essay is that we should focus on fostering local structures to promote experience and practice and extrapolate these to the international level through developing effective policies for sustainable management of ecosystems.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

IM is employed by the company Delphis S.A.

The remaining authors declare that the present review paper was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1. WHO. Air Pollution . WHO. Available online at: http://www.who.int/airpollution/en/ (accessed October 5, 2019).

Google Scholar

2. Moores FC. Climate change and air pollution: exploring the synergies and potential for mitigation in industrializing countries. Sustainability . (2009) 1:43–54. doi: 10.3390/su1010043

CrossRef Full Text | Google Scholar

3. USGCRP (2009). Global Climate Change Impacts in the United States. In: Karl TR, Melillo JM, Peterson TC, editors. Climate Change Impacts by Sectors: Ecosystems . New York, NY: United States Global Change Research Program. Cambridge University Press.

4. Marlon JR, Bloodhart B, Ballew MT, Rolfe-Redding J, Roser-Renouf C, Leiserowitz A, et al. (2019). How hope and doubt affect climate change mobilization. Front. Commun. 4:20. doi: 10.3389/fcomm.2019.00020

5. Eze IC, Schaffner E, Fischer E, Schikowski T, Adam M, Imboden M, et al. Long- term air pollution exposure and diabetes in a population-based Swiss cohort. Environ Int . (2014) 70:95–105. doi: 10.1016/j.envint.2014.05.014

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Kelishadi R, Poursafa P. Air pollution and non-respiratory health hazards for children. Arch Med Sci . (2010) 6:483–95. doi: 10.5114/aoms.2010.14458

7. Manucci PM, Franchini M. Health effects of ambient air pollution in developing countries. Int J Environ Res Public Health . (2017) 14:1048. doi: 10.3390/ijerph14091048

8. Burden of Disease from Ambient and Household Air Pollution . Available online: http://who.int/phe/health_topics/outdoorair/databases/en/ (accessed August 15, 2017).

9. Hashim D, Boffetta P. Occupational and environmental exposures and cancers in developing countries. Ann Glob Health . (2014) 80:393–411. doi: 10.1016/j.aogh.2014.10.002

10. Guo Y, Zeng H, Zheng R, Li S, Pereira G, Liu Q, et al. The burden of lung cancer mortality attributable to fine particles in China. Total Environ Sci . (2017) 579:1460–6. doi: 10.1016/j.scitotenv.2016.11.147

11. Hou Q, An XQ, Wang Y, Guo JP. An evaluation of resident exposure to respirable particulate matter and health economic loss in Beijing during Beijing 2008 Olympic Games. Sci Total Environ . (2010) 408:4026–32. doi: 10.1016/j.scitotenv.2009.12.030

12. Kan H, Chen R, Tong S. Ambient air pollution, climate change, and population health in China. Environ Int . (2012) 42:10–9. doi: 10.1016/j.envint.2011.03.003

13. Burroughs Peña MS, Rollins A. Environmental exposures and cardiovascular disease: a challenge for health and development in low- and middle-income countries. Cardiol Clin . (2017) 35:71–86. doi: 10.1016/j.ccl.2016.09.001

14. Kankaria A, Nongkynrih B, Gupta S. Indoor air pollution in india: implications on health and its control. Indian J Comm Med . 39:203–7. doi: 10.4103/0970-0218.143019

15. Parajuli I, Lee H, Shrestha KR. Indoor air quality and ventilation assessment of rural mountainous households of Nepal. Int J Sust Built Env . (2016) 5:301–11. doi: 10.1016/j.ijsbe.2016.08.003

16. Saud T, Gautam R, Mandal TK, Gadi R, Singh DP, Sharma SK. Emission estimates of organic and elemental carbon from household biomass fuel used over the Indo-Gangetic Plain (IGP), India. Atmos Environ . (2012) 61:212–20. doi: 10.1016/j.atmosenv.2012.07.030

17. Singh DP, Gadi R, Mandal TK, Saud T, Saxena M, Sharma SK. Emissions estimates of PAH from biomass fuels used in rural sector of Indo-Gangetic Plains of India. Atmos Environ . (2013) 68:120–6. doi: 10.1016/j.atmosenv.2012.11.042

18. Dherani M, Pope D, Mascarenhas M, Smith KR, Weber M BN. Indoor air pollution from unprocessed solid fuel use and pneumonia risk in children aged under five years: a systematic review and meta-analysis. Bull World Health Organ . (2008) 86:390–4. doi: 10.2471/BLT.07.044529

19. Kassomenos P, Kelessis A, Petrakakis M, Zoumakis N, Christides T, Paschalidou AK. Air Quality assessment in a heavily-polluted urban Mediterranean environment through Air Quality indices. Ecol Indic . (2012) 18:259–68. doi: 10.1016/j.ecolind.2011.11.021

20. Dockery DW, Pope CA, Xu X, Spengler JD, Ware JH, Fay ME, et al. An association between air pollution and mortality in six U.S. cities. N Engl J Med . (1993) 329:1753–9. doi: 10.1056/NEJM199312093292401

21. Schwela DH, Köth-Jahr I. Leitfaden für die Aufstellung von Luftreinhalteplänen [Guidelines for the Implementation of Clean Air Implementation Plans]. Landesumweltamt des Landes Nordrhein Westfalen. State Environmental Service of the State of North Rhine-Westphalia (1994).

22. Newlands M. Environmental Activism, Environmental Politics, and Representation: The Framing of the British Environmental Activist Movement . Ph.D. thesis. University of East London, United Kingdom (2015).

23. NEPIS (National Service Center for Environmental Publications) US EPA (Environmental Protection Agency) (2017). Available online at: https://www.epa.gov/clean-air-act-overview/air-pollution-current-and-future-challenges (accessed August 15, 2017).

24. NRC (National Research Council). Available online at: https://www.nap.edu/read/10728/chapter/1,2014 (accessed September 17, 2019).

25. Bull A. Traffic Congestion: The Problem and How to Deal With It . Santiago: Nationes Unidas, Cepal (2003).

26. Spiegel J, Maystre LY. Environmental Pollution Control, Part VII - The Environment, Chapter 55, Encyclopedia of Occupational Health and Safety . Available online at: http://www.ilocis.org/documents/chpt55e.htm (accessed September 17, 2019).

27. European Community Reports. Assessment of the Effectiveness of European Air Quality Policies and Measures: Case Study 2; Comparison of the EU and US Air Quality Standards and Planning Requirements. (2004). Available online at: https://ec.europa.eu/environment/archives/cafe/activities/pdf/case_study2.pdf (accessed September 22, 2019).

28. Gibson R, Ward S. Parties in the digital age; a review. J Represent Democracy . (2009) 45:87–100. doi: 10.1080/00344890802710888

29. Kaun A, Uldam J. Digital activism: after the hype. New Media Soc. (2017) 20:2099–106. doi: 10.1177/14614448177319

30. Sivitanides M, Shah V. The era of digital activism. In: 2011 Conference for Information Systems Applied Research(CONISAR) Proceedings Wilmington North Carolina, USA . Available online at: https://www.arifyildirim.com/ilt510/marcos.sivitanides.vivek.shah.pdf (accessed September 22, 2019).

31. Möller L, Schuetzle D, Autrup H. Future research needs associated with the assessment of potential human health risks from exposure to toxic ambient air pollutants. Environ Health Perspect . (1994) 102(Suppl. 4):193–210. doi: 10.1289/ehp.94102s4193

32. Jacobson MZ, Jacobson PMZ. Atmospheric Pollution: History, Science, and Regulation. Cambridge University Press (2002). p. 206. doi: 10.1256/wea.243.02

33. Stover RH. Flooding of soil for disease control. In: Mulder D, editor. Chapter 3. Developments in Agricultural and Managed Forest Ecology . Elsevier (1979). p. 19–28. Available online at: http://www.sciencedirect.com/science/article/pii/B9780444416926500094 doi: 10.1016/B978-0-444-41692-6.50009-4 (accessed July 1, 2019).

34. Maipa V, Alamanos Y, Bezirtzoglou E. Seasonal fluctuation of bacterial indicators in coastal waters. Microb Ecol Health Dis . (2001) 13:143–6. doi: 10.1080/089106001750462687

35. Bezirtzoglou E, Dimitriou D, Panagiou A. Occurrence of Clostridium perfringens in river water by using a new procedure. Anaerobe . (1996) 2:169–73. doi: 10.1006/anae.1996.0022

36. Kjellstrom T, Lodh M, McMichael T, Ranmuthugala G, Shrestha R, Kingsland S. Air and Water Pollution: Burden and Strategies for Control. DCP, Chapter 43. 817–32 p. Available online at: https://www.dcp-3.org/sites/default/files/dcp2/DCP43.pdf (accessed September 17, 2017).

37. Pathak RK, Wang T, Ho KF, Lee SC. Characteristics of summertime PM2.5 organic and elemental carbon in four major Chinese cities: implications of high acidity for water- soluble organic carbon (WSOC). Atmos Environ . (2011) 45:318–25. doi: 10.1016/j.atmosenv.2010.10.021

38. Bonavigo L, Zucchetti M, Mankolli H. Water radioactive pollution and related environmental aspects. J Int Env Appl Sci . (2009) 4:357–63

39. World Health Organization (WHO). Preventing Disease Through Healthy Environments: Towards an Estimate of the Environmental Burden of Disease . 1106 p. Available online at: https://www.who.int/quantifying_ehimpacts/publications/preventingdisease.pdf (accessed September 22, 2019).

40. Stansfeld SA. Noise effects on health in the context of air pollution exposure. Int J Environ Res Public Health . (2015) 12:12735–60. doi: 10.3390/ijerph121012735

41. Ethical Unicorn. Everything You Need To Know About Aerosols & Air Pollution. (2019). Available online at: https://ethicalunicorn.com/2019/04/29/everything-you-need-to-know-about-aerosols-air-pollution/ (accessed October 4, 2019).

42. Colbeck I, Lazaridis M. Aerosols and environmental pollution. Sci Nat . (2009) 97:117–31. doi: 10.1007/s00114-009-0594-x

43. Incecik S, Gertler A, Kassomenos P. Aerosols and air quality. Sci Total Env . (2014) 355, 488–9. doi: 10.1016/j.scitotenv.2014.04.012

44. D'Amato G, Pawankar R, Vitale C, Maurizia L. Climate change and air pollution: effects on respiratory allergy. Allergy Asthma Immunol Res . (2016) 8:391–5. doi: 10.4168/aair.2016.8.5.391

45. Bezirtzoglou C, Dekas K, Charvalos E. Climate changes, environment and infection: facts, scenarios and growing awareness from the public health community within Europe. Anaerobe . (2011) 17:337–40. doi: 10.1016/j.anaerobe.2011.05.016

46. Castelli F, Sulis G. Migration and infectious diseases. Clin Microbiol Infect . (2017) 23:283–9. doi: 10.1016/j.cmi.2017.03.012

47. Watson JT, Gayer M, Connolly MA. Epidemics after natural disasters. Emerg Infect Dis . (2007) 13:1–5. doi: 10.3201/eid1301.060779

48. Fenn B. Malnutrition in Humanitarian Emergencies . Available online at: https://www.who.int/diseasecontrol_emergencies/publications/idhe_2009_london_malnutrition_fenn.pdf . (accessed August 15, 2017).

49. Lindh E, Argentini C, Remoli ME, Fortuna C, Faggioni G, Benedetti E, et al. The Italian 2017 outbreak Chikungunya virus belongs to an emerging Aedes albopictus –adapted virus cluster introduced from the Indian subcontinent. Open Forum Infect Dis. (2019) 6:ofy321. doi: 10.1093/ofid/ofy321

50. Calba C, Guerbois-Galla M, Franke F, Jeannin C, Auzet-Caillaud M, Grard G, Pigaglio L, Decoppet A, et al. Preliminary report of an autochthonous chikungunya outbreak in France, July to September 2017. Eur Surveill . (2017) 22:17-00647. doi: 10.2807/1560-7917.ES.2017.22.39.17-00647

51. Menne B, Murray V. Floods in the WHO European Region: Health Effects and Their Prevention . Copenhagen: WHO; Weltgesundheits organisation, Regionalbüro für Europa (2013). Available online at: http://www.euro.who.int/data/assets/pdf_file/0020/189020/e96853.pdf (accessed 15 August 2017).

52. Schneider SH. The greenhouse effect: science and policy. Science . (1989) 243:771–81. doi: 10.1126/science.243.4892.771

53. Wilson WE, Suh HH. Fine particles and coarse particles: concentration relationships relevant to epidemiologic studies. J Air Waste Manag Assoc . (1997) 47:1238–49. doi: 10.1080/10473289.1997.10464074

54. US EPA (US Environmental Protection Agency) (2018). Available online at: https://www.epa.gov/pm-pollution/particulate-matter-pm-basics (accessed September 22, 2018).

55. Cheung K, Daher N, Kam W, Shafer MM, Ning Z, Schauer JJ, et al. Spatial and temporal variation of chemical composition and mass closure of ambient coarse particulate matter (PM10–2.5) in the Los Angeles area. Atmos Environ . (2011) 45:2651–62. doi: 10.1016/j.atmosenv.2011.02.066

56. Zhang L, Yang Y, Li Y, Qian ZM, Xiao W, Wang X, et al. Short-term and long-term effects of PM2.5 on acute nasopharyngitis in 10 communities of Guangdong, China. Sci Total Env. (2019) 688:136–42. doi: 10.1016/j.scitotenv.2019.05.470.

57. Kloog I, Ridgway B, Koutrakis P, Coull BA, Schwartz JD. Long- and short-term exposure to PM2.5 and mortality using novel exposure models, Epidemiology . (2013) 24:555–61. doi: 10.1097/EDE.0b013e318294beaa

58. New Hampshire Department of Environmental Services. Current and Forecasted Air Quality in New Hampshire . Environmental Fact Sheet (2019). Available online at: https://www.des.nh.gov/organization/commissioner/pip/factsheets/ard/documents/ard-16.pdf (accessed September 22, 2019).

59. Kappos AD, Bruckmann P, Eikmann T, Englert N, Heinrich U, Höppe P, et al. Health effects of particles in ambient air. Int J Hyg Environ Health . (2004) 207:399–407. doi: 10.1078/1438-4639-00306

60. Boschi N (Ed.). Defining an educational framework for indoor air sciences education. In: Education and Training in Indoor Air Sciences . Luxembourg: Springer Science & Business Media (2012). 245 p.

61. Heal MR, Kumar P, Harrison RM. Particles, air quality, policy and health. Chem Soc Rev . (2012) 41:6606–30. doi: 10.1039/c2cs35076a

62. Bezirtzoglou E, Alexopoulos A. Ozone history and ecosystems: a goliath from impacts to advance industrial benefits and interests, to environmental and therapeutical strategies. In: Ozone Depletion, Chemistry and Impacts. (2009). p. 135–45.

63. Villányi V, Turk B, Franc B, Csintalan Z. Ozone Pollution and its Bioindication. In: Villányi V, editor. Air Pollution . London: Intech Open (2010). doi: 10.5772/10047

64. Massachusetts Department of Public Health. Massachusetts State Health Assessment . Boston, MA (2017). Available online at: https://www.mass.gov/files/documents/2017/11/03/2017%20MA%20SHA%20final%20compressed.pdf (accessed October 30, 2017).

65. Lorenzini G, Saitanis C. Ozone: A Novel Plant “Pathogen.” In: Sanitá di Toppi L, Pawlik-Skowrońska B, editors. Abiotic Stresses in Plant Springer Link (2003). p. 205–29. doi: 10.1007/978-94-017-0255-3_8

66. Fares S, Vargas R, Detto M, Goldstein AH, Karlik J, Paoletti E, et al. Tropospheric ozone reduces carbon assimilation in trees: estimates from analysis of continuous flux measurements. Glob Change Biol . (2013) 19:2427–43. doi: 10.1111/gcb.12222

67. Harmens H, Mills G, Hayes F, Jones L, Norris D, Fuhrer J. Air Pollution and Vegetation . ICP Vegetation Annual Report 2006/2007. (2012)

68. Emberson LD, Pleijel H, Ainsworth EA, den Berg M, Ren W, Osborne S, et al. Ozone effects on crops and consideration in crop models. Eur J Agron . (2018) 100:19–34. doi: 10.1016/j.eja.2018.06.002

69. Alexopoulos A, Plessas S, Ceciu S, Lazar V, Mantzourani I, Voidarou C, et al. Evaluation of ozone efficacy on the reduction of microbial population of fresh cut lettuce ( Lactuca sativa ) and green bell pepper ( Capsicum annuum ). Food Control . (2013) 30:491–6. doi: 10.1016/j.foodcont.2012.09.018

70. Alexopoulos A, Plessas S, Kourkoutas Y, Stefanis C, Vavias S, Voidarou C, et al. Experimental effect of ozone upon the microbial flora of commercially produced dairy fermented products. Int J Food Microbiol . (2017) 246:5–11. doi: 10.1016/j.ijfoodmicro.2017.01.018

71. Maggio A, Fagnano M. Ozone damages to mediterranean crops: physiological responses. Ital J Agron . (2008) 13–20. doi: 10.4081/ija.2008.13

72. McCarthy JT, Pelle E, Dong K, Brahmbhatt K, Yarosh D, Pernodet N. Effects of ozone in normal human epidermal keratinocytes. Exp Dermatol . (2013) 22:360–1. doi: 10.1111/exd.12125

73. WHO. Health Risks of Ozone From Long-Range Transboundary Air Pollution . Available online at: http://www.euro.who.int/data/assets/pdf_file/0005/78647/E91843.pdf (accessed August 15, 2019).

74. Thiele JJ, Traber MG, Tsang K, Cross CE, Packer L. In vivo exposure to ozone depletes vitamins C and E and induces lipid peroxidation in epidermal layers of murine skin. Free Radic Biol Med. (1997) 23:365–91. doi: 10.1016/S0891-5849(96)00617-X

75. Hatch GE, Slade R, Harris LP, McDonnell WF, Devlin RB, Koren HS, et al. Ozone dose and effect in humans and rats. A comparison using oxygen- 18 labeling and bronchoalveolar lavage. Am J Respir Crit Care Med . (1994) 150:676–83. doi: 10.1164/ajrccm.150.3.8087337

76. Lippmann M. Health effects of ozone. A critical review. JAPCA . (1989) 39:672–95. doi: 10.1080/08940630.1989.10466554

77. Gryparis A, Forsberg B, Katsouyanni K, Analitis A, Touloumi G, Schwartz J, et al. Acute effects of ozone on mortality from the “air pollution and health: a European approach” project. Am J Respir Crit Care Med . (2004) 170:1080–7. doi: 10.1164/rccm.200403-333OC

78. Soon W, Baliunas SL, Robinson AB, Robinson ZW. Environmental effects of increased atmospheric carbon dioxide. Climate Res . (1999) 13:149–64 doi: 10.1260/0958305991499694

79. Richmont-Bryant J, Owen RC, Graham S, Snyder M, McDow S, Oakes M, et al. Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway gradients from near-road monitoring data. Air Qual Atm Health . (2017) 10:611–25. doi: 10.1007/s11869-016-0455-7

80. Hesterberg TW, Bunn WB, McClellan RO, Hamade AK, Long CM, Valberg PA. Critical review of the human data on short-term nitrogen dioxide (NO 2 ) exposures: evidence for NO2 no-effect levels. Crit Rev Toxicol . (2009) 39:743–81. doi: 10.3109/10408440903294945

81. Chen T-M, Gokhale J, Shofer S, Kuschner WG. Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects. Am J Med Sci . (2007) 333:249–56. doi: 10.1097/MAJ.0b013e31803b900f

82. US EPA. Table of Historical SO 2 NAAQS, Sulfur US EPA . Available online at: https://www3.epa.gov/ttn/naaqs/standards/so2/s_so2_history.html (accessed October 5, 2019).

83. WHO Regional Office of Europe (2000). Available online at: https://euro.who.int/_data/assets/pdf_file/0020/123086/AQG2ndEd_7_4Sulfuroxide.pdf

84. Pruss-Ustun A, Fewrell L, Landrigan PJ, Ayuso-Mateos JL. Lead exposure. Comparative Quantification of Health Risks . World Health Organization. p. 1495–1542. Available online at: https://www.who.int/publications/cra/chapters/volume2/1495-1542.pdf?ua=1

PubMed Abstract | Google Scholar

85. Goyer RA. Transplacental transport of lead. Environ Health Perspect . (1990) 89:101–5. doi: 10.1289/ehp.9089101

86. National Institute of Environmental Health Sciences (NIH). Lead and Your Health . (2013). 1–4 p. Available online at: https://www.niehs.nih.gov/health/materials/lead_and_your_health_508.pdf (accessed September 17, 2019).

87. Farhat A, Mohammadzadeh A, Balali-Mood M, Aghajanpoor-Pasha M, Ravanshad Y. Correlation of blood lead level in mothers and exclusively breastfed infants: a study on infants aged less than six months. Asia Pac J Med Toxicol . (2013) 2:150–2.

88. Assi MA, Hezmee MNM, Haron AW, Sabri MYM, Rajion MA. The detrimental effects of lead on human and animal health. Vet World . (2016) 9:660–71. doi: 10.14202/vetworld.2016.660-671

89. Abdel-Shafy HI, Mansour MSM. A review on polycyclic aromatic hydrocarbons: source, environmental impact, effect on human health and remediation. Egypt J Pet . (2016) 25:107–23. doi: 10.1016/j.ejpe.2015.03.011

90. Kumar A, Singh BP, Punia M, Singh D, Kumar K, Jain VK. Assessment of indoor air concentrations of VOCs and their associated health risks in the library of Jawaharlal Nehru University, New Delhi. Environ Sci Pollut Res Int . (2014) 21:2240–8. doi: 10.1007/s11356-013-2150-7

91. Molhave L, Clausen G, Berglund B, Ceaurriz J, Kettrup A, Lindvall T, et al. Total Volatile Organic Compounds (TVOC) in Indoor Air Quality Investigations. Indoor Air . 7:225–240. doi: 10.1111/j.1600-0668.1997.00002.x

92. Gibb T. Indoor Air Quality May be Hazardous to Your Health . MSU Extension. Available online at: https://www.canr.msu.edu/news/indoor_air_quality_may_be_hazardous_to_your_health (accessed October 5, 2019).

93. Ebersviller S, Lichtveld K, Sexton KG, Zavala J, Lin Y-H, Jaspers I, et al. Gaseous VOCs rapidly modify particulate matter and its biological effects – Part 1: simple VOCs and model PM. Atmos Chem Phys Discuss . (2012) 12:5065–105. doi: 10.5194/acpd-12-5065-2012

94. WHO (World Health Organization). Dioxins and Their Effects on Human Health. Available online at: https://www.who.int/news-room/fact-sheets/detail/dioxins-and-their-effects-on-human-health (accessed October 5, 2019).

95. EEA (European Environmental Agency). Air Quality Standards to the European Union and WHO . Available online at: https://www.eea.europa.eu/themes/data-and-maps/figures/air-quality-standards-under-the

96. Nakano T, Otsuki T. [Environmental air pollutants and the risk of cancer]. (Japanese). Gan To Kagaku Ryoho . (2013) 40:1441–5.

97. Kurt OK, Zhang J, Pinkerton KE. Pulmonary health effects of air pollution. Curr Opin Pulm Med . (2016) 22:138–43. doi: 10.1097/MCP.0000000000000248

98. Guarnieri M, Balmes JR. Outdoor air pollution and asthma. Lancet . (2014) 383:1581–92. doi: 10.1016/S0140-6736(14)60617-6

99. Jiang X-Q, Mei X-D, Feng D. Air pollution and chronic airway diseases: what should people know and do? J Thorac Dis . (2016) 8:E31–40.

100. Bourdrel T, Bind M-A, Béjot Y, Morel O, Argacha J-F. Cardiovascular effects of air pollution. Arch Cardiovasc Dis . (2017) 110:634–42. doi: 10.1016/j.acvd.2017.05.003

101. Hoffmann B, Moebus S, Möhlenkamp S, Stang A, Lehmann N, Dragano N, et al. Residential exposure to traffic is associated with coronary atherosclerosis. Circulation . (2007) 116:489–496. doi: 10.1161/CIRCULATIONAHA.107.693622

102. Katholi RE, Couri DM. Left ventricular hypertrophy: major risk factor in patients with hypertension: update and practical clinical applications. Int J Hypertens . (2011) 2011:495349. doi: 10.4061/2011/495349

103. Leary PJ, Kaufman JD, Barr RG, Bluemke DA, Curl CL, Hough CL, et al. Traffic- related air pollution and the right ventricle. the multi-ethnic study of atherosclerosis. Am J Respir Crit Care Med . (2014) 189:1093–100. doi: 10.1164/rccm.201312-2298OC

104. Genc S, Zadeoglulari Z, Fuss SH, Genc K. The adverse effects of air pollution on the nervous system. J Toxicol . (2012) 2012:782462. doi: 10.1155/2012/782462

105. Calderon-Garciduenas L, Azzarelli B, Acuna H, et al. Air pollution and brain damage. Toxicol Pathol. (2002) 30:373–89. doi: 10.1080/01926230252929954

106. Rückerl R, Greven S, Ljungman P, Aalto P, Antoniades C, Bellander T, et al. Air pollution and inflammation (interleukin-6, C-reactive protein, fibrinogen) in myocardial infarction survivors. Environ Health Perspect . (2007) 115:1072–80. doi: 10.1289/ehp.10021

107. Peters A, Veronesi B, Calderón-Garcidueñas L, Gehr P, Chen LC, Geiser M, et al. Translocation and potential neurological effects of fine and ultrafine particles a critical update. Part Fibre Toxicol . (2006) 3:13–8. doi: 10.1186/1743-8977-3-13

108. Kelly FJ. Dietary antioxidants and environmental stress. Proc Nutr Soc . (2004) 63:579–85. doi: 10.1079/PNS2004388

109. Bellinger DC. Very low lead exposures and children's neurodevelopment. Curr Opin Pediatr . (2008) 20:172–7. doi: 10.1097/MOP.0b013e3282f4f97b

110. Balbo P, Silvestri M, Rossi GA, Crimi E, Burastero SE. Differential role of CD80 and CD86 on alveolar macrophages in the presentation of allergen to T lymphocytes in asthma. Clin Exp Allergy J Br Soc Allergy Clin Immunol . (2001) 31:625–36. doi: 10.1046/j.1365-2222.2001.01068.x

111. Drakaki E, Dessinioti C, Antoniou C. Air pollution and the skin. Front Environ Sci Eng China . (2014) 15:2–8. doi: 10.3389/fenvs.2014.00011

112. Weisskopf MG, Kioumourtzoglou M-A, Roberts AL. Air pollution and autism spectrum disorders: causal or confounded? Curr Environ Health Rep . (2015) 2:430–9. doi: 10.1007/s40572-015-0073-9

113. Mo Z, Fu Q, Lyu D, Zhang L, Qin Z, Tang Q, et al. Impacts of air pollution on dry eye disease among residents in Hangzhou, China: a case-crossover study. Environ Pollut . (2019) 246:183–9. doi: 10.1016/j.envpol.2018.11.109

114. Klopfer J. Effects of environmental air pollution on the eye. J Am Optom Assoc . (1989) 60:773–8.

115. Ashfaq A, Sharma P. Environmental effects of air pollution and application of engineered methods to combat the problem. J Indust Pollut Control . (2012) 29.

116. Madronich S, de Gruijl F. Skin cancer and UV radiation. Nature . (1993) 366:23–9. doi: 10.1038/366023a0

117. Teramura A. Effects of UV-B radiation on the growth and yield of crop plants. Physiol Plant . (2006) 58:415–27. doi: 10.1111/j.1399-3054.1983.tb04203.x

118. Singh E, Tiwari S, Agrawal M. Effects of elevated ozone on photosynthesis and stomatal conductance of two soybean varieties: a case study to assess impacts of one component of predicted global climate change. Plant Biol Stuttg Ger . (2009) 11(Suppl. 1):101–8. doi: 10.1111/j.1438-8677.2009.00263.x

119. Manderson L. How global Warming is Adding to the Health Risks of Poor People . The Conversation. University of the Witwatersrand. Available online at: http://theconversation.com/how-global-warming-is-adding-to-the-health-risks-of-poor-people-109520 (accessed October 5, 2019).

120. Ministers of Energy and Environment. Federal/Provincial/Territorial Ministers of Energy and Environment (Canada), editor. The Canada-Wide Acid Rain Strategy for Post-2000 . Halifax: The Ministers (1999). 11 p.

121. Zuhara S, Isaifan R. The impact of criteria air pollutants on soil and water: a review. (2018) 278–84. doi: 10.30799/jespr.133.18040205

122. WHO. First WHO Global Conference on Air Pollution and Health. (2018). Available online at: https://www.who.int/airpollution/events/conference/en/ (accessed October 6, 2019).

123. What is the Kyoto Protocol? UNFCCC . Available online at: https://unfccc.int/kyoto__protocol (accessed October 6, 2019).

124. CopenhagenClimate Change Conference (UNFCCC) . Available online at: https://unfccc.int/process-and-meetings/conferences/past-conferences/copenhagen-climate-change-conference-december-2009/copenhagen-climate-change-conference-december-2009 (accessed October 6, 2019).

125. Durban Climate Change Conference,. UNFCCC (2011). Available online at: https://unfccc.int/process-and-meetings/conferences/past-conferences/copenhagen-climate-change-conference-december-2009/copenhagen-climate-change-conference-december-2009 (accessed October 6, 2019).

126. Paris Climate Change Agreement,. (2016). Available online at: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement

Keywords: air pollution, environment, health, public health, gas emission, policy

Citation: Manisalidis I, Stavropoulou E, Stavropoulos A and Bezirtzoglou E (2020) Environmental and Health Impacts of Air Pollution: A Review. Front. Public Health 8:14. doi: 10.3389/fpubh.2020.00014

Received: 17 October 2019; Accepted: 17 January 2020; Published: 20 February 2020.

Reviewed by:

Copyright © 2020 Manisalidis, Stavropoulou, Stavropoulos and Bezirtzoglou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ioannis Manisalidis, giannismanisal@gmail.com ; Elisavet Stavropoulou, elisabeth.stavropoulou@gmail.com

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Advances in air quality research – current and emerging challenges

Ranjeet s. sokhi, nicolas moussiopoulos, alexander baklanov, john bartzis, isabelle coll, sandro finardi, rainer friedrich, camilla geels, tiia grönholm, tomas halenka, matthias ketzel, androniki maragkidou, volker matthias, jana moldanova, leonidas ntziachristos, klaus schäfer, peter suppan, george tsegas, greg carmichael, vicente franco, steve hanna, jukka-pekka jalkanen, guus j. m. velders, jaakko kukkonen.

This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

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Sokhi, R. S., Moussiopoulos, N., Baklanov, A., Bartzis, J., Coll, I., Finardi, S., Friedrich, R., Geels, C., Grönholm, T., Halenka, T., Ketzel, M., Maragkidou, A., Matthias, V., Moldanova, J., Ntziachristos, L., Schäfer, K., Suppan, P., Tsegas, G., Carmichael, G., Franco, V., Hanna, S., Jalkanen, J.-P., Velders, G. J. M., and Kukkonen, J.: Advances in air quality research – current and emerging challenges, Atmos. Chem. Phys., 22, 4615–4703, https://doi.org/10.5194/acp-22-4615-2022, 2022.

We wish to dedicate this article to the following eminent scientists who made immense contributions to the science of air quality and its impacts: Paul J. Crutzen (1933–2021), atmospheric chemist, awarded the Nobel Prize in Chemistry 1995; Mario Molina (1943–2020), atmospheric chemist, awarded the Nobel Prize in Chemistry 1995; Samohineeveesu Trivikrama Rao (1944–2021), air pollution meteorology and atmospheric modelling; Kirk Smith (1947–2020), global environmental health; Martin Williams (1947–2020), air quality science and policy; Sergej Zilitinkevich (1936–2021), atmospheric turbulence, awarded the IMO Prize 2019.

Air pollution remains one of the greatest environmental risks facing humanity. WHO (2016) estimated that over 90 % of the global population is exposed to air quality that does not meet WHO guidelines, and Shaddick et al. (2020) report that 55 % of the world's population were exposed to PM 2.5 concentrations that were increasing between 2010 and 2016. Shaddick et al. (2020) also highlighted marked inequalities between global regions, with decreasing trends in annual average population-weighted concentrations in North America and Europe but increasing trends in central and southern Asia. WHO (2016) has evaluated that approximately 7 million people died prematurely in 2012 throughout the world as a result of air pollution exposure originating from emissions from outdoor and indoor anthropogenic sources. The recent update from the World Health Organization (WHO) of air quality guidelines (WHO, 2021) has emphasized the need to further curtail air pollution emissions and improve air quality globally.

Over the past decade there have been significant developments in the field of air quality research spanning improvements in characterizing sources and emissions of air pollution, new measurement technologies offering the possibility of low-cost sensors, advances in air quality prediction and forecasting, understanding interactions with meteorology and climate, and exposure assessment and management. However, there has not been a broader and comprehensive review of recent developments that push the boundaries of air quality research forward. This was recognized as a major gap in the literature at the last International Conference on Air Quality – Science and Application held online due to the COVID 19 restrictions during 18–26 May 2020. While the concept of this review originated at the International Conference on Air Quality and was stimulated by the presentations and discussions at the conference, this article has been extended to incorporate a wider landscape of research literature in the field of air quality, spanning in particular the developments occurring over the last decade. It is hoped that such a review will help to pave the path for further research in key areas where significant gaps of knowledge still exist and also to make recommendations to guide the direction for future research within the wider community. Although this paper has been written to be accessible to readers from a wide scientific and policy background, it does not seek to provide an introduction to the topic of air quality science. For readers less familiar with the research area, an introductory lecture with a focus on air quality in megacities has been published by Molina (2021). There are also other recent specific reviews, e.g. Manisalidis et al. (2020) on health impacts and Fowler et al. (2020) on air quality developments. This section begins with a short historical perspective on air quality research, before providing the underlying rationale for the key areas considered in this paper.

1.1  A brief historical perspective

In order to provide context to the topics considered in this review, this section briefly touches upon developments of air quality research since the last century. For a more thorough historical survey of air quality issues, the reader is referred to Fowler et al. (2020). Over the previous century there have been a number of landmark events of elevated air pollution that have brought air quality increasingly to prominence, especially in relation to the adverse health impacts. It has been well-known since the early 1900s that cold weather in winter can lead to increased mortality (e.g. Russell, 1926).

The perception that air pollution can have severe health impacts significantly changed when a high-air-pollution episode occurred from 1–5 December 1930 over an industrial town in the Meuse Valley in Belgium (Firket, 1936). The atmospheric conditions were foggy and stagnant. A large proportion of the population experienced acute respiratory symptoms; in addition, health conditions of people with pre-existing cardiorespiratory problems worsened (e.g. Nemery et al., 2001; Anderson, 2009). A similar event was recorded in Donora, Pennsylvania, USA, during October 1948, reported by Schrenk (1949). Although air pollution was generally treated as a nuisance, this “unusual episode” along with that over the Meuse Valley raised awareness and acceptance of the seriousness of air pollution for human health. Both air pollution events, Meuse Valley and Donora, were associated with air pollution from industrial emissions, which accumulated during cold winter periods exhibiting atmospheric stagnation caused by thermal inversions.

The so-called “Great London Smog” occurred from 5–9 December 1952, when similar stagnant atmospheric conditions were prevalent. However, in this case the cause of the severe air pollution was mainly the burning of low-grade, sulfur-rich coal for home heating (e.g. Anderson, 2009). Estimates of deaths resulting from this smog episode range from 4000 to 12 000 (e.g. Stone, 2002).

Since these historical events, the prominence of air pollution sources has changed from industrial and heating to road traffic and become a global threat to health. Trends of air pollution emissions over the past decades have been markedly different for different regions of the world, which has led to similar disparities in air quality concentrations (e.g. Sokhi, 2012). These disparities still exist, as shown in Fig. 1. Spatial distributions in this figure are based on recent analysis showing the large variations in population-weighted annual mean PM 2.5 concentrations across the globe. Commonly, now some of the highest concentrations occur in parts of Asia, Africa, and Latin America as reported by Health Effects Institute (HEI, 2020).

Figure 1 Global distribution of population-weighted annual PM 2.5 concentrations for 2019 (HEI, 2020). Figure produced from https://www.stateofglobalair.org/data/#/air/map (last access: 10 December 2021).

As the recognition of poor air quality has increased, so has the need for the capability to assess levels of key air pollutants not only through monitoring but also through modelling. Historically, although air pollution was obviously poor prior to the first World War (WWI), the primary impetus for development of transport and dispersion (T&D) models during and after WWI was the widespread use of chemical weapons. Fundamental theoretical advances were made by Lewis Fry Richardson, George Keith Batchelor, and many other famous fluid dynamicists. The earliest models were analytical (e.g. Gaussian and K-theory) models used for surface boundary layer releases. With the advent of nuclear weapons in WWII, new emphasis was placed on plume rise and dispersion of large thermal radiological explosions. Thus, the full troposphere and stratosphere had to be modelled.

Later in the 1980s the first investigations came up about the atmospheric consequences of a hypothetical nuclear war initiated by Paul Crutzen (Crutzen and Birks, 1982) and others (Aleksandrov and Stenchikov, 1983; Turco et al., 1983). The concept of a nuclear winter was created. It is one of the first examples that enormous emissions of dust into the atmosphere cause global effects and catastrophic long-term climate change. Also, the nuclear winter scenario was examined in recent years with current model tools for certain nuclear war scenarios (Robock et al., 2007; Toon et al., 2019).

Deposition (wet and dry) was a main concern for many radiological substances, especially for accidental plume dispersion monitoring and modelling of nuclear power plants. In the US, a major change was the introduction of the Clean Air Act in the 1970s. A similar legislation was also issued in other countries. This effort initially focused on T&D models for industrial sources, such as the stacks of fossil power plants. The first applied models were analytical plume rise and Gaussian T&D models. Soon computer codes were written to solve these equations and produce outputs at many spatial locations and for every hour of the year.

1.2  Sources and emissions of air pollutants

From a human health perspective, the key emission sources are those affecting concentration of particulate matter and its size fractions (PM 2.5 and PM 10 ), but also sources affecting other air pollutants, such as ozone and nitrogen dioxide (NO 2 ), especially in highly populated urban areas. Sources in the direct vicinity of urban areas could also be considered especially important, including vehicular traffic and shipping, local industrial sources, various abrasive processes, and residential and commercial heating.

An important component of PM is secondary; regional sources of the precursors of secondary PM are therefore of major importance. These include volatile organic compounds (VOCs), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ammonia (NH 3 ), the first two also being precursors of ozone (O 3 ). Important regional precursor sources are biogenic and industrial emissions of VOCs, agriculture (NH 3 ), road traffic (nitrogen oxides, NO x = NO + NO 2 ), shipping ( NO x and SO 2 ) , and industrial and power generation sources, along with biomass burning and forest fires (VOC, NO x , also primary PM). An important source of PM is the resuspension of dust, especially in arid regions and seasonally also in areas with intensive agriculture.

While Europe and many other parts of the world have experienced decreasing anthropogenic emissions since 1990, climate change and its associated impacts can lead to an increase in dust and wildfire emissions, as a result of increased drought and desertification. Climate change is also expected to lead to significantly higher biogenic VOC emissions in different regions, e.g. Arctic and China (Kramshøj et al., 2016; Liu et al., 2019), also from urban vegetation (Churkina et al., 2017).

The emission inventory work in Europe is harmonized through the official reporting of EU member states of their emissions to the European Commission through an e-reporting scheme (Implementing Provisions for Reporting, IPR of EU Air Quality Directive, 2008/50/EC). The methodologies applied by the individual member states can, however, differ, which can sometimes bring inconsistencies into the reported national emissions. Within the last decade the EU-funded MACC project and the on-going Copernicus service have been developing consistent European-wide and global gridded emission inventories, which are suitable for air quality modelling. The access to the different inventories and analysis of differences have been facilitated by centralized databases like Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD, https://eccad.aeris-data.fr/ , last access: 7 July 2021).

Developing innovative methods to refine the emission inventories feeding the models and conducting studies to discriminate the role of different sources in local air quality have become essential to reduce uncertainties in predictions of urban air quality and help target effective abatement measures (Borge et al., 2014). The emission compilation that needs to be carried out also requires (i) the involvement of all stakeholders (e.g. citizens, decision-makers, service providers, and industrialists) and (ii) the implementation of dedicated and specific tools for assessing quality of the urban environment. This type of research can be used for quantifying the impacts of different emission control scenarios and supporting incentive policies (Fulton et al., 2015).

One area that has been receiving increased attention recently is ship emissions, which are an important source of air pollution, especially in coastal areas and harbour cities. Detailed bottom-up emission inventories based on ship position data have been established for SO 2 , NO x , PM, carbon monoxide (CO), and VOCs for various marine regions and also globally (Jalkanen et al., 2009, 2012, 2016; Aulinger et al., 2016; Johansson et al., 2017). Despite these advances, the evaluation of the shipping emissions for products of incomplete combustion, such as black carbons (BC), CO, and VOCs, is uncertain, as these may depend on characteristics which are not known accurately, such as the service history of ships. Regional model applications have quantified the contribution of shipping to air pollution to be of the order of up to 30 %, depending on pollutant and region (e.g. Matthias et al., 2010; Jonson et al., 2015; Aulinger et al., 2016; Karl et al., 2019a; Kukkonen et al., 2018, 2020a). More recent studies focus on the harbour and city scale, where relative contributions from ships to NO 2 concentrations may be even higher (Ramacher et al., 2019, 2020). Effects of in-plume chemistry, e.g. regarding the NO x removal and secondary aerosol formation, are not sufficiently well considered in larger-scale dispersion models (e.g. Prank et al., 2016).

1.3  Air quality in cities

Extensive and growing urban sprawl in different cities of the world is leading to environmental degradation and the depletion of natural resources, including the availability of arable land, thereby resulting in per capita increases of resource use and greenhouse gas emissions as well as air pollution, with significant impacts on health (WHO, 2016). Urban features have a profound influence on air quality in cities due to diurnal changes in urban air temperature; the urban heat island, which develops in particular during heat waves (Halenka et al., 2019); stable stratification and air stagnations; and wind flow and turbulence near and around streets and buildings affecting air pollution hotspots. Climate change will modify urban meteorology patterns which will affect air quality in cities and may even affect atmospheric chemistry reaction rates. The relative role of urban meteorology and climate compared to local emissions and chemistry is complex, non-linear, and subject to continued research, especially with boundary layer feedback (Baklanov et al., 2016).

With air quality standards being regularly exceeded in many urban areas across the globe, air quality issues are today strongly centred on the phenomena of proximity to emitters such as traffic – or certain industrial activities present in urban areas – but they also call for better understanding of contributions from long-range regional, diffuse, or specific local sources (e.g. residential wood combustion and maritime traffic) to the daily exposure of city dwellers (e.g. EEA, 2020b). In particular, the prevalent issue of individual exposure calls for a better understanding of the variability of concentrations at street level and the dispersion of emissions in the built environment. However, the approach implemented should not only be local, since urban air quality management involves a set of scales going beyond the city limits, in terms of the economic, societal, or logistical levers involved, but also include the interplay of pollutant sources and transport extending to regional and even global scales.

Beyond the scales of governance and urban functioning, it becomes essential to take into account the fact that scale interactions also exist in a geophysical context. The urban dweller has become especially exposed and vulnerable to the impacts of natural disasters, weather, and climate extreme events and their environmental consequences. These events often result in domino effects in the densely populated, complex urban environment in which system and services have become interdependent. There has never been a bigger need for user-focused urban weather, climate, water, and related environmental services in support of safe, healthy, and resilient cities (Baklanov et al., 2018b; Grimmond et al., 2020). The 18th World Meteorological Congress (2019) noted the current rapid urbanization and recognized the need for an integrated approach providing weather, climate, water, and related environmental services tailored to the urban needs (WMO, 2019).

1.4  Measuring air pollution

Measurements in the atmosphere are necessary not only for air quality monitoring but also for different purposes in weather forecast and climate change study, energy production, agriculture, traffic, industry, health protection, or tourism (e.g. Foken, 2021). Additional areas of application include the detection of emissions into the atmosphere, disaster monitoring, and the initialization and evaluation of modelling. Depending on the different objectives, in situ measuring, and ground-based, aircraft-based, and space-based remote sensing techniques and integrated measuring techniques are available. Satellite observations are a growing field of development due to increasingly small and thus cost-effective platforms (down to nanosatellites). Another area of growth is the use of unmanned aerial vehicles (UAVs) for air pollution measurements (Gu et al., 2018).

Networks of ground-level measurements with continuous monitoring stations remain a major effort, but the coverage is starkly regionally dependent and with scarce measurements in the continent of Africa (Rees et al., 2019; Bauer et al., 2019).

Over the past decade, there has been increasing recognition that measuring air pollution at outdoor locations may not necessarily reflect the health impact on individuals or populations. The research should therefore be directed to the evaluation of both personal exposure and dynamic population exposure (Kousa et al., 2002; Soares et al., 2014). Temporal concentration and location information is needed on air pollution concentrations at all the relevant outdoor and indoor microenvironments. The actual exposure of individuals and populations cannot realistically be represented by selected concentrations at fixed outdoor locations, due to the fine-resolution spatial variability of concentrations in urban areas and the mobility of people (Kukkonen et al., 2016b; Singh et al., 2020b).

Further development of the installation of a larger number of cheap measurement devices, especially for PM 2.5 , that are operated by people interested in air quality in so-called citizen science projects is ongoing ( https://www.eea.europa.eu/publications/assessing-air-quality-through-citizen-science , last access: 21 February 2022). Examples of such projects are the Open Knowledge Foundation Germany; OK Labs ( https://luftdaten.info/ , last access: 21 February 2022), Opensense (open air quality, meteorological, and noise data platform), connected with OK Labs ( https://opensensemap.org/ , last access: 21 February 2022); or AirSensEUR, an open framework for air quality monitoring ( https://airsenseur.org/website/airsenseur-air-quality-monitoring-open-framework/ , last access: 21 February 2022). However, the accuracy of these measurements is still debated (Duvall et al., 2021; Concas et al., 2021), although the development of more accurate but still low-cost devices is ongoing for denser measurement networks, 3D measurements, and new modelling. Measurements are not only required for compliance and for monitoring long-term trends. Observations are used more and more for evaluating models and where measurements might also be used to nudge the model results, for example through data assimilation (see for example Campbell et al., 2015; K. Wang et al., 2015).

1.5  Air quality modelling from local to regional scales

Air pollution models have played and continue to play a pivotal role in furthering scientific understanding and supporting policy. Additionally, for air quality assessments by regulatory methods, it is also important to predict or even forecast peak pollutant concentrations to prevent or reduce health impacts from acute episodes. Both complex and simple models have also been developed for dispersion on urban and local scales. A review has been provided by Thunis et al. (2016) that examines local- and regional-scale models, especially from an air quality policy perspective. Briefly, the spectrum of finer- and urban-scale air quality models applied for urban areas is very broad and includes urbanized chemistry–transport models (CTMs) coupled with high-resolution meso-scale numerical weather prediction (NWP) models, computational fluid dynamics (CFD) obstacle-resolved models in Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) formulations (the latest mostly only for research studies), and statistical and land use regression (LUR) models. Developments in local-scale air quality models continue. For example, the dispersion on local or urban scales that also considers obstacle effects has recently been investigated using wind tunnels and CFD models (e.g. Badeke et al., 2021).

During the last decades many countries have established real-time air quality forecasting (AQF) programmes to forecast concentrations of pollutants of special health concerns. The history of AQF can be traced back to the 1960s, when the US Weather Bureau provided the first forecasts of air stagnation or pollution potential using numerical weather prediction (NWP) models to forecast conditions conducive to poor air quality (e.g. Niemeyer, 1960). Accurate AQF can offer tremendous societal and economic benefits by enabling advanced planning for individuals, organizations, and communities in order to avoid exposure and reduce adverse health impacts resulting from air pollution. Forecasts can also assist urban authorities, for example, in changing and managing traffic and hence reduce road emissions in a particular area. Air quality modelling, however, can provide a more holistic assessment of air pollution for policy makers and decision makers to develop strategies that do not compromise benefits in one area while worsening air pollution in another.

Two main approaches can be generally distinguished in AQF: empirical/statistical methods and chemical transport modelling. Until the mid-1990s, AQF was mainly performed using empirical approaches and statistical models trained with or fitted to historical air quality and meteorological data (e.g. Aron, 1980). The empirical/statistical approaches have several common drawbacks for AQF which are reviewed and discussed by Zhang et al. (2012a) and Baklanov and Zhang (2020).

The chemical transport models (CTMs) are more commonly used today for air quality assessment and forecasting. Over the last decade AQF systems based on CTMs have been developed rapidly and are currently in operation in many countries. Progress in CTM development and computing technologies has allowed daily AQFs using simplified or more comprehensive 3D CTMs, such as offline-coupled and online-coupled meteorology–chemistry models. There are several comprehensive review papers, e.g. Kukkonen et al. (2012), Zhang et al. (2012a, b), Baklanov et al. (2014), Bai et al. (2018), and Baklanov and Zhang (2020), which have more thoroughly examined the development and principles of 3D global and regional AQF models and identified areas of improvement in meteorological forecasts, chemical inputs, and model treatments of atmospheric physical, dynamic, and chemical processes.

Interest in regional pollution arose in the 1980s, initially spurred by the acid rain problem (Sokhi, 2012; Fowler et al., 2020). In the past few years, these regional air pollution models have become routinely linked with outputs of NWP models such as WRF and ECMWF. Models such as WRF coupled with CTMs are often run in a nested mode down to an inner domain with a grid size of 1 km. As computer speed and storage continually improve with developments in parameterization, in the future, these nested models may potentially take over most applied T&D analyses on local scales. Another development over the last decade is the increasing use of ensemble techniques which have also progressed and make it possible to cover an increasing range of pollutants and physical parameters, using a multiplicity of observations (e.g. ground, airborne, satellite) that enable the different dimensions of models to be investigated. At the same time that the use of regional Eulerian models has grown (e.g. Rao et al., 2020), the puff, particle, and plume T&D models for small scales and mesoscales have been improved. Several agencies and countries now have Lagrangian particle or puff models that are linked with an NWP model and are applied at all scales (Ngan et al., 2019).

1.6  Interactions of air quality, meteorology, and climate

Meteorological processes are the main driver for atmospheric pollutant dispersion, transformation, and removal. However, as studies have shown (e.g. Baklanov et al., 2016; Pfister et al., 2020), the chemistry dynamics feedbacks exist among the Earth system components, including the atmosphere. Potential impacts of aerosol feedbacks can be broadly explained in terms of four types of effects: direct, semidirect, first indirect, and second indirect (e.g. Kong et al., 2015; Fan et al., 2016). Such feedbacks, forcing mechanisms, and two-way interactions of atmospheric composition and meteorology can be important not only for air pollution modelling but also for NWP and climate change prediction (WMO, 2016).

There is a strong scientific need to increase interfacing or even coupling of prediction capabilities for weather, air quality, and climate. The first driver for improvement is the fact that information from predictions is needed at higher spatial resolutions (and longer lead times) to address societal needs. Secondly, there is the need to estimate the changes in air quality in the future driven by climate change. Thirdly, continued improvements in prediction skill require advances in observing systems, models, and assimilation systems. In addition, there is also growing awareness of the benefits of more closely integrating atmospheric composition, weather, and climate predictions, because of the important feedbacks resulting from the role that aerosols (and atmospheric composition in general) play in these systems. Recently, this trend for further integration has led to greater coupling of atmospheric dynamics and composition models to deliver seamless Earth system modelling (ESM) systems.

1.7  Air quality and health perspectives

Air pollution has serious impacts on our health by reducing our life span and exacerbating numerous illnesses. The Global Burden of Disease Study 2019 (GBDS, 2020) summarizes a comprehensive assessment of the impact of a large number of stressors including air pollution. One of the most hazardous air pollutants is particulate matter. Primary particles are directly released into the atmosphere and originate from natural and anthropogenic sources. Secondary particles are formed in the atmosphere by chemical reactions involving, in particular, gas-to-particle conversion. Primary particles tend to be larger than secondary particles. Ultra-fine and fine particles, on the other hand, deposit into the respiratory system; these may reach human lungs and blood circulation and may therefore cause severe adverse health effects (e.g. Maragkidou, 2018; Stone et al., 2017).

When considering numbers of particles, most of these in the atmosphere are smaller than 0.1  µm in diameter (e.g. Jesus et al., 2019). On the other hand, the majority of the particle volume and mass is found in particles larger than 0.1  µm (e.g. Filella, 2012). The particle number concentrations are therefore in most cases dominated by the ultra-fine aerosols, whereas the mass or volume concentrations are dominated by the coarse and accumulation mode aerosols (e.g. Seinfeld and Pandis, 2016). Other characteristics of PM have also been shown to be important in relation to health impact. The characteristics of atmospheric particles in addition to the size include mass, surface area, chemical composition, and shape and morphology (Gwaze, 2007).

It has been convincingly shown in previous literature that the exposure to particulate matter (PM) in ambient air can be associated with negative health impacts (e.g. Hime et al., 2018; Thurston et al., 2017). It is also known that PM can cause health effects combined with other environmental stressors, such as heat waves and cold spells, allergenic pollen, or airborne microorganisms. For understanding such associations, reliable methods are needed to evaluate the exposure of human populations to air pollution.

The strong association between the exposure to mass-based concentrations of ambient PM air pollution and severe health effects has been found by numerous epidemiological studies (e.g. Pope et al., 2020). In particular, there is extensive scientific evidence to suggest that exposure to PM air pollution can have acute effects on human health, resulting in respiratory, cardiovascular and lung problems, chronic obstructive pulmonary diseases (COPDs), asthma, oxidative stress, immune response, and even lung cancer (e.g. Chen et al., 2017; Hime et al., 2018; Falcon-Rodriguez et al., 2016; Thurston et al., 2017). For instance, a cohort study conducted across Montreal and Toronto (Canada) on 1.9 million adults during four cycles (1991, 1996, 2001, and 2006) resulted in a possible connection between ambient ultra-fine particles and incident brain tumours in adults (Weichenthal et al., 2020). Recent work has also investigated assessment of the health impacts of particulate matter in terms of its oxidative potential (e.g. Gao et al., 2020; He et al., 2021).

1.8  Air quality management and legislative and policy responses

Air quality management and policy is an important but also complex task for political decision makers. It started in the middle of the last century when concerns about smoke and London smog arose. The national authorities at that time reacted by stipulating efficient dust filters and high stacks for large firings. In the 1980s, forest dieback led to a shift in focus to other important air pollutants, especially SO 2 , NO x , and later ozone, and so also on the ozone precursors including VOCs. In the 1990s studies showed a relation between PM 10 and “chronic” mortality, thus drawing particular attention to the health effects of fine particles (WHO, 2013b). Also, in the 1990s, the European Commission (EC) increasingly took over the responsibility for air pollution control from the authorities of the member states, on the basis that there is free trade of goods in the European Union and also transboundary air pollutants.

The EC launched the first Air Quality Framework Directive 96/62/EC and its daughter directives, which regulated the concentrations for a range of pollutants including ozone, PM 10 , NO 2 , and SO 2 . The first standard for vehicles (Euro 1) was established in 1991. The sulfur content in many oil products was reduced starting in the late 1990s. Some of the problems with air pollution in the EU, e.g. the acidification of lakes, were caused by the transport of air pollutants from eastern Europe to the EU. This problem was discussed in the United Nations Economic Commission for Europe (UNECE), as all countries involved were members of this commission. The Convention on Long-range Transboundary Air Pollution within the UNECE agreed on eight protocols, which set aims for reducing emissions, starting in 1985 with reducing national SO 2 emissions, with the latest protocol being the revised Protocol to Abate Acidification, Eutrophication and Ground-level Ozone (Gothenburg Protocol), which limits national SO 2 , NO x , VOC, NH 3 , and PM 2.5 emissions.

Over time, regulation of air pollution has become more stringent and thus more complex and more costly. To achieve acceptance, it had to be demonstrated that the measures would achieve the environmental and climate protection goals safely and efficiently, i.e. with the lowest possible costs and other disadvantages, and that the advantages of environmental protection outweigh the disadvantages (Friedrich, 2016). It is a scientific task to support this demonstration, mainly by developing and applying integrated assessments of air pollution control strategies, e.g. by carrying out cost–effectiveness and cost–benefit analyses. With a cost–effectiveness analysis (CEA) the net costs (costs minus monetizable benefits) for improving an indicator used in an environmental aim with a certain measure are calculated, e.g. the costs of reducing the emission of 1 t of CO 2,eq . The lower the unit costs, the higher the effectiveness of a policy or measure. The CEA is mostly used for assessing the effects associated with climatically active species, as the effects are global. The situation is different for air pollution, where the avoided damage of emitting 1 t of a pollutant varies widely depending on time and place of the emission.

The more general methodology is cost–benefit analysis (CBA). In a CBA, the benefits, i.e. the avoided damage and risks due to an air pollution control measure or bundle of measures, are quantified and monetized. Then, costs including the monetized negative impacts of the measures are estimated. If the net present value of benefits minus costs is positive, benefits outweigh the costs. Thus the measure is beneficial for society; i.e. it increases welfare. Dividing the benefits minus the nonmonetary costs by the monetary costs will result in the net benefit per euro spent, which can be used for ranking policies and measures.

Of course, for performing mathematical operations like summing or dividing costs and benefits, they have first to be quantified and then converted into a common unit, for which a monetary unit, i.e. euros, is usually chosen.

The term “integrated” in the context of integrated assessment means that – as far as possible – all relevant aspects (disadvantages, benefits) should be considered, i.e. all aspects that might have a non-negligible influence on the result of the assessment. Given the high complexity of answering questions related to managing the impacts of air quality, a scientific approach is required to conduct an integrated assessment, which is defined here as “a multidisciplinary process of synthesizing knowledge across scientific disciplines with the purpose of providing all relevant information to decision makers to help to make decisions” (Friedrich, 2016).

The focus of this review is on research developments that have emerged over approximately the past decade. Where needed, older references are given, but these either provide a historical perspective or support emerging work or where no recent references were available. The following areas of air quality research have been examined in this review:

air pollution sources and emissions;

air quality observations and instrumentation;

air quality modelling from local to regional scales;

interactions between air quality, meteorology, and climate;

air quality exposure and health;

air quality management and policy development.

Each section begins with a brief overview and then examines the current status and challenges before proceeding to highlight emerging challenges and priorities in air quality research. In terms of climate research, the focus is more on the interactions between air quality and meteorology with climate and not on climate change per se.

The section on air quality observations focuses on new technological developments that have led to remote sensing, low-cost sensors, crowdsourcing, and modern methods of data mining rather than attempting to cover the more traditional instrumentations and measurements which are dealt with, e.g. in Foken (2021). After considering these themes of research, the Discussion section pulls together common strands on science and implications for policy makers.

3.1  Brief overview

A fundamental prerequisite of successful abatement strategies for reduction of air pollution is understanding the role of emission sources in ambient concentration levels of different air pollutants. This requires a good knowledge of air pollution sources regarding their strength, chemical characterization, spatial distribution, and temporal variation along with knowledge on their atmospheric transport and processing. In observations of ambient air pollution, typically a complex mixture of contributions from different pollution sources is observed. These source contributions have to be disentangled before efficient reduction strategies targeting specific sources can be set up. Consequently, our discussion below is divided into two main topics: (i) emission inventories and emission pre-processing for model applications and (ii) source apportionment methods and studies.

This paper cannot give a full overview of the status of and the emerging challenges in all emissions sectors. For example, we do not deal with aviation as the impact on air quality in cities is generally rather small or concentrated around the major airports, or with construction machinery or industrial sources which make significant contributions to air pollution in some areas. Instead, we put emphasis on two emission sectors that have experienced important methodology developments in recent years in terms of emission inventories and that are of major concern for health effects: exhaust emissions from road traffic and shipping. We also touch other anthropogenic emissions, e.g. from agriculture and wood burning, As later in this paper we will explain, since individual exposure including the exposure to indoor pollution should gain importance in assessing air pollution, emissions from indoor sources will be addressed in a subchapter. Natural and biogenic emissions encompass VOC emissions from vegetation, NO emissions from soil, primary biological aerosol particles, windblown dust, methane from wetlands and geological seepages, and various pollutants from forest fires and volcanoes; these are described in a series of papers edited by Friedrich (2009). As natural and biogenic emissions depend on meteorological data, which are input data for the atmospheric model, they are usually estimated in a submodule of the atmospheric model. They are not further discussed here.

3.2  Current status and challenges

3.2.1  emissions inventories.

In the European Union, emissions of the most important gaseous air pollutants have decreased during the last 30 years (see Fig. 2). SO 2 and CO show reductions of at least 60 % (CO) or almost 90 % (SO 2 ). Also, NO x and non-methane volatile organic compound (NMVOC) emissions decreased by approx. 50 % while NH 3 shows much lower reductions of 20 % only. Similar to NH 3 , PM emissions also stay at similar levels compared to 2000 (Fig. 2b). Only black carbon shows considerably larger reductions, because of larger efforts to reduce BC, in particular from traffic. While traffic is the most important sector for NO x emissions and an important source for BC, PM emissions stem mainly from numerous small emission units like households and commercial applications (Fig. 2c).

Figure 2 EU-28 emission trends in absolute and relative numbers for (a)  the main gaseous air pollutants and (b)  particulate matter. Panel  (c) shows the share of EU emissions of the main pollutants by sector in 2018 (EEA, 2020b).

In parallel, research came on the path of accompanying and evaluating local emission control measures in a more comprehensive and systemic approach to urban space. The main technical advances of this research field have consisted in producing a more reliable assessment of the predominant emissions on the scale of an agglomeration/region. This has been done in order to feed the models with activity-based emission data such as population energy-consuming practices or local characteristics of road traffic, with the concern to better include their temporal variability or weather condition dependency. The originality of these approaches has been to develop the emissions inventories and modelling efforts in collaboration with stakeholders, for better data reliability and greater realism in policy support.

Improved and innovative representation of emissions, such as real configuration of residential combustion emission sources (location of domestic households using biomass combustion and surveys regarding the characteristics and use of wood stoves, boilers, and other relevant appliances) allows more realistic diagnoses (e.g. Ots et al., 2018; Grythe et al., 2019; Savolahti et al., 2019; Plejdrup et al., 2016; Kukkonen et al., 2020b). Also, increased use of traffic flow models for the representation of mobile emissions have provided refined traffic and emission estimates in cities and on national levels, as a path for improved scenarios (e.g. Matthias et al., 2020a). Kukkonen et al. (2016a) presented an emission inventory for particulate matter numbers (PNs) in the whole of Europe, and in more detail in five target cities. The accuracy of the modelled PN concentrations (PNCs) was evaluated against experimental data on regional and urban scales. They concluded that it is feasible to model PNCs in major cities within a reasonable accuracy, although major challenges remained in the evaluation of both the emissions and atmospheric transformation of PNCs.

For shipping, and in most recent development also aviation, inventories based on position data from transponders on individual vessels are becoming more widely used and provide refined emission inventories with high spatial resolution for use in harbour-city and airport studies (e.g. Johansson et al., 2017; Ramacher et al., 2019, 2020). Refined emission inventory and emission modelling are in many cases integrated into a complete regional-to-local modelling chain, which allows these refined data to be taken into account and ensures the consistency of the final results. This links to the subsequent chapters on air quality and exposure modelling.

3.2.2  Preprocessing emission data for use in atmospheric models

Emission inventories usually contain annual data for administrative units apart from data for large point sources and line sources. Atmospheric models, however, need hourly emission data for the grid cells of the model domain. Furthermore the height of the emissions (above ground), and for NMVOC, PM, and NO x a breakdown into species or classes of species according to the chemical scheme of the atmospheric model, is necessary. For PM, information is also required on the size distribution. Thus, a transformation of the available data into structure and resolution as needed by the models has to be made (Matthias et al., 2018).

For the spatial resolution, standard procedures for several emission sectors are described in Chap. 7 of the EMEP/EEA air pollutant emission inventory guidebook 2019 (EMEP/EEA, 2019). In principle, proxy data that are available in high spatial resolution and that are correlated to the activity data of the emission sources are used. For point sources (larger sources like power plants) these are coordinates of the stack. For road transport, shape files with coordinates at least for the main road network are used together with traffic counts (for past times) or traffic flow modelling for scenarios for future years. Figure 3 shows as an example the result of a distribution of road transport emissions to grid elements for the EU countries Norway and Switzerland. The major roads as well as the urban areas can be identified as sites for the NO x emitters. For households, land use data (e.g. residential area with a certain density) combined with statistical data (number of inhabitants, use of heating technologies) are used. Especially for heating with wood-specific algorithms using data on forest density and specific residential wood combustion, emission inventories and models have been developed (Aulinger et al., 2011; Bieser et al., 2011a; Mues et al., 2014; Paunu et al., 2020; Kukkonen et al., 2020b). Thiruchittampalam (2014) contains a comprehensive description of the methodology for the spatial resolution of emissions for Europe for all emission source categories.

The algorithms for disaggregating annual emission data into hourly data follow a similar scheme. All kinds of available data containing information about the temporal course of activities leading to emissions are used for temporal disaggregation. For road transport, data from continuously monitoring the traffic volume are available, and statistical data provide the electricity production from power plants. The activity of firings for heating depends on the outside temperature or more precisely on the degree days, an indicator for the daily heating demand, together with an empirical daily course of the use of the heating (Aulinger et al., 2011; Bieser et al., 2011a; Mues et al., 2014).

Figure 3 Spatial distribution of national PM 10 emissions from road transport in the EU28 on a 5 km×5 km grid (Schmid, 2018).

A detailed description of the methodology for the temporal resolution of emission data for all source sectors in Europe is contained in Thiruchittampalam (2014). A compilation of temporal profiles for disaggregating annual into hourly data is published by Denier van der Gon (2011) and in Matthias et al. (2018). New sets of global time profiles for numerous emission sectors have recently been provided by Crippa et al. (2020) and Guevara et al. (2021). Crippa et al. (2020) provide high-resolution temporal profiles for all parts of the world including Europe. Guevara et al. (2021) developed temporal profiles as part of the Copernicus Atmosphere Monitoring Service and also include higher-resolution European profiles designed for regional air pollution forecasting. The temporal profiles include time-dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, country-specific weekly and daily profiles, and a flexible system to compute hourly emissions. Thus, a harmonized temporal distribution of emissions is given, which can be applied to any emission database as input for atmospheric models up to the global scale.

For the temporal and spatial distribution of agricultural emissions a number of approaches have been established; these are based on information on farmer practice, available proxy data, and meteorological data, e.g. farmland and animal densities and the consideration of temperature and wind speed for agricultural emissions (e.g. Skjøth et al., 2011; Backes et al., 2016; Hendriks et al., 2016; see Fig. 4).

Figure 4 Break-down of agricultural emissions into sub-sectors in order to improve the spatial and temporal distribution (from Backes et al., 2016).

Comprehensive VOC split vectors are provided by Theloke and Friedrich (2007) and more recently by Huang et al. (2017). Region- and source-specific speciation profiles of NMVOC species or species groups are compiled and provided, with corresponding quality codes specifying the quality of the mapping. They can then be allocated to the reduced number of VOC species used in the chemical reaction schemes implemented in atmospheric chemistry–transport models. Typical heights for the release of emissions, e.g. typical stack heights, are given by Pregger and Friedrich (2009) and Bieser et al. (2011b).

Model systems have been developed that perform the entire temporal and spatial emission distribution and the NMVOC and PM speciation in order to provide hourly gridded emission data for use in different chemistry–transport models. Recent examples are the HERMES model (Baldasano et al., 2008; Guevara et al., 2013, 2019, 2020), FUME (Benešová et al., 2018), and the Community Emissions Data System (CEDS) model system (Hoesly et al., 2018). Because natural emissions, e.g. biogenic emissions, sea spray, and dust, depend strongly on the meteorological conditions, these emissions are frequently calculated within the chemistry–transport models (CTMs). Other established CTMs like the EMEP model (Simpson et al., 2012) or LOTOS-EUROS (Manders et al., 2017) do not use emissions preprocessors but distribute gridded emissions in time based on standard temporal and speciation profiles alongside the chemistry–transport calculations in order to avoid storing and reading large emission data sets.

3.2.3  Road transport emissions

Exhaust emissions from road transport have been a significant source of primarily NO x and ultra-fine particles (UFPs) in urban areas around the world. In the EU, road transport is the single most important source of NO x , producing 28.1 % of total NO x emissions (EEA, 2019b). In terms of PM 10 , its contribution is 7.7 % when both exhaust and non-exhaust sources are counted and 2.9 % when only exhaust emissions are considered (EEA, 2019b). Road transport contributes 32 %–97 % of total UFP in urban areas (Kumar et al., 2014). The difference between PM 10 and UFP contributions from road transport is a direct outcome of the small size of exhaust particles that mostly reside in the UFP range (Vouitsis et al., 2017).

The proximity of people to the emission source (vehicles) significantly increases exposure to traffic-induced pollution (Żak et al., 2017). Consequently, traffic exhaust emissions have been extensively studied, and comprehensive sets of emission factors have been available for a long time. The two most widespread methods to estimate emissions in Europe include COPERT ( https://www.emisia.com/utilities/copert/ , last access: 22 February 2022) and HBEFA ( https://www.hbefa.net , last access: 22 February 2022). These methods share the same experimental database of vehicular emissions – the so-called ERMES database ( https://www.ermes-group.eu/ , last access: 22 February 2022) – but express emission factors in different modelling terms. COPERT is also a part of the EMEP/CORINAIR Emission Inventory Guidebook (EMEP/EEA, 2019).

These models define the emissions for several pollutant species, for a wide range of vehicles and operating conditions. Emission factors are regularly being updated in an effort to reflect the best knowledge of on-road vehicle emission levels. Despite this, there are still some uncertainties in estimating emissions from road transport, in particular when these are to be used as input to air quality models. More attention is therefore needed in the following directions.

Emission factors for the latest vehicle technologies always come with some delay. This is the result of the time lag between placement of a new vehicle technology on the road and the organization of measurement campaigns to collect the experimental information required to develop the emission factors. The latest regulation (Reg. (EU) 2018/858) – mandating a minimum number of market surveillance tests in the different member states – may help to reduce this lag and to extend the availability of vehicle tests on which to base emission factors.

The availability of measurements of pollutants which are currently not included in emissions regulations (NH 3 , N 2 O, CH 4 , PAHs, etc.) is limited compared to regulated pollutants. Moreover, any available measurements have been mostly collected in the laboratory, due to instrumentation limitations for on-road measurements. Therefore, emission models may miss on-road operation conditions that potentially lead to high emissions rates of non-regulated pollutants.

The increase in emissions with vehicle age is still subject to high uncertainty. Emission increases with age may be due to normal system degradation, the presence of high emitters on the road (Murena and Prati, 2020) or vehicle tampering to improve performance or decrease operational costs. Current models use degradation functions based on remote sensing data (e.g. Borken-Kleefeld and Chen, 2015). This is a useful source of information, but remote sensing data need to be collected in additional locations in the EU, covering a range of climatic and operation conditions.

Emission models may be conservative in their approach of estimating emissions in extreme conditions of temperature (Lozhkina et al., 2020), altitude, road gradient, or creeping speeds. Although such conditions may not be substantial for estimating the total emissions of most countries, they can potentially lead to a significant underestimation of emissions that have to be locally calculated for high-resolution air quality modelling.

Despite uncertainties in modelling emissions, there is a high level of confidence that exhaust gas emissions of mobile sources will continue to decrease in the years to come. For example, Matthias et al. (2020b) projected that the contribution of road traffic to ambient NO 2 concentrations will decrease from 40 %–60 % in 2010 to 10 %–30 % in 2040. This is the result of relevant technological development driven by demanding CO 2 reduction targets and air pollutant emission standards applicable to new vehicles. An example of such technological development is the increase in the availability of plug-in hybrid vehicles, which have exhibited great potential in reducing both pollutant emissions and CO 2 emissions from traffic (Doulgeris et al., 2020).

Technological improvement in decreasing emissions from internal combustion engines will be accelerated in the EU market due to the current Euro 6d emission standard and the upcoming Euro 7 regulation but also the proliferation of electric power trains to meet CO 2 targets. The only road transport pollutant not significantly affected by the introduction of electric vehicles is non-exhaust PM coming from tyre, brake, and road wear, with estimates suggesting both increases due to heavier vehicles and reductions due to wider exploitation of regenerating braking systems (Beddows and Harrison, 2021).

New techniques are also being developed with the capacity to monitor emissions of vehicles in operation. This can verify that emissions remain below limits in actual use and not just in type approval testing conditions. A current example of such on-board monitoring systems is the on-board fuel consumption measurement (OBFCM) device which is already mandatory for new light-duty vehicles and is being extended for heavy-duty vehicles (Zacharof et al., 2020). Information from such systems, together with new computation methods (big data), can provide very useful information for improving the reliability and temporal and spatial resolution of current emissions inventories.

3.2.4  Shipping emissions

Ships consume high amounts of fossil fuels. On the global scale they emit amounts of CO 2 comparable to big industrialized countries like Germany and Japan. Because ships use high-sulfur fuels, regardless of the global introduction of the 0.5 % sulfur cap in 2020, and typically are not equipped with advanced exhaust gas cleaning systems, their share from global CO 2 is 2.9 %, but corresponding shares of NO x and SO x are considerably higher, 13 % and 12 %, respectively (IPCC, 2014; Smith et al., 2015; Faber et al., 2020). Ship routes are frequently located in the vicinity of the coast, which may go along with significant contributions to air pollution in coastal areas. Effects on ozone formation and secondary aerosol formation also need to be considered.

The environmental regulation concerning the sulfur emissions from ships has been in place in the Baltic Sea since 2006, with the North Sea following in 2007. Currently, also North America and some Chinese coastal areas have stringent sulfur limits for ship fuels. Everywhere else the use of high-sulfur fuel in ships was allowed until the start of 2020, when sulfur reductions of a maximum of 0.5 % S were extended to all ships (IMO, 2019). This has been estimated to reduce the premature deaths by 137 000 each year (Sofiev et al., 2018). Nitrogen oxide emissions from ships are regulated by NO x Emission Control Areas (ECAs), which currently exist only in the coastlines of Canada and the US. The Baltic Sea and the North Sea areas will quickly follow, because in 2021 all new ships sailing these areas must comply with 80 % NO x reduction.

The introduction of the automatic identification system (AIS), long-range identification and tracking (LRIT), and vessel monitoring systems (VMSs) have enabled tracking of individual ships in unprecedented detail. These navigational aids offer an excellent description of vessel activities on both local and global scales.

Currently, ship emission models using AIS data as an activity source are most popular. They can have accurate information about quantity, location, and time of the emissions. Most of the model systems applied today use a bottom-up approach to calculate shipping emissions (e.g. Jalkanen et al., 2009, 2012, 2016; Johansson et al., 2017; Aulinger et al., 2016). The combination of vessel activity, technical description, and an emission model allows for prediction of emissions for individual ships. This also facilitates comparisons to fuel reports, like those of the EU Monitoring, Reporting, and Verification (MRV) scheme or IMO Data Collection System (DCS). Emission models may also include external contributions, like wind, waves, ice, or sea currents in vessel performance prediction, which brings them closer to realistic conditions experienced by ships than the assumptions applied for ideal conditions (Jalkanen et al., 2009; Yang et al., 2020). A vessel-level modelling approach allows for very high spatio-temporal resolution and flexible 4D grids (lat, long, height, time) on which the data can be given. New information about modified or new emission factors for certain chemical species can easily be adopted in the models. Ship emission data are available on a global grid at 0.1 ∘ × 0.1 ∘ and in higher resolution for regional domains in Europe (see Fig. 5), North America, and East Asia (e.g. Johansson et al., 2017). The emission model systems also allow for the construction of future scenarios; see e.g. Matthias et al. (2016) for the North Sea, Karl et al. (2019c) for the Baltic Sea or Geels et al. (2021) for a possible opening of new routes in the Arctic.

Figure 5 The predicted SO x emissions from ships in Europe in 2018, computed using the STEAM model (e.g. Johansson et al., 2017). Use of low-sulfur fuels and SO x scrubbers is concentrated to the North Sea and Baltic Sea ECAs. Background map © US Geological Survey, Landsat8 imagery.

Emissions from ships in ports can be quantified for arrival and departure following the same AIS-based approach as for regional and global shipping emissions. Emissions for ships at berth are estimated based on ship type and size, but with large uncertainties.

Introduction of emission limits gives shipowners a choice to comply with at least three options. The first of these is the use of low-sulfur fuels, and the second option involves the use of aftertreatment devices ( SO x scrubbers), which remove air pollutants by spraying the exhaust with seawater. The third option probably applies only to new ships, because it involves the use of liquid natural gas (LNG) as a marine fuel.

Exhaust aftertreatment systems, which are commonly used to remove NO x , SO x , or PM often involve chemical additives (urea, caustic soda) or large amounts of seawater. Use of so-called open-loop SO x scrubbers, which use seawater spray to wash the ship exhaust, releases the effluent back to the sea. This may lead to a creation of a new water quality problem, especially in areas where water volumes are small (estuaries, ports) or water exchange is slow (e.g. the Baltic Sea) (Teuchies et al., 2020).

The use of low-sulfur or LNG fuels is a fossil-based solution, unless the fuel was made using renewable or fully synthetic sources. However, emissions of NO x , SO x , and PM from LNG engines can be very low, but this depends very much on the engine type selected.

Methane, methanol, and ammonia are three fuels which can be produced by fossil, bio, and synthetic pathways. These three fuels are also suitable for use in internal combustion engines as well as fuel cells. All three are hydrogen carriers and processes, which lead to synthesis of these three fuels and have hydrogen production as an intermediary step. This could offer a viable pathway towards hydrogen-based shipping but also allows the use of current engine setups and existing fuel infrastructure (DNV-GL, 2019).

3.2.5  Emissions of indoor sources

The shift in focus from regulating the outdoor concentration of pollutants to putting more emphasis on reducing the individual exposure to pollutants, which is described later in Sects. 7.4 and 8 of this paper, makes it necessary to analyse not only possibilities for reducing emissions from outdoor sources, but also those from indoor sources. Thus, detailed knowledge about emission factors from indoor sources is needed.

Smoking, combustion appliances, and cooking are important sources of PM 2.5 , NO, NO 2 , and PAHs (Hu, 2012; Li, 2020; Weschler and Carslaw, 2018). Particularly important indoor sources of NO and NO 2 are gas appliances such as stoves and boilers (Farmer et al., 2019). For PM 2.5 , apart from diffuse abrasion processes, passive smoking is still the most important source, although the awareness that passive smoking is unhealthy has been increasing with the EU ban of smoking in public buildings. Schripp et al. (2013) report that not only smoking but also consuming e-cigarettes leads to a high emission of VOCs and fine and ultra-fine particles. Frying and baking lead to the evaporation and later condensation of fat and are a large source for PM 2.5 , especially if no kitchen hood is used; a larger number of studies on frying are available and listed in Li (2020) and Hu et al. (2012). Hu et al. (2012) reviewed emissions of PM 2.5 from the use of candles and incense sticks and found that incense sticks have much higher emission rates than candles. Zhao et al. (2020a) simultaneously measured indoor and outdoor concentrations of PM in homes in Germany and report abrasion and resuspension processes as major contributors of coarser particles (PM 2.5−10 ) and toasting, frying, baking, and burning of candles and incense sticks as important sources for ultra-fine particles. Also, the use of open chimneys and older wood stoves in the living area is an important source. For wood stoves, mostly measured indoor concentrations of PM 2.5 are used to characterize the pressure coming from indoor emissions, or emissions are estimated as a fraction of the overall emissions of a stove. As only a few studies measuring emissions from wood stoves into the interior exist (Li et al., 2019b; Salthammer et al., 2014), more measurements are necessary. Schripp et al. (2014) report very high emission factors of ethanol-burning fireplaces, as these have no chimney.

Laser printers emit ultra-fine particles, especially longer-chained alkanes (C21–C45) and siloxanes (Morawska et al., 2009). Also the new 3D printers are a source of nanoparticles, as Gu et al. (2019) found out. Schripp et al. (2011) analysed the emissions from electric household appliances and reported high emission rates in particular from toasters, raclette grills, flat irons, and hair blowers.

New furniture is often a source of formaldehyde. The use of chemicals such as cleaning agents and personal care products leads to VOC and semi-volatile organic compounds (SVOC) emissions, which are partly oxidized and condensate and thus transform into fine particles. McDonald et al. (2018) point out that with rapidly decreasing emissions of VOC from transportation, emissions from the use of volatile chemical products indoors are becoming the dominant sources in the urban VOC emission inventory, so that VOC concentrations often are higher indoors than outdoors (Kristensen et al., 2019).

Excreta of house dust mites use of fan heaters; vacuum cleaning; especially without HEPA filters; and pets are further indoor emission sources. Furthermore, all kinds of human activities produce abrasion. As there are numerous different processes causing these emissions, instead of estimating emissions, measured concentrations, which typically stem from abrasion processes, are used.

Apart from reducing emissions, the concentration of pollutants indoors can also be reduced by ventilation, i.e. by opening windows or using mechanical ventilation, or by filtering the air, e.g. with HEPA filters for the removal of fine particles.

3.2.6  Source apportionment methods and studies

The question of how much the different sources are contributing to the ambient levels of different air pollutants is critical for the design of effective strategies for urban air quality planning. Different methods are used for source apportionment of ambient concentrations, each including certain limitations given by the intrinsic assumptions underpinning the individual methods and by availability and robustness of data underpinning the source apportionment. In many cases these methods are complementary to each other, and implementation of a combination of different methods decreases the uncertainties (Thunis et al., 2019). There are two principally different source apportionment models: the receptor models apportioning the measured mass of an atmospheric pollutant at a given site to its emission sources and the source-oriented models based on sensitivity analyses performed with different types of air quality models (Gaussian, Lagrangian, or Eulerian chemistry–transport models) (Viana et al., 2008; Hopke, 2016; Mircea et al., 2020). Another method addressing the source–receptor relation of air pollution is inverse modelling used for improvement of emission inventories from global scale to individual industrial sources (e.g. Stohl et al., 2010; Henne et al., 2016; Bergamaschi et al., 2018).

The main receptor models are the incremental (Lenschow) method, the chemical mass balance (CMB) method, and the positive matrix factorization (PMF) (Mircea et al., 2020). The Lenschow method is based on the assumption that source contributions can be derived from the differences in measured concentrations at specific locations not affected and affected by the emission sources. This approach is based on the assumptions that the regional contribution is constant at both locations and that the sources do not contribute to the regional background. The CMB is based on known source composition profiles and measured receptor species concentrations. The result depends strongly on the availability of source profiles, which ideally are from the region where the receptor is located and that should be contemporary with the underpinning ambient air measurements. PMF is the most commonly used analytical technique operating linear transformation of the original variables to create a new set of variables, which better explain cause–effect patterns. Hopke (2016) provides a complete review of receptor models.

The source-oriented apportionment methods utilizing source-specific gridded emission inventories and air pollution models include two in principal different methods, the widely used sensitivity analysis, also called brute-force method, or emission reduction potential (Mircea et al., 2020) or emission reduction impact (ERI) method (Thunis et al., 2019), and the tagged species methodology which involves computational algorithms solving reactive tracer concentrations within the chemistry–transport models. ERI and tagged species methods are conceptually different and address different questions. Generally, the ERI method analyses how the concentrations predicted by an air quality model respond to variations in input emissions and their uncertainties. An important aspect to consider when using this method is that the relationship between precursor emissions and concentrations of secondary air pollutants may include non-linear effects. In non-linear situations, the sum of the concentrations of each source is different from the total concentration obtained in the base case. The magnitude of the emission variations considered in ERI may vary from small perturbations, studying the model response in the same chemical and physical regime as the base case, to removing 100 % of the studied emissions (the zero-out method), which may include non-linear effects present in the model response (Mircea et al., 2020). The tagged species method is based on CTM simulations with the tagging/labelling technique, which keeps track of the origin of air pollutants through the model simulation. This accountability makes it possible to quantify the mass contributed by every source or area to the pollutant concentration (Thunis et al., 2019; Im et al., 2019).

The principal differences between the different source-apportionment methods and implications of these differences on apportionment of sources to the observed or modelled ambient concentration levels are in detail explained and discussed in Clappier et al. (2017) and Thunis et al. (2019). Belis et al. (2020) evaluated 49 independent source apportionment results produced by 40 different research groups deploying both receptor and source-oriented models in the framework of the FAIRMODE intercomparison study of PM 10 source apportionment. The results have shown good performance and intercomparability of the receptor models for the overall data set while results for the time series were more diverse. The source contributions of the source-oriented models to PM 10 were less than the measured concentrations.

In this section we further focus on new developments in source characterization with the help of receptor-oriented models and in construction of emission inventories while the air quality models and emission sensitivity studies are the subject of Sect. 5 of this paper. Several new studies reported on characterization of local composition of particulate matter as well as of NMVOCs and PAHs, tracking the contribution of main emission sources (Christodoulou et al., 2020; Diémoz et al., 2020; Saraga et al., 2021; Liakakou et al., 2020; Kermenidou et al., 2020). The particulate matter has been characterized in terms of carbonaceous matter – elemental or black and organic carbon, organic matter, metals, ionic species, and elemental composition. An Aethalometer model to identify BC related to fossil fuel combustion and biomass burning has been applied in several studies (Grange et al., 2020; Christodoulou et al., 2020; Diémoz et al., 2020). Combination of the different analytical methods and analysis of temporal and spatial variation in the data allowed for identification of chemical fingerprints of different emission sources. Belis et al. (2019) present a multistep PMF approach where a high-time-resolution data set from Italy of aerosol organic and inorganic species measured with several online and offline techniques gave internally consistent results and could identify additional emission sources compared to earlier studies.

The local studies characterizing the local composition of PM, as well as NMVOCs and PAHs, revealed the important roles of road traffic and residential combustion for concentration levels of air pollutants in both urban and rural areas. Wood burning has an important share in many residential areas, especially those outside the city centres and in the countryside (Saraga et al., 2021; Fameli et al., 2020). Fuel oil is another important fuel in residential combustion; in some cities such as Athens it is the dominating one (Fameli et al., 2020). The studies show important differences in the diurnal and seasonal patterns of these two emission sources. While road traffic emissions have maxima in the morning and afternoon hours, contributions from residential combustion dominate at night-time and in the cold season. Important contributions of traffic are found in all studies. Saraga et al. (2021) show, as results from the ICARUS study performed in six European cities, that the main contribution to road-traffic-related PM 2.5 is the tyre and brake wear and resuspension of the particles. The fuel oil combustion source is, apart from residential heating, also associated with industrial emissions and shipping emissions. Contributions from these sources become important at specific locations, like in cities with certain industrial plants or in harbour cities.

Analyses of data from longer time series show a decreasing trend for exhaust gas emissions in road traffic. Its contributions to BC in the last decade decreased while the residential combustion, especially the wood burning contribution, does not show any clear trend (Grange et al., 2020). Efficient abatement measures for improvement of the local air quality need to address the important sources. In most cases these are the local traffic and residential combustion, but in many cases these also include industrial sources and in some cases shipping. Targeting these different sources requires a different approach for each.

Inverse modelling is mainly used for improvement of emission inventories with the help of measurements. Different inversion methods applied in Lagrangian dispersion models (e.g. Stohl et al., 2010; Manning et al., 2011; Henne et al., 2016) and global and regional Eulerian models have been widely used for improvements of emission inventories of greenhouse gases on a wide range of geographical scales from global to national, urban, and local. An overview of different inverse modelling approaches applied to a European CH 4 emission inventory is presented by Bergamaschi et al. (2018). Inverse modelling has the potential to reduce uncertainties of emission inventories comparable to other approaches, e.g. an incremental method combining aircraft measurements and a high-resolution emission inventory (Gurney et al., 2017).

3.3  Emerging challenges

3.3.1  emission inventories and preprocessors.

Emission inventories still have large uncertainties. In particular, PM emissions stemming from all kinds of diffuse processes, especially from abrasion processes in industry, households, agriculture, and traffic, show a large variability and uncertainty. For example, abrasion processes of trains may cause very large PM concentrations in underground train stations, but emission factors and total emissions are not well-known. With the ongoing reduction of exhaust gas emissions and the continuing introduction of electric vehicles, abrasion will become the most important process for traffic emissions.

For residential wood combustion many uncertainties relate to the quality and refinement of information about the use of wood and the heating device technologies, tree species, wood storage conditions, or combustion procedures implemented. Their impact on emission inventories is not well evaluated, but new research underlines how national characteristics need to be taken into account and also shows what type of data can be used in order to improve the spatial representation of these emissions.

Despite the activities to improve temporal profiles of agricultural emissions, more detailed information about the amount of NH 3 and PM emissions is still needed for many regions of the world. Also, natural emissions like dust, marine VOCs, and marine organic aerosols remain a challenge, in particular when climate change might lead to the formation of new source regions in high latitudes.

Chemical composition of NMVOC emissions from combustion processes remains highly uncertain, especially when new fuels enter the market like low-sulfur residual fuels in shipping or when new exhaust gas cleaning technologies are introduced that modify the chemical composition of the exhaust gas. Advanced instrumentation for the characterization of new emission profiles are needed here. Measurement techniques employed in the characterization of emissions impact the results; for example, the dilution methods used have a large impact on the measured gas-to-particle partitioning. Better understanding of these impacts and a robust assessment of the uncertainties and variabilities remain a challenge. Emission inventories should include air pollutants and greenhouse gases at the same time. Integrated assessments analyse measures and policies targeting air pollution control as well as climate protection at the same time and potential, and their co-benefits need to be investigated.

Emissions preprocessors aim at increasing the level of detail they take into account for calculating the spatial and temporal resolution of emissions. However, the availability of input data sets (e.g. traffic data from mobile phone positions, AIS ship position data), the huge size of these data sets, and also data protection rules currently hinder their use. Still, there is big potential in extending the data sources used for emissions preprocessing towards big data, e.g. from mobile phone positions, traffic counts, or online emission reporting, in order to reach real-time emission data and improved dynamic emission inventories to be used in air quality forecast systems. Monitoring data from numerous air quality sensors at multiple locations might help in advancing these inventories.

3.3.2  Road emissions

The accuracy and relevance of our current emission estimation and modelling approaches may in the future be challenged by relevant developments, the most important ones being the following.

The exhaust emissions from road transport are continuously decreasing, as exhaust filters become increasingly efficient and are used in a wider range of vehicle technologies, including gasoline vehicles, while the market share of electric cars is also increasing. However, PM 2.5 , PM 10 , and heavy metal emissions from wear and abrasion processes increase with increasing traffic volume as they are not regulated, and electric cars also produce emissions from tyre wear and road abrasion. For instance, the emissions of PM 2.5 reported by Germany to the EEA for 2018 show 9.9 kt a −1 for exhaust gases of cars, trucks, and motorcycles; 7.6 kt for tyre and brake wear; and 4.3 kt from road abrasion. A scenario reported by Germany for 2030 shows only 2.0 kt PM 2.5 for exhaust emissions, but 7.9 kt from tyre and brake wear and 4.4 kt from road abrasion (EIONET, 2019). Emissions from wear of tyres and brakes and abrasion of road surfaces are less studied than exhaust emissions. Wear emissions depend on a range of parameters including driving behaviour (acceleration and braking pattern), vehicle weight and loading, structure and material of brakes and tyres, road surface material, and weather conditions (e.g. road water coverage) (e.g. Denby et al., 2013; Stojiljkovic et al., 2019; Beddows and Harrison, 2021). Capturing the effect of technological developments in this area would be therefore important for relevant air quality estimates.

The profile of non-methane organic gases (NMOGs) is important to estimate the contribution of exhaust to secondary organic aerosol formation. NMOGs depend on fuel and lube oil use, combustion, aftertreatment, and operation conditions. The profile of emission species may be differentiated as new fuels, including renewable, oxygenated, and other organic components are being increasingly used to decarbonize fuels. Hence, although total hydrocarbon emissions are still controlled by emission standards, the speciation of these emissions may vary in the future. Monitoring those changes is cumbersome as the study of the chemistry and/or volatility of organic species is a tedious and expensive procedure. Hence any changes may escape relevant experimental campaigns.

Questions remain about the suitability of widespread emission factors and models to capture the effects of lane layouts, vehicle interactions, and driving behaviour, while lane-wide average traffic parameters are a structural limitation to emission modelling. As urban policies are advancing in an effort to decrease the usage of private vehicles in cities, the impact of traffic calming and banning measures may not be satisfactorily captured by today's available emission models. In order to take driving behaviours into account, it is necessary to improve so-called microscopic models such as the “Passenger car and Heavy duty Emission Model“ (PHEM) (Hausberger et al., 2003) that calculate emissions from high-temporal-frequency information on network configuration as well as traffic and driving conditions (see review by Franco et al., 2013). Their use calls for the development of new methodologies to provide the simulation with individual speed profiles, taking into account the actual road usage and the specificities of the emissions of the most recent vehicles.

3.3.3  Shipping emissions

The efforts of decarbonizing shipping have thus far concentrated on minimizing the energy need of ships, but a shift to carbon-neutral or non-carbon fuels is necessary. Methane, methanol, and ammonia are three fuels that could offer a viable pathway towards hydrogen-based shipping but also allow for the use of current engine setups and existing fuel infrastructure (DNV-GL, 2019). Regardless of the fuel or aftertreatment technique used, detailed emission factor measurements for various combinations of fuels and engines are needed (Anderson et al., 2015; Winnes et al., 2020) to reliably model the emissions.

Little is known about emissions of VOCs from ships and how much they contribute to particle formation and ozone formation. VOC emissions from ships are not included in most ship emission models, because emission factors are not available or stem from comparably old observations. In addition, VOC emissions are expected to vary considerably with the type of fuel burned and the lubricants used on board, both of which have changed considerably with the introduction of low-sulfur fuels in 2015 (in ECAs) and in 2020 (on a global level). The most recent greenhouse gas emission report from IMO (2021) states that evaporation might be the most important source for VOCs from shipping, which is not considered in any emission inventory, yet.

Current exhaust gas cleaning technologies, in particular scrubbers applied for removing SO 2 from the ship exhaust, dump large parts of the scrubbed pollutants into the sea. More comprehensive research is therefore urgently needed on the combined effects of shipping, which will treat both the impacts via the atmosphere and those on the marine environments. The impacts via the atmosphere include the health effects on humans, the deposition of pollutants to the sea, and climatic forcing. The impacts on the marine environment include acidification, eutrophication, accumulation of pollution in the seas, and marine biota. Recently, there have been attempts to combine the expertise of oceanic and atmospheric researchers for resolving these issues (Kukkonen et al., 2020a).

Ships have high emissions when they arrive in ports and also when they depart a short time later. In addition, they need electricity and heat when they stay at berth, leading to additional emissions in ports stemming from their auxiliary engines and boilers. The impact of these emissions on urban air quality in port areas is of high interest because of their large impact on human exposure.

3.3.4  Indoor sources

Even though people in industrialized countries spend more than 80 % of their time indoors, systematic knowledge on indoor air quality, source strength of the indoor air pollution sources, and physico-chemical transformation of indoor air pollutants is still limited. Therefore, systematic quantification of different indoor air pollution sources, such as building material, consumer products, and human activities, is needed, including exploitation of the already existing test chamber, and other relevant laboratory data are needed. Special attention is also needed to the outdoor source component. Besides obtaining new data on indoor-to-outdoor (I  /  O) ratios, the existing data need to be systematically analysed. One of the key challenges here is how to translate such data from the outdoor contribution into a real indoor environment with considerable heterogeneity in terms of ventilation, volume, microclimatic characteristics, and multiple indoor sources (Bartzis et al., 2015).

Development of indoor air quality models with accurate description of the key chemical and physical processes involved in outdoor–indoor air interaction as well as processing and transport of indoor air pollution inside the buildings is needed to properly address connection between the outdoor air quality and indoor air pollution sources. Additional advanced modelling is needed for air–surface interactions targeting emissions and sinks on different surfaces including those in the ventilation set-up (Liu et al., 2013) along with verification of the indoor air models with measurements in a variety of indoor air environments.

3.3.5  Source apportionment

Continuous improvement of emission inventories with help of verification with source- and receptor-oriented source apportionment methods is needed, especially as large changes in emissions, in terms of both the emission totals and profiles of emission species from individual sources, are expected as a result of upcoming new technologies, fuels, and changes in lifestyle emerging mainly from the Paris Agreement climate change targets.

Currently, apportionments of the overall measurement data sets usually give consistent results while source apportionment of data with high temporal resolution still remains challenging. With rapid development of both advanced online measurement instruments and low-cost measurement sensors, development of source apportionment methods towards high-temporal-resolution data and increasing number of parameters is necessary. This also requires improvements in characterization of sources in terms of both speciation and temporal profiles. This in particular concerns emission profiles for NMVOCs, PAHs, and particulate organic matter (e.g. most existing profiles for PAH emission from vehicles are quite old and do not follow vehicle technology evolution; Cecinato et al., 2014; Finardi et al., 2017). Inverse modelling methods are very powerful and promising tools for source estimation and improvement of emission inventories, but the current models provide large spread in results and need to be further improved and intercompared.

Here we concentrate on another growing field of development: low-cost sensor (LCS) networks, crowdsourcing, and citizen science together with small-scale air quality model simulations to provide personal air pollution exposure. Modern satellite and remote sensing techniques are not in focus here.

4.1  Brief overview

Europe's air quality has been improved over the past decade. This has led to a significant reduction in premature deaths over the same period in Europe, but all Europeans still suffer from air pollution (EEA, 2020a). The most serious air pollutants, in terms of harm to human health, are particulate matter (PM), NO 2 , and ground-level ozone (O 3 ). The analysis of concentrations in relation to the defined EU and World Health Organization (WHO) standards is based on measurements at fixed monitoring points, officially reported by the member states. Supplementary assessment by modelling is also considered, particularly when it results in exceeding the legislated EU standards. But in parallel new monitoring techniques and strategies for observation of ambient air quality are available and applied, which are discussed below.

The motivations for new developments in observation and instrumentation are, on the one hand, obtaining necessary information about air pollutant concentrations and exposure as a basis for compliance and health protection measures and on the other hand supporting improvements in weather, climate, and air quality forecasts. Remote sensing techniques are developed further to get 3D coverage of observations globally by establishment of networks with mini-lidar for example (so-called ceilometers), for evaluation of satellite measurements, to contribute to atmospheric super sites (extension of in situ measurements), or for chemistry–transport model (CTM) evaluations. These techniques can provide nearly continuous monitoring data, only interrupted by certain weather conditions. Satellite measurements are becoming more important for air quality management because their spatial resolution can reach down to 1 km, while their information content is suitable for the assessment of modelling results and combination with modelling tasks (Hirtl et al., 2020). All these techniques enable unattended detection at different altitudes and thus of the composition, clouds, structure, and radiation fluxes of the atmosphere as well as Earth surface characteristics, relevant for atmosphere–surface feedback processes.

Some examples of modern remote sensing techniques as described in Foken (2021) are the sun photometer networks (determination of aerosol optical depth), MAX-DOAS (e.g. NO 2 and HCHO column densities), lidar (e.g. water vapour, temperature, wind, and air pollutants), and more recently ceilometers (e.g. cloud altitude and mixing layer height). Machine learning algorithms, such as neural networks, are now deployed for remote sensing applications (Feng et al., 2020). Satellite observations have become available for column densities of aerosol, NO 2 , CO, HCHO, O 3 , PM 10 , CH 4 , and CO 2 as well as aerosol optical depth and various image analyses (Foken, 2021). Together with improved spatial coverage and high resolution, these data become increasingly important for assessment in urban areas (Letheren, 2016).

The distribution of ambient air composition exhibits large spatial variations; therefore high-resolution measurement networks are required. This has become possible with LCS networks, which are used in both research and operational applications of air pollution measurement and in global networks of observations such as the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) programme (Lewis et al., 2017). WMO/GAW (Global Atmosphere Watch Programme of the World Meteorological Organization; https://public.wmo.int/en , last access: 21 February 2022) addresses atmospheric composition on all scales from global and regional to local and urban (see GAW Station Information System, https://gawsis.meteoswiss.ch/GAWSIS/#/ , last access: 21 February 2022) and thus provides information and services on atmospheric composition to the public and to decision-makers, which requires quality assurance elements and procedures as described by the WMO/GAW Implementation Plan: 2016–2023 (WMO, 2017). This topic is further discussed with respect to the related sensor, network, and data analysis requirements.

4.2  Current status and challenges

To describe the current trends of air quality monitoring, certain lines of research and technical development are formulated in the following section. This section concentrates on high-resolution measurement networks by the installation of a larger number of small and low-cost measurement sensors. The measurements by traditional in situ measuring as well as ground-based, aircraft-based, and space-based remote sensing techniques or integrated measuring techniques are no longer considered. Also, satellite observations, which are a growing field of development towards even smaller and thus cost-effective platforms, are not the focus here.

The configurations of ambient air measurements can be described as a space, time, and precision-dimensional feature space shown as large arrows in Fig. 6 where crowds with LCS (green) are distributed irregularly in space and time at low precision and high number. Stationary measurements (yellow) are performed at high precision and thus of the highest quality as well as continuously over time but only at a few points in space requiring high effort and cost. Between the two layers, mobile measurements are available on a medium level of precision: in one case regularly on certain routes (red) and in another case with high spatial density at a few points during intensive measurement campaigns (blue). The crowd measurements by LCS can be geo-statistically projected onto a higher quality level together with the high-precision measurements (thin black arrows). Following this, an overall higher information density at an elevated quality level than the sum of the individual measurements alone is possible, so that continuous data by LCS can be applied (Budde et al., 2017).

There is an increasing interest in air quality forecast and assessment systems by decision makers to improve air quality and public health; mitigate the occurrence of acute air pollution episodes, particularly in urban areas; and reduce the associated impacts on agriculture, ecosystems, and climate. Current trends in the development of modern atmospheric composition modelling and air quality forecast systems are described in review by Baklanov and Zhang (2020), which includes for instance the multi-scale prediction approach, multi-platform observations, and data assimilation as well as data fusion, machine learning methods, and bias correction techniques. This shows the general development towards spatial and temporal high resolution as well as better knowledge of personal air pollution exposure.

Figure 6 Configuration of ambient air measurements modelled as a space, time, and precision-dimensional feature space (large arrows): crowds with low-cost sensors (green) scatter irregularly in space and time at low precision but high number (source: Budde et al., 2017).

4.2.1  Low-cost sensors and citizen science for atmospheric research

Many manufacturers (more than 50 worldwide, with their numbers growing fast) are working in the market for air quality monitoring with different business models (Alfano et al., 2020). There are companies which produce and/or sell medium-cost sensors (MCSs) with a cost per compound on the order of EUR 100 and EUR 1000 and LCS on the order of EUR 10 and EUR 100 for all key air pollutants (Concas et al., 2021). Furthermore, manufacturers and integrators often provide installation of LCS and MCS for networks and on mobile monitoring platforms. The operation of such networked and mobile platform measurements is also often supported by the companies which install the sensors. However, the monitoring of air pollutant limit value exceedances is still a task of governmental agencies which are responsible for air quality.

These developments point to a new era in detecting the quality of air which we breathe (Munir et al., 2019; Schade et al., 2019; Schäfer et al., 2021) where virtually everybody can measure air pollutants. Following this potential high number of sensors, fine-granular assessment of air quality in urban areas is possible at lower costs. The data platforms of these LCS and MCS networks collect enormous amounts of data, and new data products like personal exposure of air pollutants, spatial distribution of air pollutants down to 1 m resolution, information about least polluted areas, and forecast of air quality are supplied for users. Figure 7 shows these possibilities on the Internet of Everything with things, sensor data, open data platforms, and citizen actions.

Figure 7 Exploitation of Internet of Everything technology with things, sensor data, open data platforms, and actions of people.

Algorithms from machine learning and big data, together with data from reference instruments as well as monitoring data owned by governmental agencies, are often working on a central data server. Thus, an overall higher information density at an elevated quality level than the sum of the individual measurement components is possible. Also, a dynamic evaluation technique can be applied, which is built upon mobile sensors on board vehicles, for example, trams, buses, and taxis combined with the existing monitoring infrastructure by intercomparison between any two devices which requires a corresponding high dynamic of their sensitivity. Pre-/post-calibrations are possible by using high-end instruments or adjustment in a reference atmosphere under prescribed laboratory and/or field conditions. Based on these achievements in the monitoring networks it is possible to identify emission hot spots and thus to assess spatially resolved, high-resolution emission inventories. Such emission inventories are a prerequisite for supporting high-resolution numerical simulations of air pollutant concentrations and eventually the forecast of air quality.

Furthermore, because of the small size and low weight, sensors can be installed on board unmanned aerial vehicles (UAVs) so that these platforms become complex air quality (Burgués and Marco, 2020) and meteorological instruments. This means vertical profiling is possible with aerial atmospheric monitoring to understand the influence of air pollutant emissions upon air quality.

4.2.2  Quality of sensor-measured and numerical simulation data

An increasing number of evaluations of MCS and LCS as well as of networks based on such sensors are being performed, and conclusions are available from these studies such as Thompson (2016), Morawska et al. (2018), and Karagulian et al. (2019). It is well-known that these sensors suffer from drift and ageing (Brattich et al., 2020). The drift can vary even among the same model sensors that come from the same factory. Furthermore, sensor data evaluation is necessary due to cross-sensitivities of sensors with other air pollutants in ambient air and the influences of different temperatures and humidity in ambient air upon the sensor response.

Activities for the standardization of a protocol for evaluation of MCS and LCS at an international level and for inter-comparison exercises are ongoing, where MCS and LCS are tested at the same sites and at the same time (e.g. Williams et al., 2019). The European Committee for Standardization/Technical Committee (CEN/TC) 264/Working Group (WG) 42 “Ambient air – Air quality sensors” works for a Technical Specification of LCS (CEN/TS 17660-1; https://standards.cencenelec.eu/dyn/www/f?p=CEN:105::RESET:::: last access: 21 February 2022). Such guidelines and sensor certifications are required for data products such as personal air pollution exposure, emission source identification, and nowcasting of air quality as well as for applications as traffic management (Lewis et al., 2018; Morawska et al., 2018).

In the area of high-resolution modelling, the creation of a model data standard for obstacle-resolving models ( https://www.atmodat.de/ , last access: 21 February 2022) has started (Voss et al., 2020) as already done for coupled models (CESM – CMIP6 (ucar.edu); https://www.cesm.ucar.edu/projects/CMIP6/ , last access: 21 February 2022).

4.2.3  Importance of crowdsourcing, big data analysis, and data assimilation

Data from high-resolution measurement networks can provide the base for application of small-scale 3D process-based CTMs by means of assessment of emission inventory and model results. Additionally, it can support the operation of statistical, artificial intelligence, neural network, machine learning, and hybrid modelling methods (Bai et al., 2018; WMO, 2020; Baklanov and Zhang, 2020). Statistical methods are simple but require a large amount of historical data and are extremely sensitive to them. Artificial intelligence, neural network, and machine learning methods can have better performance but can be unstable and depend on data quality. Hybrid or combined methods often provide better performance. Such methods can also improve the CTM forecast by utilizing added observation data. For example, Mallet et al. (2009) have applied machine learning methods for the ozone ensemble forecast, performing sequential aggregation based on ensemble simulations and past observations. The latest results of the integration of air quality sensor network data with numerical simulation and neural network modelling results by data assimilation methods are for the Balkan region (Barmpas et al., 2020); Grenoble, France (Zanini et al., 2020); Leipzig, Germany (Heinold et al., 2020); and the inner city of Paris, France (Otalora et al., 2020), and they show how modelling can be used to support and consolidate information from observation data products.

The trend to improve air quality forecasting systems leads to the development of new methods of utilizing modern observational data in models, including data assimilation and data fusion algorithms, machine learning methods, and bias correction techniques (Baklanov and Zhang, 2020). Typically, as a first step data verification and validation of different data sources are performed, including data from LCS and MCS networks, permanent monitoring networks, and UAV-based, aircraft-based, and satellite-based measurements (in situ and remote sensing). Subsequently, emission information data assimilation methods are applied for integration with urban-scale CTM or neural network modelling or fluid dynamics modelling or combining these to provide a flexible framework for air quality modelling (Barmpas et al., 2020). Such approaches that combine the use of observations with models can lead to improved new tools to deliver high-quality information about air quality, spatial high-resolution forecasts of air quality for hours up to days, and health protection to the public.

Further, literature already provides QA–QC methods for MCS and LCS based on big data analyses and machine learning as well as data analyses in the cloud (Foken, 2021). Evaluation methods for measurement and modelling results are selected and combined to show the application potential of data sets of the new sensors, networks, and air quality model simulations. The further development and application of assimilation and quality evaluation methods is ongoing with the aim that distributed data sources will form the basis for new data products, making possible new applications for citizens, local authorities, and stakeholders.

4.2.4  Applicability of sensor observations

Crowdsourcing of sensor observations is applied to get information for personal air pollution exposure and for supporting decisions on personal health protection measures such as information about the least polluted areas for outdoor activities. Using this data-based information, citizens can recognize heavily polluted areas, which could be especially important for sensitive groups.

The platforms for the combination of ground-based stationary and mobile sensors, the complementation with 3D measurement data by in situ and remote sensing observations, and model evaluation and assessment can support such applications. This trend of cost-effective air quality monitoring includes user-oriented data services and education about air pollution and climate change to best exploit the knowledge and information content of measured data. Local authorities already use such data (e.g. English et al., 2020) for identifying emission hot spots, management of city infrastructure, and road traffic management towards improving air quality.

MCS and LCS and their advantages in operation and data availability via citizen sciences can also support the understanding of indoor air quality. The investigations of indoor air pollution in conjunction with outdoor air pollution monitoring provide more realistic data of personal air pollution exposure and for assessing measures of health protection.

4.2.5  Modelling for urban air quality to support observation data products

Numerical modelling results are traditionally evaluated against data from air quality monitoring networks (see also Sect. 5). At high resolution, this process requires the use of a sensor network specifically configured to meet the needs of the exercise. Conversely, modelling can also be used to support air quality mapping based on observational data. Indeed, while the use of LCS for high-density observations can provide information on the variability of pollutant concentration on a fine spatial scale, the spatial (and temporal) global coverage of the areas being monitored nevertheless can prove to be irregular and incomplete.

Data-driven modelling over combined stationary- and mobile-generated pollution data requires the deployment of dedicated statistical methodologies. Although little research effort has been devoted to such developments so far, recent advances in machine learning and artificial intelligence have highlighted the exciting potential of several statistical analysis tools (data envelopment analysis, unsupervised neural learning algorithms, decision trees, etc.) to predict air quality at the city scale from data generated by mobile sensors, which are supported by citizen involvement (Mihăiţă et al., 2019).

Another approach that appears very promising to meet the operational challenges associated with fine spatial mapping is to combine sensor data with mapped data from models. The technique used is geostatistical data fusion, an approach similar to data assimilation and based on kriging interpolation. It produces a new map whose added value lies in obtaining the most probable field of concentration, at the time when the sensor observations were made but also the combination of information provided by the two data sources (Ahangar et al., 2019; Schneider et al., 2017). A study carried out on a medium-density urban area in France showed that the bias found between the outputs of an urban model and the data from the local air quality network was reduced from 8 % to 2.5 % following fusion with the sensor data. However, the results of the fusion technique are characterized by a lower dispersion than the input data sets, which leads to a smoothing of the peaks and thus an underestimation of the maximum values. Finally, the performance of fusion is logically degraded by the uncertainty in the sensor measurements and the low correlation between the two data sources due to biases in the LCS measurements (Gressent et al., 2020). This underlines the importance of accurate calibration of portable devices to achieve reliable air quality mapping on a fine scale.

4.3  Emerging challenges

4.3.1  use of low-cost sensors.

Providing citizens and stakeholders with innovative information from large networks of sensors can yield added value and is fast becoming one of the main emerging challenges in air quality management. Nevertheless, with the greater range of observational techniques available now, there is a need for the application of instrumentation consistency, involving operation of mobile sensors by citizen for routine inter-calibrations and approaches for sensor intercomparison in networks, using correction algorithms for sensors which should be described in a common way. When sensors are installed on board vehicles or UAV, detailed information about the sensor response time should be provided taking account of the compatibility with its movement speed and data gathering frequency.

There is also the need to strengthen the linkages between existing measurement data sets. For example, air pollution monitoring networks of governmental agencies operating at local and national levels incorporating reference data with certified QA–QC methods need to be explored to exploit numerical algorithms, especially from artificial intelligence or dynamic data assimilation, for example as part of sensor and network certifications and standardization, so that these measurement methodologies and the available enormous amount of data can be useful for air quality research and assessment, including legislative reporting.

In the case of low-cost sensors, guidelines and sensor certifications for LCS and MCS are prerequisites for their application. Because such documentation has not been consistently available up to now, LCS and MCS data cannot be used for official assessment of WHO or EU limit value exceedances. Furthermore, the level of acceptable data quality of LCS and MCS is difficult to ascertain, and presently the LCS and MCS networks are difficult to integrate into or extend the air pollution monitoring networks of responsible authorities.

4.3.2  Multi-pollutant instruments

Depending on the monitoring task of air quality or personal exposure, sensors for detection of all air pollutants including ultra-fine particles (UFPs) and particle size distribution (PSD) but also greenhouse gases (GHGs) are necessary. In the application case of sensors embedded at the surface of clothes or carried by individuals, extended miniaturization of LCS and MCS must measure the personal air pollution exposure. Relevant developments could also include personal measurements of bioaerosols (e.g. pollen and fungi). Such data are required to study the combined health effects of air pollutants, bioaerosols, and meteorological parameters. In this sense the speciation or chemical composition and physical characteristics of particles of all sizes are needed too.

4.3.3  Modelling for urban air quality to support observation data products

The small-scale forecast of air quality for different applicants and personal health protection must be improved by adaptation of corresponding numerical simulations of air pollution, based on online input data, which requires readily accessible sources like traffic counting and household heating activities. Alternatively, inverse modelling approaches can help quantify the strengths of diffusive emission sources and identify hot spots. Running spatial and temporal highly resolved numerical simulations requires online evaluation data from the combination of different platforms and the application of data algorithms from the area of machine learning or artificial intelligence.

The assimilation of small-scale data from measurements and numerical simulation of air pollution should be used for reduction of the space-time gaps of measurement networks. This is needed because measurement networks cannot be as dense as the spatial grids of numerical simulations. This implies further development of integration of observations by different platforms and methods as well as the assessment of numerical simulation results together with the application of crowdsourcing. Big data analyses and data assimilation methods can provide new areas of modelling applications in the field of improvement of air quality, determination of air pollution emissions and emission inventories, and development of personal health protection measures. Finally, it is necessary that these data eventually become suitable for monitoring and assessment of air quality in agreement with national and international guidelines.

Measurements and numerical simulation of coupled outdoor and indoor air quality must be supported for obtaining more realistic personal air pollution exposure information, given that most people are mainly exposed to indoor air, which, in turn, is strongly influenced by the quality of the outdoor air.

5.1  Brief overview

Over the last years, it became obvious that our understanding of pollution and exposure processes at the urban scale could be improved by combining multi-scale models and creating new dedicated numerical approaches and that the representation of scale interactions for dynamic phenomena, pollutant emission sources, and pollutant ageing would be a critical element in the realism of the simulation outputs. New developments have therefore aimed at restoring the spatial variability and heterogeneity of air pollution due to the turbulent transport of pollutants, whether in urbanized valleys, city centres, or confined urban spaces such as canyon streets.

The motivation of these works is to address societal issues with a focus on street-level representations of pollutant concentration fields to support the assessment of individual exposure to pollution. In this context, it is now acknowledged that statistical and other data analysis techniques such as machine learning have an important role to play in identifying underlying patterns and trends as well as relationships between different parameters. At the same time, air quality monitoring has been progressing by improving ensemble techniques that allow for more in-depth model evaluation and provide a solid basis for consistent operational work on air quality. The following section reviews current challenges and highlights emerging areas of research covering the development, application, and evaluation of air quality models.

5.2  Current status and challenges

5.2.1  innovative combinations of models.

To meet the need to represent concentration gradients of primary pollutants in large agglomerations, the use of urban-scale dispersion models has increased since the 2010s (Singh et al., 2014; Soulhac et al., 2012). These models indeed allowed the resolution of dispersion effects in a complex emitting and built environment, whereas chemistry–transport models (CTMs) cannot provide an explicit representation of near-source characteristics and meet computational time issues as the resolution increases. However, both the lack of connection between local emission effects and the regional transport of pollutants and the absence of a relevant representation of atmospheric reactivity limit the scope of this type of model. Therefore, interest is progressively turned to the nesting of CTMs and urban models, which allows the exploitation of the advantages of both approaches. Over the last decade, approaches either coupling or nesting Eulerian models with Gaussian source dispersion models (Hood et al., 2018; Hamer et al., 2020), microscale CFD models (Tsegas et al., 2015), obstacle-resolving Lagrangian particle models (Veratti et al., 2020), and/or street models (Jensen et al., 2017; Kim et al., 2018; Khan et al., 2021) have thus been developed with the aim of producing comprehensive cross-scale simulations of air quality in the city. An organization chart for such combined models is illustrated in Fig. 8.

Figure 8 Schematic diagram of the EPISODE model with the CityChem extension (EPISODE–CityChem model), from Karl et al. (2019b).

The interest of the “CTM-Urban dispersion model” approaches called plume-in-grid or street-in-grid lies in the fact that they allow in a single time step the simulation of urban background and to solve at low cost the dispersion of near-field emissions, for more resolved and realistic pollutant concentration fields. Compared to an urban model alone, those systems improve NO 2 scores in areas upwind of urban sources, as well as the average concentration levels of compounds that have a strong long-range transport component such as PM 2.5 , PM 10 , and ozone (Hood et al., 2018). Implemented at the scale of an agglomeration or a region, this approach demonstrated its ability to represent the diversity of urban microenvironments (e.g. proximity to road traffic versus urban background, effect of building density, and street configuration) that were until now poorly considered by the Eulerian approach alone. The representation of road traffic and its influence on urban air quality have been the main focus of these studies. Reaching a resolution from a few metres to a few tens of metres, the simulation outputs indeed accurately reproduce the gradients observed along road axes (see Fig. 9) and show greater comparability with urban-scale measurement data than CTMs alone (especially for NO 2 ). Particularly improved performances have been observed under stable winter conditions, and for some studies, the deviation from measurements is within the 15 % maximum uncertainty allowed by the EU directive for continuous measurements (Hamer et al., 2020). Mostly, the results show a better representation of the amplitude of the local signal than an improvement of the correlation with the observed concentrations, and it is concluded that these multi-scale approaches are a significant advance to predict local peaks and episodes. These skills set them apart as essential tools for providing high-resolution air quality data for street-level exposure purposes (Singh et al., 2020b). Statistical evaluations of the model outputs based on the EU DELTA Tool have been carried out as part of several studies: they show that the models comply well with the quality objectives of the FAIRMODE approach ( https://fairmode.jrc.ec.europa.eu/document/fairmode/WG1/MQO_GuidanceV3.2_online.pdf , last access: 23 Febraury 2022). In the end, although the performances of the models remain dependent on the relative importance of local emissions, as well as transport and chemical processes at each computation grid point, most of the residual biases could be attributed to a lack of realism in the emissions. This includes the presence of poorly characterized local sources (works on the street, road particulate resuspension processes) but also insufficient temporal refinement of road traffic profiles. In this respect, it should be emphasized that the improvement of particulate representation in the model and the restitution of near-field chemical equilibria are also expected as major evolution pathways for the models.

Figure 9 NO 2 annual average concentrations from the coupled ADMS-Urban–EMEP4UK model for (a)  the whole of Greater London and (b)  an area of central London. Monitoring data are overlaid as coloured symbols (Hood et al., 2018).

The study of the impact of shipping activities on urban air quality has also benefited from these multi-scale modelling approaches. Indeed, while conventional CTM approaches simulating the effect of shipping emissions in coastal areas of the North and Baltic seas agreed on the average contribution of shipping to air pollution (around 15 %–30 % of elevated concentrations of SO 2 , NO 2 , ozone, and PM 2.5 ; see Aulinger et al., 2016; Jonson et al., 2015; Karl et al., 2019a; Geels et al., 2021; Moussiopoulos et al., 2019, 2020), the use of urban and plume dispersion models made it possible to refine this diagnosis and assess near-field effects. As for road traffic, the influence of ship emissions on air quality induces pollution gradients in the city. Karl et al. (2020) thus found out that, in residential areas up to 3600 m from a major harbour, the ultra-fine particle concentrations were increased by a factor of 2 or more compared with the urban background.

5.2.2  Improved turbulence and dynamics for higher-resolution assessment of urban air quality

In parallel, the need for higher-resolution assessment of urban air quality poses new demands on flow and dispersion modelling. As an additional difficulty besides complex-geometry-induced phenomena, we are reaching a spatial resolution of metres and a temporal resolution of seconds, thus entering the space scales and timescales of atmospheric turbulence. Therefore, the exposure-related parameters cannot be described only deterministically without considering their stochastic component. A recent step forward in this direction is the increased use of large-eddy simulation (LES) methodology dealing directly with the stochastic behaviour of flow and concentration parameters (Wolf et al., 2020).

Advanced computational fluid dynamics (CFD), including Reynolds-averaged Navier–Stokes (RANS) equations models that provide concentration standard deviation, have also appeared in literature for some time (Andronopoulos et al., 2019). More precisely, the implementation of LES class models solving the most energetic part of turbulence explicitly as well as 3D primitive hydro-thermodynamical equations and the structural details of the complex urban surface has been carried out at the scale of agglomerations, in meteorological conditions corresponding to typical stratified winter pollution situations, and fed with emission data from the city authorities (residential combustion as well as maritime and road traffic in particular). More specifically, advanced CFD models such as LESs, have shown to better characterize the very fine-scale variability of primary urban pollution, for example regarding the irregular spatial distribution of concentrations in proximity to road traffic at complex built-up intersections, which makes it possible to open a reflection on the representativeness of the levels measured and their regulatory use and to define criteria for the optimization of measurement networks. LES local-scale modelling has been used to refine urban air quality predictions either alone (Esau et al., 2020) or embedded in an urban-scale model (San José et al., 2020). Also, wider use of CFD has taken place to improve understanding of pollution distribution inside a built environment, especially for critical infrastructure protection (Karakitsios et al., 2020).

Microscale models are particularly powerful to resolve the turbulent flow and pollutant dispersion around urban obstacles to reconstruct pollutant concentration variability within the urban canopy. Recent microscale model simulations also showed the importance of barrier effects for emissions from large ships. It was thus shown that turbulence at the stern of the ship may cause a significant decrease in exhaust pollutants, leading to higher concentrations near the ground and, most likely, higher exposure of the nearby urban population (Badeke et al., 2021). The application of LES (Esau et al., 2020; Wolf-Grosse et al., 2017; Resler et al., 2020; Werhahn et al., 2020; Hellsten et al., 2020; Khan et al., 2021) and CFD (San José et al., 2020; Gao et al., 2018; Flageul et al., 2020; Koutsourakis et al., 2020; Nuterman et al., 2011; Buccolieri et al., 2021; Kurppa et al., 2018, 2019; Karttunen et al., 2020; Kurppa et al., 2020) models for air quality assessment in urban environments is becoming a frequent approach. Many papers implementing the PALM LES model (Maronga et al., 2015) have been presented at the 12th International Conference on Air Quality – Science and Application. Yet, their application is still limited by difficulties dealing with urban-scale atmospheric chemistry and by the relevant computational resources required – as the use of advanced models such as LESs requires increased computational capabilities. On the other hand, the heavy computational burden of urban LES computations can be reduced by approximately 80 % or even more by employing the two-way coupled LES–LES nesting technique, recently developed within the LES model PALM (Hellsten et al., 2021). Precomputation of LES in operational modelling can be an acceptable solution, especially combined with big data compression methodologies (Sakai et al., 2013). Another possibility is to focus on limited urban areas with special interest (e.g. street canyons and “hot spots”); however, one should in this case take into account the effect on turbulent transport from the surrounding larger-scale turbulent phenomena. In the problem of urban air quality, an assisted approach in the selection/classification process is the use of clustering (Chatzimichailidis et al., 2020) and artificial intelligence/machine learning technologies (Gariazzo et al., 2020).

5.2.3  Use of advanced numerical approaches and statistical models

At the same time, the complementary role of prognostic and diagnostic approaches has been explored. New methodologies based on artificial neural network models, machine learning, or autoregressive models have been developed in order to achieve a more realistic representation of air quality in inhabited areas than achieved by CTMs (Kukkonen et al., 2003; Niska et al., 2005; Carbajal-Hernández et al., 2012; P. Wang et al., 2015; Zhan et al., 2017; Just et al., 2020; Alimissis et al., 2018). Likewise, Pelliccioni and Tirabassi (2006) employed neural networks to improve the outputs of Gaussian and puff atmospheric dispersion models. Also, Mallet et al. (2009) applied machine learning methods for ozone ensemble forecast and performed sequential aggregation based on ensemble simulations and past observations.

Kukkonen et al. (2003), through an extensive evaluation of the predictions of various types of neural network and other statistical models, concluded that such approaches can be accurate and easily usable tools of air quality assessment but that they have inherent limitations related to the need to train the model using appropriate site- and time-specific data. This dependence has prevented their use in the evaluation of air pollution abatement scenarios or for the evaluation of multidecadal time series of pollutant concentrations. The works of X. Li et al. (2017) confirmed that methods based on machine learning, and more specifically neural networks, can accurately predict the temporal variability of PM 2.5 concentrations in urban areas but that the model performance may be improved using explanatory training variables. Prospective neural network modelling works were also conducted in a canyon street by Goulier et al. (2020). They proposed a comparison of model outputs with measurements (based mainly on Pearson correlation, rank correlation by Spearman, modelling quality indicator's index from FAIRMODE), for a set of gaseous and particulate pollutants. They confirmed that the modelled data were able to reproduce with a very good accuracy the variability of the concentrations of some gaseous pollutants (O 3 , NO 2 ) but that there was still a significant margin for improvement of the models, notably for particles. Again, an important part of the expected progress lies in the choice of model predictors.

As for multi-scale modelling, the main research efforts associated with these numerical approaches are directed towards the downscaling of simulated pollutant concentration fields in urban areas, the improvement of CTM forecast using additional observation data, and a refined representation of individual exposure at the street scale (Berrocal et al., 2020; Elessa Etuman et al., 2020). Gariazzo et al. (2020) used a random forest model to enhance CTM results and produce improved population exposure estimates at 200 m resolution, in a multi-pollutant, multi-city, and multi-year study conducted over Italy. In addition to reduced bias, the outputs presented much greater physical consistency in their temporal evolution, when compared to measurements.

Other applications, such as advancing knowledge about exposure in urban microenvironments, have also been made possible by these approaches Thus, the use of Bayesian statistics has shown an ability to predict the concentration gradients of primary pollutants in the immediate vicinity of an air quality monitoring station, by iterating between observations and the outputs of a microscale simulation approach – including both a CFD and a Lagrangian dispersion model (Rodriguez et al., 2019).

5.2.4  Implementation of activity-based data

To take full advantage of the high-resolution simulation capability of these new modelling tools, and to achieve a more comprehensive approach to the determinants of air quality in urban areas, modellers have relied on a new generation of activity-based emissions data.

As for traffic, new methodologies relying on individual data collected through surveys, geocoded activities, improved emission factors, and measured traffic flows (Gioli et al., 2015; Sun et al., 2017) or involving traffic models simulating origin–destination matrices for city dwellers on the road network (Fallah-Shorshani et al., 2017) have been developed to serve as input to the urban dispersion models. Their implementation in a case study in Italy, with a horizontal resolution of 4 m, showed that detailed traffic emission estimates were very effective in reproducing observed NO x variability and trends (Veratti et al., 2020).

Residential wood combustion has also proven to act as a major source of harmful air pollutants in many cities in Europe, and especially in northern-central and northern European countries which have a strong tradition of wood combustion. Yet, until the early 2010s, residential wood combustion (RWC) inventories were still heavily burdened with uncertainties related to actual wood consumption, the location of emitters, emission factors depending on heating equipment, and practices driving the temporality of emissions. To represent RWC emissions more accurately in urban air quality models, new emission estimation methods based on environmental and activity variables that drive pollutant emissions have been developed. They include for example outdoor temperature, housing characteristics and equipment, available heating technologies and associated emission factors, or temporal activity profiles from official wood consumption statistics (Grythe et al., 2019; Kukkonen et al., 2020b). Kukkonen et al. (2020b) notably showed with this approach that the annual average contribution of RWC to PM 2.5 levels could be as high as 15 % to 22 % in Helsinki, Copenhagen, and Umeå and up to 60 % in Oslo. Overall, although the results show a better horizontal and vertical spatial distribution of emissions compared to non-specific inventories, improvements are expected, especially on the use of meteorological parameters and regarding emission factors for specific devices.

Finally, for emissions associated with maritime activity in port areas, the inventories developed specifically for high-resolution modelling approaches include information on the fleet, the ship rotations in the harbour, and the emission heights. The implementation of the EPISODE-CityChem model within a CTM showed that in Baltic Sea harbour cities such as Rostock (Germany), Riga (Latvia), and Gdańsk–Gdynia (Poland), shipping activity could have contributed to 50 % to 80 % of NO 2 concentrations within the port area (Ramacher et al., 2019). As for the other sources, improvements are expected. They concern for instance the energy consumption of the different ships and the propulsion power of the auxiliary systems of the ships during their stay in port.

Because they allow detailed mapping of air quality in urban areas, and realistically represent emitting activities, those approaches allow tackling issues such as chronic exposure and source–concentration relationships, but they also provide elements for increased policy and technical measures, as discussed below: regulation, information campaigns, and economic steering.

5.2.5  Contribution of modelling to policy making and urban management strategies

Applying air quality and emission models allows for projections of future developments in air quality that can shed light on the different effects of alternative policy options, e.g. new regulations or effects of changes in the emissions from certain emission sectors. As an example (Fig. 10), the OSCAR model was run over London to quantify the contribution of sources – such as traffic – to the urban PM 2.5 concentration gradients.

Figure 10 (a)  Predicted spatial distributions of the annual mean PM 2.5 concentrations in µg m −3 , and (b)  urban traffic contributions to the total PM 2.5 concentrations, in %, for London for the year 2008 (Singh et al., 2014).

Air quality modelling is expected to gain relevance following the review of air quality legislation announced as part of the European Green Deal (EC, 2019), whereby the European Commission will also propose strengthening provisions on monitoring, modelling, and air quality plans to help local authorities achieve cleaner air. The construction of these future air quality modelling scenarios can be demanding, in particular when the goal is to be realistic and consistent with technological potentials as well as economic and societal developments (in particular reductions in the use of fossil fuels driven by climate policies).

Another field of action recently explored is that of technology-based and management-based traffic control strategies, and in particular the implementation of low-emission zones (LEZs) in urban areas (e.g. in Portugal, Dias et al., 2016; France, Host et al., 2020; and India, Sonawane et al., 2012). The quantification of the expected gains in terms of pollutant concentrations in ambient air, but also of economic benefits and reduction in the occurrence of chronic respiratory diseases or vascular accidents, provides concrete and robust elements for political and citizen debate and helps to move towards greater acceptability of the measures. In this framework, the degree of realism of the simulated scenarios, the spatial refinement of the approaches used, and also the capacity to evaluate them at the sub-urban scale (street, individual) can become determining elements of their scientific relevance and their legitimacy in the policy debate. Therefore, an increasing number of studies favour the use of multi-scale models with the introduction of puff or Gaussian dispersion models, as well as canyon-street models, with CTMs. When modelled scenarios serve as a basis for political decisions, it is highly valuable to include relevant authorities and decision makers from the beginning in the scenario design. This can be done in common workshops with relevant stakeholders where questions about technological trends and possibilities for emission reduction are discussed.

The analysis of simulation data for the estimation of health impacts can be ensured by integrated approaches – such as the EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) – or more simply by algorithms derived from epidemiology such as population-attributable fractions, which are standard methodology used to assess the contribution of a risk factor to disease. In terms of emissions, depending on the focus of the study, survey data on residential practices or activity-based road traffic models (as well as marine traffic models where appropriate) are increasingly used. Supplementary traffic algorithms can sometimes more accurately represent the effects of congestion on roadway emissions. Finally, for more realism, the scenarios considered can be derived from either the relevant air quality plans implemented at the scale of agglomerations or projections on vehicle fleet evolution (Andre et al., 2020). Some of the models also include the feedback effects of changes in practice, such as the estimate of emission increase due to the energy demand for electric vehicle charging (Soret et al., 2014).

Very small-scale modelling has also been used in other fields such as support in evaluating the effect of roadside structures on near-road air quality. Several studies, mainly based on CFD models, including LES approaches have thus focused on the performance of air pollution dispersion by green infrastructures in open areas and street canyons, even characterizing the capacity of parked vehicles to reduce pedestrian exposure to pollutants (see review article in Abhijith et al., 2017). Also, the link between the morphology of urban buildings, the dispersion of emissions, and air quality is often apprehended through CFD models (Hassan et al., 2020). At an even more operational level, LUR models (based on the spatial analysis of air quality data) have been coupled to high-resolution CTM runs to allow a precise identification of land use classes more exposed to PM 10 , SO 2 , and NO 2 . The results provided a methodological framework that could be used by authorities to assess the impact of specific plans on the exposed population and to include air quality in urban development policies (Ajtai et al., 2020).

Examples also exist in the area of shipping emissions, where several EU-funded projects either involved stakeholders such as IMO and HELCOM from the beginning (e.g. Clean North Sea Shipping, ENVISUM, CSHIPP, EMERGE) or made use of their knowledge in dedicated expert elicitation workshops (e.g. SHEBA). Future scenarios for shipping, some of them developed in these projects, were presented for the North and Baltic seas (Johansson et al., 2013; Matthias et al., 2016; Karl et al., 2019a; Jonson et al., 2015), for Chinese waters (Zhao et al., 2020b), and globally (Sofiev et al., 2018; Geels et al., 2020). However, the process of scenario generation in cooperation with authorities and other stakeholders is rarely described in scientific literature or fully detailed in publications that address various policy options.

5.2.6  Ensemble modelling for air quality research applications

In parallel, statistical developments also serve the evolution of ensemble models. During the last decade, ensemble-building methodologies have been questioned and improved in several international collaborations, and the inclusion of new observational data has allowed a better assessment of the relevance of these approaches. Ensemble forecasting can be implemented using multiple models or one model but with different inputs (e.g. varying meteorological input forcings, emission scenarios, chemical initial conditions), different process parameters (e.g. varying chemical reaction rates), different model configurations (e.g. varying grid spacings), or different models (Hu et al., 2017; Galmarini et al., 2012). A comprehensive study on ensemble modelling of surface O 3 was done as part of the Air Quality Model Evaluation International Initiative (AQMEII), including 11 CTMs operated by European and North American modelling groups (Solazzo et al., 2012). One of the main conclusions was that even if the multi-model ensemble based on all models performed better than the individual models, a selection of both top- and low-ranking models can lead to an even better ensemble (Kioutsioukis et al., 2016). It was also shown that outliers are needed in order to enhance the performance of the ensemble.

Within the CAMS regional forecasting system for Europe, multi-model ensemble modelling is a part of daily operational production ( https://www.regional.atmosphere.copernicus.eu/ , last access: 28 February 2022) for several air quality components. Statistical analyses have shown that an ensemble based on the median of the individual model gives a robust and efficient setup, also in the case of outliers and missing data (Marécal et al., 2015). By combining global- and regional-scale models, Galmarini et al. (2018) have taken this kind of ensemble modelling a step further, by setting up a hybrid ensemble to explore the full potential benefit of the diversity between models covering different scales. The analysis indeed showed that the multi-scale ensemble leads to a higher performance than the single-scale (e.g. regional-scale) ensemble, highlighting the complementary contribution of the two types of models.

5.3  Emerging challenges

5.3.1  on multiscale interaction and subgrid modelling.

The advances in computational capacity, the progress on big data management, and the recent developments on low-cost sensor technology, together with the significant developments in closing the gaps of knowledge when dealing with finer spatial and temporal scales (up to the order of metres and seconds, respectively) give the opportunity for further achievements in terms of innovation and outcome reliability in urban- to local-scale flow and air quality assessment. In such applications, very high spatial resolution modelling outputs are required together with dynamic and geocoded demographic data to conduct health monitoring on the impacts of air pollutants. However, new sub-grid/local approaches such as LESs, advanced CFD-RANS, machine learning statistical tools, and interfaces among different modelling scales (regional, urban, local/sub-grid) require further R&D work, especially when interfacing models using different parameterizations or computational approaches.

Of specific interest here is the case of model nesting in regimes where it has not been extensively applied in the past, as is the case of implementation and validation of multiply nested LESs (see e.g. Hellsten et al., 2021), as well as coupling of urban-scale deterministic models with local probabilistic models. In both areas, complications arise due to the nature of different parameterizations and the way boundary conditions are traditionally treated in LES models, highlighting the need for further validation and tools for the numerical evaluation of coupling implementations. Further areas of development include the better articulation between CTMs and subgrid models, towards solving overlay problems like emission double counting and mass conservation across interpolated interfaces, both critical points for their successful application as assessment tools.

5.3.2  On chemistry and aerosol modelling

One important aspect is the fact that local-scale models often include simple approaches to tropospheric chemistry. Although such an approach can be justified from the fact that computation domain timescales are usually well below lifetime scales of priority pollutants, it also poses limitations that need to be addressed. For example, the lack of full representation of NO x –VOC chemistry, or not considering a delay in establishing the photostationary NO–NO 2 –O 3 equilibrium, can introduce a significant bias in the restitution of concentration gradients at very fine scales. Particle-size-resolved schemes, including for example the discrimination of particle removal phenomena, are also expected to be important developments for these local models. How do simplified chemistry and physics impact on treating traffic emissions in cities? What is their role in the restitution of particle growth, secondary organic aerosol (SOA) formation, and ozone chemistry? These issues require special attention. They are also relevant to the treatment of other urban sources generating strong concentration gradients, such as shipping. Thus, the impact of the representation of VOC behaviour on particle formation and ageing, or the effect of NO 2 removal, both in the early phases of ship plume dispersion, should also be investigated.

More globally, there remain issues in the representation of reactivity in multi-scale modelling approaches and air quality forecasting. On the one hand, although some studies have shown that high-resolution models are good at predicting the occurrence (or non-occurrence) of local pollution events, it has been observed that they do not always capture the full range of pollutant concentrations and, especially, the amplitude of the strongest concentration peaks. On the other hand, there remains a very strong interaction between locally emitted pollutants and those resulting from long-range transport (LRT) to the city. This may be determinant for the operational forecasting of air quality at the urban scale. Thus, the representation, on a fine scale, of the fundamental processes of reactivity is one next challenging issue of multi-scale modelling. For local-scale modelling it is indeed important to make sure that at least we include chemical transformation with timescales significantly smaller than the time ranges imposed by the considered computational domain.

5.3.3  On fine-scale model input and emission data

As we move to finer scales and more advanced modelling, the input data – whether meteorological, descriptive of the urban environment, or related to the sources of pollutant – also require additional knowledge of their time and space variation, even including sufficiently detailed statistical behaviour. The refinement of meteorological and chemical input fields for statistical approaches is an important challenge. Indeed, the application of LES or statistical models in a fine domain embedded into a larger domain where ensemble-average modelling data are available and needed raises the question of how to generate fine-scale or statistical input data that are both mathematically consistent and physically correct. It was highlighted that the role of statistical models based on machine learning is increasing, especially for urban AQ applications. This is due to growing computer and IT networking possibilities, but also to new types of numerous observations, e.g. crowdsourcing, low-cost sensors, or citizen science approaches. The ability of machine learning to capture these new data sources and identify new applications in fine-scale air quality and personal exposure is therefore a great challenge for the coming years.

As far as emissions are concerned, the gain in realism has become a prerequisite to produce decision-support scenarios and requires a strong grounding in reality – i.e. emissions must be based on a census of the activities and on the specificities of the emitters (e.g. car engines, heating equipment, and rotation of boats in the port), which requires increasingly complex phases of model implementation over a territory and the intervention of a multiplicity of actors for data supply. In this context, tabulated emission inventories – even those based on actual activity data – have limited scope for use in future air quality and exposure scenarios. To be realistic, the scenarios must be able to reproduce the variation in emitting activity in relation to changes in transport supply, urban planning, energy costs, and individual or collective energy consumption practices. Therefore, a significant part of the work is now focused on developing air quality modelling platforms integrating emission models centred on the individual (see Fig. 11 in this paper; Elessa Etuman and Coll, 2018).

Figure 11 Schematic representation of OLYMPUS emission operating system (Elessa Etuman and Coll, 2018).

There, the main challenges are related to the representation of individual mobility for both commuting and private activities as well as domestic heating and more broadly energy consumption practices on one side and the consideration of traffic parameters such as urban freight, the distribution of traffic and its speciation, driving patterns, or the effects of road congestion on the other side (Lejri et al., 2018; Coulombel et al., 2019).

Another emerging issue is also how to cope with short-time hazardous emissions in urban areas. Such emissions can be related to accidents or deliberate releases that are of increased concern today. An important characteristic of associated exposures is their inherent stochastic behaviour (Bartzis et al., 2020). Novel modelling approaches are needed to properly assess the impact and support relevant mitigation measures.

5.3.4  On model evaluation

To act on these numerous and expected developments, and use their results for operational decision support, multi-scale models need validation. An often-overseen basic prerequisite here is the availability and representativeness of validation data, particularly at smaller scales. The model's performance indeed needs to be explored in more spatial detail and in all covered spatial scales, preferably as part of multi-scale urban-to-rural intercomparison projects, in order to be able to provide finer assessment on air quality and exposure. Such efforts can be supported by networks of inexpensive sensors as well as smart tags (Sevilla et al., 2018) and other sources of distributed information acting complementary to traditional local monitoring and flow-profiling technologies. To obtain methodology and data refinement as well as outcome reliability, more experience through additional case studies is also needed. Finally, consideration should be given to specific model performance evaluation criteria for various regulatory purposes, including prospective mode operation, i.e. the ability of a model to accurately predict the air quality response to changes in emissions. To this end, evaluations can draw on the very large methodological work that has been carried out since 2007 by the Forum for AIR quality MODelling in Europe (FAIRMODE) for the assessment of CTMs (Monteiro et al., 2018). The objective was to develop and support the harmonized use of models for regulatory applications, based on PM 10 , NO 2 , and O 3 assessments. The main strength of this approach was to produce an in-depth analysis of the performance of different model applications, combining innovative and traditional indicators (Modelling Quality Index and Modelling Quality Objectives) and considering measurement uncertainty. Although FAIRMODE was successful in promoting a harmonized reporting process, there remain major ways of improvement that can be critical for its regulatory acknowledgement – in particular regarding inconsistencies between indicators of different time horizons – and a methodology dedicated to data assimilation assessments.

6.1  Brief overview

There is a need to increase prediction capabilities for weather, air quality, and climate. The new trend in developing integrated atmospheric dynamics and composition models is based on the seamless Earth system modelling (ESM) approach (WWRP, 2015) to evolve from separate model components to seamless meteorology–composition–environment modelling systems, where the different components of the Earth system are taken into account in a coupled way (WMO, 2016). The Coupled Model Intercomparison Project (CMIP) is the main reference for the development ESM models that serve as input to the IPCC assessment reports (Eyring et al., 2016; IPCC, 2022). One driver for improvement is the fact that information from predictions is needed at higher spatial resolutions and longer lead times. In addition, we have to consider two-way feedbacks between meteorological and chemical processes on the one hand and aerosol–meteorology feedback on the other hand, where both are needed to meet societal needs. Continued improvements in prediction will require advances in observing systems, models, and assimilation systems. There is also growing awareness of the benefits of closely integrating atmospheric composition, weather, and climate predictions, because of the important role that aerosols (and atmospheric composition in general) play in these systems. Because the proposed review is focused on air quality and its atmospheric forcings, the present section discusses the atmospheric component of ESMs focusing on coupled chemistry–meteorology models.

While this section also considers challenges related to air quality modelling, it differs in emphasis to Sect. 5, by examining interactions that operate on multiple scales and including multiple processes that affect air quality, especially for cities.

6.2  Current status and challenges

6.2.1  interactions and coupled chemistry–meteorology modelling (ccmm).

Meteorology is one of the main uncertainties of air quality modelling and prediction. Many studies have investigated the role of meteorology in air quality in the past (e.g. Fisher et al., 2001, 2005, 2006; Kukkonen et al., 2005a, b) and even more recently (e.g. McNider and Pour-Biazar, 2020; Rao et al., 2020; Gilliam et al., 2015; Parra, 2020). The relationship between meteorology and air pollution cannot be interpreted as a one-way input process due to the complex two-way interaction between the atmospheric circulation and physical and chemical processes involving trace substances in both gas and aerosol form. The improvement of atmospheric phenomena prediction capability is, therefore, tied to progress in both fields and to their coupling.

The advances made by mesoscale planetary boundary layer meteorology during the last decades have been recently reviewed by Kristovich et al. (2019). During the last decade significant advances have been made even in the capabilities to predict air quality and to model the many feedbacks between air quality, meteorology, and climate, including radiative and microphysical responses (WMO, 2016, 2020; Pfister et al., 2020). Due to advances in air quality models themselves and the availability of more computing resources, air quality models can be run at high spatial resolution and can be tightly (online) or weakly linked to meteorological models (through couplers). This is a pre-requisite to improve prediction skills further, while air quality models themselves will be improved as our knowledge of key processes continues to advance.

Online-coupled meteorology and atmospheric chemistry models have greatly evolved during the last decade (Flemming et al., 2009; Zhang et al., 2012a, b; Pleim et al., 2014; WWRP, 2015; Baklanov et al., 2014; Mathur et al., 2017; Bai et al., 2018; Im et al., 2015a, b), a comprehensive evaluation of coupled model results has been provided by the outcome of AQMEII project (Galmarini and Hogrefe, 2015). Although mainly developed by the air quality modelling community, these integrated models are also of interest for numerical weather prediction and climate modelling as they can consider both the effects of meteorology on air quality and the potentially important effects of atmospheric composition on weather (WMO, 2016). Migration from offline to online integrated modelling and seamless environmental prediction systems are recommended for consistent treatment of processes and allowance of two-way interactions of physical and chemical components, particularly for AQ and numerical weather prediction (NWP) communities (WWRP, 2015; Baklanov et al., 2018a).

It has been demonstrated that prediction skills can be improved through running an ensemble of models. Intercomparison studies such as MICS and AQMEII (Tan et al., 2020; Galmarini et al., 2017; Zhang et al., 2016) serve as important functions of demonstrating the effectiveness of ensemble predictions and helping to improve the individual models. Predictions can also be improved through the assimilation of atmospheric composition data. Weather prediction has relied on data assimilation for many decades. In comparison, assimilation in air quality prediction is much more recent, but important advances have been made in data assimilation methods for atmospheric composition (Carmichael et al., 2008; Bocquet et al., 2015; Benedetti et al., 2018). Community available assimilation systems for ensemble and variational methods make it easier to utilize assimilation (Delle Monache et al., 2008; Mallet, 2010). Furthermore, the amount of atmospheric composition data available for assimilation is increasing, with expanding monitoring networks and the growing capabilities to observe aerosol and atmospheric composition from geostationary satellites (e.g. Kim et al., 2020). Operational systems such as CAMS (Copernicus Atmospheric Monitoring Service) have advanced current capabilities for air quality prediction (Marécal et al., 2015; Barré et al., 2021).

Currently, NWP centres around the world are moving towards explicitly incorporating aerosols into their operational forecast models. Demonstration projects are also showing a positive impact on seasonal to sub-seasonal forecast by including aerosols in their models (Benedetti and Vitart, 2018). Even the usual subdivision between global-scale NWP models and limited-area models employed to resolve regional to local scales is going to be revised. Many groups are building new Earth system models and taking advantage of global refined grid capabilities that facilitate multiscale simulations in a single model run, as in the case of the Model for Prediction Across Scales (MPAS) (Skamarock et al., 2018; Michaelis et al., 2019) and MUSICA (Pfister et al., 2020) approaches.

6.2.2  Aerosol–meteorology feedbacks for predicting and forecasting air quality for city scales

Multiscale CTMs are increasingly used for research and air quality assessment but less for urban air quality. Recently, there have been examples of coupled urban and regional models which allow the prediction and assessment of local, urban, and regional air quality affecting cities (Baklanov et al., 2009; Kukkonen et al., 2012; Sokhi et al., 2018; Kukkonen et al., 2018; Khan et al., 2019b). In particular, a downscaling modelling chain for prediction of weather and atmospheric composition on the regional, urban, and street scales is described and evaluated against observations by Nuterman et al. (2021). Kukkonen et al. (2018) described a modelling chain from global to regional (European and northern European domains) and urban scales and a multidecadal hindcast application of this modelling chain.

There are still uncertainties in prediction of PM components such as secondary organic aerosols (SOAs), especially during stable atmospheric conditions in urban areas which can cause severe air pollution conditions (Beekmann et al., 2015). Moreover, aerosol feedback and interaction with urban heat island (UHI) circulation is a source of uncertainty in CTM predictions. Several studies (Folberth et al., 2015; Baklanov et al., 2016; Huszar et al., 2016) demonstrated that urban emissions of pollutants, especially aerosols, are leading to climate forcing, mostly at local and regional scales through complex interactions with air quality (Fig. 12). These, in addition to almost 70 % of global CO 2 emissions, arise from urban areas, and hence urban areas pose a considerable source of climate forcing species.

Figure 12 The main linkages between urban emissions, air quality, and climate. (Baklanov et al., 2010).

It is necessary to highlight that the effects of aerosols and other chemical species on meteorological parameters have many different pathways (e.g. direct, indirect, semidirect effects) and must be prioritized in integrated modelling systems. Chemical species influencing weather and atmospheric processes over urban areas include greenhouse gases (GHGs), which warm near-surface air, and aerosols, such as sea salt, dust, and primary and secondary particles of anthropogenic and natural origin. Some aerosol particle components (black carbon, iron, aluminium, polycyclic and nitrated aromatic compounds) warm the air by absorbing solar and thermal-infrared radiation, while others (water, sulfate, nitrate, and most organic compounds) cool the air by backscattering incident short-wave radiation to space. It has been demonstrated (Sokhi et al., 2018; Baklanov et al., 2011; Huszar et al., 2016) that the indirect effects of urban aerosols modulate dispersion by affecting atmospheric stability (the difference in deposition fields is up to 7 %). In addition its effects on the urban boundary layer (UBL) thickness could be of the same order of magnitude as the effects of the UHI (a few hundred metres for the nocturnal boundary layer).

6.2.3  Urban-scale interactions

Meteorology is one of the main uncertainties in air quality assessment and forecast in urban areas where meteorological characteristics are very inhomogeneous (Hidalgo et al., 2008; Ching, 2013; Huszar et al., 2018, 2020). For these reasons, models used at the urban level must achieve greater accuracy in the meteorological fields (wind speed, temperature, turbulence, humidity, cloud water, precipitation).

Due to different characteristics of the surface properties (e.g. heat storage, reflection properties), a heat island effect occurs in cities. Urban areas can therefore be up to several degrees Celsius warmer than the surrounding rural areas and experience lighter winds due to the increased drag of urban canopy. This heating impacts the local environment directly, as well as affecting the regional air circulation with complex interactions that can induce pollutant recirculation, worsen stagnation episodes, and influence ozone and secondary aerosol formation and transport.

Studies over the past decade (e.g. McCarthy et al., 2010; Cui and Shi, 2012; González-Aparicio et al., 2014; Fallmann et al., 2016; Molina, 2021) have shown that the effects of the built environment, such as the change in roughness and albedo, the anthropogenic heat flux, and the feedbacks between urban pollutants and radiation, can have significant impacts on the urban air quality levels. A reliable urban-scale forecast of air flows and meteorological fields is of primary importance for urban air quality and emergency management systems in the case of accidental toxic releases, fires, or even chemical, radioactive, or biological substance releases by terrorists.

Improvements (so-called “urbanization”) are required for meteorological and NWP models that are used as drivers for urban air quality (UAQ) models. The requirements for the urbanization of UAQ models must include a better resolution in the vertical structure of the urban boundary layer and specific urban feature description. One of the key important characteristics for UAQ modelling is the mixing height, which has a strong specificity and inhomogeneity over urban areas because of the internal boundary layers and blending heights from different urban roughness neighbourhoods (Sokhi et al., 2018; Scherer et al., 2019).

Modern urban meteorology and UAQ models (e.g. WRF, COSMO, ENVIRO-HIRLAM) successfully implemented (a hierarchy of) urban parameterizations with different complexities and reached suitable spatial resolutions (Baklanov et al., 2008; Salamanca et al., 2011, 2018; Sharma et al., 2017; Huang et al., 2019; Mussetti et al., 2020; Trusilova et al., 2016; Wouters et al., 2016; Schubert and Grossman-Clarke, 2014) for an effective description of atmospheric flow in urban areas. The application of urban parameterizations implemented inside limited-area meteorological models is becoming a common approach to drive urban air quality analysis, allowing the improved urban meteorology description in different climatic and environmental conditions (Ribeiro et al., 2021; Salamanca et al., 2018; Gariazzo et al., 2020; Pavlovic et al., 2020; Badia et al., 2020). However, activities to improve the parameterizations (Gohil and Jin, 2019) and provide reliable estimation of the input urban features (Brousse et al., 2016) are continuing.

6.2.4  Integrated weather, air quality, and climate modelling

Since cities are still growing, intensification of urban effects is expected, contributing to regional or global climate changes, including intensification of floods, heat waves, and other extreme weather events; air quality issues caused by pollutant production; and transport. This requires a more integrated assessment of environmental hazards affecting towns and cities.

The numerical models most suitable to address the description of mentioned phenomena within integrated operational urban weather, air quality, and climate forecasting systems are the new-generation limited-area models with coupled dynamic and chemistry modules (so-called coupled chemistry–meteorology models, CCMMs). These models have benefited from rapid advances in computing resources, along with extensive basic science research (Martilli et al., 2015; WMO, 2016; Baklanov et al., 2011, 2018a). Current state-of-the-art CCMMs encompass interactive chemical and physical processes, such as aerosols–clouds–radiation, coupled to a non-hydrostatic and fully compressible dynamic core that includes monotonic transport for scalars, allowing feedbacks between the chemical composition and physical properties of the atmosphere. These models incorporate the physical characteristics of the urban built environment. However, simulations using fine resolutions, large domains, and detailed chemistry over long time durations for the aerosol and gas/aqueous phase are computationally demanding given the models' high degree of complexity. Therefore, CCMM weather and climate applications still make compromises between the spatial resolution, domain size, simulation length, and degree of complexity for the chemical and aerosol mechanisms.

Over the past decade integrated approaches have benefited from coupled modelling of air quality and weather, enabling a range of hazards to be assessed. Research applications have demonstrated the advantages of such integration and the capability to assimilate aerosol information in forecast cycles to improve emission estimates (e.g. for biomass burning) impacting both weather and air quality predictions (Grell and Baklanov, 2011; Kukkonen et al., 2012; Klein et al., 2012; Benedetti et al., 2018).

6.3  Emerging challenges

6.3.1  earth systems modelling for air quality research.

Full integration of aerosols across the various applications requires advances in Earth system modelling, with explicit coupling between the biosphere, oceans, and atmosphere, taking advantage of global refined grid capabilities that facilitate multiscale simulations in a single ESM run. The Earth system models offer many advantages but also create new challenges. Data assimilation in these tightly coupled systems is a future research area, and we can anticipate advances in assimilation of soil moisture and surface fluxes of pollutants and greenhouse gases.

The expected advance of the Earth system approach requires an increased research effort for the different communities to work more closely together to expand and to evolve the Earth observing system capacity. For what concerns the atmospheric models, the improvement of aerosol–cloud interaction description, related sulfate production, and oxidation processes in the aqueous phase are important to provide a better estimate of aerosol and cloud condensation nuclei (CCN) production impacting weather and climate. Their impact on surface PM concentrations, especially in areas with very low SO x emissions like Europe, still needs to be investigated (Schrödner et al., 2020; Genz et al., 2020; Suter and Brunner, 2020).

6.3.2  Constraining models with observations

The use of coupled regional-scale meteorology–chemistry models for AQF represents a desirable advancement in routine operations that would greatly improve the understanding of the underlying complex interplay of meteorology, emission, and chemistry. Chemical species data assimilation along with increased capabilities to measure plume heights will help to better constrain emissions in forecast applications.

While important advances have been made, present challenges require advances in observing systems and assimilation systems to support and improve air quality models. From the perspective of air quality modelling, there are still uncertainties in the emission estimates (especially those driven by meteorology and other conditions such as biomass burning and dust storms).

The impacts of data assimilation of atmospheric composition are limited by the remaining major gaps in spatial coverage in our observing systems. Major parts of the world have limited or no observations (Africa is an obvious case). This is changing thanks to the forthcoming new constellation of geostationary satellites (Sentinel-4, TEMPO, and GEMS; Kim et al., 2020) measuring atmospheric composition and with the advances in low-cost sensor technologies. Machine learning applications will play important roles in improving predictions through better parameterizations, better ways to deal with bias, and new approaches to utilize heterogeneous observations, for example new models for relating aerosol optical depth (AOD) to surface PM 2.5 mass and composition.

Reanalysis products of aerosols and other atmospheric constituents are now being produced (Inness et al., 2019). These can support many applications, and continued development is strongly encouraged and will benefit from the observations and data assimilation advances discussed above.

6.3.3  Multiscale interactions affecting urban areas

For urban applications the main science challenges related to multiscale interactions involved the non-linear interactions of urban heat island circulation and aerosol forcing and urban aerosol interactions with clouds and radiation. In order to improve air quality modelling for cities, advances are needed in data assimilation of urban observations (including meteorological, chemical, and aerosol species), development of model dynamic cores with efficient multi-tracer transport capability, and the general effects of aerosols on the evolution of weather and climate on different scales. All these research areas are concerned with optimized use of models on massively parallel computer systems, as well as modern techniques for assimilation or fusion of meteorological and chemical observation data (Nguyen and Soulhac, 2021).

In terms of atmospheric chemistry, the formation of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols) in urban environments is still an active research area, and there is an important need to improve the understanding and treatment within two-way coupled chemistry–meteorology models.

Urban areas interact at many scales with the atmosphere through their physical form, geographical distribution, and metabolism from human activities and functions. Urban areas are the drivers with the greatest impact on climate change. The exchange processes between the urban surface and the free troposphere need to be more precisely determined in order to define and implement improved climate adaptation strategies for cities and urban conglomerations. The knowledge of the 3D structure of the urban airshed is an important feature to define temperature, humidity, wind flow, and pollutant concentrations inside urban areas. Although computational resources had great improvement, time and spatial resolution are still imposing some limitations to the correct representation of urban features, especially for the street scale. Urban areas are responsible for the urban heat island circulation, which interacts with other mesoscale circulations, such as the sea breeze and mountain valley circulations, determining the pathways of primary pollutants emitted in the atmosphere but even the production and transport of ozone (see e.g. Finardi et al., 2018) and secondary aerosols (Fig. 13).

Figure 13 Near-surface ozone concentrations ( µg m −3 ) predicted for 15 July 2015 at (a)  08:00, (b)  12:00, and (c)  17:00 LST over Naples. Wind field at 10 m height is represented by grey arrows. (Finardi et al., 2018; © American Meteorological Society. Used with permission.)

Challenges remain on how to include scale-dependent processes and interactions for urban- and sub-urban-scale modelling. These include spatial and temporal distribution of heat, chemical, and aerosol emission source activities down to building-size resolution, flow modification at the micro-scale level by the urban canopy structure and by the urban surface heat balance, enhancement/damping of turbulent fluxes in the urban boundary layer due to surface and emission heterogeneity, and chemical transformation of pollutants during their lifetime within the urban canopy sublayer. Obviously, the scale interaction issues facing air quality–meteorology–climate models are quite in line with those described in Sect. 5 for multi-scale air quality modelling. Thus, on coupling regional to urban and building scales, CTMs coupled with urbanized meteorological models are needed to describe the city-scale atmospheric circulation and chemistry in the urban airshed and the building and evolution of the urban heat island, especially strong during heat waves (Halenka et al., 2019), including the combined effects of urban, sub-urban, and rural pollutant emissions. High spatial resolution is also needed to capture pollutant concentration spatial variability at the pedestrian level in an urban environment, answering epidemiological research questions or emergency preparedness issues. In the near future, microscale CFD, including LES modelling, will probably become an appropriate tool for urban air quality assessment and forecasting purposes due to the expected continuous increase in computational resources enabling the inclusion of chemical reactions (Fig. 14). Nevertheless, today computational resources still limit their application to short-term episodes and often to stationary conditions, while climatological studies require for instance a multi-year approach. Parameterized street-scale models (Singh et al., 2020a; Hamer et al., 2020; Kim et al., 2018) or a database created with CFD simulations of several scenarios (Hellsten et al., 2020) can be alternative ways for the downscaling from the mesoscale to the city and street scale, together with obstacle-resolving Lagrangian particle models driven by Rokle-type diagnostic flow models (Veratti et al., 2020; Tinarelli and Trini Castelli et al., 2019) that can be coupled with CTMs for long-term air quality assessment (Barbero et al., 2021).

Figure 14 Modelled distribution of ground-level nitrogen dioxide  (a) and ozone  (b) at 20:00 CEST for a 6.7 km×6.7 km subarea of Berlin around Ernst-Reuter-Platz. The simulation was performed with the chemistry mechanism CBM4 and a horizontal grid size of 10 m (Khan et al., 2021).

6.3.4  Nature-based solutions for improving air quality

The growing interest for nature-based solutions requires the improvement of models' capability to describe biogenic emissions (Cremona et al., 2020) and deposition processes (Petroff et al., 2008; Petroff and Zhang, 2010), resolving the different species leaf features, biomass density, and physiology. The balance between vegetation drag, pollutant absorption, and biogenic volatile organic compound (BVOC) emissions determines the net positive or negative air quality impact at local and city scales (Karttunen et al., 2020; San José et al., 2020; Santiago et al., 2017; Jeanjean et al., 2017; Jones et al., 2019; Anderson and Gough, 2020). In most cases this feature cannot be explicitly considered, with some parameterized approach, such as the canyon one being necessary, to deal with it. Nevertheless, the present capabilities of UAQ models to describe biogenic emissions together with gas and particle deposition over vegetation covered surfaces (including green roofs and vertical green surfaces) need to be improved to include nature-based solutions' impact in air quality plan evaluation.

7.1  Brief overview

A substantial amount of research has been conducted regarding the health effects of air pollution, especially those attributed to particulate matter (PM). Nevertheless, it is not conclusively known which properties of PM are the most important ones in terms of the health impacts (e.g. Brook et al., 2010; Beelen et al., 2014; Pope et al., 2019; Schraufnagel et al., 2019). For example, a review article by Hoek et al. (2013) addressed cohort studies and reported an excess risk for all-cause and cardiovascular mortality due to long-term exposure to PM 2.5 .

In this section, we have therefore addressed three topical research areas, associated with air quality and health: (i) the health impacts of particulate matter in ambient air; (ii) the combined effects on human health of various air pollutants, heat waves, and pandemics; and (iii) the assessment of the exposure of populations to air pollution. Research that has been reviewed is based on selected international research projects and publications, but generally these are expected to reflect the general consensus, as both the projects and resulting publications involved a significant section of the air quality and health research community. Regarding pandemics, we will focus on the most recent one that has been caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The research and interdependencies of these topics have been illustrated in Fig. 15.

Figure 15 A schematic diagram that illustrates some of the main factors in the evaluation of the exposure and health impacts of particulate matter.

As illustrated in the figure, particulate matter pollution originates from a wide range of anthropogenic and natural sources, and its characteristics can vary in terms of size distributions, chemical composition, and other properties. The resulting health outcomes also vary substantially, depending on the target physiological system or organ of an individual. In addition, the assessments of the interrelations of PM pollution and health outcomes are challenged by various combined and in some cases synergetic effects caused by, for example heat waves and cold spells, allergenic pollen, and airborne microorganisms.

7.2  Current status and challenges

7.2.1  health impacts of particulate matter, (i) overview of the health impacts of particulate matter pollution.

In addition to cardiovascular and respiratory diseases, exposure to ambient air PM may result in acute and severe health problems, such as cardiovascular mortality, cardiac arrhythmia, myocardial infarction (MI), myocardial ischemia, and heart failure (Dockery et al., 1993; Schwartz et al., 1996; Peters et al., 2001; Pope et al., 2002). The Organization for Economic Co-operation and Development (OECD) concluded in its outlook (OECD, 2012) that PM pollution will be the primary cause of deaths of the African population by 2050, in comparison to hazardous water and poor hygiene. Pražnikar and Pražnikar (2012) comprehensively addressed in their review several epidemiological studies throughout the world; they reported a strong association between the PM concentrations and respiratory morbidity, cardiovascular morbidity, and total mortality.

Global assessments of air quality and health require comprehensive estimates of the exposure to air pollution. However, in many developing countries (e.g. Africa; see Rees at al., 2019; Bauer et al., 2019) ground-based monitoring is sparse or non-existent; quality control and the evaluation of the representativeness of stations may also be insufficient. An inter-disciplinary approach to exposure assessment for burden of disease analyses on a global scale has been recently suggested jointly by WHO, WMO, and CAMS (Shaddick et al., 2021). Such an approach would combine information from available ground measurements with atmospheric chemical transport modelling and estimates from remote sensing satellites. The aim is to produce information that is required for health burden assessment and the calculation of air-pollution-related Sustainable Development Goal (SDG) indicators.

(ii) Health effects associated with the long-term exposure to particulate matter

Long-term exposure may potentially affect every organ in the body and hence worsen existing health conditions, and it may even result in premature mortality (see for example a recent review by Schraufnagel et al., 2019; Brook et al., 2010; Brunekreef and Holgate, 2002; Beelen et al., 2015; Im et al., 2018; Liang et al., 2018; Vodonos et al., 2018; Pope et al., 2019). For example, a review article by Hoek et al. (2013) addressed cohort studies and reported an excess risk for all-cause and cardiovascular mortality due to long-term exposure to PM 2.5 . Beelen et al. (2015) analysed an extensive set of data from 19 European cohort studies; they found that long-term exposure to PM 2.5 sulfur was associated with natural-case mortality. Similar results regarding long-term exposure to PM 2.5 and mortality were also presented in other recent studies conducted by Vodonos et al. (2018) and Pope et al. (2019).

Studies conducted in the framework of the European Study of Cohorts for Air Pollution Effects (ESCAPE) project showed that long-term exposure to PM air pollution was linked to incidences of acute coronary (Cesaroni et al., 2014), cerebrovascular events (Stafoggia et al., 2014), and lung cancer in adults (Adam et al., 2015). Moreover, findings from the same project revealed that other health effects related to PM air pollution were reduced lung function in children (Gehring et al., 2013), pneumonia in early childhood and possibly otitis media (MacIntyre et al., 2014), low birthweight (Pedersen et al., 2013), and the incidence of lung cancer (Raaschou-Nielsen et al., 2013). In addition, another finding of the ESCAPE project was the connection between traffic-related PM 2.5 absorbance and malignant brain tumours (Andersen et al., 2018).

The Biobank Standardisation and Harmonisation for Research Excellence in the European Union (BioSHaRE-EU) project, which included three European cohort studies, presented the association between long-term exposure to ambient PM 10 and asthma prevalence (Cai et al., 2017). In the framework of three major cohorts (HUNT, EPIC-Oxford, and UK Biobank) it was shown that, after adjustments for road traffic noise, incidences of cardiovascular disease (CVD) diseases were attributed to long-term PM exposure (Cai et al., 2018). Hoffmann et al. (2015) suggested that long-term exposure to both PM 10 and PM 2.5 is linked to an increased risk for stroke, and it might be responsible for incidences of coronary events.

(iii) Health effects associated with the short-term exposure to particulate matter

Collaborative studies such as the APHENA (Air Pollution and Health: A European and North American Approach) and the MED-PARTICLES project in Mediterranean Europe have evidenced that short-term exposure to PM has been associated with all-cause cardiovascular and respiratory mortality (Katsouyanni et al., 2009; Zanobetti and Schwartz, 2009; Samoli et al., 2013; Dai et al., 2014), hospital admissions (Stafoggia et al., 2013), and occurrence of asthma symptom episodes in children (Weinmayr et al., 2010).

(iv) Health effects associated with the chemical constituents of PM

The chemical composition of PM is associated with the health effects related to PM concentrations, in addition to the mass concentrations of particulate matter (e.g. Maricq, 2007). Chemical composition of particles is complex; generally, it depends on the source origin of particles and their chemical and physical transformations in the atmosphere (e.g. Prank et al., 2016). Some prominent examples of the components of PM are sulfate (SO 4 ), nitrate (NO 3 ), metals, elemental and organic carbon (Yang et al., 2018), ammonium (NH 3 ) (Pražnikar and Pražnikar, 2012), sea salt, and dust (Prank et al., 2016).

The PM components also include biological organisms (e.g. bacteria, fungi, and viruses) and organic compounds (e.g. polycyclic aromatic hydrocarbons, PAHs, and their nitro-derivatives, NPAHs) (Morakinyo et al., 2016; Kalisa et al., 2019). Their content can vary significantly with regard to time and for various climatic regions (Maki et al., 2015; Gou et al., 2016).

Hime et al. (2018) have reviewed studies which investigated which PM components could be mostly responsible for severe health effects. Such studies included the National Particle Component Toxicity (NPACT) initiative, which combined epidemiologic and toxicologic studies. That study concluded that the concentrations of SO 4 , EC, OC, and PM mainly originated from traffic and combustion and had a significant impact on human health (Adams et al., 2015). The European Study of Cohorts for Air Pollution Effects (ESCAPE) project aimed at examining the association of elemental components of PM (copper, Cu; iron, Fe; potassium, K; nickel, Ni; sulfur, S; silicon, Si; vanadium, V; and zinc, Zn) with inflammatory blood markers in European cohorts (Hampel et al., 2015). They focused, together with the TRANSPHORM project (Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter), on investigating the relationship of these components with cardiovascular (CVD) mortality (Wang et al., 2014).

Moreover, other studies conducted within the framework of ESCAPE and TRANSPHORM projects provided evidence that mortality was linked to long-term exposure to PM 2.5 sulfur (Beelen et al., 2015), as well as to the particle mass and nitrogen oxides (NO 2 and NO x ) (Beelen et al., 2014). As part of the NordicWelfAir project, Hvidtfeldt et al. (2019b) connected the risks of being exposed long-term to PM 2.5 , PM 10 , BC, and NO 2 with all-cause and CVD mortality. In another paper, Hvidtfeldt et al. (2019a) demonstrated the association between long-term exposure to PM 2.5 , elemental and primary organic carbonaceous particles ( BC / OC ), secondary organic aerosols (SOA), and all-cause mortality. They also demonstrated the connection between PM 2.5 , BC / OC , and secondary inorganic aerosols (SIAs) and CVD mortality. Recently, a continuation of this study included all Danes born between 1921 and 1985, showing higher mortality related to exposure to NO 2 , O 3 , PM 2.5 , and BC (Raaschou-Nielsen et al., 2020).

In the framework of the Particle Component Toxicity (NPACT) project, Lippmann et al. (2013) showed that PM 2.5 mass and EC were linked to all-cause mortality; EC was also connected with ischemic heart disease mortality. The latter result was quite similar to the findings of Ostro et al. (2010, 2011, 2015), including OC, SO 4 , NO 3 , and SO in addition to EC. Concerning cardiopulmonary disease mortality, a strong association was observed for the exposure to NO 3 and SO 4 (Ostro et al., 2010, 2011). Luben et al. (2017) and Hoek et al. (2013) in their reviews observed the association of BC with cardiovascular disease hospital admissions and mortality.

In a meta-analysis work conducted by Achilleos et al. (2017), elemental carbon (EC), black carbon (BC), black smoke (BS), organic carbon (OC), sodium (Na), silicon (Si), and sulfate (SO 4 ) were associated with all-cause mortality, and BS, EC, nitrate (NO 3 ), ammonium (NH 4 ), chlorine (Cl), and calcium (Ca) were linked to CVD mortality. In addition, some American cohort studies pointed out that long-term exposure to SO 4 was positively connected with all-cause, cardiopulmonary disease, and lung cancer mortality (Dockery et al., 1993; HEI, 2000; Pope et al., 2002; Ostro et al., 2010).

In addition, other kinds of severe health effects related to PM components have been reported. For example, Wolf et al. (2015) showed that long-term exposure to PM constituents, especially of K, Si, and Fe, which are indicators of road dust, provoked coronary events. The findings of a systematic review, where 59 studies were included, indicated that chronic obstructive pulmonary disease (COPD) emergency risk was attributed to short-term exposure to O 3 and NO 2 , whereas short-term exposure to SO 2 and NO 2 was responsible for acute COPD risk in developing countries (Li et al., 2016). The review of Li et al. (2016) also reported that short-term exposure to O 3 , CO, NO 2 , SO 2 , PM 10 , and PM 2.5 was linked to respiratory risks.

Poulsen et al. (2020), using detailed modelling and Danish registers from 1989–2014, showed stronger relationships between primarily emitted black carbon (BC), organic carbon (OC), and combined carbon ( OC / BC ) and malignant brain tumours. Furthermore, the risk for lung cancer was linked to several different compounds and sources of aerosol particles; they found that particles containing S and Ni might be two of the most important components associated with lung cancer (Raaschou-Nielsen, 2016). Park et al. (2018) found that PM 2.5 particles emitted from diesel and gasoline engines were more toxic for humans than, for example, particles from biomass burning or coal combustion. In a recent study, it was concluded that traffic-specific PM components, and in particular NH 4 and SO 4 , lead to higher risks of stroke than PM components linked to industrial sources (Rodins et al., 2020).

(vi) The uncertainties associated with concentration–response functions

Based on previous research, WHO and Europe recommended in 2015 a set of linear concentration–response functions for the main air pollutants and related health outcomes (Héroux et al., 2015). These functions are currently widely used for health assessments, e.g. on a European scale by EEA. EEA (2019) estimated that more than 340 000 premature deaths per year in Europe could be related to the exposure to PM 2.5 . However, it is currently widely debated what the optimal shape of the concentration–response functions is and whether there should be a threshold or lower limit.

A prominent example is the highly cited study by Burnett et al. (2018) on the developments of the Global Exposure Mortality Model (GEMM). By combining data from 41 cohorts from 16 different countries, Burnett et al. (2018) have constructed new hazard ratio functions that to a wider degree than previous studies include the full range of the global exposure to outdoor PM 2.5 . The GEMM functions for PM 2.5 and nonaccidental mortality generally follow a supralinear association at lower concentrations and near-linear association at higher concentrations (Burnett et al., 2018).

The GEMM functions would indicate that health impacts related to PM 2.5 exposure have been underestimated, at both the global and regional scales. In a recent European study on cardiovascular mortality, the GEMM functions were combined with concentration fields from a global atmospheric chemistry–climate model. The results pointed towards a total of 790 000 premature deaths attributed to air pollution in Europe per year, which is significantly higher than the value previously estimated by EEA for example (Lelieveld et al., 2019). Several reviews or meta-analyses have focused on low exposure levels; the conclusion has been that significant associations can be found between PM 2.5 and health effects also at levels below the concentrations of 10–12  µg m −3 . These values are equal to or below the WHO guidelines (10  µg m −3 ) and the US EPA standards (12  µg m −3 ) (Vodonos et al., 2018; Papadogeorgou et al., 2019).

(vii) The use of high-resolution multi-decadal data sets for extensive regions

Developments of air pollution modelling and more efficient computing resources have made it possible to compute high-resolution air pollution data sets that cover larger regions, as well as longer, even multi-decadal, time periods (Fig. 16). The combination of such data with national or international health registers, or cohorts from several countries, improves the representativeness of statistical analyses. The use of more extensive data sets will also reduce the selection biases related to the sizes of the cohorts.

This has resulted in, for example, a better detection of the links between air pollution exposure and new health endpoints, such as psychiatric disorders (e.g. Khan et al., 2019a; Antonsen et al., 2020) and cognitive abilities (e.g. Zhang et al., 2018). Based on high-resolution ( 1 km×1 km ) air pollution data covering the period 1979–2015 and population-based data from the Danish national registers, Thygesen et al. (2020) found that exposure to air pollution (specifically NO 2 ) during early childhood was associated with the development of attention-deficit/hyperactivity disorder (ADHD).

Figure 16 An illustration of how concentration predictions at a high spatial and temporal resolution (panels on the left-hand side) could be used for high-resolution health impact assessments (panel on the right-hand side). The concentration distributions were predicted with the chemical transport model SILAM. The health impact assessment was made with the EVA model in a high-resolution setup for the Nordic region, giving an estimate of the number of premature deaths due to exposure to air pollution (Lehtomäki et al., 2020). The concentrations used in EVA were from the chemical transport system DEHH-UBM, providing 1 km×1 km concentration across the Nordic region.

Kukkonen et al. (2018) presented a multi-decadal global- and European-scale modelling of a wide range of pollutants and the finer-resolution urban-scale modelling of PM 2.5 in the Helsinki metropolitan area. All of these computations were conducted for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the chemical transport model SILAM. These results have been used for health impact assessments by Siddika et al. (2019, 2020). The predicted air quality and meteorological data are also available to be used in any other region globally in health impact assessments.

7.2.2  Combined effects of air pollution, heat waves, and pandemics on human health

It is widely known that poor air quality has severe impacts on the human immune system (Genc et al., 2012). In particular, some of the acute health effects include chronic respiratory and cardiovascular diseases (Ghorani-Azam et al., 2016), respiratory infection (e.g. Conticini et al., 2020), and even cancer and death (IOM, 2011; Villeneuve et al., 2013). Polluted air can cause, for example, damage in epithelial cilia (Cao et al., 2020), which leads to a chronic inflammatory stimulus (Conticini et al., 2020). It has also been shown that the SARS-CoV-2 can stay viable and infectious on aerosol particles that are smaller than 5  µm in diameter for more than 3 h (van Doremalen et al., 2020). Therefore, atmospheric pollutants might play an important role in spreading the virus.

(i) The role of air pollution in pandemics

Previously, Cui et al. (2003) found that the long-term exposure to moderate or high air pollution levels was positively correlated with mortality caused by SARS-CoV-1 in the Chinese population. Therefore, it is possible that poor air quality would enhance the risk of mortality during epidemics or pandemics, such as the COVID-19 disease, caused by SARS-CoV-2. Moreover, poor air quality can enhance the human health effects of heat waves, cold spells, and allergenic pollen. This is because exposure to ambient air pollutants together with microorganisms tend to make the health impacts of pathogens more severe; at the same time, they weaken human immunity, resulting in an increased risk of respiratory infection (e.g. Xu et al., 2016; Horne et al., 2018; Xie et al., 2019; Phosri et al., 2019).

Conticini et al. (2020) concluded that weakened lung defence mechanisms due to continuous exposure to air pollution could partly explain the higher morbidity and mortality caused by SARS-CoV-2 in areas of poor air quality in Italy. Zhu et al. (2020) used the data of daily confirmed COVID-19 cases, air pollution, and meteorology from 120 cities in China to study the association between the concentrations of ambient air pollutants (PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 ), and COVID-19 cases. By applying a generalized additive model, they found a significant correlation between PM 2.5 , PM 10 , CO, NO 2 , and O 3 and daily counts of confirmed COVID-19 patients, while SO 2 was negatively associated with the daily number of new COVID-19 cases.

Ogen (2020) studied 66 regions in Italy, Spain, France, and Germany; he also found a spatial correlation between high NO 2 concentration and fatality from COVID-19. According to this study, 83 % of all fatalities occurred in the regions having a maximum NO 2 concentration above 100  µ molec . m - 2 , and only 1.5 % of all fatalities took place in areas in which the maximum NO 2 concentration was below 50  µ molec . m - 2 . However, Pisoni and Van Dingenen (2020) did not find a similar phenomenon in the UK, where the number of deaths was higher than in Italy, despite a significantly lower NO 2 concentration.

Xie and Zhu (2020) used temperature data from 122 cities mainly in the eastern part of China and observed a linear relationship between ambient temperature and daily number of confirmed COVID-19 counts in cases when the temperature was below 3  ∘ C. At higher temperatures, no correlation was found. This indicates that daily counts of COVID-19 did not decline at warmer atmospheric conditions, although such a dependency was expected based on the previous studies related to coronaviruses SARS-CoV and MERS-CoV (e.g. van Doremalen et al., 2013; Bi et al., 2007; Tan et al., 2005). However, the study of Xie and Zhu (2020) was conducted in winter; the highest temperatures were around 27  ∘ C.

Chen et al. (2017) statistically investigated the correlation between influenza incidences and the concentrations of PM 2.5 in 47 Chinese cities for 14 months during 2013–2014. Based on the results, they concluded that about 10 % of the influenza cases were induced by the exposure to ambient PM 2.5 . They also classified the days as cold, moderately cold, moderately hot, and hot separately for each city and found that the risk for influenza transmission associated with ambient air PM 2.5 was enhanced during cold days.

(ii) Combined effects of air pollutants and heat waves

Siddika et al. (2019) found that prenatal exposure to both PM 2.5 and O 3 increased the risk of preterm birth in Finland in the 1980s. The risk was more pronounced if the mother was exposed to both higher PM 2.5 and higher O 3 concentrations. They explained that O 3 might deplete antioxidants in the lung, and therefore the defence mechanism needed against reactive oxygen species formation was reduced due to the exposure to PM 2.5 . Also, the O 3 concentrations can cause changes in lung epithelium so that it is more permeable for particles to absorb into the circulatory system. The population selected for the study were living in southern Finland in the 1980s, in relatively good air quality. However, the concentrations of many pollutants, e.g. those of PM 2.5 , have been shown to have been twice as high in the 1980s, compared with the corresponding pollutant levels in the same region during the second decade of the 21st century (Kukkonen et al., 2018).

Wang et al. (2020) presented that PM 2.5 exposure strengthened the effect of moderate heat waves (short or only moderate temperature rise) associated with preterm births during January 2015–July 2017 in Guangdong Province, China. However, during the intensive heat waves, the effects were not additive.

Analitis et al. (2018) studied synergetic effects of temperature, PM 10 , O 3 , and NO 2 on cardiovascular and respiratory deaths. They found some correlation between the effects of high ambient temperatures and those caused by O 3 and PM 10 concentrations. However, during the heat waves, no clear synergetic effect was found. In a review article, Son et al. (2019) concluded that there is some evidence between the mortality related to high temperatures and air pollution.

J. Li et al. (2017) wrote a comprehensive literature review about the role of temperature and air pollution in mortality. They determined individual spatial temperature ranges and grouped them in “low”, “medium”, and “high” based on the information given in each study about typical local weather conditions. After a careful selection based on the quality of the data sets, they performed a meta-analysis by using data of 21 studies; they found that high temperature significantly increased the risk of non-accidental and cardiovascular mortality, caused by the exposure to PM 10 or O 3 . The risk of cardiovascular mortality due to PM 10 decreased during low-temperature days in the prevailing climate. However, the exposure to both low temperature and the concentrations of O 3 increased the risk of non-accidental mortality. Similar effects were not found for the concentrations of SO 2 or NO 2 and temperature. Lepeule et al. (2018) found that short-term rise in outdoor air temperature and relative humidity was linked to deteriorated lung function of elderly people. A simultaneous exposure to black carbon amplified the health effects.

7.2.3  Estimation of exposures

(i) modelling of individual exposure.

The currently available epidemiological studies use measured or modelled outdoor concentrations in residential areas or at home addresses, to correlate the concentrations with health effects. However, several studies have pointed out that it is critical to use the exposure of people as indicators for the health effects (Kousa et al., 2002; Soares et al., 2014; Kukkonen et al., 2016b; Smith et al., 2016; Singh et al., 2020a; Li and Friedrich, 2019; Li, 2020). It is obvious that the effects of air pollutants on human health are caused by the inhaled pollutants, instead of the pollutants at a certain point or area outdoors. Thus, exposure is a much better indicator for estimating health risks than outdoor concentrations. The individual exposure of a person to air pollutants is defined here as the concentration of pollutants at the sites where the person is staying weighted by the length of stay at each of the places of stay and averaged over a certain time span, e.g. a year. The places of stay are in this context called microenvironments. Exposure of a group of people with certain features (e.g. sex, age, place of living) is the average exposure of the individuals in the subgroup. In general, the exposure of a person is calculated by first estimating the concentration of air pollutants in the microenvironments where the person or population subgroup is staying and then by weighting this concentration with the length of time the person has been at the respective microenvironment (Li and Friedrich, 2019; Li, 2020). The result of modelling exposure can be verified by measuring the exposure with personal sensors (e.g. Dessimond et al., 2021).

Exposures to ambient concentrations of PM 2.5 can be substantially different in different microenvironments. The concentrations in microenvironments can be either measured or modelled. Computational results of activity-based dynamic exposures by Singh et al. (2020a) demonstrate that the total population exposure was over one-quarter ( −28  %) lower on a city-wide average level, compared with simply using outdoor concentrations at residential locations, in the case of London in the 2010s. Smith et al. (2016) have shown by modelling that exposure estimates based on space-time activity were 37 % lower than the outdoor exposure evaluated at residential addresses in London. However, this proportion will be different for other urban regions and time periods, or when addressing specific population sub-groups.

The exposure to particulate matter is substantially influenced by indoor environments, as people spend 80 %–95 % of their time indoors (e.g. Hänninen et al., 2005). Indoor air quality mainly depends on the penetration of pollutants in outdoor air, on ventilation, and on indoor pollution sources. For estimating the indoor concentration, commonly a mass-balance model is applied (Hänninen et al., 2004; Li, 2020). With a mass-balance model, the indoor concentration is calculated based on the outdoor concentration, a penetration factor, the air exchange rate, the decay rate, the emission rates of the indoor sources and the room volume, and, if available, by parameters of the mechanical ventilation system.

A complex stochastic model has been developed for estimating the annual individual exposure of people or groups of people in the European Union to PM 2.5 and NO 2 , using characteristics of the analysed subgroup, such as age, gender, place of residence, and socioeconomic status (Li et al., 2019a, c; Li and Friedrich, 2019; Li, 2020). The probabilistic model incorporates an atmospheric model for estimating the ambient pollutant concentrations in outdoor microenvironments and a mass-balance model for estimating indoor concentrations stemming from outdoor concentrations and from emissions from indoor sources. Time-activity patterns (which specify how long a person stays in each microenvironment) were derived from an advancement of the Multinational Time Use Study (MTUS) (Fisher and Gershuny, 2016). The exposures can also be estimated for past years. It is therefore possible to analyse the exposure for the whole lifetime of a person, by using a lifetime trajectory model that retrospectively predicts the possible transitions in the past life of a person.

An exemplary result from Li and Friedrich (2019) is shown in Fig. 17. It displays that the PM 2.5 annual average exposure averaged over all adult persons living in the EU increased since the 1950s from 19.0 (95 % confidence interval, CI: 3.3–55.7)  µg m −3 to a maximum of 37.2 (95 % CI: 9.2–113.8)  µg m −3 in the 1980s. The exposure then declined gradually afterwards until 2015 to 20.1 (95 % CI: 5.8–51.2)  µg m −3 . Indoor air pollution contributes considerably to exposure. In recent years more than 45 % of the PM 2.5 exposure of an average EU citizen has been caused by indoor sources.

Figure 17 Temporal evolution of the annual average exposure of EU adult persons to PM 2.5 from 1950 to 2015 (Li and Friedrich, 2019). All sources, except “outdoor”, refer to indoor sources. ETS denotes environmental tobacco smoke (passive smoking).

The most important indoor sources are environmental tobacco smoke (passive smoking), frying, wood burning in open fireplaces and stoves in the living area, and the use of incense sticks and candles. In addition, nearly all indoor activities include abrasion processes that produce fine dust. For NO 2 , indoor sources cause around 24 % of the exposure, with the main contributions from cooking with gas and from biomass burning in stoves and open fireplaces (Li and Friedrich, 2019). The solid black line in Fig. 7.3 shows the background outdoor concentration at the places where EU citizens spend their lives on average. Urban background concentrations refer to urban concentrations that are not in the immediate vicinity of the emission sources, especially of streets.

The average exposure is higher than the average outdoor background concentration. Epidemiological studies correlate outdoor concentrations with health risks and thus neglect the exposure caused by indoor sources. Such studies therefore implicitly assume that the contribution of indoor sources is the same at all places and for all people. Thus, calculating the burden of disease using exposures to PM 2.5 will yield years of lives lost and other chronic diseases that are about 40 % higher than those calculated with outdoor background concentrations (Li, 2020). Using exposure data, a 70-year-old male EU citizen will have experienced a reduction of life expectancy of about 13 (CI 2–43) d yr −1 of exposure to PM 2.5 , since the age of 30 (Li, 2020). For a person who is now 40 years old or younger, the life expectancy loss per year will be less than half as much as that of a 70-year-old person.

A similar approach for estimating the “integrated population-weighted exposure” of the Chinese population to PM 2.5 has been used by Aunan et al. (2018) and Zhao et al. (2018). Aunan et al. (2018) estimated a mean annual averaged PM 2.5 exposure of 103 [86–120]  µg m −3 in urban areas and 200 [161–238]  µg m −3 in rural areas, with 50 % in urban areas and 78 % in rural areas originating from domestic biomass and coal burning.

(ii) Measurements of indoor concentrations and individual exposure

Zhao et al. (2020a) took measurements of PM concentrations of different size classes in 40 homes in the German cities of Leipzig and Berlin. Measurements were taken in different seasons simultaneously inside and directly outside the homes. Only homes without smokers were analysed. Mean annual indoor PM 10 concentrations were 30 % larger than the outdoor concentrations near the houses. However, the mean indoor concentration of PM 2.5 was 6 % smaller than the outdoor concentration. The infiltration factor was evaluated to be 0.5. They therefore concluded that the indoor concentration of PM 2.5 was considerably influenced by both indoor and outdoor sources; the former included cooking and burning of candles.

Vardoulakis et al. (2020) made a comprehensive literature review on indoor concentration of selected air pollutants associated with negative health effects and listed the main results (concentrations) and other features (e.g. main sources) for the analysed studies. They express the need for “standardized IAQ (indoor air quality) measurement and analytical methods and longer monitoring periods over multiple sites”.

Some studies have focused on the measurements of personal exposure to ambient air concentrations using portable instruments in different microenvironments. For instance, Dessimond et al. (2021) describe the development and use of a personal sensor for measuring PM 1 , PM 2.5 PM 10 , BC, NO 2 , and VOC together with climate parameters, location, and sleep quality. Clearly, such measurements can provide valuable and accurate information on the spatial and temporal variations in exposure, and they can be used to validate exposure models.

7.3  Emerging challenges

7.3.1  emerging challenges for health impacts of particulate matter, (i) classification of particulate matter measures and characteristics and potential health outcomes.

Various studies have described PM in terms of the overall aerosol properties, such as the mass fractions (most commonly PM 2.5 and PM 10 ), the size distributions (mass, area, volume), the chemical composition, primary versus secondary PM, the morphology of particles, and source-attributed PM. Some studies have adopted more specific properties of PM derived based on the above-mentioned overall properties. Such properties include, to mention a few of the most common ones, particle number concentrations (PNCs), PNCs evaluated separately for each particulate mode, ultra-fine PM, nanoparticles, secondary organic PM, primary PM, other combinations of chemical composition, suspended dust (specific class of source-attributed PM), the content of metals, and toxic or hazardous pollutants.

An important emerging area is therefore to understand better which PM properties or measures would optimally describe the resulting health impacts. As mentioned above, one potentially crucial candidate for such a property is particulate number concentration (PNC). Kukkonen et al. (2016a) presented the modelling of the emissions and concentrations of particle numbers on a European scale and in five European cities. Frohn et al. (2021) and Ketzel et al. (2021) performed modelling of particle number concentrations for all Danish residential addresses for a 40-year time period. For all studies, the comparison of the predicted PNCs to measurements on regional and urban scales showed a reasonable agreement. However, there are still substantial uncertainties, especially in the modelling of the emissions of particulate numbers.

Health outcomes can also be classified as overall outcomes and physiologically more specific outcomes. Prominent examples of overall outcomes are mortality and morbidity. Relatively more specific impacts include respiratory and cardiovascular impacts, bronchitis, asthma, neurological impacts, and impacts on specific population groups (such as infants, children, the elderly, prenatal impacts, and persons suffering various diseases).

(ii) Uncertainties and challenges on evaluating the health impacts of particulate matter

Additional uncertainty is included in the concentration versus health response functions, which may be linear or logarithmic or a combination of both of these, and including or excluding a threshold value. When applied in health assessments, the shape of the response functions translates into large differences in the estimated number of premature deaths (Lehtomäki et al., 2020). EEA has made a sensitivity analysis showing that the application of a 2.5  µg m −3 threshold (equivalent to a natural background) will reduce the estimated number of premature deaths related to PM 2.5 by about 20 % in Europe (EEA, 2019a). Clearly, in the evaluation of the health impacts of PM, there are also numerous confounding factors. For population-based studies, these include active and passive smoking, sources and sinks that influence indoor pollution, gaseous pollutants, allergenic pollen, socio-economic effects, age, health status, and gender.

In addition, the health impacts of PM are related to the impacts of other environmental stressors, such as heat waves and cold spells, allergenic pollen, and airborne microorganisms. Commonly, it is challenging to decipher such effects in terms of each other. The factors may also have either synergistic or antagonistic effects. For instance, the health impacts of PM may be enhanced in the presence of a heat wave. The impacts of various PM properties are also known to be physiologically specific; i.e. such a property may contribute to a certain health outcome but not to some other outcomes.

In summary, there are many associations of various PM properties and measures to various health outcomes. Some of these inter-dependencies are known relatively better, either qualitatively or quantitatively, while there are also numerous associations, which are currently known poorly.

(iii) Research recommendations for deciphering the impacts of various particulate matter properties

For evaluating the relative significance of various PM properties and measures on human health, a denser measurement network on advanced PM properties would be needed, on both regional and urban scales. The required PM properties would include, in particular, size distributions and chemical composition. Clearly, such a network would be especially valuable in cities and regions which include high-quality population cohorts. The most important requirement in terms of PM modelling would be improved emission inventories, which would also include sufficiently accurate information on particle size distributions and chemical composition.

Pražnikar and Pražnikar (2012) and Rodins et al. (2020) stressed the importance of the identification of the specific sources and the evaluation of the chemical composition of PM responsible for acute health effects. For instance, Hime et al. (2018) reported that there is a severe lack of epidemiological studies investigating the health impacts originating from exposure to ambient diesel exhaust PM. In addition, they pointed out that there is no clear distinction between PM originating from diesel emissions and from other sources; thus, there is a limited number of studies assessing the respective health impacts.

Despite the substantial amount of research on the impacts of various PM properties and measures, the results on the importance of the more advanced measures (in addition to PM mass fractions) are to some extent inconclusive. One reason for this uncertainty is that there are so many associations of various PM properties and measures to various health outcomes. An emerging area related to assessing the health impact of PM is the associated oxidative stress when the particles are inhaled (e.g. see Gao et al., 2020; He et al., 2021). A possible explanation for the health effects from PM is based on PM-bound reactive oxygen species (ROS) being introduced to the surface of the lung, which leads to the depletion of the lung-lining fluid antioxidants as well as other damage (Gao et al., 2020).

One prominent emerging area is the evaluation of long-term, multi-decadal concentrations and meteorology on a sufficient spatial resolution. Long-term and lifetime exposures are known to be more important in terms of human health, compared with short-term exposures. Comprehensive data sets are therefore needed, which will include multi-decadal evaluation of air quality, meteorology, exposure, and a range of health impacts. Some first examples of such data sets have already been reported (Kukkonen et al., 2018; Siddika et al., 2019; Raaschou-Nielsen et al., 2020; Thygesen et al., 2020; Siddika et al., 2020). Although it is clear that chronic diseases and chronic mortality are caused by exposure to fine PM over many years, information is scarce regarding the critical length of the exposure period in terms of premature death for example.

Elderly people are generally regarded as more sensitive to air pollution. It is well-known that the overall trend towards an ageing population can counteract improvements in air pollution levels in the future (e.g. Geels et al., 2015). However, detailed knowledge is scarce regarding whether exposure during specific periods in life can increase the risk of chronic morbidity or mortality. Inequalities in both the exposure to PM and the related risks across different population groups (like gender, ethnicity, socioeconomic position, etc) due to underlying differences in health status will also need further investigations, to ascertain that future mitigation strategies will benefit all population groups (Fairburn et at., 2019; Raaschou-Nielsen et al., 2020). With regard to chronic diseases caused by NO 2 , it is still uncertain whether NO 2 is the cause of the diseases or whether other pressures or a combination of pressures that are correlated with the NO 2 concentration are responsible.

The introduction of green spaces in urban areas can contribute either negatively or positively to air quality. Green spaces can also potentially act as sources of allergenic pollen. The health impacts of introducing green spaces would therefore need to be clarified (Hvidtfeldt et al., 2019b; Engemann et al., 2020).

7.3.2  Emerging challenges for the combined effects of air pollution and viruses

Studying the combined effects of air pollution, heat waves or cold spells, and viruses is challenging, due to numerous confounding factors and incidental correlations. For instance, air pollution is commonly a serious problem in areas where the population density is also high. The high population density tends to allow viruses to spread more easily, compared with the situation in more sparsely populated areas.

Morbidity or mortality due to pandemics is also dependent on the age distribution of the population, cultural and social differences, the level of health care, living conditions, common hygiene, and other factors. Clearly, such demographic differences should be taken into account, when comparing the frequencies of virus infections in different areas.

Due to limited data and the still evolving COVID-19 pandemic, it is difficult to draw definite conclusions related to the role of air pollution or meteorological drivers (like temperature or relative humidity) in transmission rates or in the severity of the disease. Global interdisciplinary studies, open data sharing, and scientific collaboration are the key words towards better understanding of the interaction of COVID-19 and meteorological and environmental variables. Moreover, it is important to know what the role of, for example, PM is in spreading SARS-CoV-2. Indoor or laboratory dispersion experiments are needed to find out if the virus is spreading not only in droplets but also in smaller aerosol particles. Together with a fully validated computational fluid dynamics model, it is possible to get facts about dispersion distances in different conditions and to study for example the effect of ventilation systems, furniture placements, and air cleaners to give information-based recommendations to make the environment as safe as possible without complete lockdowns.

Allergenic pollen can periodically cause substantial health impacts for numerous people. As PM is transported in the atmosphere, microbial pathogens such as bacteria, fungi, and viruses can be attached on the surfaces of particles (Morakinyo et al., 2016); clearly, these may provide an additional risk (Kalisa et al., 2019). The combination of both biological and chemical components of PM can further enhance some health effects, such as asthma and COPD (Kalisa et al., 2019).

Adverse health impacts can also be associated with short-term exposure to atmospheric particles. The short-term impacts may be important, especially during air pollution episodes. Such episodes may be caused, for example, by the emissions originating from wildfires, dust storms, or severe accidents. Episodes can also be caused by extreme meteorological conditions; two prominent examples are heat waves and extremely stable atmospheric conditions.

7.3.3  Other emerging challenges

First attempts have been made to quantify exposures by estimating concentrations in microenvironments, combined with space-time activity data. However, improvements will be necessary for virtually all the components of exposure modelling. Regarding the emissions used for concentration modelling, in particular the evaluation of the emission rates from indoor sources should be improved on a broader empirical basis.

Emission rates depend on human behaviour, for which more detailed information is needed. For example, how many people smoke indoors, and how many family members are exposed to passive smoking? Are kitchen hoods used when cooking and frying? How often are chimneys open, and how often are wood stoves used? For estimating indoor concentrations, one would need further information of ventilation habits in different seasons. Better information would be needed regarding the use of mechanical ventilation with heat recovery in new homes and office buildings.

To validate the results of the indoor air pollution models, one would need more measurements of indoor air concentrations in rooms with different emission sources and ventilation systems. Furthermore, measurements of concentrations are needed in various microenvironments, such as in cars, buses, and the underground.

With growing knowledge of the relation between exposure and health impacts, more detailed exposure indicators might be necessary. For instance, a further differentiation according to size and species of PM 2.5 and PM 10 might be needed, as well as the specification of the temperature and of the breathing rate, in other words the intake of pollutants with respiration. The use of more dynamic exposure data in epidemiological studies in the future could substantially improve the accuracy of health impact assessments.

8.1  Brief overview

While decisions about air quality management and policy development are based on political considerations, it is a scientific task to provide evidence and decision support for designing efficient air pollution control strategies that lead to an optimization of welfare of the population. To do this, integrated assessments of the available policies and measures to reduce air pollution and their impacts are made. In such assessments, two questions are addressed.

Is a policy or measure or a bundle of policies or measures beneficial for society? Does it increase the welfare of society; i.e. do the benefits outweigh the costs (including disadvantages, risks, utility losses)?

If several alternative policy measures or bundles of policy measures are proposed, how can we prioritize them according to their efficiency; i.e. which should be used first to fulfil the environmental aims?

To analyse these questions, two methodologies have been developed: cost–effectiveness analyses and cost–benefit analyses. The concept of “costs” is used here in a broad sense, referring to all negative impacts including – in addition to financial costs – also non-monetary risks and disadvantages, such as time losses, increased health risks, risks caused by climate change, biodiversity losses, comfort losses, and so on, which are monetized to be able to add them to the monetary costs. Benefits encompass all positive impacts including avoided monetary costs, avoided health risks, avoided biodiversity losses, avoided material damage, reduced risks caused by climate change, and time and comfort gains.

With a cost–effectiveness analysis (CEA) the net costs (costs plus monetized disadvantages minus monetized benefits) for improving a non-monetary indicator used in an environmental aim with a certain measure are calculated, e.g. the costs of reducing the emission of 1 t of CO 2,eq . The lower the unit costs, the higher the cost–effectiveness or efficiency of a policy or measure. The CEA is mostly used for assessing the costs associated with climatically active species, as the effects are global. The situation is different for air pollution, where the damage caused by emitting 1 t of a pollutant varies widely depending on time and place of the emission.

Cost–benefit analysis (CBA) is a more general methodology. In a CBA, the benefits, i.e. the avoided damage and risks due to an air pollution control measure or bundle of measures, are quantified and monetized. Then, costs including the monetized negative impacts of the measures are estimated. If the net present value of benefits minus costs is positive, benefits outweigh the costs. Thus the measure is beneficial for society; i.e. it increases welfare. Dividing the benefits minus the nonmonetary costs by the monetary costs of a specific measure will result in the net benefit per euro spent, which can be used for ranking policies and measures. For performing mathematical operations like summing or dividing costs and benefits, they have first to be quantified and then converted into a common unit, for which a monetary unit, e.g. euros, is usually chosen. Integrated assessment means that – as far as possible – all relevant aspects (disadvantages, benefits) should be considered, i.e. all aspects that might have a non-negligible influence.

When setting up air pollution control plans, it is essential to also consider the effect of these plans on greenhouse gas emissions. Air pollution control measures usually lead to a decrease but sometimes also to an increase in GHG emissions. And vice versa, most measures for GHG reduction influence in fact reduce the emissions of air pollutants in most cases. Thus, an optimized combined air pollution control and climate protection plan is necessary to avoid contradictions and inconsistencies.

Looking at the current praxis in the EU countries, still separate plans are made for air pollution control and climate protection. Air pollution control plans currently estimate the reduction (or sometimes increase) of GHG emissions more and more, but they do not assess these reductions by monetizing them, and thus they cannot be accounted for in a cost–benefit analysis. In the assessment of the National Energy and Climate Plans the EC states:

Despite some efforts made, there continues to be insufficient reporting of the projected impacts of the planned policies and measures on the emissions of air pollutants by Member States in their final plans. Only 13 Member States provided a sufficient level of detail and/or improved analysis of the air impacts compared to the draft plans. The final plans provide insufficient analysis of potential trade-offs between air and climate/energy objectives (mostly related to increasing amounts of bioenergy). (EC, 2020)

So, in an integrated assessment, the assessment of air pollution control measures should always take into account the impact of changes in greenhouse gas emissions. Correspondingly, climate protection plans should take changes in air pollution into account (Friedrich, 2016). In the following, advancements in the quantification and monetizing of avoided impacts from reducing emissions from air pollutants and greenhouse gases are described.

8.2  Current status and challenges

8.2.1  estimation and monetization of impacts from air pollution.

Integrated assessments, which include as a relevant element the assessment of air pollution, encompass many areas, especially the assessment of energy and transport technologies and of policies for air pollution control and climate protection. The development of such integrated assessments started in the early 1990s with a series of EU research projects, which have been called “ExternE-external costs of energy”. A summarized description of the developed methodology can be found in Bickel and Friedrich (2005); further descriptions and project results are addressed in ExternE (2012), Friedrich and Kuhn (2011), Friedrich (2016) and Roos (2017). The framework for integrated environmental assessments has been further consolidated and developed within the EU research projects INTARESE and HEIMTSA. The advanced methodology and its application are described in Friedrich and Kuhn (2011). The processes of an integrated assessment are shown in Fig. 18 (Briggs, 2008; IEHIAS, 2014), where important elements like issue framing, scenario construction, provision of data and models, uncertainty estimation, and stakeholder consultation are addressed. In the beginning of an assessment, the relevant air pollutants have to be identified, which are those that cause substantial damage. In many cases, primary and secondary particulate matter of different size classes and NO 2 will cause the worst damage, followed by O 3 .

The element in the framework that is representing the assessment of air pollution, i.e. the “impact pathway approach”, is shown in detail in Fig. 19. This figure already includes one of the emerging developments described in Sect. 8.3, namely the estimation of individual exposure instead of outdoor concentration. First, scenarios of activities are collected, for instance the distance driven with a Euro 5 diesel car or the amount of wood used in wood stoves. Multiplying the activity data with the appropriate emission factors will result in emissions. The emission data are input for chemical–transport models that are used to calculate concentrations on regional, continental, or global scales; for Europe the EMEP model (Simpson et al., 2012) and worldwide the TM5-FASST model (van Dingenen et al., 2018) are often used – see Sect. 5 of this paper.

In the next phase, concentration–response functions derived from epidemiological studies are used to estimate health impacts. For the most relevant pollutants PM 10 , PM 2.5 , NO 2 , and O 3 , the WHO (2013a) made a meta-analysis of the epidemiological studies available until 2012 and recommended exposure–response relationships for use in integrated assessments, which are still widely used. Newer epidemiological studies in particular investigating the relation between fine particulate air pollution and human mortality have been analysed by Pope et al. (2020), who present a nonlinear exposure–response function with a decreasing slope for cardiopulmonary disease mortality caused by PM 2.5 . The most important concentration–response functions for impacts of air pollution on human health are described in Sarigiannis and Karakitsios (2018) and Friedrich and Kuhn (2011).

Beneath health damage, which is the most important damage category, impacts on ecosystems, especially biodiversity losses, and on materials and crop losses should also be considered. Impacts on ecosystems are usually quantified as pdf, “potentially disappeared fraction of species” per square metre land (Dorber et al., 2020) and thus as biodiversity losses. A first methodology was developed by Ott et al. (2006), which is still used in some studies. Further approaches, partly adopted from methods developed for LCIA (life cycle impact assessment), were developed later (e.g. Souza et al., 2015; Förster et al., 2019), but because of the simplifications and uncertain assumptions made, none of these approaches reached the same full acceptance as the approaches for the other damage categories. For material damage and crop loss, deposition–response relationships have been developed in the ExternE – External Costs of Energy (ExternE, 2012) project series and are described in Bickel and Friedrich (2005); they are still used.

Finally, the health effects and the other impacts are monetized, which means that they are converted into financial costs; for the non-monetary part of the impacts results of contingent valuation (willingness to pay) studies are used (as described in OECD, 2018). As numerous contingent valuation studies have been made in the past, it is not necessary to carry out a further willingness-to-pay study; instead results of existing studies which found monetary values for the damage endpoints to be analysed can be used. Of course, as the contingent valuation studies are usually made at another time, in another area, and with other cultural situations than the planned assessment, the monetary values must be transformed with a methodology called “benefit transfer” from the original time, place, and cultural features to the ones of the assessment (see Navrud and Ready, 2007). The most important monetary value in the context of air pollution is the value for a statistical life year lost (VLYL) caused by a premature death at the end of life after lifelong exposure to air pollutants. It is often based on a study of Desaigues et al. (2011). The result for average EU citizens – transformed to 2020 – is EUR 2020 75 200 (47 000–269 450) per VLYL. A list of monetary values for health endpoints, which are used in most studies, can be found in Friedrich and Kuhn (2011).

Figure 18 Integrated assessment process involving air pollution (Briggs, 2008).

Figure 19 Schematic presentation of the use of models and the flow of data in the enhanced impact pathway approach (Friedrich, 2016).

Based on this principal approach, a growing number of tools have been developed and applied for supporting air quality control for urban, national, and regional to global scales. The tool used for the assessments for DG Environment and for the Convention on Long-range Transboundary Air Pollution of the UN ECE is GAINS (Greenhouse gas – Air pollution Interactions and Synergies) developed by IIASA (Amann et al., 2017; Klimont, 2021).

A specific development in GAINS is the use of source–receptor matrices as a proxy for using an atmospheric model. A limitation of chemical transport models has been the substantial computational requirements for running the models for estimating hourly concentration values caused by an emission scenario for an entire year. To be able to simulate many scenarios within a short time, results of certain runs with the complex atmospheric model EMEP (Simpson et al., 2012) were transformed into source–receptor matrices, which provided information of the relationship between a change of emissions in a country and the change of the concentration in grid cells of a European grid. However, because of the relatively large size of the grid cells for European-wide models, concentrations in cities were underestimated; thus an “urban increment” was introduced for cities (Vautard et al., 2007; Torras and Friedrich, 2013; Torras, 2012). Thunis (2018), however, points out that this approach has certain weaknesses. Thus, newer approaches use nested modelling with regional atmospheric models using varying grid sizes (e.g. Brandt, 2012) or modelling of typical days instead of a whole year with a finer grid (Bartzis et al., 2020; Sakellaris et al., 2022). The ECOSENSE model uses a similar method as GAINS, however, distinguishing between parts of larger countries and emission heights. Furthermore a monetary assessment of greenhouse gas emissions is made (ExternE, 2012; Friedrich, 2016; Roos, 2017).

As a major application of the GAINS tool, the European Commission, DG Environment regularly assesses its directives for air pollution control. A well-known example is the impact assessment carried out for assessing the Thematic Strategy on Air Pollution and the Directive on “Ambient Air Quality and Cleaner Air for Europe” (EC, 2005). It was shown that the monetized benefits of implementing the thematic strategy for air pollution control are much higher than the costs. In the most recent assessment, DG Environment assessed the costs and benefits of the so-called NAPCPs, the national air pollution control programmes, which the member states had to provide by 2019 to show how they plan to comply with the emission reduction commitments of the National Emission Reduction Commitments Directive (NEC Directive). The benefits considered were the monetized reduced health and environmental impacts caused by the requested air pollution control measures. The results show that the health benefits alone with EUR 8 billion per year to EUR 42 billion per year are much larger than the costs of the considered measures with EUR 1.4 billion per year (EC, 2021) and that further emission reductions might also be efficient. The UN ECE (UN Economic Commission for Europe) has launched eight so-called protocols guided by the Convention on Long-range Transboundary Air Pollution, which require the member states to provide information on air pollution in their countries and to take actions to improve it (UNECE, 2020). The latest protocol entering into force was the revised Protocol to Abate Acidification, Eutrophication and Ground-level Ozone, as amended on 4 May 2012. To prepare for these protocols, the effects of air pollution on ecosystems, health, crops, and materials have been assessed with the same methods as used by the EC, i.e. using the GAINS model.

The OECD recommends carrying out cost–benefit analyses with the impact pathway methodology (OECD, 2018). Similarly, national authorities, e.g. the German Federal Environmental Agency, have proposed using the methodology for the assessment of environmental policies and infrastructure projects (Matthey and Bünger, 2019). In Denmark, the method has been used in the EVA system (Economic Valuation of Air pollution, Brandt et al., 2013) to estimate the external costs related to air pollution, as part of the national air quality monitoring programme (Ellermann et al., 2018). The same system has been used to assess the impact from different emission sectors and countries within the Nordic area, by using a CTM model with a tagging method (Im et al., 2019). Kukkonen et al. (2020c) developed an integrated assessment tool based on the impact pathway principle that can be used for evaluating the public health costs. The model was applied for evaluating the concentrations of fine particulate matter (PM 2.5 ) in ambient air and the associated public health costs of domestic PM 2.5 emissions in Finland. Several further integrated assessment models have been described in Thunis et al. (2016). Not only in Europe but also in the USA, integrated assessment of air pollution is an issue. Keiser and Muller (2017) provide an overview of integrated assessment models for air and water in the US and hint at the intersections between air and water pollution.

Several studies are using the impact pathway approach from Fig. 8.2 for estimating health impacts and aggregate them to DALYS (disability adjusted life years), but without monetizing the impacts; i.e. they calculate the burden of disease or the overall health impacts stemming from air pollution. The WHO has estimated the burden of disease from different causes, including air pollution, in the Global Burden of Disease Study (GBDS, 2020). The European Environmental Agency regularly estimates the health impacts from air pollution in Europe and found 4 381 000 life years lost attributable to the emissions of PM 2.5 in 2018 in the EU28 (EEA, 2020a). Hänninen et al. (2014) analysed the burden of disease of nine environmental stressors, including particulate matter, for Europe. Lehtomäki et al. (2020) quantified the health impacts of particles, ozone, and nitrogen dioxide in Finland and found a burden of 34 800 DALYs per year, with fine particles being the main contributor (74 %). Recent studies also include future projections of emissions and climate. Huang (2018) assessed and monetized the health impacts of air pollution in China for 2010 and for several scenarios until 2030. Likewise, Tarín-Carrasco et al. (2021) projected the number of premature deaths in Europe towards 2050 and found that a shift to renewable energy sources (to a share of 80 %) is effective in reducing negative health impacts.

In the following, we address recent improvements in the methodology of integrated assessment with a focus on air pollution control. A milestone was the publication of concentration–response functions for NO 2 by the WHO (2013a). Following this, more and more studies calculated health impacts from exposure not only to PM 10 , PM 2.5 , and ozone but also to NO 2 (e.g. Balogun et al., 2020; Siddika et al., 2019, 2020),

Ideally, human health risks should be evaluated based on exposures instead of ambient concentrations (see Sect. 7). Until now, measured or modelled ambient (outdoor) air concentrations are input to the concentration–response functions used to estimate health risks. However, it is obvious that people are affected by the pollutants that they inhale, and that is decisive for the health impact. Therefore, a better indicator for estimating health impacts than the outside background concentration is exposure, which is the concentration of pollutants in the inhaled air averaged over a certain time interval. Only recently, in the EU projects HEALS and ICARUS, have methodologies been developed to estimate personal exposure, i.e. the concentration in the inhaled air averaged over a year or a number of years as the basis for estimating health impacts from air pollutants. Furthermore, the time span used in the exposure–response relationships commonly ranges from hourly to annual mean concentration values. By far the most important health effects are chronic effects. Although the indicator used to estimate chronic impacts is the annual mean concentrations, chronic diseases develop over several years, or even during the whole lifetime. This is the reason why the EC regulates a 3-year “average exposure index” of PM 2.5 in the air quality directive. But the relevant time period for the exposure might be larger than 3 years. Thus, exposure over a lifetime is important for estimating risks to develop chronic diseases and premature deaths, which are the most important health impacts. The methods for evaluating lifetime exposure have been addressed in Sect. 7.2.3 (see Li and Friedrich, 2019; Li et al., 2019a, c).

Thus, as a major improvement of the impact pathway approach, the exposure to pollutants should be used as an indicator for health impacts, instead of the exposure estimated from outdoor air concentrations at permanent locations. However, epidemiological studies that directly relate health impacts to exposures to air pollutants are not yet available. Instead, the existing concentration–response functions are transformed into exposure–response functions by calculating the increase in the exposure (e.g. x   µg m −3 ) caused by the increase of 1  µg m −3 in the outdoor concentration. Dividing the concentration–response relationship by x will then convert it into an exposure–response relationship (Li, 2020). Of course, it would be better to use results of epidemiological studies that directly relate exposure data with health effects. Thus, such studies should be urgently conducted.

Clearly, indoor pollution sources also influence exposure. It is therefore important to assess possibilities to reduce the contributions of indoor sources to exposure. These might include raising awareness of the dangers of smoking at home indoors, the development of more effective kitchen hoods and promoting their use, ban of incense sticks, and mandatory use of inserts in open fireplaces.

Secondly, a reduction of exposure is also possible by increasing the air exchange rate with ventilation or by filtering the indoor air. For example, if old windows are replaced by new ones, the use of mechanical ventilation with heat recovery might be recommended or even made mandatory. Also, the enhancement of HEPA filters and their use in vacuum cleaners will help as well as using air purifiers/filters. These systems will also be helpful to reduce the indoor transmission of SARS-CoV-2.

Furthermore, there is growing evidence that PM 10 and PM 2.5 concentrations in underground trains and in metro stations can be much higher than the concentration in street canyons with dense traffic (e.g. Nieuwenhuijsen et al., 2007; Loxham and Nieuwenhuijsen, 2019; Mao et al., 2019; Smith et al., 2020). Using ventilation systems with filters might improve this situation.

8.2.2  Monetization of impacts of greenhouse gas emissions

As explained above, air pollution control strategies usually influence and, in most cases, reduce emissions of greenhouse gases (GHGs). Thus, in an integrated assessment, both the reduction of air pollution and of greenhouse gas emissions should be quantitatively assessed. In practice, however, many national air pollution control strategies do not take changes in GHGs into account in the assessment; instead the national authorities develop separate climate protection plans. Similarly, although DG Environment estimates the changes in GHG emissions in their assessment of air pollution control strategies, the changes are not assessed or monetized.

An exception is the UK, where estimations of the “social costs of carbon” are used in assessments (Watkiss and Downing, 2008; DBEIS, 2019). The UK government currently recommends using a carbon price of GBP 69 per tonne of CO 2,eq in 2020 rising to GBP 355 per tonne of CO 2,eq in 2075–2078 at 2018 prices.

How can the benefits of a reduction of greenhouse gas emissions be monetized? A possibility is to use the same approach as with air pollution; i.e. estimate the marginal damage costs (i.e. the monetized damages and disadvantages) of emitting 1 additional tonne of CO 2 . These marginal costs would then be internalized for example as a tax per tonne of CO 2 emitted to allow the market to create optimal solutions (Baumol, 1972). Thus, first scenarios of greenhouse gas (GHG) emissions would be set up, then concentrations of GHGs in the atmosphere would be calculated, and finally changes of the climate followed by the estimation of changes of risks and damages would be calculated. However, this does not lead to useful results. Uncertainties are too high and assumptions of economic parameters like the discount rate or the use of equity weighting influence the result considerably, so that the range of results encompass several orders of magnitude. Furthermore, the precautionary principle tells us that we should avoid possible impacts, even if they cannot (yet) be quantified and thus not be included in the quantitative estimation of impacts.

An alternative approach to estimating marginal damage costs is to use marginal abatement costs. A basic law of environmental economics is that for pollution control a pareto-optimal state should be achieved, where marginal damage costs (MDCs) are equal to marginal abatement costs (MACs). Thus, if MAC at the pareto-optimal state are known, they could be used instead of the MDCs. However, the pareto-optimal state is not known if MDCs are not known. But one could use an environmental aim that is universally agreed upon by society and assume that they represent the optimal solution in the view of society and then estimate the MACs to reach this aim, which is then used for the assessment. This approach was first proposed by Baumol and Oates (1971).

For assessing GHG emissions, especially the aim of the so-called Paris Agreement, which was agreed on at the 2015 United Nations Climate Change Conference, COP 21 in Paris by a large number of countries, the most important aim was to keep a global temperature rise this century well below 2  ∘ C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5  ∘ C. This objective could be used as the basis for generating MACs.

Bachmann (2020) has carried out a literature research of MDCs and MACs for GHG emissions. Based on this review, MACs calculated by a meta-analysis of Kuik et al. (2009) are used here as the basis for the calculation of marginal abatement costs for reaching the above aim, resulting in EUR 2019  286 (162–503) per tonne of CO 2,eq in 2050. However, we propose starting with the most efficient measures now and gradually increasing the specific costs, until they reach the costs mentioned above in 2050. If future innovations lead to a reduction of the avoidance costs, the costs of carbon can be adjusted accordingly. With a real discount rate of 3 % a −1 , social costs of CO 2,eq to be used in 2020 would be EUR 2020  118 (67–207) per tonne of CO 2,eq .

8.2.3  Effect of integrating air pollution control and climate protection

In most cases, especially if a substitution of fossil fuels with carbon-free energy carriers or a reduction of energy demand is foreseen, a reduction of emissions of greenhouse gases and air pollutants is foreseen; thus, taking both air pollution control and climate protection into account will considerably improve the efficiency of such measures.

An example, showing the choice and ranking of measures for combined air pollution control and climate protection are different from the ranking in separate plans is shown in Fig. 20. In the frame of the EU TRANSPHORM project, 24 measures to reduce air pollution and climate change caused by transport in the EU have been assessed with an integrated assessment. Figure 20 shows the 8 most effective measures for both avoided health impacts and reduced climate change, where both benefits are converted into monetary units and combined (Friedrich, 2016). As can be seen, measures with benefits in both air pollution control and climate protection improve their rank compared to the separate rankings for these damage categories. The most effective measure is travel with trains instead of aeroplanes for routes of less than 500 km.

Figure 20 Ranking of measures in transport according to their effectiveness in mitigating damage from air pollutant and greenhouse gas emissions in 2019. Recalculation of results of Friedrich (2016) with abatement costs of EUR 2020  118 per avoided tonne of CO 2,eq as recommended in Sect. 8.2.2.

With another example, Markandya et al. (2018) demonstrate that especially for developing and emerging countries the costs for meeting the aims of the Paris Agreement will be outweighed by the benefits that are achieved by avoiding health impacts from air pollution, so that the climate protection comes without net costs. This is due to the fact that in developing countries the use of fossil fuels is less accompanied by the use of emission reduction technologies (filters), so replacing fossil fuels by electricity from wind or solar energy or saving energy will result in a much higher reduction in air pollution than doing the same in OECD countries. For Europe, the effects of integrating the damage costs of air pollution into the optimization of energy scenarios have been analysed by Korkmaz et al. (2020) and Schmid et al. (2019). Two effects are important: firstly, biomass burning in particular in smaller boilers is significantly reduced, as firing biomass is climate friendly but leads to air pollution. Secondly, the marginal avoidance costs per tonne of avoided carbon are reduced, especially for the period 2020–2035. The reason is that in this period more efficient measures like the replacement of oil and coal with electricity from carbon-free energy carriers (except biomass) and measures for energy savings will also reduce emissions of air pollution significantly, while later more expensive measures like producing and using fuels that are produced from renewable electricity (power to X) will have a lower effect on air pollution reduction.

In most cases, integrated assessment improves the efficiency of measures for environmental and climate protection. In the following an example is shown where an efficient climate protection measure gets inefficient if air pollution is included in the assessment. This example is the use of small wood firings in cities. Wood firings are climate friendly but emit lots of fine particles and NO x . Huang et al. (2016) show that for wood firings that are operated in cities, the damage of more health impacts outweighs the benefit of less greenhouse gas emissions.

Figure 21 shows the social costs per year; this is the annuity of the monetary costs, the monetized impacts of climate change, and the monetized health impacts caused by air pollution for different heating techniques that are used in an older single-family house in the centre of the city of Stuttgart. The social costs are calculated for newly built state-of-the-art technologies fulfilling the currently valid strict regulations for small firings in Germany (BImSchV, 2021). Older stoves have emissions and thus impacts that are much larger than those shown. The social costs are highest for wood and pellet combustion caused by their high air pollution costs, although the climate change costs of wood combustion are very low. This means that the benefit of less greenhouse gas emissions of wood firings is much smaller than the additional burden caused by air pollution. Furthermore, even if we further enhance the emission reduction by equipping the wood and pellet combustion with an efficient particulate filter – these are represented by the columns marked with “ + part. filter” – the ranking is not changed. The reason is the high NO x emissions of wood combustion. The results suggest that wood combustion in rural areas should be equipped with a particulate filter, while in cities a ban on small wood combustion might be considered, unless wood and pellet firings are equipped not only with particulate filters but also with selective catalytic reduction (SCR) filters.

Figure 21 Social costs per year (annuity) of different heating boilers for an older single-family house in Stuttgart. Boilers are state-of-the-art technologies, and + part. filter means that wood or pellet heating is additionally equipped with efficient particulate filters (Huang et al., 2016).

8.3  Emerging challenges

8.3.1  challenges in improving the methodology for integrated assessments.

Estimations of damage costs caused by air pollution and climate change still show large uncertainties. Li (2020) reports a 95 % confidence interval of EUR  3.5×10 11 to EUR  2.4×10 12 for the damage costs caused by the exposure to PM 2.5 and NO 2 for 1 year (2015) for the adult EU28 + 2 population. Kuik et al. (2009) report an uncertainty range for the marginal avoidance costs to reach the “2 ∘ aim” of EUR 2020  162 to EUR 2020  503 per tonne of CO 2,eq in 2050. In addition, systematic errors might occur, for instance still unknown exposure–response relationships. Thus, methodological improvements are necessary.

In principle, all model steps and related input data shown in Fig. 19 would need improvement. Most of the improvements necessary for models and the data shown in Fig. 19 have already been addressed in the previous sections. Challenges for improving the estimation of emissions of indoor and outdoor sources are described in Sect. 3.3. Improvements in atmospheric modelling are addressed in Sect. 5.3. Exposure modelling is a relatively new field, so a lot of gaps have to be filled (see Sect. 7.3.3). Further epidemiological studies, especially for analysing the health impacts of specific PM species and PM size classes, are urgently needed, and contingent valuation studies are needed to improve the methodology. The challenges for these topics are addressed in the relevant sections above and will thus not be repeated here. However, two further methodological improvements have not been mentioned and are thus described in the following.

When assessing a policy measure for the reduction of air pollution, the first step is to estimate the reduction of emissions caused by the policy measure. Measures can be roughly classified in technical measures that improve emission factors (e.g. by demanding filters) and non-technical measures that change the behaviour or choices of emission source operators (e.g. by increasing prices of polluting goods). Especially if non-technical measures are chosen, e.g. the increase in the price for a good that is less environmentally friendly, the identification of the reaction of the operators of the emission sources is not straightforward. Do they keep using the good although it is more expensive? Do they substitute the good or do they renounce the utility of the good by using neither the good nor substitutes anymore? For energy-saving measures, it is well-known that after implementing such a measure, the users do not save the full expected energy amount but instead increase their comfort, for instance by increasing the room temperature. This is known as the rebound effect. The traditional way to deal with behavioural changes is using empirically found elasticity factors. For the transport sector, where most of the applications are made, Schieberle (2019) compiled a literature search for elasticities in the transport sector and demonstrated their use in integrated assessments. However, as a further development, recently first attempts to use agent-based modelling have been made to estimate the behavioural changes of people confronted with policy measures (Chapizanis et al., 2021).

With regard to the marginal costs of CO 2 reduction used in the assessments, further investigations taking into account emerging innovations are necessary. Furthermore, the stated estimates are quite high, so that the question arises of whether the values and thus the Paris aim gain worldwide social acceptance. More emphasis might be laid on research to develop measures for more efficient climate protection as well as measures to remove GHGs from the atmosphere and also to develop adaptation measures.

8.3.2  Challenges for the reduction of ambient air pollution

In recent years, regulations have been implemented that will decrease emissions in two important sectors considerably.

For ships, the IMO (International Maritime Organization) adopted a revised annex VI to the international Convention for the Prevention of Pollution from Ships, now known universally as MARPOL, which reduces the global sulfur limit from 3.50 % to 0.50 %, effective from 1 January 2020 (IMO, 2019). This will drastically reduce the SO 2 emissions around Europe outside of the sulfur emission control areas of the Baltic Sea, North Sea, and English Channel. Furthermore, the IMO has adopted a strategy to reduce greenhouse gas emissions by at least 40 % by 2030, pursuing efforts towards 70 % by 2050, compared to 2008. A revised, more ambitious plan is currently being discussed. Geels et al. (2021) assess the effects of these new regulations by generating several future emission scenarios and assessing their impact on air pollution and health in northern Europe.

Diesel cars now must comply with the Euro 6d norm, which will drastically reduce the real driving emissions of NO x on streets and roads. In addition, electric vehicles are now promoted and subsidized in many EU countries.

In the context of a revision of the EU rules on air quality announced in the European Green Deal, the European Commission is expected to strengthen provisions on monitoring, modelling, and air quality plans to help local authorities achieve cleaner air, notably proposing to revise air quality standards to align them more closely with the World Health Organization recommendations (which was updated in 2021).

The European Commission is also expected to introduce a new Euro 7 norm for passenger cars in 2025. The Industrial Emissions Directive demands permanent reviews of the EU Best Available Techniques reference documents (BREFs), resulting in decreasing emissions from large industrial emitters. The EU has decided to reduce greenhouse gas emissions by at least 55 % compared to 1990. Furthermore, national reduction plans for GHG lead to a further reduction of the combustion of fossil fuels. Thus, emissions of air pollutants from combustion processes will significantly decrease with one exception: wood and pellet firings <500  kW th . Hence, regarding combustion, the main challenge is the development of further PM 2.5 and NO x reduction measures for small wood firings. Similar trends will be observed in a number of other countries. For example the USA wants to reduce their greenhouse gas emissions from 2005 to 2030 by 50 % and China wants to reach carbon neutrality by 2060.

As emissions of particulates from combustion decrease, diffuse emissions, e.g. from abrasion processes, bulk handling, or demolition of buildings, and those from evaporation of volatile organic compounds get more and more dominant. So more emphasis should be put on the determination and reduction of these emissions. In particular, the processes leading to diffuse emissions are not well-known. In transport, emissions from tyre and brake wear and road abrasion heavily depend on driving habits, speed, weather conditions, and especially the traffic situation and layout of the road network. However, emission factors for diffuse emissions are still largely expressed in grammes per vehicle kilometre, not taking situations where braking is necessary, e.g. because of traffic jams or crossroads, into account. Furthermore, reduction measures like the development of tyres and brakes with longer durability should be considered and assessed.

A key challenge for reducing secondary particulates, especially ammonium nitrates, is a further reduction of NH 3 emissions from agriculture. Certain national reduction commitments for EU countries from 2005 until 2030 are regulated by the National Emission Reduction Commitments Directive (NEC Directive) of the EU, but further reductions might be necessary.

8.3.3  Challenges for the reduction of indoor air pollution

A more precise understanding of personal exposure to air pollution and the use of exposure–response relationships (instead of relationships linking outdoor concentration with responses) will potentially change the focus of air pollution control. As people are indoors most of the time, now the reduction of indoor pollution is becoming important. Of course, reducing ambient concentration will also reduce indoor pollution, as pollutants penetrate from outside into the houses. However, around 46 % of the total exposure with PM 2.5 for an average EU citizen stems from indoor sources; for NO 2 about 25 % is caused by indoor sources (Li and Friedrich, 2019). Thus, indoor sources cannot be neglected. The reduction of exposure to emissions from passive smoking, frying, and baking in the kitchen; using open fireplaces and older wood stoves; and incense sticks and candles is especially important. Indoor concentrations can be reduced by reducing the emission factor of the source, changing behaviour when using the source; by banning the use of a source; by increasing the air exchange rate with ventilation; and by using air filters.

This review has covered a larger number of research areas and identified not only the current status but also the emerging research needs. There are of course cross-cutting needs that are a prerequisite to further air quality research and develop more robust strategies for reducing the impact of air pollution on health. The following section discusses some of the key areas and synthesizes these in the form of recommendations for further research.

9.1  Connecting emissions and exposure to air pollution

There is a progressively important need to move from static annual inventories to those that are dynamic in terms of activity patterns and of higher temporal resolution. This is driven partly by the need for activity-dependent exposure modelling and because there is an increasing availability of online observations from sensors to arrive at a better spatial and temporal resolution of emission rates and factors. Clearly community efforts are necessary for identifying and reducing uncertainties in emissions that have a large impact on the resulting air quality and exposure predictions including benefiting from source apportionment methods.

One gap is the evaluation of agricultural emissions, which are still poorly understood, and improvements will support both air quality and climate change assessment, leading to co-benefits. While considerable effort has been devoted to estimating NO x emissions, there are still uncertainties in the estimation of VOC emissions. These uncertainties have direct implications when quantifying changes in ozone levels and contributions from secondary organic aerosols to regional and global scales. One prominent example of such uncertainties is the estimation of VOC profiles in terms of the chemical species and their evaporation rates, including in particular those from shipping activities in the vicinity of ports, as a shift has occurred to both low-sulfur and carbon-neutral or non-carbon fuels.

Similarly, as exhaust emissions decrease with the increase in electric vehicles, the assessment of the consequences of airborne non-exhaust emissions is becoming more and more important. However, this needs to be examined in the context of tighter policy-driven controls on petrol and diesel vehicles. Emission factors for ultra-fine particles are also uncertain; these are also spatially and temporally highly variable, which reduces the reliability of particle number predictions necessary for estimating exposure of people (Kukkonen et al., 2016a).

Exposure connects emissions to concentrations and their impact on health. As exposure to a particular air pollutant is determined by all sources of that air pollutant, both indoor and outdoor sources are important. Indoor sources are considerable in number and variety from tobacco smoking to cooking and heating fuels, indoor furniture, body care and cleaning products, and perfumes. Not only are emission factor data for these sources needed, stricter regulations are necessary for indoor sources (e.g. indoor cleaning products and wood burning for residential heating).

9.2  Extending observations for air quality research

Our review has highlighted the urgent need to strengthen the integration of observations from different platforms, including from reference instruments, mobile and networked low-cost sensors, and other data sources, such as satellite instruments and other forms of remote sensing. In addition to providing greater spatial extent and fine-scale resolution of observations in urban and other areas, these integrated data sets can form the basis of inputs for dynamic data assimilation. Data assimilation can also be performed using machine learning and/or artificial intelligence approaches. These developments can improve the accuracy of chemistry–transport models, including air quality forecasts.

Additional requirements for low-cost sensors are (i) improving their reliability for both the gaseous and particulate matter measurements (including in particular VOCs), (ii) extending the measured size range of particulate matter up to ultra-fine particles, and (iii) including their scope to also measure bioaerosols, such as allergenic pollen species and fungi. Integrating these sensors into existing infrastructures, such as permanent air quality measurement networks, traffic counting sites, and indoor monitoring, would provide a richer data set for air quality and exposure research. Further effort is required to determine the health-relevant PM information, including in particular the chemical composition of PM.

As citizen science and crowdsourcing increase, their use in air quality research needs to be more clearly defined. It could potentially provide near-real-time air pollution information as well as information to be used for personal health protection and lifestyle decisions. Most challenging for this objective is data quality characterization and acceptance of these new data provisioning tools, which do not easily allow an analytical quality assurance and control.

9.3  Bridging scales and processes with integrated air pollution modelling

Continuing developments in fine, urban, and regional modelling have elevated scale interactions as a key area of interest. As highlighted above, research is needed to develop new approaches to connect processes operating on different scales. Baklanov et al. (2014) reviewed online approaches that include coupling of meteorology and chemistry within an Eulerian nested framework, but on the whole air pollution applications are limited to different modelling systems including Eulerian for regional, Gaussian for urban and street, and LES and advanced CFD-RANS for even finer scales. Challenges remain on how to best integrate the fast-emerging machine learning statistical tools and how parameterizations and computational approaches have to be adapted. These scale interactions are of critical importance when examining the impact of air pollution in cities which are subject to heterogeneous distribution of emissions and rapidly changing dispersion gradients of concentrations. New modelling approaches will enable multiple air quality hazards that affect cities to be examined within a consistent multi-scale framework for air quality prediction and forecasting and local air quality management, quantifying the impact of episodic high-air-pollution events involving LRT and even meteorological and climate hazards as cities prepare for the future.

One major development in this vain is that of Earth system model (ESM) approaches, which in the past have been focussed on global scales but have the potential of higher-resolution applications (e.g. WWRP, 2015). Within Earth system models, there is potential for integration of observations (e.g. through data assimilation of soil moisture and surface fluxes of short-lived pollutants and greenhouse gases). These developments are to some degree being aided by the rapidly evolving area of parallel computer systems. While the representation of urban features and processes within ESMs still require further effort, these models have the potential to include dynamical and chemical interactions on a much wider scale than is possible with traditional approaches (e.g. mesoscale circulations, urban heat island circulation, sea-breeze and mountain-valley circulations, floods, heat waves, wildfires, air quality issues, and other extreme weather events).

As primary air pollution emissions are decreasing, the role of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols) in urban environments requires more research. Here coupled systems and potential ESMs in the future will have a key role based on two-way interaction chemistry–meteorology models combining the effects of urban, sub-urban, and rural pollutant emissions with dynamics. This is especially true in a changing climate scenario.

Cities are routinely facing multiple hazards in addition to high levels of air pollution including storm surges, flooding, heat waves, and a changing climate. Moving towards integrated urban systems and services poses research challenges but is viewed as essential to meet sustainable and environmentally smart city development goals, e.g. SDG11: Sustainable Cities and Communities (Baklanov et al., 2018b; Grimmond et al., 2020). More integrated assessment of risk to urban areas necessitates observation and modelling that brings together data from hydrometeorological, soil, hydrology, vegetation, and air quality communities including sophisticated and responsive early warning and forecast capabilities for city and regional administrations.

9.4  Improving air quality for better health

As air quality science continues to develop, the need to improve our understanding of PM properties and resulting health impacts remains a priority. In particular the areas that stand out are the need to better quantify particle number concentrations (PNCs), particle size distributions (PSDs), and the chemical composition of PM, especially in urban areas where population density is higher. An ongoing challenge for the science community is to investigate which of the PM properties or measures optimally describe the resulting health impacts. To aid research, a denser measurement network on advanced PM properties is needed for quantifying chemical and physical characteristics of PM in cities and regionally. Another important requirement is the availability of improved higher-resolution emission inventories of PM components and for different sizes (see Sect. 9.1). To support epidemiological studies, comprehensive long-term data sets are needed including both (i) multi-decadal evaluations of air quality, meteorology, and exposure and (ii) information on a range of health impacts.

9.5  Challenges of global pandemics

In addition to the multiple hazards facing cities mentioned in Sect. 9.3, the COVID-19 pandemic has starkly demonstrated how society can be dramatically affected across the world. Studies are indicating a dramatic impact on air quality due to the lockdown as well as possible connections between air pollutants such as aerosols in spreading the SARS-CoV-2 virus (e.g. Baldasano, 2020; Gkatzelis et al., 2021; Sokhi et al., 2021). To fully assess the interactions of viruses and air pollutants, studies need to consider both indoor and outdoor transmission as well as meteorological and climatological influences. A recent preliminary review (WMO, 2021) has concluded that there are mixed indications of links between meteorology and air quality with COVID-19, and more thorough studies are needed to ascertain the direct and indirect effects. Given the complexity of the topic, cross- and interdisciplinary studies would be needed, including a collaboration of microbiologists, epidemiologists, health professionals, and atmospheric and indoor pollution air scientists.

9.6  Integrating policy responses for air quality, climate, and health

Most control policies and measures targeted at air pollution will also change GHG emissions, which implies that taking them both into account in integrated assessments will in most cases provide considerable co-benefits. There are cases, for example in the case of biomass burning, which will increase air pollution emissions, and hence additional abatement measures (e.g. cleaning systems) will be required. On the whole, however, integrating climate change and air pollution policies where possible has the potential of making the integrated policy more efficient than separate policies for improving air quality and limiting the impact of climate change. Thus, integrated environmental policies based on assessing reductions of impacts on health, the environment, and materials caused by air pollution control and reductions of impacts of climate change caused by measures for climate protection simultaneously should be implemented. The assessment should be made following the impact pathway approach described in Sect. 8.2. The impact pathway approach uses the methods and data from all the sections of this paper, i.e. emission modelling, atmospheric modelling, exposure modelling, and health impact modelling. Thus, addressing the challenges described in this paper would help to reduce uncertainties and improve efficiency in the scientific recommendations for setting up integrated environmental policy plans. Within an integrated air pollution control and climate protection assessment, a particularly important new development would be to use the individual exposure (the concentration of a pollutant where it is inhaled by an individual averaged over a year) instead of some outdoor concentration as an indicator for health impacts, i.e. as input for the exposure–response relationship. In this case, the indoor concentration of air pollutants and thus indoor source emission rates and ventilation air exchange rates would be important elements in the assessment along with contributions from outdoor sources when planning air pollution control strategies.

9.7  Key recommendations

Below in Table 1 we present a synthesis of key recommendations for scientific research and the importance for air quality policy that have emerged from this review. The table also provides an indication of the confidence in the scientific knowledge in each of the areas, the urgency to complete the science gaps in our knowledge, and the importance of each of the listed areas for supporting policy. It should be noted that our approach provides more of an overview and does not consider the needs of specific areas or of national needs which may differ from the regional status of knowledge. For example, in the case of emission inventories for Europe and North America, there is generally high confidence but that may not be the case in other regions of the world or for specific countries or sub-regions.

Table 1 A synthesis of key recommendations for scientific research and the importance for air quality policy. A three-level scale is used to indicate the current confidence in the scientific knowledge and understanding and a measure of the urgency to fill the science gaps where they exist. Similarly, a three-level scale is used to indicate the importance of the specific issues for policy support. Scientific confidence – h: high (progress is useful but may not require significant specific research effort); m: medium (some further research is required); l: low (concerted research effort is required). Scientific urgency to meet gaps in knowledge – v: very urgent need to fill science gap; u: urgent need to fill scientific gap; w: widely accepted with less urgency to fill the science gap. Importance for policy support – H: high (is highly important for developments of new policies); M: medium (can lead to refinements of current policies); L: low (progress is useful but may not require significant effort in the short or medium term).

1  PM properties refer here to particulate matter size distributions, particle number, and chemical composition for example. 2  Dynamic exposure assessment refers to exposure studies, which treat the pollutant concentrations in different microenvironments as well as the infiltration of outdoor air to indoors. In dynamic exposure assessment, one can also treat pollution sources and sinks in indoor air.

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This review has mainly examined research developments that have emerged over the last decade. As part of the review, we have provided a short historical survey, before assessing the current status of the research field and then highlighting emerging challenges. We have had to be selective in the key areas of air quality research that have been examined. While the concept of this review emerged from the 12th International Conference on Air Quality (held virtually during 18–26 May 2020), each of the sections not only provided an air quality research community perspective but also included a wider literature examination of the areas.

10.1  Emissions of air pollution

The emphasis has been on air pollution emissions of major concern for health effects, namely exhaust and non-exhaust emissions from road traffic and shipping, and other anthropogenic emissions, e.g. those from agriculture and wood burning. Developments are continuing to improve global and regional emission inventories and integrating local emissions data into the larger-scale inventories. With increasing demand for cleaner vehicles, there is still the need to assess whether electric and hybrid vehicles actually reduce total PM 2.5 and PM 10 emissions, as emissions from non-exhaust PM from tyre, brake, and road wear are still present. Developments in on board monitoring to help improve estimation of real-world emission estimation is another growing area. Understanding the effects of non-exhaust emission will be important to design robust air quality management strategies in the context of other emissions, including windblown dust.

Uncertainties still exist in estimating emissions from diffuse processes, such as abrasion processes in industry, households, agriculture, and traffic, where large variabilities are still present. Other sources, which are not well characterized, include residential wood combustion as well as the spatial representation of these emissions across regions. While progress in source apportionment models has continued, inverse modelling used for improvement of emission inventories has the potential to reduce their uncertainties.

In terms of chemical speciation, while some improvements have taken place in estimating temporal profiles of agricultural emissions, the amount of NH 3 and PM emissions originating from agriculture are still uncertain for many regions. The impact of new fuels on the chemical composition of NMVOC emissions from combustion processes remains highly uncertain (e.g. low-sulfur residual fuels in shipping and new exhaust gas cleaning technologies).

Bringing together air pollution emission inventories with those of greenhouse gases will facilitate integrated assessment measures and policies benefitting from co-benefits. On the urban and street scales, emission models need to be able to simulate the spatial and temporal variations in emissions at a higher resolution from road traffic, taking account of traffic and driving conditions.

The importance of shipping emissions is growing, as there is a shift to carbon-neutral or zero-carbon fuels. Emission factors for VOC from shipping are generally less certain, and hence little is known about their contribution to particle and ozone formation. To estimate the total environmental impact of shipping, integrated approaches are needed that bring together (i) impacts from atmospheric emissions on air quality and health, deposition of pollutants to the sea; (ii) impacts of discharges to the sea on the marine environments and biota; and (iii) climatic forcing.

The greater emphasis on reducing exposure to air pollution requires consideration of both emissions from outdoor and indoor sources, as well as their exchange between indoor and outdoor environments. Emissions of VOC for example, from transportation and the use of volatile chemical products, such as pesticides, coatings, inks, personal care products, and cleaning agents, are becoming more important, as are combustion gas appliances such as stoves and boilers, smoking, heating, and cooking, which are important sources of PM 2.5 , NO, NO 2 , and PAHs. The complexity of integrated exposure models is expected to increase, as they have to include both indoor and outdoor emissions of air pollution, accurate description of the key chemical and physical processes, and treatment of dispersion of air pollution inside and outside and exchange between buildings and the ambient environment (Liu et al., 2013; Bartzis et al., 2015).

10.2  Observations to support air quality research

Regarding observation of air quality, this review has focused on low-cost sensor (LCS) networks, crowdsourcing, and citizen science and on the development of modern satellite and remote sensing technics. Connecting observational data with small-scale air quality model simulations to provide personal air pollution exposure has also been discussed.

Remote sensing measurements including satellite observations have a significant role in air quality management because of their spatial coverage, improving spatial resolution and their use in combination with modelling tasks (Hirtl et al., 2020), even for urban areas (Letheren, 2016). Machine learning algorithms are increasingly being used with remote sensing applications (e.g. Foken, 2021), and recent advances have highlighted the potential of statistical analysis tools (e.g. neural learning algorithms) for predicting air quality at the city scale based on data generated by stationary and mobile sensors (Mihăiţă et al., 2019). Geostatistical data fusion is allowing fine spatial mapping by combining sensor data with modelled spatial distribution of air pollutant concentrations (Johansson et al., 2015; Ahangar et al., 2019; Schneider et al., 2017).

Applications of LCS as well as networks based on such sensors have increased over the past decade (e.g. Thompson, 2016; Karagulian et al., 2019; Barmpas et al., 2020; Schäfer et al., 2021). These applications have also highlighted the need for proper evaluation, quality control, and calibration of these sensors. The analysis of LCS data should take account of cross-sensitivities with other air pollutants, effects of ageing, and the dependence of the sensor responses on temperatures and humidity in ambient air (e.g. Brattich et al., 2020).

10.3  Air quality modelling

Air quality research, including approaches to manage air pollution, has relied heavily on the continuing developments, applications, and evaluation of air quality models. Air quality models span a wide range of modelling approaches including CFD and RANS models used for very high resolution dispersion applications (e.g. Nuterman et al., 2011; Andronopoulos et al., 2019), and Lagrangian plume models to Eulerian grid CTMs used for urban to regional scales. An interesting development is that of the implementation of multiply nested LESs and coupling of urban-scale deterministic models with local probabilistic models (e.g. Hellsten et al., 2021), although complexities arise because of the different parameterizations and the treatment of boundary conditions. A limitation that needs addressing with CFD, including LES models, is that they are currently suited mainly for dispersion of tracer contaminants or where only simple tropospheric chemistry is relevant. Lack of more sophisticated or realistic description of NO x –VOC chemistry can cause significant bias in the concentration gradients at very fine scales.

Over the last decade new developments have focused on improving scale interactions and model resolution to resolve the spatial variability and heterogeneity of air pollution (e.g. Jensen et al., 2017; Singh et al., 2014, 2020a) at street scales in a city area. New approaches of artificial neural network models and machine learning have shown a more detailed representation of air quality in complex built-up areas (e.g. P. Wang et al., 2015; Zhan et al., 2017; Just et al., 2020; Alimissis et al., 2018). CTMs have also been developed to improve spatial resolution, for example, through downscaling approaches for predicting air quality in urban areas, forecasting air quality, and simulation of exposure at the street scale (Berrocal et al., 2020; Elessa Etuman et al., 2020; Jensen et al., 2017). Ensemble simulations have proven to be successful to provide more reliable air quality prediction and forecasting (e.g. Galmarini et al., 2012; Hu et al., 2017), and complementary hybrid approaches have been explored for multi-scale applications (Galmarini et al., 2018).

The strong interaction between local and regional contributions, especially to secondary air pollutants (PM 2.5 and O 3 ), has motivated the coupling of urban- and regional-scale models (e.g. Singh et al., 2014; Kukkonen et al., 2018). With the importance of exposure assessment increasing, the incorporation of finer spatial scales within a larger spatial domain is required, which introduces the challenging issue of representing multiscale dynamical and chemical processes, while maintaining realistic computational constraints (e.g. Tsegas et al., 2015). Similarly, machine learning approaches offer possibilities to use observational data to improve fine-scale air quality and personal exposure predictions (Shaddick et al., 2021).

10.4  Interactions between air quality, meteorology, and climate

Our review has highlighted the need to integrate predictions of weather, air quality, and climate where Earth system modelling (ESM) approaches play an increasing role (WWRP, 2015; WMO, 2016). There are also continued improvements from higher-spatial-resolution modelling and interconnected multiscale processes, while maintaining realistic computational times. Many advances have taken place in the development and use of coupled regional-scale meteorology–chemistry models for air quality prediction and forecasting applications (e.g. Kong et al., 2015; Baklanov et al., 2014, 2018a). These advances contribute to assess complex interactions between meteorology, emission, and chemistry, for example, relating to dust intrusion and wildfires (e.g. Kong et al., 2015). Data assimilation of chemical species data into CTM systems is still an evolving field of research; it has the potential to better constrain emissions in forecast applications. An example would be data assimilation of urban observations (including meteorological, chemical, and aerosol species) to investigate multiscale effects of the impacts of aerosols on weather and climate (Nguyen and Soulhac, 2021).

Urban- and finer-scale (e.g. built environment) studies are showing that improvements in the treatment of albedo, the anthropogenic heat flux, and the feedbacks between urban pollutants and radiation can influence urban air quality significantly (e.g. González-Aparicio et al., 2014; Fallmann et al., 2016; Molina, 2021). These considerations can be very important for urban air quality forecasting, as temporal variations in air pollutant concentrations in the short term are largely due to variabilities in meteorology. Understanding and parameterizing multiscale and non-linear interactions, for example evolution and dynamics of urban heat island circulation and aerosol forcing and urban aerosol interactions with clouds and radiation, remains an ongoing atmospheric science challenge. Another remaining research challenge that involves multiscale interactions includes the formation of secondary air pollutants (e.g. ozone and secondary organic and inorganic aerosols), especially to describe air quality over urban, sub-urban, and rural environments.

Development and evaluation of nature-based solutions to improve air quality demand an improved understanding of the role of biogenic emissions (Cremona et al., 2020) as a function of vegetation species and characteristics. Interactions are influenced by several factors, such as vegetation drag, pollutant absorption, and biogenic emissions. These factors will determine the impact on air quality, be it positive or negative (Karttunen et al., 2020; San José et al., 2020; Santiago et al., 2017). Advanced approaches are needed to describe biogenic emissions together with gas and particle deposition over vegetation surfaces to further assess the effectiveness of nature-based solutions to improve air quality in cities.

10.5  Air quality exposure and health

Air-quality-related observations to support air quality health impact studies are heterogeneous; for many developing regions, such as Africa, ground-based monitoring is sparse or non-existent (Rees at al., 2019). The motivation is growing for an inter-disciplinary approach to assess exposure and the burden of disease from air pollution (Shaddick et al., 2021); this could benefit from the combined use of ground and remote sensing measurements, including satellite data, with atmospheric chemical transport and urban-scale dispersion modelling.

Air quality impact on health can occur on short and long timescales. PM, which is one of the most health-relevant air pollutants, is associated with many health effects, such as all-cause, cardiovascular, and respiratory mortality and childhood asthma (e.g. Dai et al., 2014; Samoli et al., 2013; Stafoggia et al., 2013; Weinmayr et al., 2010). There have been significant advances that reveal new evidence of the health impact of PM components, such as SO 4 , EC, OC, and metals (Wang et al., 2014; Adams et al., 2015; Hampel et al., 2015; Hime et al., 2018). Challenges remain to elucidate the relative role of PM components and measures in determining the total health impact. These include particle number concentrations (PNCs), secondary organic PM, primary PM, various chemical components, suspended dust, the content of metals, and toxic or hazardous pollutants.

Improved knowledge on the health impacts of PM components has also stimulated further debate on the optimal concentration–response functions and on the necessity of threshold or lower limit values, below which health impacts might not manifest (Burnett et al., 2018). These challenges will feed into health impact studies, such as EEA (2020a), which estimated that more than 3 848 000 years of life lost (about 374 000 premature deaths) were linked to exposure to PM 2.5 in 2018 in the EU-28. However, another study by Lelieveld et al. (2019) indicated that health impacts from PM 2.5 exposure may have been considerably underestimated.

The worldwide impact from the COVID-19 pandemic caused by the SARS-CoV-2 virus has raised global interest in the links between air quality and the spread of viruses (van Doremalen et al., 2020). However, the exact role and mechanisms are not yet clear and require concerted effort (e.g. Pisoni and Van Dingenen, 2020). There is also evidence that poor air quality can exacerbate health effects from other environmental stressors, including heat waves, cold spells, and allergenic pollen (e.g. Klein et al., 2012; Horne et al., 2018; Xie et al., 2019; Phosri et al., 2019).

The link between population activity and actual exposure is also becoming clearer, where dynamic diurnal activity patterns provide more accurate representation of exposures to air pollution (Soares et al., 2014; Kukkonen et al., 2016b; Smith et al., 2016; Singh et al., 2020a). Recent work by Ramacher et al. (2019), for example, has also demonstrated the importance of the movements of people to assess exposure.

For evaluating the relative significance of various PM properties and measures on human health, a denser measurement network on advanced PM properties would be needed, on both regional and urban scales. The required PM properties would include, in particular, size distributions and chemical composition. Clearly, such a network would be especially valuable in cities and regions that include high-quality population cohorts. The most important requirement in terms of PM modelling would be improved emission inventories, which would also include sufficiently accurate information on particle size distributions and chemical composition.

10.6  Air quality management and policy

Integrated assessment of air pollution control policies has progressively developed over the last 2 decades and has been widely used as a tool for air quality management (e.g. EC, 2021). Relatively recently, integrated assessment for air pollution control in research projects has started to take account of climate change. Correspondingly, integrated assessment activities for climate protection have started to include impacts of air pollution in the assessment (Friedrich, 2016). Some national authorities, such as the German Federal Environmental Agency or the UK Department for Business, Energy and Industrial Strategy, have also recommended an integrated assessment, combining the assessment of climate and air pollution impacts (Matthey and Bünger, 2019; DBEIS, 2019). Impact pathway approaches are also currently increasingly incorporating exposure to air pollutants as an indicator of health impacts, instead of the previously applied concentration of air pollutants at fixed outdoor locations (Li and Friedrich, 2019). This has an implication for epidemiological studies, which usually are based on correlation between modelled or measured concentrations at outdoor locations and health risks (e.g. Singh et al., 2020b).

Interdependence of air pollution and climatically active species allows co-benefits to be optimized. This approach also shows that costs of meeting policy obligations for climate protection (e.g. for the Paris Agreement) can be reduced or offset by the benefits of reduced health impacts from improved air quality (Markandya et al., 2018). On the other hand, for some climate protection measures, the benefits of reduced climate change are much smaller than the impacts caused by increased air pollution. This has been demonstrated for wood combustion, which while being more climate friendly than fossil fuels, will give rise to PM 2.5 and NO x emissions (Huang et al., 2016; Kukkonen et al., 2020b). Some recent studies (e.g. Schmid et al., 2019) have provided evidence on the advantages of using costs and benefits for both climate and air pollution abatement measures in integrated assessments.

Air quality management must adapt to the tightening of policy-driven regulations. Recently, the sulfur content of the fuel for ships has been reduced to 0.5 % worldwide (IMO, 2019). The EURO 6d norm has led to a significant reduction of NO x in the exhaust gas of diesel cars, whereas the EURO 7 norm planned to be implemented in 2025 will further reduce PM and NO x emissions from vehicle engines. The European Council has recently (in September 2020) agreed to reduce the EU's greenhouse gas emissions in 2030 by at least 55 % compared to the corresponding emissions in 1990. Together with national reduction plans for GHG, this will significantly reduce emissions of air pollutants from the combustion of fossil fuels. However, there is one exception: small wood and pellet firings ( <500  kW), where still further measures should be developed for reducing the PM and NO x emissions (e.g. Kukkonen et al., 2020b).

While direct combustion emissions are expected to decrease, a particular challenge will be to control diffuse emissions, e.g. from abrasion processes, bulk handling, demolition of buildings, and use of paints and cleaning agents. Despite cleaner vehicles, emissions from tyre and brake wear and road abrasion remain an important challenge. Other areas that pose challenges for air quality management are the need to reduce agricultural emissions, especially of ammonia, which can lead to the production of secondary aerosols (especially ammonium nitrates).

Using personal exposure instead of outdoor concentration as an indicator in health impact assessments offers the opportunity to assess the impacts of indoor air pollution control. Possibilities to reduce emissions from indoor sources, such as smoking, frying and cooking, candles and incense sticks, open chimneys and wood stoves, and cleaning agents, should be assessed. Furthermore, using HEPA filters in vacuum cleaners, air filters, and cooker bonnets and using mechanical ventilation with heat recovery should be analysed. In addition, possibilities for reducing PM concentrations in underground rail stations should be explored.

Finally, we consider cross-cutting needs as a synthesis of our findings and suggest recommendations for further research.

Specifically, we indicate the confidence in the scientific knowledge, the urgency to complete the science gaps and the importance of each area for supporting policy.

No data sets were used in this article.

All co-authors contributed to conceptualization and design of study, coordination, methodology development of the review, validation and checking, formal analysis, investigation and examination of the literature, writing of original draft, and review and editing of paper.

The contact author has declared that neither they nor their co-authors have any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The support of the following institutions and enterprises is gratefully acknowledged: University of Hertfordshire; Aristotle University Thessaloniki; TITAN Cement S.A., TSI GmbH; APHH UK-India Programme on Air Pollution and Human Health (funded by NERC, MOES, DBT, MRC, Newton Fund); and American Meteorological Society (AMS) Air & Waste Management Association (A&WMA).

We especially acknowledge the tireless effort of Ioannis Pipilis, Afedo Koukounaris, and Eva Angelidou.

World Meteorological Organization (WMO) GAW Urban Research Meteorology and Environment (GURME) programme for supporting and contributing to this review.

Klaus Schäfer is grateful for funding within the frame of the project Smart Air Quality Network by the German Federal Ministry of Transport and Digital Infrastructure – Bundesministerium für Verkehr und digitale Infrastruktur (BMVI).

Tomas Halenka is grateful for funding within the activity PROGRES Q16 by the Charles University, Prague.

Vikas Singh is thanked for providing Fig. 10.

This work reflects only the authors' view, and the Innovation and Networks Executive Agency is not responsible for any use that may be made of the information it contains.

We are also thankful for the funding of NordForsk.

We wish to thank Antti Hellsten (FMI) for his useful comments on CFD modelling.

This research has been supported by the European Union's Horizon 2020 Research and Innovation programme (HEALS (grant agreement no. 603946), ICARUS (grant agreement no. 690105), SCIPPER (grant agreement no. 814893), EXHAUSTION (grant agreement no. 820655), and EMERGE (grant agreement no. 874990)), the EU LIFE financial programme through the project VEG-GAP “Vegetation for Urban Green Air Quality Plans” (LIFE18 PRE IT003), German Federal Ministry of Transport and Digital Infrastructure – Bundesministerium für Verkehr und digitale Infrastruktur (BMVI; grant no. 19F2003A-F), and the funding of NordForsk under the Nordic Programme on Health and Welfare (project no. 75,007: NordicWelfAir – Understanding the link between Air pollution and Distribution of related Health Impacts and Welfare in the Nordic countries).

This paper was edited by Pedro Jimenez-Guerrero and reviewed by two anonymous referees.

Abhijith, K. V., Kumar, P., Gallagher, J., McNabola, A., Baldauf, R., Pilla, F., Broderick, B., Di Sabatino, S., and Pulvirenti, B.: Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments – A review, Atmos. Environ., 162, 71–86, 2017. 

Achilleos, S., Kioumourtzoglou, M.-A., Wu, C.-D., Schwartz, J. D., Koutrakis, P., and Papatheodorou, S. I.: Acute effects of fine particulate matter constituents on mortality: A systematic review and meta-regression analysis, Environ. Int., 109, 89–100, https://doi.org/10.1016/j.envint.2017.09.010 , 2017. 

Adam, M., Schikowski, T., Carsin, A. E., Cai, Y., Jacquemin, B., Sanchez, M., Vierkötter, A., Marcon, A., Keidel, D., Sugiri, D., Al Kanani, Z., Nadif, R., Siroux, V., Hardy, R., Kuh, D., Rochat, T., Bridevaux, P.-O., Eeftens, M., Tsai, M.-Y., Villani, S., Phuleria, H. C., Birk, M., Cyrys, J., Cirach, M., Nazelle, A. d., Nieuwenhuijsen, M. J., Forsberg, B., Hoogh, K. d., Declerq, C., Bono, R., Piccioni, P., Quass, U., Heinrich, J., Jarvis, D., Pin, I., Beelen, R., Hoek, G., Brunekreef, B., Schindler, C., Sunyer, J., Krämer, U., Kauffmann, F., Hansell, A. L., Künzli, N., and Probst-Hensch, N.: Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis, Eur. Respir. J., 45, 38–50, https://doi.org/10.1183/09031936.00130014 , 2015. 

Adams, K., Greenbaum, D. S., Shaikh, R., van Erp, A. M., and Russell, A. G.: Particulate matter components, sources, and health: Systematic approaches to testing effects, J. Air Waste Manage., 65, 544–558, https://doi.org/10.1080/10962247.2014.1001884 , 2015. 

Ahangar, F., Freedman, F., and Venkatram, A.: Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps, Int. J. Env. Res. Pub. He., 16, 1252, https://doi.org/10.3390/ijerph16071252 , 2019. 

Ajtai, N., Stefanie, H., Botezan, C., Ozunu, A., Radovici, A., Dumitrache, R., Iriza-Burcă, A., Diamandi, A., and Hirtl, M.: Support tools for land use policies based on high resolution regional air quality modelling, Land Use Policy, 95, 103909, https://doi.org/10.1016/j.landusepol.2019.03.022 , 2020. 

Aleksandrov, V. V. and Stenchikov, G. I.: On the modeling of the climatic consequences of the nuclear war, The Proceeding of Appl. Mathematics, The Computing Center of the AS USSR, Moscow, 21 pp., http://climate.envsci.rutgers.edu/pdf/AleksandrovStenchikov.pdf (last access: 21 February 2022), 1983. 

Alfano, B., Barretta, L., Del Giudice, A., De Vito, S., Di Francia, G., Esposito, E., Formisano, F., Massera, E., Miglietta, M. L., and Polichetti, T.: A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives, Sensors, 20, 6819, https://doi.org/10.3390/s20236819 , 2020. 

Alimissis, A., Philippopoulos, K., Tzanis, C. G., and Deligiorgi, D.: Spatial estimation of urban air pollution with the use of artificial neural network models, Atmos. Environ., 191, 205–213, 2018. 

Amann, M., Holland, M., Maas, R., Vandyck, T., and Saveyn, B.: Costs, benefits and economic impacts of the EU Clean Air Strategy and their implications on innovation and competitiveness, IIASA, https://ec.europa.eu/environment/air/pdf/clean_air_outlook_economic_impact_report.pdf (last access 25 February 2022), 2017. 

Analitis, A., de' Donato, F., Scortichini, M., Lanki, T., Basagana, X., Ballester, F., Astrom, C., Paldy, A., Pascal, M., Gasparrini, A., Michelozzi, P., and Katsouyanni, K.: Synergistic Effects of Ambient Temperature and Air Pollution on Health in Europe: Results from the PHASE Project, Int. J. Env. Res. Pub. He., 15, 1856, https://doi.org/10.3390/ijerph15091856 , 2018. 

Andersen, Z. J., Pedersen, M., Weinmayr, G., Stafoggia, M., Galassi, C., Jørgensen, J. T., Sommar, J. N., Forsberg, B., Olsson, D., Oftedal, B., Aasvang, G. M., Schwarze, P., Pyko, A., Pershagen, G., Korek, M., Faire, U. d., Östenson, C.-G., Fratiglioni, L., Eriksen, K. T., Poulsen, A. H., Tjønneland, A., Bräuner, E. V., Peeters, P. H., Bueno-de-Mesquita, B., Jaensch, A., Nagel, G., Lang, A., Wang, M., Tsai, M.-Y., Grioni, S., Marcon, A., Krogh, V., Ricceri, F., Sacerdote, C., Migliore, E., Vermeulen, R., Sokhi, R., Keuken, M., Hoogh, K. d., Beelen, R., Vineis, P., Cesaroni, G., Brunekreef, B., Hoek, G., and Raaschou-Nielsen, O.: Long-term exposure to ambient air pollution and incidence of brain tumor: the European Study of Cohorts for Air Pollution Effects (ESCAPE), Neuro-Oncology, 20, 420–432, https://doi.org/10.1093/neuonc/nox163 , 2018. 

Anderson, H. A.: Air pollution and mortality: A history, Atmos. Environ., 43, 142–152, 2009. 

Anderson, M., Salo, K., and Fridell, E.: Particle- and Gaseous Emissions from an LNG Powered Ship, Environ. Sci. Technol., 49, 12568–12575, 2015. 

Anderson, V. and Gough, W. A.: Evaluating the potential of nature-based solutions to reduce ozone, nitrogen dioxide, and carbon dioxide through a multi-type green infrastructure study in Ontario, Canada, City and Environment Interactions, 6, 100043, https://doi.org/10.1016/j.cacint.2020.100043 , 2020. 

Andre, M., Sartelet, K., Moukhtar, S., Andre, J. M., and Redaelli, M.: Diesel, petrol or electric vehicles: What choices to improve urban air quality in the Ile-de-France region? A simulation platform and case study, Atmos. Environ., 241, 117752, https://doi.org/10.1016/j.atmosenv.2020.117752 , 2020. 

Andronopoulos, S., Bartzis, J. G., Efthimiou, G. C., and Venetsanos, A. G.: Puff-dispersion variability assessment through Lagrangian and Eulerian modelling based on the JU2003 campaign, Bound.-Lay. Meteorol., 171, 395–422, 2019. 

Antonsen, S., Mok, P. L. H., Webb, R. T., Mortensen, P. B., McGrath, J. J., Agerbo, E., Brandt, J., Geels, C., Christensen, J. H., and Pedersen, C. B.: Exposure to air pollution during childhood and risk of developing schizophrenia: a national cohort study, The Lancet, 4, E64–E73, https://doi.org/10.1016/s2542-5196(20)30004-8 , 2020. 

Aron, R. H.: Forecasting high level oxidant concentrations in the Los Angeles basin, J. Air Pollut. Control Assoc., 20, 1227–1228, 1980. 

Aulinger, A., Matthias, V., and Quante, M.: An Approach to Temporally Disaggregate Benzo(a)pyrene Emissions and Their Application to a 3D Eulerian Atmospheric Chemistry Transport Model, Water Air Soil Poll., 216, 643–655, 2011. 

Aulinger, A., Matthias, V., Zeretzke, M., Bieser, J., Quante, M., and Backes, A.: The impact of shipping emissions on air pollution in the greater North Sea region – Part 1: Current emissions and concentrations, Atmos. Chem. Phys., 16, 739–758, https://doi.org/10.5194/acp-16-739-2016 , 2016. 

Aunan, K., Ma, Q., Lund, M. T., and Wang, S.: Population-weighted exposure to PM 2.5 pollution in China: An integrated approach, Environ. Int., 120, 111–120, https://doi.org/10.1016/j.envint.2018.07.042 , 2018. 

Bachmann, T. M.: Considering environmental costs of greenhouse gas emissions for setting a CO 2 tax: A review, Sci. Total Environ., 720, 137524, https://doi.org/10.1016/j.scitotenv.2020.137524 , 2020. 

Backes, A., Aulinger, A., Bieser, J., Matthias, V., and Quante, M.: Ammonia emissions in Europe, part I: Development of a dynamical ammonia emission inventory, Atmos. Environ., 131, 55–66, 2016. 

Badeke, R., Matthias, V., and Grawe, D.: Parameterizing the vertical downward dispersion of ship exhaust gas in the near field, Atmos. Chem. Phys., 21, 5935–5951, https://doi.org/10.5194/acp-21-5935-2021 , 2021. 

Badia, A., Segura, R., Gilabert, J., Ventura, S., Vidal, V., and Villalba, G.: Air quality modeling study using wrf-chem over Barcelona, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Bai, L., Wang, J., Ma, X., and Lu, H.: Air Pollution Forecasts: An Overview, Int. J. Env. Res. Pub. He., 15, 780, https://doi.org/10.3390/ijerph15040780 , 2018. 

Baklanov, A. and Zhang, Y.: Advances in air quality modeling and forecasting, Global Transitions, 2, 261–270, https://doi.org/10.1016/j.glt.2020.11.001 , 2020. 

Baklanov, A., Lawrence, M., Pandis, S., Mahura, A., Finardi, S., Moussiopoulos, N., Beekmann, M., Laj, P., Gomes, L., Jaffrezo, J.-L., Borbon, A., Coll, I., Gros, V., Sciare, J., Kukkonen, J., Galmarini, S., Giorgi, F., Grimmond, S., Esau, I., Stohl, A., Denby, B., Wagner, T., Butler, T., Baltensperger, U., Builtjes, P., van den Hout, D., van der Gon, H. D., Collins, B., Schluenzen, H., Kulmala, M., Zilitinkevich, S., Sokhi, R., Friedrich, R., Theloke, J., Kummer, U., Jalkinen, L., Halenka, T., Wiedensholer, A., Pyle, J., and Rossow, W. B.: MEGAPOLI: concept of multi-scale modelling of megacity impact on air quality and climate, Adv. Sci. Res., 4, 115–120, https://doi.org/10.5194/asr-4-115-2010 , 2010. 

Baklanov, A., Mestayer, P. G., Clappier, A., Zilitinkevich, S., Joffre, S., Mahura, A., and Nielsen, N. W.: Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description, Atmos. Chem. Phys., 8, 523–543, https://doi.org/10.5194/acp-8-523-2008 , 2008. 

Baklanov, A., Mahura, A., Grimmond, S., and Athanassiadou, M.: Meteorological and Air Quality Models for Urban Areas, Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg, 184 pp., https://doi.org/10.1007/978-3-642-00298-4 , 2009. 

Baklanov, A., Mahura, A., and Sokhi, R.: Integrated Systems of Meso-Meteorological and Chemical Transport Models, Springer Berlin Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-13980-2 , 2011. 

Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014 , 2014. 

Baklanov, A., Molina, L. T., and Gauss, M.: Megacities, air quality and climate, Atmos. Environ., 126, 235–249, https://doi.org/10.1016/j.atmosenv.2015.11.059 , 2016. 

Baklanov, A., Brunner, D., Carmichael, G., Flemming, J., Freitas, S., Gauss, M., Hov, Ø., Mathur, R., Schlünzen, K., Seigneur, C., and Vogel, B.: Key Issues for Seamless Integrated Chemistry-Meteorology Modeling, B. Am. Meteorol. Soc., 98, 2285–2292, https://doi.org/10.1175/bams-d-15-00166.1 , 2018a. 

Baklanov, A., Grimmond, C. S. B., Carlson, D., Terblanche, D., Tang, X., Bouchet, V., Lee, B., Langendijk, G., Kolli, R. K., and Hovsepyan, A.: From urban meteorology, climate and environment research to integrated city services, Urban Climate, 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004 , 2018b. 

Baldasano, J. M.: COVID-19 lockdown effects on air quality by NO 2 in the cities of Barcelona and Madrid (Spain), Sci. Total Environ., 741, 140353, https://doi.org/10.1016/j.scitotenv.2020.140353 , 2020. 

Baldasano, J. M., Güereca, L. P., López, E., Gassó, S., and Jiménez-Guerrero, P.: Development of a high resolution (1 km × 1 km, 1 h) emission model for Spain: the High-Elective Resolution Modeling Emission System (HERMES), Atmos. Environ., 42, 7215–7233, https://doi.org/10.1016/j.atmosenv.2008.07.026 , 2008. 

Balogun, H. A., Rantala, A. K., Antikainen, H., Siddika, N., Amegah, A. K., Ryti, N. R. I., Kukkonen, J., Sofiev, M., Jaakkola, M. S., and Jaakkola, J. J. K.: Effects of Air Pollution on the Risk of Low Birth Weight in a Cold Climate, Appl. Sci., 10, 6399, https://doi.org/10.3390/app10186399 , 2020. 

Barbero, D., Tinarelli, G., Silibello, C., et al.: A microscale hybrid modelling system to assess the air quality over a large portion of a large European city, Atmos. Environ., 264, 118656, https://doi.org/10.1016/j.atmosenv.2021.118656 , 2021. 

Barmpas, F., Tsegas, G., Moussiopoulos, N., and Chourdakis, E.: Interpreting measurements from air quality sensor networks: data assimilation and physical modelling, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 112, https://doi.org/10.18745/pb.22217 , 2020. 

Barré, J., Petetin, H., Colette, A., Guevara, M., Peuch, V.-H., Rouil, L., Engelen, R., Inness, A., Flemming, J., Pérez García-Pando, C., Bowdalo, D., Meleux, F., Geels, C., Christensen, J. H., Gauss, M., Benedictow, A., Tsyro, S., Friese, E., Struzewska, J., Kaminski, J. W., Douros, J., Timmermans, R., Robertson, L., Adani, M., Jorba, O., Joly, M., and Kouznetsov, R.: Estimating lockdown-induced European NO 2 changes using satellite and surface observations and air quality models, Atmos. Chem. Phys., 21, 7373–7394, https://doi.org/10.5194/acp-21-7373-2021 , 2021. 

Bartzis, J., Wolkoff, P., Stranger, M., Efthimiou, G., Tolis, E. I., Maes, F., Nørgaard, A. W., Ventura, G., Kalimeri, K. K., Goelen, E., and Fernandes, O.: On organic emissions testing from indoor consumer products' use, J. Hazard. Mater., 285, 37–45, https://doi.org/10.1016/j.jhazmat.2014.11.024 , 2015. 

Bartzis, J. G., Andronopoulos, S., and Efthimiou, G. C.: Simplified approaches in quantifying exposure statistical behaviour due to airborne hazardous releases of short time duration, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, https://doi.org/10.18745/pb.22217 , 2020. 

Bauer, S. E., Im, U., Mezuman, K., and Gao, C. Y.: Desert dust, industrialization and agricultural fires: Health impacts of outdoor air pollution in Africa, J. Geophys. Res.-Atmos., 124, 4104–4120, https://doi.org/10.1029/2018JD029336 , 2019. 

Baumol, W. J.: On Taxation and the Control of Externalities, Am. Econ. Rev., 62, 307–322, 1972. 

Baumol, W. J. and Oates, W. E.: The Use of Standards and Prices for Protection of the Environment, Swed. J. Econ., 73, 42–54, https://doi.org/10.2307/3439132 , 1971. 

Beddows, D. C. S. and Harrison, R. M.: PM 10 and PM 2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles, Atmos. Environ., 244, 117886, https://doi.org/10.1016/j.atmosenv.2020.117886 , 2021. 

Beekmann, M., Prévôt, A. S. H., Drewnick, F., Sciare, J., Pandis, S. N., Denier van der Gon, H. A. C., Crippa, M., Freutel, F., Poulain, L., Ghersi, V., Rodriguez, E., Beirle, S., Zotter, P., von der Weiden-Reinmüller, S.-L., Bressi, M., Fountoukis, C., Petetin, H., Szidat, S., Schneider, J., Rosso, A., El Haddad, I., Megaritis, A., Zhang, Q. J., Michoud, V., Slowik, J. G., Moukhtar, S., Kolmonen, P., Stohl, A., Eckhardt, S., Borbon, A., Gros, V., Marchand, N., Jaffrezo, J. L., Schwarzenboeck, A., Colomb, A., Wiedensohler, A., Borrmann, S., Lawrence, M., Baklanov, A., and Baltensperger, U.: In situ, satellite measurement and model evidence on the dominant regional contribution to fine particulate matter levels in the Paris megacity, Atmos. Chem. Phys., 15, 9577–9591, https://doi.org/10.5194/acp-15-9577-2015 , 2015. 

Beelen, R., Hoek, G., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z. J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P. H., Nieuwenhuijsen, M. J., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Marcon, A., Vartiainen, E., Lanki, T., Yli-Tuomi, T., Oftedal, B., Schwarze, P. E., Nafstad, P., Faire, U. d., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K. T., Overvad, K., Sørensen, M., Eeftens, M., Peeters, P. H., Meliefste, K., Wang, M., Bueno-de-Mesquita, H. B., Sugiri, D., Krämer, U., Heinrich, J., Hoogh, K. d., Key, T., Peters, A., Hampel, R., Concin, H., Nagel, G., Jaensch, A., Ineichen, A., Tsai, M.-Y., Schaffner, E., Probst-Hensch, N. M., Schindler, C., Ragettli, M. S., Vilier, A., Clavel-Chapelon, F., Declercq, C., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Katsoulis, M., Trichopoulou, A., Keuken, M., Jedynska, A., Kooter, I. M., Kukkonen, J., Sokhi, R. S., Vineis, P., and Brunekreef, B.: Natural-Cause Mortality and Long-Term Exposure to Particle Components: An Analysis of 19 European Cohorts within the Multi-Center ESCAPE Project, Environ. Health Persp., 123, 525–533, https://doi.org/10.1289/ehp.1408095 , 2015. 

Beelen, R., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z. J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P., Nieuwenhuijsen, M., Vineis, P., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Oudin, A., Forsberg, B., Modig, L., Havulinna, A. S., Lanki, T., Turunen, A., Oftedal, B., Nystad, W., Nafstad, P., Faire, U. d., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K. T., Overvad, K., Ellermann, T., Eeftens, M., Peeters, P. H., Meliefste, K., Wang, M., Bueno-de-Mesquita, B., Sugiri, D., Krämer, U., Heinrich, J., Hoogh, K. d., Key, T., Peters, A., Hampel, R., Concin, H., Nagel, G., Ineichen, A., Schaffner, E., Probst-Hensch, N., Künzli, N., Schindler, C., Schikowski, T., Adam, M., Phuleria, H., Vilier, A., Clavel-Chapelon, F., Declercq, C., Grioni, S., Krogh, V., Tsai, M.-Y., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Tamayo, I., Amiano, P., Dorronsoro, M., Katsoulis, M., Trichopoulou, A., Brunekreef, B., and Hoek, G.: Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project, Lancet, 383, 785–795, https://doi.org/10.1016/s0140-6736(13)62158-3 , 2014. 

Belis, C. A., Pikridas, M., Lucarelli, F., Petralia, E., Cavalli, F., Calzolai, G., Berico, M., and Sciare, J.: Source apportionment of fine PM by combining high time resolution organic and inorganic chemical composition datasets, Atmos. Environ. X, 3, 100046, https://doi.org/10.1016/j.aeaoa.2019.100046 , 2019. 

Belis, C. A., Pernigotti, D., Pirovano, G., Favez, O., Jaffrezo, J.L., Kuenen, J., Denier van Der Gon, H., Reizer, M., Riffault, V., Alleman, L.Y., Almeida, M., Amato, F., Angyal, A., Argyropoulos, G., Bande, S., Beslic, I., Besombes, J.-L., Bove, M.C., Brotto, P., Calori, G., Cesari, D., Colombi, C., Contini, D., De Gennaro, G., Di Gilio, A., Diapouli, E., El Haddad, I., Elbern, H., Eleftheriadis, K., Ferreira, J., Vivanco, M.G., Gilardoni, S., Golly, B., Hellebust, S., Hopke, P.K., Izadmanesh, Y., Jorquera, H., Krajsek, K., Kranenburg, R., Lazzeri, P., Lenartz, F., Lucarelli, F., Maciejewska, K., Manders, A., Manousakas, M., Masiol, M., Mircea, M., Mooibroek, D., Nava, S., Oliveira, D., Paglione, M., Pandolfi, M., Perrone, M., Petralia, E., Pietrodangelo, A., Pillon, S., Pokorna, P., Prati, P., Salameh, D., Samara, C., Samek, L., Saraga, D., Sauvage, S., Schaap, M., Scotto, F., Sega, K., Siour, G., Tauler, R., Valli, G., Vecchi, R., Venturini, E., Vestenius, M., Waked, A.,, and Yubero, E.: Evaluation of receptor and chemical transport models for PM 10 source apportionment, Atmos. Environ. X, 5, 100053, https://doi.org/10.1016/j.aeaoa.2019.100053 , 2020. 

Benedetti, A. and Vitart, F.: Can the Direct Effect of Aerosols Improve Subseasonal Predictability?, Mon. Weather Rev., 146, 3481–3498, https://doi.org/10.1175/MWR-D-17-0282.1 , 2018. 

Benedetti, A., Reid, J. S., Knippertz, P., Marsham, J. H., Di Giuseppe, F., Rémy, S., Basart, S., Boucher, O., Brooks, I. M., Menut, L., Mona, L., Laj, P., Pappalardo, G., Wiedensohler, A., Baklanov, A., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Popp, T., Quinn, P. K., Sekiyama, T. T., Tanaka, T., and Terradellas, E.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018 , 2018. 

Benešová, N., Belda, M., Eben, K., Geletič, J., Huszár, P., Juruš, P., Krč, P., Resler, J., and Vlček, O.: New open source emission processor for air quality models, in: Proceedings of Abstracts 11th International Conference on Air Quality Science and Application, edited by: Sokhi, R., Tiwari, P. R., Gállego, M. J., Craviotto Arnau, J. M., Castells Guiu, C., and Singh, V., University of Hertfordshire, UK, p. 27, https://doi.org/10.18745/pb.19829 , 2018. 

Bergamaschi, P., Karstens, U., Manning, A. J., Saunois, M., Tsuruta, A., Berchet, A., Vermeulen, A. T., Arnold, T., Janssens-Maenhout, G., Hammer, S., Levin, I., Schmidt, M., Ramonet, M., Lopez, M., Lavric, J., Aalto, T., Chen, H., Feist, D. G., Gerbig, C., Haszpra, L., Hermansen, O., Manca, G., Moncrieff, J., Meinhardt, F., Necki, J., Galkowski, M., O'Doherty, S., Paramonova, N., Scheeren, H. A., Steinbacher, M., and Dlugokencky, E.: Inverse modelling of European CH 4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations, Atmos. Chem. Phys., 18, 901–920, https://doi.org/10.5194/acp-18-901-2018 , 2018. 

Berrocal, V. J., Guan, Y., Muyskens, A., Wang, H., Reich, B. J., Mulholland, J. A., and Chang, H. H.: A comparison of statistical and machine learning methods for creating national daily maps of ambient PM 2.5 concentration, Atmos. Environ., 222, 117130, https://doi.org/10.1016/j.atmosenv.2019.117130 , 2020. 

Bi, P., Wang, J., and Hiller, J. E.: Weather: driving force behind the transmission of severe acute respiratory syndrome in China?, Intern. Med. J., 37, 550–554, https://doi.org/10.1111/j.1445-5994.2007.01358.x , 2007. 

Bickel, P. and Friedrich, R. (Eds.): ExternE – Externalities of Energy, Methodology 2005 Update, EUR 21951, https://doi.org/10.18419/opus-11923 , 2005. 

Bieser, J., Aulinger, A., Matthias, V., Quante, M., and Builtjes, P.: SMOKE for Europe – adaptation, modification and evaluation of a comprehensive emission model for Europe, Geosci. Model Dev., 4, 47–68, https://doi.org/10.5194/gmd-4-47-2011 , 2011a. 

Bieser, J., Aulinger, A., Matthias, V., Quante, M., and Denier van der Gon, H. A. C.: Vertical emission profiles for Europe based on plume rise calculations, Environ. Pollut., 159, 2935–2946, https://doi.org/10.1016/j.envpol.2011.04.030 , 2011b. 

BImSchV: Erste Verordnung zur Durchführung des Bundes-Immissionsschutzgesetzes (Verordnung über kleine und mittlere Feuerungsanlagen) – 1. BImSchV, https://www.gesetze-im-internet.de/bimschv_1_2010/BJNR003800010.html (last access: 25 February 2022), 2021. 

Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015 , 2015. 

Borge, R., Lumbreras, J., Pérez, J., de la Paz, D., Vedrenne, M., de Andrés, J. M., and Rodríguez, M. E.: Emission inventories and modeling requirements for the development of air quality plans. Application to Madrid (Spain), Sci. Total Environ., 466-467, 809–819, https://doi.org/10.1016/j.scitotenv.2013.07.093 , 2014. 

Borken-Kleefeld, J. and Chen, Y.: New emission deterioration rates for gasoline cars – Results from long-term measurements, Atmos. Environ., 101, 58–64, https://doi.org/10.1016/j.atmosenv.2014.11.013 , 2015. 

Brandt, J., Silver, J. D., Frohn, L. M., Geels, C., Gross, A., Hansen, A. B., Hansen, K. M., Hedegaard, G. B., Skjøth, C. A., Villadsen, H., Zare, A., and Christensen, J. H.: An integrated model study for Europe and North America using the Danish Eulerian Hemispheric Model with focus on intercontinental transport, Atmos. Environ., 53, 156–176, https://doi.org/10.1016/j.atmosenv.2012.01.011 , 2012. 

Brandt, J., Silver, J. D., Christensen, J. H., Andersen, M. S., Bønløkke, J. H., Sigsgaard, T., Geels, C., Gross, A., Hansen, A. B., Hansen, K. M., Hedegaard, G. B., Kaas, E., and Frohn, L. M.: Contribution from the ten major emission sectors in Europe and Denmark to the health-cost externalities of air pollution using the EVA model system – an integrated modelling approach, Atmos. Chem. Phys., 13, 7725–7746, https://doi.org/10.5194/acp-13-7725-2013 , 2013. 

Brattich, E., Bracci, A., Zappi, A., Morozzi, P., Di Sabatino, S., Porcù, F., Di Nicola, F., and Tositti, L.: How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations, Sensors, 20, 3073, https://doi.org/10.3390/s20113073 , 2020. 

Briggs, D. J.: A framework for integrated environmental health impact assessment of systemic risks, Environ. Health, 7, 61, https://doi.org/10.1186/1476-069x-7-61 , 2008. 

Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A., Diez-Roux, A. V., Holguin, F., Hong, Y., Luepker, R. V., Mittleman, M. A., Peters, A., Siscovick, D., Smith, S. C., Whitsel, L., and Kaufman, J. D.: Particulate Matter Air Pollution and Cardiovascular Disease, Circulation, 121, 2331–2378, https://doi.org/10.1161/CIR.0b013e3181dbece1 , 2010. 

Brousse, O., Martilli, A., Foley, M., Mills, G., and Bechtel, B.: WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid, Urban Climate, 17, 116–134, https://doi.org/10.1016/j.uclim.2016.04.001 , 2016. 

Brunekreef, B. and Holgate, S. T.: Air pollution and health, Lancet, 360, 1233–1242, https://doi.org/10.1016/s0140-6736(02)11274-8 , 2002. 

Buccolieri, R., Santiago, J. L., and Martilli, A.: CFD modelling: The most useful tool for developing mesoscale urban canopy parameterizations, Build. Simul., 14, 407–419, https://doi.org/10.1007/s12273-020-0689-z , 2021. 

Budde, M., Riedel, T., Beigl, M., Schäfer, K., Emeis, S., Cyrys, J., Schnelle-Kreis, J., Philipp, A., Ziegler, V., Grimm, H., and Gratza, T.: SmartAQnet – Remote and In-Situ Sensing of Urban Air Quality, in: Remote Sensing of Clouds and the Atmosphere XXII, Bellingham, WA, USA, 6 October 2017, edited by: Comerón, A., Kassianov, E. I., Schäfer, K., Picard, R. H., Weber, K., SPIE, https://doi.org/10.1117/12.2282698 , 2017. 

Burgués, J. and Marco, S.: Environmental chemical sensing using small drones: A review, Sci. Total Environ., 748, 141172, https://doi.org/10.1016/j.scitotenv.2020.141172 , 2020. 

Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope, C. A., Apte, J. S., Brauer, M., Cohen, A., Weichenthal, S., Coggins, J., Di, Q., Brunekreef, B., Frostad, J., Lim, S. S., Kan, H., Walker, K. D., Thurston, G. D., Hayes, R. B., Lim, C. C., Turner, M. C., Jerrett, M., Krewski, D., Gapstur, S. M., Diver, W. R., Ostro, B., Goldberg, D., Crouse, D. L., Martin, R. V., Peters, P., Pinault, L., Tjepkema, M., van Donkelaar, A., Villeneuve, P. J., Miller, A. B., Yin, P., Zhou, M., Wang, L., Janssen, N. A. H., Marra, M., Atkinson, R. W., Tsang, H., Quoc Thach, T., Cannon, J. B., Allen, R. T., Hart, J. E., Laden, F., Cesaroni, G., Forastiere, F., Weinmayr, G., Jaensch, A., Nagel, G., Concin, H., and Spadaro, J. V.: Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter, P. Natl. Acad. Sci. USA, 115, 9592–9597, https://doi.org/10.1073/pnas.1803222115 , 2018. 

Cai, Y., Zijlema, W. L., Doiron, D., Blangiardo, M., Burton, P. R., Fortier, I., Gaye, A., Gulliver, J., Hoogh, K. d., Hveem, K., Mbatchou, S., Morley, D. W., Stolk, R. P., Elliott, P., Hansell, A. L., and Hodgson, S.: Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach, Eur. Respir. J., 49, 1502127, https://doi.org/10.1183/13993003.02127-2015 , 2017. 

Cai, Y., Hodgson, S., Blangiardo, M., Gulliver, J., Morley, D., Fecht, D., Vienneau, D., de Hoogh, K., Key, T., Hveem, K., Elliott, P., and Hansell, A. L.: Road traffic noise, air pollution and incident cardiovascular disease: A joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts, Environ. Int., 114, 191–201, https://doi.org/10.1016/j.envint.2018.02.048 , 2018. 

Campbell, P., Zhang, Y., Yahya, K., Wang, K., Hogrefe, C., Pouliot, G., Knote, C., Hodzic, A., San Jose, R., Perez, J. L., Jimenez Guerrero, P., Baro, R., and Makar, P.: A multi-model assessment for the 2006 and 2010 simulations under the Air Quality Model Evaluation International Initiative (AQMEII) phase 2 over North America: Part I. Indicators of the sensitivity of O 3 and PM 2.5 formation regimes, Atmos. Environ., 115, 569–586, https://doi.org/10.1016/j.atmosenv.2014.12.026 , 2015. 

Cao, Y., Chen, M., Dong, D., Xie, S., and Liu, M.: Environmental pollutants damage airway epithelial cell cilia: Implications for the prevention of obstructive lung diseases, Thorac. Cancer, 11, 505–510, https://doi.org/10.1111/1759-7714.13323 , 2020. 

Carbajal-Hernández, J. J., Luis P. Sánchez-Fernández, J. A. C.-O., and Martínez-Trinidad, J. F.: Assessment and prediction of air quality using fuzzy logic and autoregressive Models, Atmos. Environ., 60, 37–50, 2012. 

Carmichael, G. R., Sandu, A., Chai, T., Daescu, D., Constantinescu, E., and Tang, Y.: Predicting air quality: Improvements through advanced methods to integrate models and measurements, J. Comput. Phys., 227, 3540–3571, 2008. 

Cecinato, A., Guerriero, E., Balducci, C., and Muto, V.: Use of the PAH fingerprints for identifying pollution sources, Urban Climate, 10, 630–643, https://doi.org/10.1016/j.uclim.2014.04.004 , 2014. 

Cesaroni, G., Forastiere, F., Stafoggia, M., Andersen, Z. J., Badaloni, C., Beelen, R., Caracciolo, B., Faire, U. d., Erbel, R., Eriksen, K. T., Fratiglioni, L., Galassi, C., Hampel, R., Heier, M., Hennig, F., Hilding, A., Hoffmann, B., Houthuijs, D., Jockel, K.-H., Korek, M., Lanki, T., Leander, K., Magnusson, P. K. E., Migliore, E., Ostenson, C.-G., Overvad, K., Pedersen, N. L., J, J. P., Penell, J., Pershagen, G., Pyko, A., Raaschou-Nielsen, O., Ranzi, A., Ricceri, F., Sacerdote, C., Salomaa, V., Swart, W., Turunen, A. W., Vineis, P., Weinmayr, G., Wolf, K., de Hoogh, K., Hoek, G., Brunekreef, B., and Peters, A.: Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project, BMJ, 348, f7412, https://doi.org/10.1136/bmj.f7412 , 2014. 

Chapizanis, D., Karakitsios, S., Gotti, A., and Sarigiannis, D. A.: Assessing personal exposure using Agent Based Modelling informed by sensors technology, Environ. Res., 192, 110141, https://doi.org/10.1016/j.envres.2020.110141 , 2021. 

Chatzimichailidis, A. C., Argyropoulos, C. D., Assael, M. J., and Kakosimos, K. E.: Using the K-means clustering method to identify flow patterns in a street canyon, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 75, https://doi.org/10.18745/pb.22217 , 2020. 

Chen, G., Zhang, W., Li, S., Zhang, Y., Williams, G., Huxley, R., Ren, H., Cao, W., and Guo, Y.: The impact of ambient fine particles on influenza transmission and the modification effects of temperature in China: A multi-city study, Environ. Int., 98, 82–88, https://doi.org/10.1016/j.envint.2016.10.004 , 2017. 

Ching, J. K. S.: A perspective on urban canopy layer modeling for weather, climate, and air quality applications, Urban Climate, 3, 13–39, https://doi.org/10.1016/j.uclim.2013.02.001 , 2013. 

Christodoulou, A., Sauvage, S., Afif, C., Sarda-Estève, R., Stavroulas, I., Pikridas, M., Unga, F., Oikonomou, K., Iakovides, M., and Sciare, J.: Source apportionment of organic carbon at an urban site of the eastern Mediterranean during wintertime, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 25, https://doi.org/10.18745/pb.22217 , 2020. 

Churkina, G., Kuik, F., Bonn, B., Lauer, A., Grote, R., Tomiak, K., and Butler, T. M.: Effect of VOC Emissions from Vegetation on Air Quality in Berlin during a Heatwave, Environ. Sci. Technol., 51, 6120–6130, https://doi.org/10.1021/acs.est.6b06514 , 2017. 

Clappier, A., Belis, C. A., Pernigotti, D., and Thunis, P.: Source apportionment and sensitivity analysis: two methodologies with two different purposes, Geosci. Model Dev., 10, 4245–4256, https://doi.org/10.5194/gmd-10-4245-2017 , 2017. 

Concas, F., Mineraud, J., Lagerspetz, E., Varjonen, S., Liu, X., Puolamäki, K., Nurmi, P., and Tarkoma, S.: Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis, ACM T. Sensor Network., 17, 20, https://doi.org/10.1145/3446005 , 2021. 

Conticini, E., Frediani, B., and Caro, D.: Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?, Environ. Pollut., 261, 114465, https://doi.org/10.1016/j.envpol.2020.114465 , 2020. 

Coulombel, N., Dablanc, L., Gardrat, M., and Koning, M.: The environmental social cost of urban road freight: Evidence from the Paris region, Transport. Res. D-Tr. E., 63, 514–532, 2019. 

Cremona, G., Finardi, S., Mircea, M., Pepe, N., and Silibello, C.: Biogenic Emissions from Urban Vegetation: Impact of Detailed Inventories in Different European Cities, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 162, https://doi.org/10.18745/pb.22217 , 2020. 

Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution temporal profiles in the Emissions Database for Global Atmospheric Research, Scientific Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2 , 2020. 

Crutzen, P. J. and Birks, J. W.: The Atmosphere after a Nuclear War: Twilight at Noon, Ambio, 11, 114–125, https://www.jstor.org/stable/4312777 (last access: 3 June 2021), 1982. 

Cui, L. and Shi, J.: Urbanization and its environmental effects in Shanghai, China, Urban Climate, 2, 1–15, https://doi.org/10.1016/j.uclim.2012.10.008 , 2012. 

Cui, Y., Zhang, Z.-F., Froines, J., Zhao, J., Wang, H., Yu, S.-Z., and Detels, R.: Air pollution and case fatality of SARS in the People's Republic of China: an ecologic study, Environ. Health, 2, 15, https://doi.org/10.1186/1476-069x-2-15 , 2003. 

Dai, L., Zanobetti, A., Koutrakis, P., and Schwartz, J. D.: Associations of fine particulate matter species with mortality in the United States: A multicity time-series analysis, Environ. Health Persp., 122, 837–842, https://doi.org/10.1289/ehp.1307568 , 2014. 

DBEIS: Valuation of energy use and greenhouse gas, Supplementary guidance to the HM Treasury Green Book on Appraisal and Evaluation in Central Government, Data tables 1 to 19: supporting the toolkit and the guidance, Department of Business, Energy and Industrial Strategy, London, 2019. 

Delle Monache, L., Wilczak, J., Mckeen, S., Grell, G., Pagowski, M., Peckham, S., Stull, R., Mchenry, J., and Mcqueen, J.: A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone, Tellus B, 60, 238–249, https://doi.org/10.1111/j.1600-0889.2007.00332.x , 2008. 

Denby, B. R., Sundvor, I., Johansson, C., Pirjola, L., Ketzel, M., Norman, M., Kupiainen, K., Gustafsson, M., Blomqvist, G., Kauhaniemi, M., and Omstedt, G.: A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particle emissions (NORTRIP). Part 2: Surface moisture and salt impact modelling, Atmos. Environ., 81, 485–503, 2013. 

Denier van der Gon, H., Hendriks, C., Kuenen, J., Segers, A., and Visschedijk, A.: Description of current temporal emission patterns and sensitivity of predicted AQ for temporal emission patterns, EU, https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf , (last access 25 February 2022), 2011. 

Desaigues, B., Ami, D., Bartczak, A., Braun Kohlová, M., Chilton, S., Mikołaj Czajkowski, M., Farreras, V., Hunt, A., Hutchinson, M., Jeanrenaud, C., Kaderjack, P., Máca, V., Markiewicz, O., Markowska, A., Metcalf, H., Navrud, S., Seested Nielsen, J., Ortiz, R., Pellegrini, S., Rabl, A., Riera, P., Scasny, M., Stoeckl, M., Szánto, R., and Urban, J.: Economic Valuation of Air Pollution Mortality: A 9-Country Contingent Valuation Survey of a Value of a Life Year (VOLY), Ecol. Indic., 11, 902–910, 2011. 

Dessimond, B., Annesi-Maesano, I., Pepin, J.-L., Srairi, S., and Pau, G.: Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project, Sensors, 21, 1876, https://doi.org/10.3390/s21051876 , 2021. 

Dias, D., Tchepel, O., and Antunes, A. P.: Integrated modelling approach for the evaluation of low emission zones, J. Environ. Manage., 177, 253–263, https://doi.org/10.1016/j.jenvman.2016.04.031 , 2016. 

Diémoz, H., Tombolato, I., Zublena, M., Magri, T., and Ferrero, L.: The impact of biomass burning emissions on PM concentration in the Greater Alpine region, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 26, https://doi.org/10.18745/pb.22217 , 2020. 

DNV-GL: Maritime Forecast To 2050, Energy Transition Outlook, DNV GL – Maritime, 118 pp., 2019. 

Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G., and Speizer, F. E.: An Association between Air Pollution and Mortality in Six U.S. Cities, New Engl. J. Med., 329, 1753–1759, https://doi.org/10.1056/nejm199312093292401 , 1993. 

Dorber, M., Kuipers, K., and Verones, F.: Global characterization factors for terrestrial biodiversity impacts of future land inundation in Life Cycle Assessment, Sci. Total Environ., 712, 134582, https://doi.org/10.1016/j.scitotenv.2019.134582 , 2020. 

Doulgeris, S., Toumasatos, Z., Raptopoulos, A., Kontses, A., Dimaratos, A., Kolokotronis, D., and Samaras, Z.: Experimental assessment of the power management and pollutant emissions of plug-in hybrid vehicles, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Duvall, R. M., Hagler, G. S. W., Clements, A. L., Benedict, K., Barkjohn, K., Kilaru, V., Hanley, T., Watkins, N., Kaufman, A., Kamal, A., Reece, S., Fransioli, P., Gerboles, M., Gillerman, G., Habre, R., Hannigan, M., Ning, Z., Papapostolou, V., Pope, R., Quintana, P. J. E., and Lam Snyder, J.: Deliberating Performance Targets: Follow-on workshop discussing PM 10 , NO 2 , CO, and SO 2 air sensor targets, Atmos. Environ., 246, 118099, https://doi.org/10.1016/j.atmosenv.2020.118099 , 2021. 

EC: Impact Assessment – Annex to the Communication on Thematic Strategy on Air Pollution and the Directive on Ambient Air Quality and Cleaner Air for Europe, Commission of the European Communities, SEC (2005) 1133, https://ec.europa.eu/environment/archives/cafe/pdf/ia_report_en050921_final.pdf (last access: 22 February 2022), 2005. 

EC: Communication from the Commission COM(2019) 640 final: The European Green Deal, https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en (last access: 25 February 2022), 2019. 

EC: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: An EU-wide assessment of National Energy and Climate Plans, Driving forward the green transition and promoting economic recovery through integrated energy and climate planning, https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2020)564&lang=en (last access: 22 February 2022), 2020. 

EC: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: The Second Clean Air Outlook, https://op.europa.eu/en/publication-detail/-/publication/453fbba1-519a-11eb-b59f-01aa75ed71a1/language-en/format-PDF/source-225358842 (last access: 22 February 2022), 2021. 

EEA: Air quality in Europe – 2019 report, European Environment Agency, https://doi.org/10.2800/02825 , 2019a. 

EEA: Contribution of the transport sector to total emissions of the main air pollutants, EEA, https://www.eea.europa.eu/data-and-maps/daviz/contribution-of-the-transport-sector-6#tab-chart_4 (last access: 22 February 2022), 2019b. 

EEA: Air quality in Europe: 2020 report, European Environmental Agency, Publications Office, https://doi.org/10.2800/602793 , 2020a. 

EEA: European Union emission inventory report 1990–2018 under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP), European Environment Agency, Copenhagen, Denmark, 1990–2018, https://doi.org/10.2800/233574 , 2020b. 

EIONET: EIONET Central Data Repository – Data for Germany, European Environment Agency, https://cdr.eionet.europa.eu/de (last access: 22 February 2022), 2019. 

Elessa Etuman, A. and Coll, I.: OLYMPUS v1.0: development of an integrated air pollutant and GHG urban emissions model – methodology and calibration over greater Paris, Geosci. Model Dev., 11, 5085–5111, https://doi.org/10.5194/gmd-11-5085-2018 , 2018. 

Elessa Etuman, A., Coll, I., Makni, I., and Benoussaid, T.: Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris, Atmos. Environ., 239, 117661, https://doi.org/10.1016/j.atmosenv.2020.117661 , 2020. 

Ellermann, T., Nygaard, J., Nøjgaard, J. K., Nordstrøm, C., Brandt, J., Christensen, J., Ketzel, M., Massling, A., Bossi, R., Frohn, L. M., Geels, C., and Jensen, S. S.: The Danish Air Quality Monitoring Programme. Annual Summary for 2018, Aarhus University, DCE – Danish Centre for Environment and Energy, 83 pp., ISBN: 978-87-7156-293-4, 2018. 

EMEP/EEA: Chapter 7: Spatial mapping of emissions, in: EMEP/EEA air pollutant emission inventory guidebook 2019: Technical guidance to prepare national emission inventories, EEA report No 13/2019, https://doi.org/10.2800/293657 , 2019. 

Engemann, K., Svenning, J.-C., Arge, L., Brandt, J., Geels, C., Mortensen, P. B., Plana-Ripoll, O., Tsirogiannis, C., and Pedersen, C. B.: Natural surroundings in childhood are associated with lower schizophrenia rates, Schizophr. Res., 216, 488–495, https://doi.org/10.1016/j.schres.2019.10.012 , 2020. 

English, P., Amato, H., Bejarano, E., Carvlin, G., Lugo, H., Jerrett, M., King, G., Madrigal, D., Meltzer, D., Northcross, A., Olmedo, L., Seto, E., Torres, C., Wilkie, A., and Wong, M.: Performance of a Low-Cost Sensor Community Air Monitoring Network in Imperial County, CA, Sensors, 20, 3031, https://doi.org/10.3390/s20113031 , 2020. 

Esau, I., Wolf, T., and Pettersson, L.: High-resolution assessment of urban air quality with a 3D turbulence resolving model (PALM), in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R.S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, 9–13, https://doi.org/10.18745/pb.22217 , 2020. 

ExternE: ExternE – External Costs of Energy, Universitaet Stuttgart, https://www.ExternE.info (last access: 22 February 2022), 2012. 

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016 , 2016. 

Faber, J., Hanayama, S., Zhang, S., Pereda, P., Comer, B., Hauerhof, E., Schim van der Loeff, W., Smith, T., Zhang, Y., Kosaka, H., Adachi, M., Bonello, J., Galbraith, C., Gong, Z., Hirata, K., Hummels, D., Kleijn, A., Lee, D., Liu, Y., Lucchesi, A., Mao, X., Muraoka, E., Osipova, L., Qian, H., Rutherford, D., Suárez de la Fuente, S., Yuan, H., Velandia Perico, C., Wu, L., Sun, D., Yoo, D., and Xing, H.: The Fourth IMO GHG Study, London, UK, 2020. 

Fairburn, J., Schüle, S. A., Dreger, S., Karla Hilz, L., and Bolte, G.: Social Inequalities in Exposure to Ambient Air Pollution: A Systematic Review in the WHO European Region, Int. J. Env. Res. Pub. He., 16, 3127, https://doi.org/10.3390/ijerph16173127 , 2019. 

Falcon-Rodriguez, C. I., Osornio-Vargas, A. R., Sada-Ovalle, I., and Segura-Medina, P.: Aeroparticles, Composition, and Lung Diseases, Front. Immunol., 7, 3, https://doi.org/10.3389/fimmu.2016.00003 , 2016. 

Fallah-Shorshani, M., Shekarrizfard, M., and Hatzopoulou, M.: Integrating a street-canyon model with a regional Gaussian dispersion model for improved characterisation of near-road air pollution, Atmos. Environ., 153, 21–31, https://doi.org/10.1016/j.atmosenv.2017.01.006 , 2017. 

Fallmann, J., Forkel, R., and Emeis, S.: Secondary effects of urban heat island mitigation measures on air quality, Atmos. Environ., 125, 199–211, 2016. 

Fameli, K.-M. and Assimakopoulos, V. D.: Residential heating in Athens, Greece: emissions and important parameters, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 27, https://doi.org/10.18745/pb.22217 , 2020. 

Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of Aerosol–Cloud Interactions: Mechanisms, Significance, and Challenges, J. Atmos. Sci., 73, 4221–4252, 2016. 

Farmer, D. K., Vance, M. E., Abbatt, J. P. D., Abeleira, A., Alves, M. R., Arata, C., Boedicker, E., Bourne, S., Cardoso-Saldaña, F., Corsi, R., DeCarlo, P. F., Goldstein, A. H., Grassian, V. H., Hildebrandt Ruiz, L., Jimenez, J. L., Kahan, T. F., Katz, E. F., Mattila, J. M., Nazaroff, W. W., Novoselac, A., O'Brien, R. E., Or, V. W., Patel, S., Sankhyan, S., Stevens, P. S., Tian, Y., Wade, M., Wang, C., Zhou, S., and Zhou, Y.: Overview of HOMEChem: House Observations of Microbial and Environmental Chemistry, Environ. Sci.-Proc. Imp., 21, 1280–1300, https://doi.org/10.1039/c9em00228f , 2019. 

Feng, L., Yang, T., Wang, D., Wang, Z., Pan, Y., Matsui, I., Chen, Y., Xin, J., and Huang, H.: Identify the contribution of elevated industrial plume to ground air quality by optical and machine learning methods, Environmental Research Communications, 2, 021005, https://doi.org/10.1088/2515-7620/ab7634 , 2020. 

Filella, M.: Nanomaterials, in: Comprehensive Sampling and Sample Preparation, Elsevier, 109–124, https://doi.org/10.1016/b978-0-12-381373-2.00032-6 , 2012. 

Finardi, S., Radice, P., Cecinato, A., Gariazzo, C., Gherardi, M., and Romagnoli, P.: Seasonal variation of PAHs concentration and source attribution through diagnostic ratios analysis, Urban Climate, 22, 19–34, https://doi.org/10.1016/j.uclim.2015.12.001 , 2017. 

Finardi, S., Agrillo, G., Baraldi, R., Calori, G., Carlucci, P., Ciccioli, P., D'Allura, A., Gasbarra, D., Gioli, B., Magliulo, V., Radice, P., Toscano, P., and Zaldei, A.: Atmospheric Dynamics and Ozone Cycle during Sea Breeze in a Mediterranean Complex Urbanized Coastal Site, J. Appl. Meteorol. Clim., 57, 1083–1099, https://doi.org/10.1175/jamc-d-17-0117.1 , 2018. 

Firket, J.: Fog along the Meuse Valley, T. Faraday Soc., 32, 1192–1197, 1936. 

Fisher, K. and Gershuny, J.: Multinational Time Use Study, User's Guide and Documentation – Version 9, Centre for Time Use Research, https://www.timeuse.org/sites/default/files/9727/mtus-user-guide-r9-february-2016.pdf (last access: 25 February 2022), 2016. 

Fisher, B. E. A., Kukkonen, J., and Schatzmann, M.: Meteorology applied to urban air pollution problems COST 715, Int. J. Environ. Pollut., 16, 560–570, https://doi.org/10.1504/IJEP.2001.000650 , 2001. 

Fisher, B., Joffre, S., Kukkonen, J., Piringer, M., Rotach, M., and Schatzmann, M.: Meteorology applied to urban air pollution problems: Final report COST-715 Action, Demetra Ltd. Publ, Bulgaria, 276 pp., ISBN 954-9526-30-5, 2005. 

Fisher, B., Kukkonen, J., Piringer, M., Rotach, M. W., and Schatzmann, M.: Meteorology applied to urban air pollution problems: concepts from COST 715, Atmos. Chem. Phys., 6, 555–564, https://doi.org/10.5194/acp-6-555-2006 , 2006. 

Flageul, C., Kim, Y., Ferrand, M., Bresson, R., and Carissimo, B.: Neighborhood scale air quality simulations with street network model and CFD, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 172, https://doi.org/10.18745/pb.22217 , 2020. 

Flemming, J., Inness, A., Flentje, H., Huijnen, V., Moinat, P., Schultz, M. G., and Stein, O.: Coupling global chemistry transport models to ECMWF's integrated forecast system, Geosci. Model Dev., 2, 253–265, https://doi.org/10.5194/gmd-2-253-2009 , 2009. 

Foken, T. (Ed.): Springer Handbook of Atmospheric Measurements, Springer Nature, Springer International Publishing, Cham, Germany, https://doi.org/10.1007/978-3-030-52171-4 , 2021. 

Folberth, G. A., Butler, T. M., Collins, W. J., and Rumbold, S. T.: Megacities and climate change – A brief overview, Environ. Pollut., 203, 235–242, https://doi.org/10.1016/j.envpol.2014.09.004 , 2015. 

Förster, J., Schmidt, S., Bartkowski, B., Lienhoop, N., Albert, C., and Wittmer, H.: Incorporating environmental costs of ecosystem service loss in political decision making: A synthesis of monetary values for Germany, PLOS ONE, 14, e0211419, https://doi.org/10.1371/journal.pone.0211419 , 2019. 

Fowler, D., Brimblecombe, P., Burrows, J., Heal, M. R., Grennfelt, P., Stevenson, D. S., Jowett, A., Nemitz, E., Coyle, M., Lui, X., Chang, Y., Fuller, G. W., Sutton, M. A., Klimont, Z., Unsworth, M. H., and Vieno, M.: A chronology of global air quality, Philos. T. Roy. Soc. A, 378, 20190314, https://doi.org/10.1098/rsta.2019.0314 , 2020. 

Franco, V., Kousoulidou, M., Muntean, M., Ntziachristos, L., Hausberger, S., and Dilara, P.: Road vehicle emission factors development: A review, Atmos. Environ., 70, 84–97, 2013. 

Friedrich, R. (Ed.): Natural and Biogenic Emissions of Environmentally Relevant Atmospheric Trace Constituents in Europe, Atmos. Environ., 43, 1377–1486, 2009. 

Friedrich, R.: Integrated Assessment of Policies for Reducing Health Impacts Caused by Air Pollution, in: Environmental Determinants of Human Health, edited by: Pacyna, J. and Pacyna, M., Springer International Publishing, Switzerland, 117–132, https://doi.org/10.1007/978-3-319-43142-0 , 2016. 

Friedrich, R. and Kuhn, A. (Eds.): Integrated Environmental Health Impact Assessment for Europe – Methods and Results of the HEIMTSA/INTARESE Common Case Study, Universitaet Stuttgart, https://doi.org/10.18419/opus-11913 , 2011. 

Friedrich, R. and Li, N.: Life-long Exposure to PM 2.5 and NO 2 and Resulting Health Effects for Population Subgroups in Europe, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 72, https://doi.org/10.18745/pb.22217 , 2020. 

Frohn, L. M., Ketzel, M., Christensen, J. H., Brandt, J., Im, U., Massling, A., Andersen, C., Plejdrup, M.S., Nielsen, O.-K., Manders, A., and Raaschou-Nielsen, O.: Modelling ultrafine particle number concentrations at address resolution in Denmark from 1979 to 2018 – Part 1: regional and urban scale modelling and evaluation, Atmos. Environ., 264, 118631, https://doi.org/10.1016/j.atmosenv.2021.118631 , 2021. 

Fulton, E. A., Boschetti, F., Sporcic, M., Jones, T., Little, L. R., Dambacher, J. M., Gray, R., Scott, R., and Gorton, R.: A multi-model approach to engaging stakeholder and modellers in complex environmental problems, Environ. Sci. Policy, 48, 44–56, https://doi.org/10.1016/j.envsci.2014.12.006 , 2015. 

Galmarini, S. and Hogrefe, C. (Eds.): Special Issue Section: Evaluating Coupled Models (AQMEII P2), Atmos. Environ., 115, 340–755, https://www.sciencedirect.com/journal/atmospheric-environment/vol/115/suppl/C#article-37 (last access: 25 February 2022), 2015. 

Galmarini, S., Bianconi, R., Appel, W., Solazzo, E., Mosca, S., Grossi, P., Moran, M., Schere, K., and Rao, S. T.: ENSEMBLE and AMET: Two systems and approaches to a harmonized, simplified and efficient facility for air quality models development and evaluation, Atmos. Environ., 53, 51–59, https://doi.org/10.1016/j.atmosenv.2011.08.076 , 2012. 

Galmarini, S., Koffi, B., Solazzo, E., Keating, T., Hogrefe, C., Schulz, M., Benedictow, A., Griesfeller, J. J., Janssens-Maenhout, G., Carmichael, G., Fu, J., and Dentener, F.: Technical note: Coordination and harmonization of the multi-scale, multi-model activities HTAP2, AQMEII3, and MICS-Asia3: simulations, emission inventories, boundary conditions, and model output formats, Atmos. Chem. Phys., 17, 1543–1555, https://doi.org/10.5194/acp-17-1543-2017 , 2017. 

Galmarini, S., Kioutsioukis, I., Solazzo, E., Alyuz, U., Balzarini, A., Bellasio, R., Benedictow, A. M. K., Bianconi, R., Bieser, J., Brandt, J., Christensen, J. H., Colette, A., Curci, G., Davila, Y., Dong, X., Flemming, J., Francis, X., Fraser, A., Fu, J., Henze, D. K., Hogrefe, C., Im, U., Garcia Vivanco, M., Jiménez-Guerrero, P., Jonson, J. E., Kitwiroon, N., Manders, A., Mathur, R., Palacios-Peña, L., Pirovano, G., Pozzoli, L., Prank, M., Schultz, M., Sokhi, R. S., Sudo, K., Tuccella, P., Takemura, T., Sekiya, T., and Unal, A.: Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more?, Atmos. Chem. Phys., 18, 8727–8744, https://doi.org/10.5194/acp-18-8727-2018 , 2018. 

Gao, D., Godri Pollitt, K. J., Mulholland, J. A., Russell, A. G., and Weber, R. J.: Characterization and comparison of PM 2.5 oxidative potential assessed by two acellular assays, Atmos. Chem. Phys., 20, 5197–5210, https://doi.org/10.5194/acp-20-5197-2020 , 2020. 

Gao, Z., Bresson, R., Qu, Y., Milliez, M., Demunck, C., and Carissimo, B.: High resolution unsteady RANS simulation of wind, thermal effects and pollution dispersion for studying urban renewal scenarios in a neighborhood of Toulouse, Urban Climate, 23, 114–130, 2018. 

Gariazzo, C., Carlino, G., Silibello, C., Renzi, M., Finardi, S., Pepe, N., Radice, P., Forastiere, F., Michelozzi, P., Viegi, G., and Stafoggia, M.: A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data, Sci. Total Environ., 724, 138102, https://doi.org/10.1016/j.scitotenv.2020.138102 , 2020. 

GBDS: The Global Burden of Disease Study 2019, The Lancet, 396, 1129–1306, https://www.thelancet.com/journals/lancet/issue/vol396no10258/PIIS0140-6736(20)X0042-0#closeFullCover , (last access: 28 February 2022), 2020. 

Geels, C., Andersson, C., Hänninen, O., Lansø, A., Schwarze, P., Skjøth, C., and Brandt, J.: Future Premature Mortality Due to O3, Secondary Inorganic Aerosols and Primary PM in Europe — Sensitivity to Changes in Climate, Anthropogenic Emissions, Population and Building Stock, Int. J. Env. Res. Pub. He., 12, 2837–2869, https://doi.org/10.3390/ijerph120302837 , 2015. 

Geels, C., Winther, M., Andersson, C., Jalkanen, J.-P., Brandt, J., Frohn, L. M., Im, U., Leung, W., and Christensen, J. H.: EPITOME ship emissions: Projections of shipping emissions towards 2050, Version 1, Zenodo [data set], https://doi.org/10.5281/zenodo.4322247 , 2020. 

Geels, C., Winther, M., Andersson, C., Jalkanen, J.-P., Brandt, J., Frohn, L. M., Im, U., Leung, W., and Christensen, J. H.: Projections of shipping emissions and the related impact on air pollution and human health in the Nordic region, Atmos. Chem. Phys., 21, 12495–12519, https://doi.org/10.5194/acp-21-12495-2021 , 2021. 

Gehring, U., Gruzieva, O., Agius, R. M., Beelen, R., Custovic, A., Cyrys, J., Eeftens, M., Flexeder, C., Fuertes, E., Heinrich, J., Hoffmann, B., Jongste, J. C. d., Kerkhof, M., Klümper, C., Korek, M., Mölter, A., Schultz, E. S., Simpson, A., Sugiri, D., Svartengren, M., Berg, A. v., Wijga, A. H., Pershagen, G., and Brunekreef, B.: Air Pollution Exposure and Lung Function in Children: The ESCAPE Project, Environ. Health Persp., 121, 1357–1364, https://doi.org/10.1289/ehp.1306770 , 2013. 

Genc, S., Zadeoglulari, Z., Fuss, S. H., and Genc, K.: The Adverse Effects of Air Pollution on the Nervous System, J. Toxicol., 2012, 782462 , https://doi.org/10.1155/2012/782462 , 2012. 

Genz, C., Schrödner, R., Heinold, B., Henning, S., Baars, H., Spindler, G., and Tegen, I.: Estimation of cloud condensation nuclei number concentrations and comparison to in situ and lidar observations during the HOPE experiments, Atmos. Chem. Phys., 20, 8787–8806, https://doi.org/10.5194/acp-20-8787-2020 , 2020. 

Ghorani-Azam, A., Riahi-Zanjani, B., and Balali-Mood M.: Effects of air pollution on human health and practical measures for prevention in Iran, J. Res. Med. Sci., 21, 65, https://doi.org/10.4103/1735-1995.189646 , 2016. 

Gilliam, R. C., Hogrefe, C., Godowitch, J. M., Napelenok, S., Mathur, R., and Rao, S. T.: Impact of inherent meteorology uncertainty on air quality model predictions, J. Geophys. Res.-Atmos., 120, 12259–12280, https://doi.org/10.1002/2015jd023674 , 2015. 

Gioli, B., Gualtieri, G., Busillo, C., Calastrini, F., Zaldei, A., and Toscano, P.: Improving high resolution emission inventories with local proxies and urban eddy covariance flux measurements, Atmos. Environ., 115, 246–256, https://doi.org/10.1016/j.atmosenv.2015.05.068 , 2015. 

Gkatzelis, G. I., Gilman, J. B., Brown, S. S., Eskes, H., Gomes, A. R., Lange, A. C., McDonald, B. C., Peischl, J., Petzold, A., Thompson, C. R., and Kiendler-Scharr, A.: The global impacts of COVID-19 lockdowns on urban air pollution: A critical review and recommendations, Elementa: Science of the Anthropocene, 9, 00176, https://doi.org/10.1525/elementa.2021.00176 , 2021. 

Gohil, K. and Jin, M. S.: Validation and Improvement of the WRF Building Environment Parametrization (BEP) Urban Scheme, Climate, 7, 109, https://doi.org/10.3390/cli7090109 , 2019. 

González-Aparicio, I., Baklanov, A., Hidalgo, J., Korsholm, U., and Nuterman, R.: Impact of city expansion and increased heat fluxes scenarios on the urban boundary layer of Bilbao using Enviro-HIRLAM, Urban Climate, 10, 831–845, 2014. 

Gou, H., Lu, J., Li, S., Tong, Y., Xie, C., and Zheng, X.: Assessment of microbial communities in PM 1 and PM 10 of Urumqi during winter, Environ. Pollut., 214, 202–210, https://doi.org/10.1016/j.envpol.2016.03.073 , 2016. 

Goulier, L., Paas, B., Ehrnsperger, L., and Klemm, O.: Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables, Int. J. Env. Res. Pub. He., 17, 2025, https://doi.org/10.3390/ijerph17062025 , 2020. 

Grange, S. K., Lötscher, H., Fischer, A., Emmenegger, L., and Hueglin, C.: Exploring equivalent black carbon (EBC) concentrations in Switzerland with the aethalometer model, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 17, https://doi.org/10.18745/pb.22217 , 2020. 

Grell, G. A. and Baklanov, A.: Integrated modelling for forecasting weather and air quality: A call for fully coupled approaches, Atmos. Environ., 45, 6845–6851, https://doi.org/10.1016/j.atmosenv.2011.01.017 , 2011. 

Gressent, A., Malherbe, L., Colette, A., Rollin, H., and Scimia, R.: Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value, Environ. Int., 143, 105965, https://doi.org/10.1016/j.envint.2020.105965 , 2020. 

Grimmond, S., Bouchet, V., Molina, L. T., Baklanov, A., Tan, J., Schlünzen, K. H., Mills, G., Golding, B., Masson, V., Ren, C., Voogt, J., Miao, S., Lean, H., Heusinkveld, B., Hovespyan, A., Teruggi, G., Parrish, P., and Joe, P.: Integrated urban hydrometeorological, climate and environmental services: Concept, methodology and key messages, Urban Climate, 33, 100623, https://doi.org/10.1016/j.uclim.2020.100623 , 2020. 

Grythe, H., Lopez-Aparicio, S., Vogt, M., Vo Thanh, D., Hak, C., Halse, A. K., Hamer, P., and Sousa Santos, G.: The MetVed model: development and evaluation of emissions from residential wood combustion at high spatio-temporal resolution in Norway, Atmos. Chem. Phys., 19, 10217–10237, https://doi.org/10.5194/acp-19-10217-2019 , 2019. 

Gu, J., Wensing, M., Uhde, E., and Salthammer, T.: Characterization of particulate and gaseous pollutants emitted during operation of a desktop 3D printer, Environ. Int., 123, 476–485, https://doi.org/10.1016/j.envint.2018.12.014 , 2019. 

Gu, Q., Michanowicz, D. R., and Jia, C.: Developing a Modular Unmanned Aerial Vehicle (UAV) Platform for Air Pollution Profiling, Sensors, 18, 4363, https://doi.org/10.3390/s18124363 , 2018. 

Guevara, M., Martínez, F., Arévalo, G., Gassó, S., and Baldasano, J. M.: Improved system for modeling Spanish emissions: HERMESv2.0, Atmos. Environ., 81, 209–221 https://doi.org/10.1016/j.atmosenv.2013.08.053 , 2013. 

Guevara, M., Tena, C., Porquet, M., Jorba, O., and Pérez García-Pando, C.: HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module, Geosci. Model Dev., 12, 1885–1907, https://doi.org/10.5194/gmd-12-1885-2019 , 2019. 

Guevara, M., Tena, C., Porquet, M., Jorba, O., and Pérez García-Pando, C.: HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module, Geosci. Model Dev., 13, 873–903, https://doi.org/10.5194/gmd-13-873-2020 , 2020. 

Guevara, M., Jorba, O., Tena, C., Denier van der Gon, H., Kuenen, J., Elguindi, N., Darras, S., Granier, C., and Pérez García-Pando, C.: Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling, Earth Syst. Sci. Data, 13, 367–404, https://doi.org/10.5194/essd-13-367-2021 , 2021. 

Gurney, K. R., Liang, J., Patarasuk, R., O'Keeffe, D., Huang, J., Hutchins, M., Lauvaux, T., Turnbull, J. C., and Shepson, P. B.: Reconciling the differences between a bottom-up and inverse-estimated FFCO 2 emissions estimate in a large US urban area, Elementa: Science of the Anthropocene, 5, 44, https://doi.org/10.1525/elementa.137 , 2017. 

Gwaze, P.: Physical and chemical properties of aerosol particles in the troposphere: An approach from microscopy methods, Sierke, Göttingen, 187 pp., http://hdl.handle.net/11858/00-001M-0000-0014-89C8-0 (last access: 11 August 2020), 2007. 

Halenka, T., Belda, M., Huszar, P., Karlicky, J., Novakova, T., and Zak, M.: On the comparison of urban canopy effects parameterisation, Int. J. Environ. Pollut., 65, 177–194, https://doi.org/10.1504/IJEP.2019.101840 , 2019. 

Hamer, P. D., Walker, S.-E., Sousa-Santos, G., Vogt, M., Vo-Thanh, D., Lopez-Aparicio, S., Schneider, P., Ramacher, M. O. P., and Karl, M.: The urban dispersion model EPISODE v10.0 – Part 1: An Eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions, Geosci. Model Dev., 13, 4323–4353, https://doi.org/10.5194/gmd-13-4323-2020 , 2020. 

Hampel, R., Peters, A., Beelen, R., Brunekreef, B., Cyrys, J., Faire, U. d., Hoogh, K. d., Fuks, K., Hoffmann, B., Hüls, A., Imboden, M., Jedynska, A., Kooter, I., Koenig, W., Künzli, N., Leander, K., Magnusson, P., Männistö, S., Penell, J., Pershagen, G., Phuleria, H., Probst-Hensch, N., Pundt, N., Schaffner, E., Schikowski, T., Sugiri, D., Tiittanen, P., Tsai, M.-Y., Wang, M., Wolf, K., and Lanki, T.: Long-term effects of elemental composition of particulate matter on inflammatory blood markers in European cohorts, Environ. Int., 82, 76–84, https://doi.org/10.1016/j.envint.2015.05.008 , 2015. 

Hänninen, O., Lebret, E., Ilacqua, V., Katsouyanni, K., Künzli, N., Sram, R., and Jantunen, M.: Infiltration of ambient PM 2.5 and levels of indoor generated non-ETS PM 2.5 in residences of four European cities, Atmos. Environ., 38, 6411–6423, https://doi.org/10.1016/j.atmosenv.2004.07.015 , 2004. 

Hänninen, O., Palonen, J., Tuomisto, J., Yli-Tuomi, T., Seppänen, O., and Jantunen, M. J.: Reduction potential of urban PM 2.5 mortality risk using modern ventilation systems in buildings, Indoor Air, 15, 246–256, https://doi.org/10.1111/j.1600-0668.2005.00365.x , 2005. 

Hänninen, O., Knol, A., Jantunen, M., Lim, T., Conrad, A., Rappolder, M., Carrer P., Fanetti, A., Kim, R., Buekers, J., Torfs, R., Iavarone, I., Classen, T., Hornberg, C., and Mekel, O.: Environmental burden of disease in Europe: assessing nine risk factors in six countries, Environ. Health Perspect., 122, 439–446, https://doi.org/10.1289/ehp.1206154 , 2014. 

Hassan, A. M., ELMokadem, A. A., Megahed, N. A., and Abo Eleinen, O. M.: Urban morphology as a passive strategy in promoting outdoor air quality, Journal of Building Engineering, 29, 101204, https://doi.org/10.1016/j.jobe.2020.101204 , 2020. 

Hausberger, S., Rodler, J., Sturm, P., and Rexeis, M.: Emission factors for heavy-duty vehicles and validation by tunnel measurements, Atmos. Environ., 37, 5237–5245, 2003. 

He, L., Norris, C., Cui, X., Li, Z., Barkjohn, K. K., Brehmer, C., Teng, Y., Fang, L., Lin, L., Wang, Q., Zhou, X., Hong, J., Li, F., Zhang, Y., Schauer, J. J., Black, M., Bergin, M. H., and Zhang, J. J.: Personal Exposure to PM 2.5 Oxidative Potential in Association with Pulmonary Pathophysiologic Outcomes in Children with Asthma, Environ Sci Technol., 55, 3101–3111, https://doi.org/10.1021/acs.est.0c06114 , 2021. 

Health Effects Institute (HEI): Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality: A Special Report of the Institute’s Particle Epidemiology Reanalysis Project, Health Effects Institute, Cambridge, MA, https://www.healtheffects.org/system/files/HEI-Reanalysis-2000.pdf (last access: 30 September 2020), 2000. 

Health Effects Institute (HEI): State of Global Air 2020. Special Report on Global Exposure to Air Pollution and its Health Effects, Health Effects Institute, Boston, MA, 2578–6873, 2020. 

Heinold, B., Assmann, D., Käthner, R., Knoth, O., Macke, A., Müller, T., Tõnisson, L., Voigtländer, J., Weger, M., and Wolke, R.: Assessing the spatio-temporal distribution of urban air pollutants – an integrated system based on crowdsourcing with mobile sensors and multi-scale modelling, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 116, https://doi.org/10.18745/pb.22217 , 2020. 

Hellsten, A., Aarnio, M. A., and Hannuniemi, H.: Fast pre-computed large-eddy simulation based dispersion modelling method for hazardous material releases in urban environments – Part 1: the concept, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 73, https://doi.org/10.18745/pb.22217 , 2020. 

Hellsten, A., Ketelsen, K., Sühring, M., Auvinen, M., Maronga, B., Knigge, C., Barmpas, F., Tsegas, G., Moussiopoulos, N., and Raasch, S.: A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0, Geosci. Model Dev., 14, 3185–3214, https://doi.org/10.5194/gmd-14-3185-2021 , 2021. 

Hendriks, C.: Ammonia emission time profiles based on manure transport data improve ammonia modelling across north western Europe, Elsevier Ltd., 2016. 

Henne, S., Brunner, D., Oney, B., Leuenberger, M., Eugster, W., Bamberger, I., Meinhardt, F., Steinbacher, M., and Emmenegger, L.: Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling, Atmos. Chem. Phys., 16, 3683–3710, https://doi.org/10.5194/acp-16-3683-2016 , 2016. 

Héroux, M. E., Anderson, H. R., Atkinson, R., Brunekreef, B., Cohen, A., Forastiere, F., Hurley, F., Katsouyanni, K., Krewski, D., Krzyzanowski, M., Kunzli, N., Mills, I., Querol, X., Ostro, B., and Walton, H.: Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project, Int. J. Public Health, 60, 619–627, https://doi.org/10.1007/s00038-015-0690-y , 2015. 

Hidalgo, J., Masson, V., Baklanov, A., Pigeon, G., and Gimeno, L.: Advances in Urban Climate Modeling, Ann. N.Y. Acad. Sci., 1146, 354–374, https://doi.org/10.1196/annals.1446.015 , 2008. 

Hime, N., Marks, G., and Cowie, C.: A Comparison of the Health Effects of Ambient Particulate Matter Air Pollution from Five Emission Sources, Int. J. Env. Res. Pub. He., 15, 1206, https://doi.org/10.3390/ijerph15061206 , 2018. 

Hirtl, M., Arnold, D., Briese, C., Figuera, R. M., Flandorfer, C., Haselsteiner, M., Humer, H., Maurer, C., Natali, S., Ng, T., Placho, T., Santillan, D., Scherllin-Pirscher, B., Skarbal, B., Triebnig, G., and Uhrner, U.: Innovative applications for the augmented use of satellite observations to support air quality management, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 9, https://doi.org/10.18745/pb , 2020. 

Hoek, G., Krishnan, R. M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., and Kaufman, J. D.: Long-term air pollution exposure and cardio-respiratory mortality: a review, Environ. Health, 12, 43, https://doi.org/10.1186/1476-069x-12-43 , 2013. 

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018 , 2018. 

Hoffmann, B., Weinmayr, G., Hennig, F., Fuks, K., Moebus, S., Weimar, C., Dragano, N., Hermann, D. M., Kälsch, H., Mahabadi, A. A., Erbel, R., and Jöckel, K.-H.: Air Quality, Stroke, and Coronary Events, Dtsch. Arztebl. Online, 112, 195–201, https://doi.org/10.3238/arztebl.2015.0195 , 2015. 

Hood, C., MacKenzie, I., Stocker, J., Johnson, K., Carruthers, D., Vieno, M., and Doherty, R.: Air quality simulations for London using a coupled regional-to-local modelling system, Atmos. Chem. Phys., 18, 11221–11245, https://doi.org/10.5194/acp-18-11221-2018 , 2018. 

Hopke, P. K.: Review of receptor modeling methods for source apportionment, J. Air Waste Manage., 66, 237–259, https://doi.org/10.1080/10962247.2016.1140693 , 2016. 

Horne, B. D., Joy, E. A., Hofmann, M. G., Gesteland, P. H., Cannon, J. B., Lefler, J. S., Blagev, D. P., Korgenski, E. K., Torosyan, N., Hansen, G. I., Kartchner, D., and Pope, C. A.: Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection, Am. J. Resp. Crit. Care, 198, 759–766, https://doi.org/10.1164/rccm.201709-1883OC , 2018. 

Host, S., Honoré, C., Joly, F., Saunal, A., Le Tertre, A., and Medina, S.: Implementation of various hypothetical low emission zone scenarios in Greater Paris: Assessment of fine-scale reduction in exposure and expected health benefits, Environ. Res., 185, 109405, https://doi.org/10.1016/j.envres.2020.109405 , 2020. 

Hu, J., Li, X., Huang, L., Ying, Q., Zhang, Q., Zhao, B., Wang, S., and Zhang, H.: Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China, Atmos. Chem. Phys., 17, 13103–13118, https://doi.org/10.5194/acp-17-13103-2017 , 2017. 

Hu, T., Singer, B. C., and Logue, J. M.: Compilation of Published PM 2.5 Emission Rates for Cooking, Candles and Incense for Use in Modeling of Exposures in Residences, Tech. report, United States. Dept. of Energy. Office of Science, Washington, D.C., 29 pp., https://doi.org/10.2172/1172959 , 2012. 

Huang, G.: Integrated assessment of atmospheric environmental management in China, dissertation, University of Stuttgart, https://doi.org/10.18419/opus-9852 , 2018. 

Huang, G., Schmid, D., Friedrich, R., Vogt, U., Mahami, G., Struschka, M., and Juschka, W.: Ganzheitliche Bewertung von Holzheizungen, https://doi.org/10.18419/opus-11108 , 2016. 

Huang, G., Brook, R., Crippa, M., Janssens-Maenhout, G., Schieberle, C., Dore, C., Guizzardi, D., Muntean, M., Schaaf, E., and Friedrich, R.: Speciation of anthropogenic emissions of non-methane volatile organic compounds: a global gridded data set for 1970–2012, Atmos. Chem. Phys., 17, 7683–7701, https://doi.org/10.5194/acp-17-7683-2017 , 2017. 

Huang, M., Gao, Z., Miao, S., and Chen, F.: Sensitivity of urban boundary layer simulation to urban canopy models and PBL schemes in Beijing, Meteorol. Atmos. Phys., 131, 1235–1248, https://doi.org/10.1007/s00703-018-0634-1 , 2019. 

Huszár, P., Belda, M., Karlický, J., Pišoft, P., and Halenka, T.: The regional impact of urban emissions on climate over central Europe: present and future emission perspectives, Atmos. Chem. Phys., 16, 12993–13013, https://doi.org/10.5194/acp-16-12993-2016 , 2016. 

Huszar, P., Belda, M., Karlický, J., Bardachova, T., Halenka, T., and Pisoft, P.: Impact of urban canopy meteorological forcing on aerosol concentrations, Atmos. Chem. Phys., 18, 14059–14078, https://doi.org/10.5194/acp-18-14059-2018 , 2018. 

Huszar, P., Karlický, J., Ďoubalová, J., Nováková, T., Šindelářová, K., Švábik, F., Belda, M., Halenka, T., and Žák, M.: The impact of urban land-surface on extreme air pollution over central Europe, Atmos. Chem. Phys., 20, 11655–11681, https://doi.org/10.5194/acp-20-11655-2020 , 2020. 

Hvidtfeldt, U. A., Geels, C., Sørensen, M., Ketzel, M., Khan, J., Tjønneland, A., Christensen, J. H., Brandt, J., and Raaschou-Nielsen, O.: Long-term residential exposure to PM 2.5 constituents and mortality in a Danish cohort, Environ. Int., 133, 105268, https://doi.org/10.1016/j.envint.2019.105268 , 2019a. 

Hvidtfeldt, U. A., Sørensen, M., Geels, C., Ketzel, M., Khan, J., Tjønneland, A., Overvad, K., Brandt, J., and Raaschou-Nielsen, O.: Long-term residential exposure to PM 2.5 , PM 10 , black carbon, NO 2 , and ozone and mortality in a Danish cohort, Environ. Int., 123, 265–272, https://doi.org/10.1016/j.envint.2018.12.010 , 2019b. 

Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., Denier van der Gon, H., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Jorba, O., Knote, C., Makar, P. A, Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., Pouliot, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R S., Syrakov, D., Torian, A., Tuccella, P., Wang, K., Werhahn, J., Wolke, R., Zabkar, R., Zhang, Y., Zhang, J., Hogrefe, C., and Galmarini, S.: Evaluation of operational online coupled regional air quality models over Europe and North America in the context of AQMEII phase 2, Part II: particulate matter, Atmos. Environ., 115, 421–441, 2015a. 

Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Jorba, O., Knote, C., Kuenen, J. J. P., Makar, P. A., Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., Pouliot, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Torian, A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y., Zhang, J., Hogrefe, C., and Galmarini, S.: Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2, Part I: Ozone, Atmos. Environ., 115, 404–420, 2015b. 

Im, U., Brandt, J., Geels, C., Hansen, K. M., Christensen, J. H., Andersen, M. S., Solazzo, E., Kioutsioukis, I., Alyuz, U., Balzarini, A., Baro, R., Bellasio, R., Bianconi, R., Bieser, J., Colette, A., Curci, G., Farrow, A., Flemming, J., Fraser, A., Jimenez-Guerrero, P., Kitwiroon, N., Liang, C.-K., Nopmongcol, U., Pirovano, G., Pozzoli, L., Prank, M., Rose, R., Sokhi, R., Tuccella, P., Unal, A., Vivanco, M. G., West, J., Yarwood, G., Hogrefe, C., and Galmarini, S.: Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3, Atmos. Chem. Phys., 18, 5967–5989, https://doi.org/10.5194/acp-18-5967-2018 , 2018. 

Im, U., Christensen, J. H., Nielsen, O.-K., Sand, M., Makkonen, R., Geels, C., Anderson, C., Kukkonen, J., Lopez-Aparicio, S., and Brandt, J.: Contributions of Nordic anthropogenic emissions on air pollution and premature mortality over the Nordic region and the Arctic, Atmos. Chem. Phys., 19, 12975–12992, https://doi.org/10.5194/acp-19-12975-2019 , 2019. 

IMO: Prevention of Air Pollution from Ships, International Maritime Organization, https://www.imo.org/en/OurWork/Environment/Pages/Air-Pollution.aspx (last access: 22 February 2022), 2019. 

IMO: IMO's work to cut GHG emissions from ships, International Maritime Organisation, https://www.imo.org/en/MediaCentre/HotTopics/Pages/Cutting-GHG-emissions.aspx (last access: 25 February 2022), 2021. 

Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019 , 2019. 

IOM (Institute of Medicine): Long-Term Health Consequences of Exposure to Burn Pits in Iraq and Afghanistan, The National Academies Press, Washington, DC, https://doi.org/10.17226/13209 , 2011. 

IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland, 151 pp., 2014. 

IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited be: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, https://www.ipcc.ch/report/ar6/wg1/#FullReport (last access: 28 February 2022. 

Jalkanen, J.-P., Brink, A., Kalli, J., Pettersson, H., Kukkonen, J., and Stipa, T.: A modelling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area, Atmos. Chem. Phys., 9, 9209–9223, https://doi.org/10.5194/acp-9-9209-2009 , 2009. 

Jalkanen, J.-P., Johansson, L., Kukkonen, J., Brink, A., Kalli, J., and Stipa, T.: Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide, Atmos. Chem. Phys., 12, 2641–2659, https://doi.org/10.5194/acp-12-2641-2012 , 2012. 

Jalkanen, J.-P., Johansson, L., and Kukkonen, J.: A comprehensive inventory of ship traffic exhaust emissions in the European sea areas in 2011, Atmos. Chem. Phys., 16, 71–84, 2016. 

Jeanjean, A. P. R., Buccolieri, R., Eddy, J., Monks, P. S., and Leigh, R. J.: Air quality affected by trees in real street canyons: The case of Marylebone neighbourhood in central London, Urban For. Urban Gree., 22, 41–53, https://doi.org/10.1016/j.ufug.2017.01.009 , 2017. 

Jensen, S. S., Ketzel, M., Becker, T., Christensen, J., Brandt, J., Plejdrup, M., Winther, M., Nielsen, O. K., Hertel, O., and Ellermann, T.: High resolution multi-scale air quality modelling for all streets in Denmark, Transport. Res. D-Tr. E., 52, 322–339, 2017. 

Jesus, A. L. d., Rahman, M. M., Mazaheri, M., Thompson, H., Knibbs, L. D., Jeong, C., Evans, G., Nei, W., Ding, A., Qiao, L., Li, L., Portin, H., Niemi, J. V., Timonen, H., Luoma, K., Petäjä, T., Kulmala, M., Kowalski, M., Peters, A., Cyrys, J., Ferrero, L., Manigrasso, M., Avino, P., Buonano, G., Reche, C., Querol, X., Beddows, D., Harrison, R. M., Sowlat, M. H., Sioutas, C., and Morawska, L.: Ultrafine particles and PM 2.5 in the air of cities around the world: Are they representative of each other?, Environ. Int., 129, 118–135, https://doi.org/10.1016/j.envint.2019.05.021 , 2019. 

Johansson, L., Jalkanen, J.-P., Kalli, J., and Kukkonen, J.: The evolution of shipping emissions and the costs of regulation changes in the northern EU area, Atmos. Chem. Phys., 13, 11375-11389, 2013. 

Johansson, L., Epitropou, V., Karatzas, K., Karppinen, A., Wanner, L., Vrochidis, S., Bassoukos, A., Kukkonen, J., and Kompatsiaris, I.: Fusion of meteorological and air quality data extracted from the web for personalized environmental information services, Environmental Modelling & Software 64, 143-155, 2015. 

Johansson, L., Jalkanen, J.-P., and Kukkonen, J.: Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution., Atmos. Environ., 167, 403–415, 2017. 

Jones, L., Vieno, M., Fitch, A., Carnell, E., Steadman, C., Cryle, P., Holland, M., Nemitz, E., Morton, D., Hall, J., Mills, G., Dickie, I., and Reis, S.: Urban natural capital accounts: developing a novel approach to quantify air pollution removal by vegetation, Journal of Environmental Economics and Policy, 8, 413–428, https://doi.org/10.1080/21606544.2019.1597772 , 2019. 

Jonson, J. E., Jalkanen, J. P., Johansson, L., Gauss, M., and Denier van der Gon, H. A. C.: Model calculations of the effects of present and future emissions of air pollutants from shipping in the Baltic Sea and the North Sea, Atmos. Chem. Phys., 15, 783–798, https://doi.org/10.5194/acp-15-783-2015 , 2015. 

Just, A. C., Arfer, K. B., Rush, J., Dorman, M., Shtein, A., Lyapustin, A., and Kloog, I.: Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM 2.5 ) using satellite data over large regions, Atmos. Environ., 239, 117649, https://doi.org/10.1016/j.atmosenv.2020.117649 , 2020. 

Kalisa, E., Archer, S., Nagato, E., Bizuru, E., Lee, K., Tang, N., Pointing, S., Hayakawa, K., and Lacap-Bugler, D.: Chemical and Biological Components of Urban Aerosols in Africa: Current Status and Knowledge Gaps, Int. J. Env. Res. Pub. He., 16, 941, https://doi.org/10.3390/ijerph16060941 , 2019. 

Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., and Borowiak, A.: Review of the Performance of Low-Cost Sensors for Air Quality Monitoring, Atmosphere, 10, 506, https://doi.org/10.3390/atmos10090506 , 2019. 

Karakitsios, S., Busker, R., Tjärnhage, T., Armand, P., Dybwad, M., Nielsen, M. F., Burman, J., Burke, J., Brinek, J., Bartzis, J., Maggos, T., Theocharidou, M., Gattinesi, P., Giannopoulos, G., and Sarigiannis, D.: Challenges on detection, identification and monitoring of indoor airborne chemical-biological agents, Safety Sci., 129, 104789, https://doi.org/10.1016/j.ssci.2020.104789 , 2020. 

Karl, M., Jonson, J. E., Uppstu, A., Aulinger, A., Prank, M., Sofiev, M., Jalkanen, J.-P., Johansson, L., Quante, M., and Matthias, V.: Effects of ship emissions on air quality in the Baltic Sea region simulated with three different chemistry transport models, Atmos. Chem. Phys., 19, 7019–7053, https://doi.org/10.5194/acp-19-7019-2019 , 2019a. 

Karl, M., Walker, S.-E., Solberg, S., and Ramacher, M. O. P.: The Eulerian urban dispersion model EPISODE – Part 2: Extensions to the source dispersion and photochemistry for EPISODE–CityChem v1.2 and its application to the city of Hamburg, Geosci. Model Dev., 12, 3357–3399, https://doi.org/10.5194/gmd-12-3357-2019 , 2019b. 

Karl, M., Bieser, J., Geyer, B., Matthias, V., Jalkanen, J.-P., Johansson, L., and Fridell, E.: Impact of a nitrogen emission control area (NECA) on the future air quality and nitrogen deposition to seawater in the Baltic Sea region, Atmos. Chem. Phys., 19, 1721–1752, https://doi.org/10.5194/acp-19-1721-2019 , 2019c 

Karl, M., Pirjola, L., Karppinen, A., Jalkanen, J.-P., Ramacher, M. O. P., and Kukkonen, J.: Modeling of the Concentrations of Ultrafine Particles in the Plumes of Ships in the Vicinity of Major Harbors, Int. J. Env. Res. Pub. He., 17, 777, https://doi.org/10.3390/ijerph17030777 , 2020. 

Karttunen, S., Kurppa, M., Auvinen, M., Hellsten, A., and Järvi, L.: Large-eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations – A case study from a city-boulevard, Atmos. Environ. X, 6, 100073, https://doi.org/10.1016/j.aeaoa.2020.100073 , 2020. 

Katsouyanni, K., Samet, J. M., Anderson, H. R., Atkinson, R., Le Tertre, A., Medina, S., Samoli, E., Touloumi, G., Burnett, R. T., Krewski, D., Ramsay, T., Dominici, F., Peng, R. D., Schwartz, J., and Zanobetti, A.: Air pollution and health: a European and North American approach (APHENA), Research report (Health Effects Institute), 5–90, 2009. 

Keiser, D. and Muller, A.: Air and Water: Integrated Assessment Models for Multiple Media, Annu. Rev. Resour. Econ., 9, 165–184, https://doi.org/10.1146/annurev-resource-100516-053501 , 2017. 

Kermenidou, M., Hondrogiorgis, C., Karakitsios, S., and Sarigiannis, D.: Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in aerosols and study of their effect in human health: a comparison between the warm and the cold season of the year, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 19, https://doi.org/10.18745/pb.22217 , 2020. 

Ketzel, M., Frohn, L. M., Christensen, J. H., Brandt, J., Massling, A., Andersen, C., Im, U., Jensen, S. S., Khan, J., Nielsen, O.-K., Plejdrup, M. S., Manders, A., van der Gon, H. D., Kumar, P., and Raaschou-Nielsen, O.: Modelling ultrafine particle number concentrations at address resolution in Denmark from 1979 to 2018 – Part 2: Local and street scale modelling and evaluation, Atmos. Environ., 264, 118633, https://doi.org/10.1016/j.atmosenv.2021.118633 , 2021. 

Khan, A., Plana-Ripoll, O., Antonsen, S., Brandt, J., Geels, C., Landecker, H., Sullivan, P. F., Pedersen, C. B., and Rzhetsky, A.: Environmental pollution is associated with increased risk of psychiatric disorders in the US and Denmark, PLOS Biol., 17, e3000353, https://doi.org/10.1371/journal.pbio.3000353 , 2019a. 

Khan, J., Kakosimos, K., Raaschou-Nielsen, O., Brandt, J., Jensen, S. S., Ellermann, T., and Ketzel, M.: Development and performance evaluation of new AirGIS – A GIS based air pollution and human exposure modelling system, Atmos. Environ., 198, 102–121, https://doi.org/10.1016/j.atmosenv.2018.10.036 , 2019b. 

Khan, B., Banzhaf, S., Chan, E. C., Forkel, R., Kanani-Sühring, F., Ketelsen, K., Kurppa, M., Maronga, B., Mauder, M., Raasch, S., Russo, E., Schaap, M., and Sühring, M.: Development of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applications, Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021 , 2021. 

Kim, J., Jeong, U., Ahn, M.-H., Kim, J. H., Park, R. J., Lee, H., Song, C. H., Choi, Y.-S., Lee, K.-H., Yoo, J.-M., Jeong, M.-J., Park, S. K., Lee, K.-M., Song, C.-K., Kim, S.-W., Kim, Y. J., Kim, S.-W., Kim, M., Go, S., Liu, X., Chance, K., Chan Miller, C., Al-Saadi, J., Veihelmann, B., Bhartia, P. K., Torres, O., Abad, G. G., Haffner, D. P., Ko, D. H., Lee, S. H., Woo, J.-H., Chong, H., Park, S. S., Nicks, D., Choi, W. J., Moon, K.-J., Cho, A., Yoon, J., Kim, S.-k., Hong, H., Lee, K., Lee, H., Lee, S., Choi, M., Veefkind, P., Levelt, P. F., Edwards, D. P., Kang, M., Eo, M., Bak, J., Baek, K., Kwon, H.-A., Yang, J., Park, J., Han, K. M., Kim, B.-R., Shin, H.-W., Choi, H., Lee, E., Chong, J., Cha, Y., Koo, J.-H., Irie, H., Hayashida, S., Kasai, Y., Kanaya, Y., Liu, C., Lin, J., Crawford, J. H., Carmichael, G. R., Newchurch, M. J., Lefer, B. L., Herman, J. R., Swap, R. J., Lau, A. K. H., Kurosu, T. P., Jaross, G., Ahlers, B., Dobber, M., McElroy, C. T., and Choi, Y.: New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS), B. Am. Meteorol. Soc., 101, E1–E22, https://doi.org/10.1175/bams-d-18-0013.1 , 2020. 

Kim, Y., Wu, Y., Seigneur, C., and Roustan, Y.: Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1), Geosci. Model Dev., 11, 611–629, 2018. 

Kioutsioukis, I., Im, U., Solazzo, E., Bianconi, R., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., van der Gon, H. D., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Jorba, O., Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Tuccella, P., Werhahn, J., Wolke, R., Hogrefe, C., and Galmarini, S.: Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data, Atmos. Chem. Phys., 16, 15629–15652, https://doi.org/10.5194/acp-16-15629-2016 , 2016. 

Klein, T., Kukkonen, J., Dahl, A., Bossioli, E., Baklanov, A., Vik, A. F., Agnew, P., Karatzas, K. D., and Sofiev, M.: Interactions of physical, chemical, and biological weather calling for an integrated approach to assessment, forecasting, and communication of air quality, Ambio, 41, 851–864, https://doi.org/10.1007/s13280-012-0288-z , 2012. 

Klimont, Z.: Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS), IIASA, https://iiasa.ac.at/models-and-data/greenhouse-gas-and-air-pollution-interactions-and-synergies (last access 25 February 2022), 2021. 

Kong, X., Forkel, R., Sokhi, R. S., Suppan, P., Baklanov, A., Gauss, M., Brunner, D., Baro, R., Balzarini, A., Chemel, C., Curci, G., Jimenez-Guerrero, P., Hirtl, M., Honzak, L., Im, U., Perez, J. L., Pirovano, G., San Jose, R., Schlünzen, K. H., Tsegas, G., Tuccella, P., Werhahn, J., Zabkar, R., and Galmarini, S.: Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII phase-2, Atmos. Environ., 115, 527-540, 2015. 

Korkmaz, P., Cunha Montenegro, R., Schmid, D., Blesl, M., and Fahl, U.: On the Way to a Sustainable European Energy System: Setting Up an Integrated Assessment Toolbox with TIMES PanEU as the Key Component, Energies, 13, 707, https://doi.org/10.3390/en13030707 , 2020. 

Kousa, A., Kukkonen, J., Karppinen, A., Aarnio, P., and Koskentalo, T.: A model for evaluating the population exposure to ambient air pollution in an urban area, Atmos. Environ., 36, 2109–2119, https://doi.org/10.1016/s1352-2310(02)00228-5 , 2002. 

Koutsourakis, N., Bartzis, J. G., and Venetsanos, A.: Determination of optimum positioning of atmospheric pollutant measuring instruments using computational fluid dynamics, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 72, https://doi.org/10.18745/pb.2221729 645–655, 2020. 

Kramshøj, M., Vedel-Petersen, I., Schollert, M., Rinnan, Å., Nymand, J., Ro-Poulsen, H., and Rinnan, R.: Large increases in Arctic biogenic volatile emissions are a direct effect of warming, Nat. Geosci., 9, 349–352, https://doi.org/10.1038/ngeo2692 , 2016. 

Kristensen, K., Lunderberg, D. M., Liu, Y., Misztal, P. K., Tian, Y., Arata, C., Nazaroff, W. W., and Goldstein, A. H.: Sources and dynamics of semivolatile organic compounds in a single-family residence in northern California, Indoor Air, 29, 645–655, https://doi.org/10.1111/ina.12561 , 2019. 

Kristovich, D. A. R., Takle, E., Young, G. S., and Sharma, A.: 100 Years of Progress in Mesoscale Planetary Boundary Layer Meteorological Research, Meteor. Mon., 59, 19.1–19.41, https://doi.org/10.1175/amsmonographs-d-18-0023.1 , 2019. 

Kuik, O. J., Brander, L., and Tol, R. S. J.: Marginal abatement costs of greenhouse gas emissions: A meta-analysis, Energ. Policy, 37, 1395–1403, 2009. 

Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S., Chatterton, T., Foxall, R., and Cawley, G.: Extensive evaluation of neural network models for the prediction of NO 2 and PM 10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki, Atmos. Environ., 37, 4539–4550, 2003. 

Kukkonen, J., Pohjola, M., Sokhi, R. S., Luhana, L., Kitwiroon, N., Rantamäki, M., Berge, E., Odegaard, V., Slørdal, L. H., Denby, B., and Finardi, S.: Analysis and evaluation of selected local-scale PM10 air pollution episodes in four European cities: Helsinki, London, Milan and Oslo, Atmos. Environ., 39, 2759–2773, 2005a. 

Kukkonen, J., Sokhi, R., Slordal, L. H., Finardi, S., Fay, B., Millan, M., Salvador, R., Palau, J. L., Rasmussen, A., Schayes, G., and Berge, E.: Analysis and evaluation of European air pollution episodes, in: Meteorology applied to urban air pollution problems, Final report COST Action 715, edited by: Fisher, B., Joffre, S., Kukkonen, J., Piringer, M., Rotach, M., and Schatzmann, M., Demetra Ltd Publishers, Bulgaria, 99–114, 2005b. 

Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K.: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1–87, https://doi.org/10.5194/acp-12-1-2012 , 2012. 

Kukkonen, J., Karl, M., Keuken, M. P., Denier van der Gon, H. A. C., Denby, B. R., Singh, V., Douros, J., Manders, A., Samaras, Z., Moussiopoulos, N., Jonkers, S., Aarnio, M., Karppinen, A., Kangas, L., Lützenkirchen, S., Petäjä, T., Vouitsis, I., and Sokhi, R. S.: Modelling the dispersion of particle numbers in five European cities, Geosci. Model Dev., 9, 451–478, https://doi.org/10.5194/gmd-9-451-2016 , 2016a. 

Kukkonen, J., Singh, V., Sokhi, R. S., Soares, J., Kousa, A., Matilainen, L., Kangas, L., Kauhaniemi, M., Riikonen, K., Jalkanen, J.-P., Rasila, T., Hänninen, O., Koskentalo, T., Aarnio, M., Hendriks, C., and Karppinen, A.: Assessment of Population Exposure to Particulate Matter for London and Helsinki, in: Air Pollution Modeling and its Application XXIV, edited by: Steyn, D. G. and Chaumerliac, N., Springer Proceedings in Complexity, Springer International Publishing, Cham, 99–105, https://doi.org/10.1007/978-3-319-24478-5_16 , 2016b. 

Kukkonen, J., Kangas, L., Kauhaniemi, M., Sofiev, M., Aarnio, M., Jaakkola, J. J. K., Kousa, A., and Karppinen, A.: Modelling of the urban concentrations of PM 2.5 on a high resolution for a period of 35 years, for the assessment of lifetime exposure and health effects, Atmos. Chem. Phys., 18, 8041–8064, https://doi.org/10.5194/acp-18-8041-2018 , 2018. 

Kukkonen, J., Fridell, E., Moldanova, J., Jalkanen, J.-P., Maragkidou, A., Sofiev, M., Ntziachristos, L., Borken-Kleefeld, J., Sokhi, R. S., Zervakis, V., Hassellöv, I.-M., Ytreberg, E., Williams, I., Hole, L. R., Petrovic, M., Maragkidou, S., Ktoris, A., and Monteiro, A.: Environmental impacts of shipping: from global to local scales, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/PB.22217 , 2020a. 

Kukkonen, J., López-Aparicio, S., Segersson, D., Geels, C., Kangas, L., Kauhaniemi, M., Maragkidou, A., Jensen, A., Assmuth, T., Karppinen, A., Sofiev, M., Hellén, H., Riikonen, K., Nikmo, J., Kousa, A., Niemi, J. V., Karvosenoja, N., Santos, G. S., Sundvor, I., Im, U., Christensen, J. H., Nielsen, O.-K., Plejdrup, M. S., Nøjgaard, J. K., Omstedt, G., Andersson, C., Forsberg, B., and Brandt, J.: The influence of residential wood combustion on the concentrations of PM 2.5 in four Nordic cities, Atmos. Chem. Phys., 20, 4333–4365, https://doi.org/10.5194/acp-20-4333-2020 , 2020b. 

Kukkonen, J., Savolahti, M., Palamarchuk, Y., Lanki, T., Nurmi, V., Paunu, V.-V., Kangas, L., Sofiev, M., Karppinen, A., Maragkidou, A., Tiittanen, P., and Karvosenoja, N.: Modelling of the public health costs of fine particulate matter and results for Finland in 2015, Atmos. Chem. Phys., 20, 9371–9391, https://doi.org/10.5194/acp-20-9371-2020 , 2020c. 

Kumar, P., Morawska, L., Birmili, W., Paasonen, P., Hu, M., Kulmala, M., Harrison, R. M., Norford, L., and Britter, R.: Ultrafine particles in cities, Environ. Int., 66, 1–10, https://doi.org/10.1016/j.envint.2014.01.013 , 2014. 

Kurppa, M., Hellsten, A., Auvinen, M., Raasch, S., Vesala, T., and Järvi, L.: Ventilation and Air Quality in City Blocks Using Large-Eddy Simulation–Urban Planning Perspective, Atmosphere 9, 65, https://doi.org/10.3390/atmos9020065 , 2018. 

Kurppa, M., Hellsten, A., Roldin, P., Kokkola, H., Tonttila, J., Auvinen, M., Kent, C., Kumar, P., Maronga, B., and Järvi, L.: Implementation of the sectional aerosol module SALSA2.0 into the PALM model system 6.0: model development and first evaluation, Geosci. Model Dev., 12, 1403–1422, https://doi.org/10.5194/gmd-12-1403-2019 , 2019. 

Kurppa, M., Roldin, P., Strömberg, J., Balling, A., Karttunen, S., Kuuluvainen, H., Niemi, J. V., Pirjola, L., Rönkkö, T., Timonen, H., Hellsten, A., and Järvi, L.: Sensitivity of spatial aerosol particle distributions to the boundary conditions in the PALM model system 6.0, Geosci. Model Dev., 13, 5663–5685, https://doi.org/10.5194/gmd-13-5663-2020 , 2020. 

Lehtomäki, H., Geels, C., Brandt, J., Rao, S., Yaramenka, K., Åström, S., Andersen, M. S., Frohn, L. M., Im, U., and Hänninen, O.: Deaths Attributable to Air Pollution in Nordic Countries: Disparities in the Estimates, Atmosphere, 11, 467, https://doi.org/10.3390/atmos11050467 , 2020. 

Lejri, D., Can, A., Schiper, N., and Leclercq, L.: Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale, Transport. Res. D-Tr. E., 63, 588–603, https://doi.org/10.1016/j.trd.2018.06.023 , 2018. 

Lelieveld, J., Klingmüller, K., Pozzer, A., Pöschl, U., Fnais, M., Daiber, A., and Münzel, T.: Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions, Eur. Heart J., 40, 1590–1596, https://doi.org/10.1093/eurheartj/ehz135 , 2019. 

Lepeule, J., Litonjua, A. A., Gasparrini, A., Koutrakis, P., Sparrow, D., Vokonas, P. S., and Schwartz, J.: Lung function association with outdoor temperature and relative humidity and its interaction with air pollution in the elderly, Environ. Res., 165, 110–117, https://doi.org/10.1016/j.envres.2018.03.039 , 2018. 

Letheren, B.: Air Quality Monitoring using Aircraft, Satellite or UAVs, Queensland University of Technology, Australia, https://doi.org/10.13140/rg.2.1.2455.2720 , 2016. 

Lewis, A., Zellweger, C., Schultz, M. G., and Tarasova, O. A.: Technical advice note on lower cost air pollution sensors,, World Meteorological Organization, Global Atmospheric Watch, WMOGeneva, Switzerland, 4 pp., 2017. 

Lewis, A., von Schneidemesser, E., and Peltier, R. E.: Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications, WMO-No. 1215, World Meteorological Organization, Geneva, Switzerland, 46 pp., 2018. 

Li, J., Sun, S., Tang, R., Qiu, H., Huang, Q., Mason, T., and Tian, L.: Major air pollutants and risk of COPD exacerbations: a systematic review and meta-analysis, Int. J. Chronic Obstr., 11, 3079–3091, https://doi.org/10.2147/copd.S122282 , 2016. 

Li, J., Woodward, A., Hou, X.-Y., Zhu, T., Zhang, J., Brown, H., Yang, J., Qin, R., Gao, J., Gu, S., Xu, L., Liu, X., and Liu, Q.: Modification of the effects of air pollutants on mortality by temperature: A systematic review and meta-analysis, Sci. Total Environ., 575, 1556–1570, https://doi.org/10.1016/j.scitotenv.2016.10.070 , 2017. 

Li, N.: Long-term exposure of European population subgroups to PM 2.5 and NO 2 , dissertation, Universität Stuttgart, Stuttgart, https://doi.org/10.18419/opus-11104 , 2020. 

Li, N. and Friedrich, R.: Methodology for Estimating the Lifelong Exposure to PM 2.5 and NO 2 – The Application to European Population Subgroups, Atmosphere, 10, 507, https://doi.org/10.3390/atmos10090507 , 2019. 

Li, N., Friedrich, R., Maesano, C. N., Medda, E., Brescianini, S., Stazi, M. A., Sabel, C. E., Sarigiannis, D., and Annesi-Maesano, I.: Lifelong exposure to multiple stressors through different environmental pathways for European populations, Environ. Res., 179, 108744, https://doi.org/10.1016/j.envres.2019.108744 , 2019a. 

Li, N., Huang, G., Friedrich, R., Vogt, U., Schürmann, S., and Straub, D.: Messung und Bewertung der Schadstoffemissionen von Holzfeuerungen in Innenräumen, Universität Stuttgart, Stuttgart, Forschungsbericht Band 144, https://doi.org/10.18419/opus-11139 , 2019b. 

Li, N., Maesano, C. N., Friedrich, R., Medda, E., Brandstetter, S., Kabesch, M., Apfelbacher, C., Melter, M., Seelbach-Göbel, B., Annesi-Maesano, I., and Sarigiannis, D.: A model for estimating the lifelong exposure to PM 2.5 and NO 2 and the application to population studies, Environ. Res., 178, 108629, https://doi.org/10.1016/j.envres.2019.108629 , 2019c. 

Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., and Chi, T.: Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation, Environ. Pollut., 231, 997–1004, https://doi.org/10.1016/j.envpol.2017.08.114 , 2017. 

Liakakou, E., Stavroulas, I., Kaskaoutis, D. G., Grivas, G., Paraskevopoulou, D., Dumka, U. C., Tsagkaraki, M., Bougiatioti, A., Oikonomou, K., Sciare, J., Gerasopoulos, E., and Mihalopoulos, N.: Levels and sources of black carbon long-term measurements in Athens, Greece, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, 18, https://doi.org/10.18745/pb.22217 , 2020. 

Liang, C.-K., West, J. J., Silva, R. A., Bian, H., Chin, M., Davila, Y., Dentener, F. J., Emmons, L., Flemming, J., Folberth, G., Henze, D., Im, U., Jonson, J. E., Keating, T. J., Kucsera, T., Lenzen, A., Lin, M., Lund, M. T., Pan, X., Park, R. J., Pierce, R. B., Sekiya, T., Sudo, K., and Takemura, T.: HTAP2 multi-model estimates of premature human mortality due to intercontinental transport of air pollution and emission sectors, Atmos. Chem. Phys., 18, 10497–10520, https://doi.org/10.5194/acp-18-10497-2018 , 2018. 

Lippmann, M., Chen, L.-C., Gordon, T., Ito, K., and Thurston, G. D.: National Particle Component Toxicity (NPACT) Initiative: integrated epidemiologic and toxicologic studies of the health effects of particulate matter components, Research report (Health Effects Institute), 5–13, 2013. 

Liu, S., Xing, J., Zhang, H., Ding, D., Zhang, F., Zhao, B., Sahu, S. K., and Wang, S.: Climate-driven trends of biogenic volatile organic compound emissions and their impacts on summertime ozone and secondary organic aerosol in China in the 2050s, Atmos. Environ., 218, 117020, https://doi.org/10.1016/j.atmosenv.2019.117020 , 2019. 

Liu, Z., Ye, W., and Little, J. C.: Predicting emissions of volatile and semivolatile organic compounds from building materials: A review, Build. Environ., 64, 7–25, https://doi.org/10.1016/j.buildenv.2013.02.012 , 2013. 

Loxham, M. and Nieuwenhuijsen, M. J.: Health effects of particulate matter air pollution in underground railway systems – a critical review of the evidence, Part. Fibre Toxicol., 16, 12, https://doi.org/10.1186/s12989-019-0296-2 , 2019. 

Lozhkina, O. V., Timofeev, V. D., and Lozhkin, V. N.: Modelling of air pollution by peat fire smoke and forecast of its impact on road visibility and drivers' health, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Luben, T. J., Nichols, J. L., Dutton, S. J., Kirrane, E., Owens, E. O., Datko-Williams, L., Madden, M., and Sacks, J. D.: A systematic review of cardiovascular emergency department visits, hospital admissions and mortality associated with ambient black carbon, Environ. Int., 107, 154–162, https://doi.org/10.1016/j.envint.2017.07.005 , 2017. 

MacIntyre, E. A., Gehring, U., Mölter, A., Fuertes, E., Klümper, C., Krämer, U., Quass, U., Hoffmann, B., Gascon, M., Brunekreef, B., Koppelman, G. H., Beelen, R., Hoek, G., Birk, M., Jongste, J. C. d., Smit, H. A., Cyrys, J., Gruzieva, O., Korek, M., Bergström, A., Agius, R. M., Vocht, F. d., Simpson, A., Porta, D., Forastiere, F., Badaloni, C., Cesaroni, G., Esplugues, A., Fernández-Somoano, A., Lerxundi, A., Sunyer, J., Cirach, M., Nieuwenhuijsen, M. J., Pershagen, G., and Heinrich, J.: Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project, Environ. Health Persp., 122, 107–113, https://doi.org/10.1289/ehp.1306755 , 2014. 

Maki, T., Hara, K., Kobayashi, F., Kurosaki, Y., Kakikawa, M., Matsuki, A., Chen, B., Shi, G., Hasegawa, H., and Iwasaka, Y.: Vertical distribution of airborne bacterial communities in an Asian-dust downwind area, Noto Peninsula, Atmos. Environ., 119, 282–293, https://doi.org/10.1016/j.atmosenv.2015.08.052 , 2015. 

Mallet, V.: Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation, J. Geophys. Res., 115, D24303, https://doi.org/10.1029/2010JD014259 , 2010. 

Mallet, V., Stoltz, G., and Mauricette, B.: Ozone ensemble forecast with machine learning algorithms, J. Geophys. Res., 114, D05307, https://doi.org/10.1029/2008jd009978 , 2009. 

Manders, A. M. M., Builtjes, P. J. H., Curier, L., Denier van der Gon, H. A. C., Hendriks, C., Jonkers, S., Kranenburg, R., Kuenen, J. J. P., Segers, A. J., Timmermans, R. M. A., Visschedijk, A. J. H., Wichink Kruit, R. J., van Pul, W. A. J., Sauter, F. J., van der Swaluw, E., Swart, D. P. J., Douros, J., Eskes, H., van Meijgaard, E., van Ulft, B., van Velthoven, P., Banzhaf, S., Mues, A. C., Stern, R., Fu, G., Lu, S., Heemink, A., van Velzen, N., and Schaap, M.: Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model, Geosci. Model Dev., 10, 4145–4173, https://doi.org/10.5194/gmd-10-4145-2017 , 2017. 

Manisalidis, I., Stavropoulou, E., Stavropoulos, A., and Bezirtzoglou, E.: Environmental and Health Impacts of Air Pollution: A Review, Frontiers in Public Health, 8, 14, https://doi.org/10.3389/fpubh.2020.00014 , 2020. 

Manning, A. J., O'Doherty, S., Jones, A. R., Simmonds, P. G., and Derwent, R. G.: Estimating UK methane and nitrous oxide emissions from 1990 to 2007 using an inversion modeling approach, J. Geophys. Res., 116, D02305, https://doi.org/10.1029/2010JD014763 , 2011. 

Mao, P., Li, J., Xiong, L., Wang, L., Wang,X., Tan, Y., and Li, H.: Characterization of Urban Subway Microenvironment Exposure – A Case of Nanjing in China, Int. J. Environ. Res. Public Health, 16, 625, https://doi.org/10.3390/ijerph16040625 , 2019. 

Maragkidou, A.: Exposure to coarse particles and floor dust biological and chemical contamination inside Jordanian indoor environments, University of Helsinki, Helsinki, PhD thesis, http://hdl.handle.net/10138/241380 (last access: 21 February 2022), 2018. 

Marécal, V., Peuch, V.-H., Andersson, C., Andersson, S., Arteta, J., Beekmann, M., Benedictow, A., Bergström, R., Bessagnet, B., Cansado, A., Chéroux, F., Colette, A., Coman, A., Curier, R. L., Denier van der Gon, H. A. C., Drouin, A., Elbern, H., Emili, E., Engelen, R. J., Eskes, H. J., Foret, G., Friese, E., Gauss, M., Giannaros, C., Guth, J., Joly, M., Jaumouillé, E., Josse, B., Kadygrov, N., Kaiser, J. W., Krajsek, K., Kuenen, J., Kumar, U., Liora, N., Lopez, E., Malherbe, L., Martinez, I., Melas, D., Meleux, F., Menut, L., Moinat, P., Morales, T., Parmentier, J., Piacentini, A., Plu, M., Poupkou, A., Queguiner, S., Robertson, L., Rouïl, L., Schaap, M., Segers, A., Sofiev, M., Tarasson, L., Thomas, M., Timmermans, R., Valdebenito, Á., van Velthoven, P., van Versendaal, R., Vira, J., and Ung, A.: A regional air quality forecasting system over Europe: the MACC-II daily ensemble production, Geosci. Model Dev., 8, 2777–2813, https://doi.org/10.5194/gmd-8-2777-2015 , 2015. 

Maricq, M. M.: Chemical characterization of particulate emissions from diesel engines: A review, J. Aerosol Sci., 38, 1079–1118, https://doi.org/10.1016/j.jaerosci.2007.08.001 , 2007. 

Markandya, A., Sampedro, J., Smith, S., van Dingenen, R., Pizarro-Irizar, C., Arto, I., and González-Eguino, M.: Health Co-benefits from Air Pollution and Mitigation Costs of the Paris Agreement: Modelling Study, Lancet, 2, E126–E113, 2018. 

Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015 , 2015. 

Martilli, A., Santiago, J., and Salamanca, F.: On the representation of urban heterogeneities in mesoscale models, Environ. Fluid Mech., 15, 305–328, 2015. 

Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T. L., Wong, D. C., and Young, J.: Extending the Community Multiscale Air Quality (CMAQ) modeling system to hemispheric scales: overview of process considerations and initial applications, Atmos. Chem. Phys., 17, 12449–12474, https://doi.org/10.5194/acp-17-12449-2017 , 2017. 

Matthey, A. and Bünger, B. C.: Methodenkonvention 3.0 zur Ermittlung von Umweltkosten: Kostensätze: Stand 02/2019, Stand 02/2019, Broschüren/Umweltbundesamt, Umweltbundesamt, Dessau-Roßlau, 48 pp., https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-02-11_methodenkonvention-3-0_kostensaetze_korr.pdf (last access: 25 February 2022), 2019. 

Matthias, V., Bewersdorff, I., Aulinger, A., and Quante, M.: The contribution of ship emissions to air pollution in the North Sea regions, Environ. Pollut., 158, 2241–2250, https://doi.org/10.1016/j.envpol.2010.02.013 , 2010. 

Matthias, V., Aulinger, A., Backes, A., Bieser, J., Geyer, B., Quante, M., and Zeretzke, M.: The impact of shipping emissions on air pollution in the greater North Sea region – Part 2: Scenarios for 2030, Atmos. Chem. Phys., 16, 759–776, https://doi.org/10.5194/acp-16-759-2016 , 2016 

Matthias, V., Arndt, J. A., Aulinger, A., Bieser, J., van der Denier Gon, H., Kranenburg, R., Kuenen, J., Neumann, D., Pouliot, G., and Quante, M.: Modeling emissions for three-dimensional atmospheric chemistry transport models, J. Air Waste Manage., 68, 763–800, https://doi.org/10.1080/10962247.2018.1424057 , 2018. 

Matthias, V., Bieser, J., Mocanu, T., Pregger, T., Quante, M., Ramacher, M. O. P., Seum, S., and Winkler, C.: Modelling road transport emissions in Germany – Current day situation and scenarios for 2040, Transport. Res. D-Tr. E., 87, 102536, https://doi.org/10.1016/j.trd.2020.102536 , 2020a. 

Matthias, V., Bieser, J., Quante, M., Seum, S., and Winkler, C.: Impact of traffic emissions in 2040 on air quality in Germany, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 31, https://doi.org/10.18745/pb.22217 , 2020b. 

McCarthy, M. P., Best, M. J., and Betts, R. A.: Climate change in cities due to global warming and urban effects, Geophys. Res. Lett., 37, L09705, https://doi.org/10.1029/2010GL042845 , 2010. 

McDonald, B. C., Gouw, J. A. d., Gilman, J. B., Jathar, S. H., Akherati, A., Cappa, C. D., Jimenez, J. L., Lee-Taylor, J., Hayes, P. L., McKeen, S. A., Cui, Y. Y., Kim, S.-W., Gentner, D. R., Isaacman-VanWertz, G., Goldstein, A. H., Harley, R. A., Frost, G. J., Roberts, J. M., Ryerson, T. B., and Trainer, M.: Volatile chemical products emerging as largest petrochemical source of urban organic emissions, Science, 359, 760–764, https://doi.org/10.1126/science.aaq0524 , 2018. 

McNider, R. T. and Pour-Biazar, A.: Meteorological modeling relevant to mesoscale and regional air quality applications: a review, J. Air Waste Manage., 70, 2–43, https://doi.org/10.1080/10962247.2019.1694602 , 2020. 

Michaelis, A. C., Lackmann, G. M., and Robinson, W. A.: Evaluation of a unique approach to high-resolution climate modeling using the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1, Geosci. Model Dev., 12, 3725–3743, https://doi.org/10.5194/gmd-12-3725-2019 , 2019. 

Mihăiţă, A. S., Dupont, L., Chery, O., Camargo, M., and Cai, C.: Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling, J. Clean. Prod., 221, 398–418, https://doi.org/10.1016/j.jclepro.2019.02.179 , 2019. 

Mircea, M., Calori, G., Pirovano, G., and Belis, C. A.: European guide on air pollution source apportionment for particulate matter with source-oriented models and their combined use with receptor models, JRC, 66 pp., https://doi.org/10.2760/470628 , 2020. 

Molina, L. T.: Introductory lecture: air quality in megacities, Faraday Discuss., 226, 9–52, https://doi.org/10.1039/d0fd00123f , 2021. 

Monteiro, A., Durka, P., Flandorfer, C., Georgieva, E., Guerreiro, C., Kushta, J., Malherbe, L., Maiheu, B., Miranda, A. I., Santos, G., Stocker, J., Trimpeneers, E., Tognet, F., Stortini, M., Wesseling, J., Janssen, S., and Thunis, P.: Strengths and weaknesses of the FAIRMODE benchmarking methodology for the evaluation of air quality models, Air Qual. Atmos. Hlth., 11, 373–383, https://doi.org/10.1007/s11869-018-0554-8 , 2018. 

Morakinyo, O., Mokgobu, M., Mukhola, M., and Hunter, R.: Health Outcomes of Exposure to Biological and Chemical Components of Inhalable and Respirable Particulate Matter, Int. J. Env. Res. Pub. He., 13, 592, https://doi.org/10.3390/ijerph13060592 , 2016. 

Morawska, L., He, C., Johnson, G., Jayaratne, R., Salthammer, T., Wang, H., Uhde, E., Bostrom, T., Modini, R., Ayoko, G., McGarry, P., and Wensing, M.: An investigation into the characteristics and formation mechanisms of particles originating from the operation of laser printers, Environ. Sci. Technol., 43, 1015–1022, https://doi.org/10.1021/es802193n , 2009. 

Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, M. M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., and Williams, R.: Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?, Environ. Int., 116, 286–299, https://doi.org/10.1016/j.envint.2018.04.018 , 2018. 

Moussiopoulos, N., Tsegas, G., and Chourdakis, E.: The impact of port operations on air quality in Piraeus and the surrounding urban areas, in: Air Pollution Modelling and its Application, edited by: Mensink, C., Gong, W., and Hakami, A., 159–164, https://doi.org/10.1007/978-3-030-22055-6_25 , 2019. 

Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I. (Eds.): Proceedings of 12th International Conference on Air Quality, Science and Application, Hatfield, UK, 162 pp., https://doi.org/10.18745/pb.22217 , 2020. 

Mues, A., Kuenen, J., Hendriks, C., Manders, A., Segers, A., Scholz, Y., Hueglin, C., Builtjes, P., and Schaap, M.: Sensitivity of air pollution simulations with LOTOS-EUROS to the temporal distribution of anthropogenic emissions, Atmos. Chem. Phys., 14, 939–955, https://doi.org/10.5194/acp-14-939-2014 , 2014. 

Munir, S., Mayfield, M., Coca, D., and Jubb, S. A.: Structuring an integrated air quality monitoring network in large urban areas – Discussing the purpose, criteria and deployment strategy, Atmos. Environ. X, 2, 100027, https://doi.org/10.1016/j.aeaoa.2019.100027 , 2019. 

Murena, F. and Prati, M. V.: The contribution of high emitters vehicles to FPS number concentration in the historical centre of Naples, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 161, https://doi.org/10.18745/pb.22217 , 2020. 

Mussetti, G., Brunner, D., Henne, S., Allegrini, J., Krayenhoff, E. S., Schubert, S., Feigenwinter, C., Vogt, R., Wicki, A., and Carmeliet, J.: COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees, Geosci. Model Dev., 13, 1685–1710, https://doi.org/10.5194/gmd-13-1685-2020 , 2020. 

Navrud, S. and Ready, R. C.: Environmental value transfer: issues and methods, The economics of non-market goods and resources, Springer, Dordrecht, 9, 290 pp., 2007. 

Nemery, B., Hoet, P. H. M., and Nemmar, A.: The Meuse Valley fog of 1930: an air pollution disaster, Lancet, 357, 704–708, https://doi.org/10.1016/s0140-6736(00)04135-0 , 2001. 

Ngan, F., Loughner, C. P., and Stein, A.: The evaluation of mixing methods in HYSPLIT using measurements from controlled tracer experiments, Atmos. Environ., 219, 117043, https://doi.org/10.1016/j.atmosenv.2019.117043 , 2019. 

Nguyen, C. V. and Soulhac, L.: Data assimilation methods for urban air quality at the local scale, Atmos. Environ., 253, 118366, https://doi.org/10.1016/j.atmosenv.2021.118366 , 2021. 

Nieuwenhuijsen, M. J., Gomez-Perales, J. E., and Colvile, R. N.: Levels of particulate air pollution, its elemental composition, determinants and health effects in metro systems, Atmos. Environ., 41, 7995–8006, 2007. 

Niemeyer, L. E.: Forecasting air pollution potential, Mon. Weather Rev., 88, 88–96, 1960. 

Niska, H., Rantamäki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J., Kolehmainen, M., Evaluation of an integrated modelling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations, Atmos. Environ., 39, 6524–6536, 2005. 

Nuterman, R., Starchenko, A., and Baklanov, A.: Numerical Model of Urban Aerodynamics and Pollution Dispersion, Int. J. Environ. Pollut., 44, 385–393, 2011. 

Nuterman, R., Mahura, A., Baklanov, A., Amstrup, B., and Zakey, A.: Downscaling system for modeling of atmospheric composition on regional, urban and street scales, Atmos. Chem. Phys., 21, 11099–11112, https://doi.org/10.5194/acp-21-11099-2021 , 2021. 

OECD: OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris, https://doi.org/10.1787/9789264122246-en , 2012. 

OECD: Cost-Benefit Analysis and the Environment – Further Developments and Policy Use, OECD Publishing, Paris, https://doi.org/10.1787/9789264085169-en , 2018. 

Ogen, Y.: Assessing nitrogen dioxide (NO 2 ) levels as a contributing factor to coronavirus (COVID-19) fatality, Sci. Total Environ., 726, 138605, https://doi.org/10.1016/j.scitotenv.2020.138605 , 2020. 

Ostro, B., Lipsett, M., Reynolds, P., Goldberg, D., Hertz, A., Garcia, C., Henderson, K. D., and Bernstein, L.: Long-term exposure to constituents of fine particulate air pollution and mortality: results from the California Teachers Study, Environ. Health Persp., 118, 363–369, https://doi.org/10.1289/ehp.0901181 , 2010. 

Ostro, B., Reynolds, P., Goldberg, D., Hertz, A., Burnett, R. T., Shin, H., Hughes, E., Garcia, C., Henderson, K. D., Bernstein, L., and Lipsett, M.: Assessing Long-Term Exposure in the California Teachers Study, Environ. Health Persp., 119, A242–A243, https://doi.org/10.1289/ehp.119-3114832 , 2011. 

Ostro, B., Hu, J., Goldberg, D., Reynolds, P., Hertz, A., Bernstein, L., and Kleeman, M. J.: Associations of Mortality with Long-Term Exposures to Fine and Ultrafine Particles, Species and Sources: Results from the California Teachers Study Cohort, Environ. Health Persp., 123, 549–556, https://doi.org/10.1289/ehp.1408565 , 2015. 

Otalora, M., Soulhac, L., Nguyen, C. V., and Derognat, C.: Challenges in the assimilation of mobile sensors data for urban air quality – analysis of a Paris study, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 117, https://doi.org/10.18745/pb.22217 , 2020. 

Ots, R., Heal, M. R., Young, D. E., Williams, L. R., Allan, J. D., Nemitz, E., Di Marco, C., Detournay, A., Xu, L., Ng, N. L., Coe, H., Herndon, S. C., Mackenzie, I. A., Green, D. C., Kuenen, J. J. P., Reis, S., and Vieno, M.: Modelling carbonaceous aerosol from residential solid fuel burning with different assumptions for emissions, Atmos. Chem. Phys., 18, 4497–4518, https://doi.org/10.5194/acp-18-4497-2018 , 2018. 

Ott, W., Baur, M., Kaufmann, Y., Frischknecht, R., and Steiner, R.: Assessment of Biodiversity Losses – Monetary Valuation of Biodiversity Losses due to Land Use Changes and Airborne Emissions, Deliverable D.4.2.-RS 1b/WP4 of the EU FP6 project No. 02687 NEEDS, https://www.econcept.ch/en/focus/needs-new-energy-externalities-developments-sustainability/ (last access: 25 February 2022), 2006. 

Papadogeorgou, G., Kioumourtzoglou, M.-A., Braun, D., and Zanobetti, A.: Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions, Current Environmental Health Reports, 6, 105–115, https://doi.org/10.1007/s40572-019-00235-7 , 2019. 

Park, M., Joo, H. S., Lee, K., Jang, M., Kim, S. D., Kim, I., Borlaza, L. J. S., Lim, H., Shin, H., Chung, K. H., Choi, Y.-H., Park, S. G., Bae, M.-S., Lee, J., Song, H., and Park, K.: Differential toxicities of fine particulate matters from various sources, Sci. Rep., 8, 17007, https://doi.org/10.1038/s41598-018-35398-0 , 2018. 

Parra, R.: Effects of global meteorological datasets in modeling meteorology and air quality in the andean region of southern Ecuador, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 54, https://doi.org/10.18745/pb.22217 , 2020. 

Paunu, V.-V., Karvosenoja, N., D., S., Lopez-Aparicio, S., Nielsen, O.-K., Plejdrup, M. S., Vo, D. T., Thorsteinsson, T., Denier van der Gon, H., Brandt, J., and Geels, C.: New Nordic emission inventory – spatial distribution of machinery and residential combustion emissions, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 17, https://doi.org/10.18745/pb.22217 , 2020. 

Pavlovic, R., Belair, S., Leroyer, S., Nikiema, O., Popadic, I., Munoz-Alpizar, R., and Stroud, C.: Urban meteorology and air quality as a function of different urban features, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 151, https://doi.org/10.18745/pb.22217 , 2020. 

Pedersen, M., Giorgis-Allemand, L., Bernard, C., Aguilera, I., Andersen, A.-M. N., Ballester, F., Beelen, R. M. J., Chatzi, L., Cirach, M., Danileviciute, A., Dedele, A., van Eijsden, M., Estarlich, M., Fernández-Somoano, A., Fernández, M. F., Forastiere, F., Gehring, U., Grazuleviciene, R., Gruzieva, O., Heude, B., Hoek, G., Hoogh, K. d., van den Hooven, E. H., Håberg, S. E., Jaddoe, V. W. V., Klümper, C., Korek, M., Krämer, U., Lerchundi, A., Lepeule, J., Nafstad, P., Nystad, W., Patelarou, E., Porta, D., Postma, D., Raaschou-Nielsen, O., Rudnai, P., Sunyer, J., Stephanou, E., Sørensen, M., Thiering, E., Tuffnell, D., Varró, M. J., Vrijkotte, T. G. M., Wijga, A., Wilhelm, M., Wright, J., Nieuwenhuijsen, M. J., Pershagen, G., Brunekreef, B., Kogevinas, M., and Slama, R.: Ambient air pollution and low birthweight: a European cohort study (ESCAPE), Lancet Resp. Med., 1, 695–704, https://doi.org/10.1016/s2213-2600(13)70192-9 , 2013. 

Pelliccioni, A. and Tirabassi, T.: Air dispersion model and neural network: a new perspective for integrated models in the simulation of complex situations, Environ. Modell. Softw., 21, 539–546, 2006. 

Peters, A., Dockery, D. W., Muller, J. E., and Mittleman, M. A.: Increased Particulate Air Pollution and the Triggering of Myocardial Infarction, Circulation, 103, 2810–2815, https://doi.org/10.1161/01.Cir.103.23.2810 , 2001. 

Petroff, A. and Zhang, L.: Development and validation of a size-resolved particle dry deposition scheme for application in aerosol transport models, Geosci. Model Dev., 3, 753–769, https://doi.org/10.5194/gmd-3-753-2010 , 2010. 

Petroff, A., Mailliat, A., Amielh, M., and Anselmet, F.: Aerosol dry deposition on vegetative canopies. Part II: A new modeling approach and applications, Atmos. Environ., 42, 3654–3683, 2008. 

Pfister, G., Eastham, S., Arellano, A. F., Aumont, B., Barsanti, K., Barth, M., Conley, A., Davis, N., Emmons, L., Fast, J., Fiore, A., Gaubert, B., Goldhaber, S., Granier, C., Grell, G., Guevara, M., Henze, D., Hodzic, A., Liu, X., Marsh, D., Orlando, J., Plane, J., Polvani, L., Rosenlof, K., Steiner, A., Jacob, D., and Brasseur, G.: The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), B. Am. Meteorol. Soc., 101, E1743–E1760, https://doi.org/10.1175/bams-d-19-0331.1 , 2020. 

Phosri, A., Ueda, K., Phung, V. L. H., Tawatsupa, B., Honda, A., and Takano, H.: Effects of ambient air pollution on daily hospital admissions for respiratory and cardiovascular diseases in Bangkok, Thailand, Sci. Total Environ., 651, 1144–1153, https://doi.org/10.1016/j.scitotenv.2018.09.183 , 2019. 

Pisoni, E. and Van Dingenen, R.: Comment to the paper “Assessing nitrogen dioxide (NO 2 ) levels as a contributing factor to coronavirus (COVID-19) fatality”, by Ogen, 2020, Sci. Total Environ., 738, 139853, https://doi.org/10.1016/j.scitotenv.2020.139853 , 2020. 

Pleim, J., Mathur, R., Rao, S. T., Fast, J., and Baklanov, A.: Integrated Meteorology and Chemistry Modeling: Evaluation and Research Needs, B. Am. Meteorol. Soc., 95, ES81– ES84, https://doi.org/10.1175/BAMS-D-13-00107.1 , 2014. 

Plejdrup, M. S., Nielsen, O.-K., and Brandt, J.: Spatial emission modelling for residential wood combustion in Denmark, Atmos. Environ., 144, 389–396, https://doi.org/10.1016/j.atmosenv.2016.09.013 , 2016. 

Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., and Thurston, G. D.: Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution, JAMA-J. Am. Med. Assoc., 287, 1132–1141, https://doi.org/10.1001/jama.287.9.1132 , 2002. 

Pope III, C. A., Lefler, J. S., Ezzati, M., Higbee, J. D., Marshall, J. D., Kim, S.-Y., Bechle, M., Gilliat, K. S., Vernon, S. E., Robinson, A. L., and Burnett, R. T.: Mortality Risk and Fine Particulate Air Pollution in a Large, Representative Cohort of U.S. Adults, Environ. Health Persp., 127, 077007, https://doi.org/10.1289/ehp4438 , 2019. 

Pope III, C. A., Coleman, N., Pond, Z. A., and Burnett, R. T.: Fine particulate air pollution and human mortality: 25 + years of cohort studies, Environ. Res., 183, 108924, https://doi.org/10.1016/j.envres.2019.108924 , 2020. 

Poulsen, A. H., Hvidtfeldt U. A., Sørensen, M., Puett, R., Ketzel, M., Brandt, J., Christensen, J. H., Geels, C., and Raaschou-Nielsen, O.: Components of particulate matter air-pollution and brain tumors, Environ. Int., 144, 106046, https://doi.org/10.1016/j.envint.2020.106046 , 2020. 

Prank, M., Sofiev, M., Tsyro, S., Hendriks, C., Semeena, V., Vazhappilly Francis, X., Butler, T., Denier van der Gon, H., Friedrich, R., Hendricks, J., Kong, X., Lawrence, M., Righi, M., Samaras, Z., Sausen, R., Kukkonen, J., and Sokhi, R.: Evaluation of the performance of four chemical transport models in predicting the aerosol chemical composition in Europe in 2005, Atmos. Chem. Phys., 16, 6041–6070, https://doi.org/10.5194/acp-16-6041-2016 , 2016. 

Pražnikar, Z. and Pražnikar, J.: The effects of particulate matter air pollution on respiratory health and on the cardiovascular system, Slovenian Journal of Public Health, 51, 190–199, https://doi.org/10.2478/v10152-012-0022-z , 2012. 

Pregger, T. and Friedrich, R.: Effective pollutant emission heights for atmospheric transport modelling based on real-world information, Environ. Pollut., 157, 552–560, https://doi.org/10.1016/j.envpol.2008.09.027 , 2009. 

Raaschou-Nielsen, O., Andersen, Z. J., Beelen, R., Samoli, E., Stafoggia, M., Weinmayr, G., Hoffmann, B., Fischer, P., Nieuwenhuijsen, M. J., Brunekreef, B., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Sommar, J., Forsberg, B., Modig, L., Oudin, A., Oftedal, B., Schwarze, P. E., Nafstad, P., Faire, U. d., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K. T., Sørensen, M., Tjønneland, A., Ellermann, T., Eeftens, M., Peeters, P. H., Meliefste, K., Wang, M., Bueno-de-Mesquita, B., Key, T. J., Hoogh, K. d., Concin, H., Nagel, G., Vilier, A., Grioni, S., Krogh, V., Tsai, M.-Y., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Tamayo, I., Amiano, P., Dorronsoro, M., Trichopoulou, A., Bamia, C., Vineis, P., and Hoek, G.: Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE), Lancet Oncol., 14, 813–822, https://doi.org/10.1016/s1470-2045(13)70279-1 , 2013. 

Raaschou-Nielsen, O., Beelen, R., Wang, M., Hoek, G., Andersen, Z. J., Hoffmann, B., Stafoggia, M., Samoli, E., Weinmayr, G., Dimakopoulou, K., Nieuwenhuijsen, M., Xun, W. W., Fischer, P., Eriksen, K. T., Sørensen, M., Tjønneland, A., Ricceri, F., Hoogh, K. d., Key, T., Eeftens, M., Peeters, P. H., Bueno-de-Mesquita, H. B., Meliefste, K., Oftedal, B., Schwarze, P. E., Nafstad, P., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Penell, J., Faire, U. d., Korek, M., Pedersen, N., Östenson, C.-G., Pershagen, G., Fratiglioni, L., Concin, H., Nagel, G., Jaensch, A., Ineichen, A., Naccarati, A., Katsoulis, M., Trichpoulou, A., Keuken, M., Jedynska, A., Kooter, I. M., Kukkonen, J., Brunekreef, B., Sokhi, R. S., Katsouyanni, K., and Vineis, P.: Particulate matter air pollution components and risk for lung cancer, Environ. Int., 87, 66–73, https://doi.org/10.1016/j.envint.2015.11.007 , 2016. 

Raaschou-Nielsen, O., Thorsteinson, E., Antonsen, S., Holst, G. J., Sigsgaard, T., Geels, C., Frohn, L. M., Christensen, J. H., Brandt, J., Pedersen, C. B., and Hvidtfeldt, U. A.: Long-term exposure to air pollution and mortality in the Danish population a nationwide study, eClinicalMedicine, 28, 100605, https://doi.org/10.1016/j.eclinm.2020.100605 , 2020. 

Ramacher, M. O. P., Karl, M., Bieser, J., Jalkanen, J.-P., and Johansson, L.: Urban population exposure to NO x emissions from local shipping in three Baltic Sea harbour cities – a generic approach, Atmos. Chem. Phys., 19, 9153–9179, https://doi.org/10.5194/acp-19-9153-2019 , 2019. 

Ramacher, M. O. P., Tang, L., Moldanová, J., Matthias, V., Karl, M., Fridell, E., and Johansson, L.: The impact of ship emissions on air quality and human health in the Gothenburg area – Part II: Scenarios for 2040, Atmos. Chem. Phys., 20, 10667–10686, https://doi.org/10.5194/acp-20-10667-2020 , 2020. 

Rao, S. T., Luo, H., Astitha, M., Hogrefe, C., Garcia, V., and Mathur, R.: On the limit to the accuracy of regional-scale air quality models, Atmos. Chem. Phys., 20, 1627–1639, https://doi.org/10.5194/acp-20-1627-2020 , 2020. 

Rees, N., Wickham, A., and Choi, Y.: Silent Suffocation in Africa – Air Pollution is a Growing Menace, Affecting the Poorest Children the Most, United Nations Children's Fund (UNICEF), New York, https://www.unicef.org/media/55081/file/Silentsuffocationinafricaairpollution201920.pdf (last access: 21 February 2022), 2019. 

Resler, J., Geletič, J., Krč, P., Eben, K., Belda, M., Fuka, L., Huszár, P., Karlický, J., Vlček, O., Benešová, N., Keder, J., Bauerová, P., Škáchová, H., Ďoubalová, J., Žák, M., Sühring, M., and Schwenkel, J.: Validation of the air quality and meteorological values modelled by PALM-4U model against observation campaign in Prague-Dejvice, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., Pipilis, I., Hatfield, UK, p. 160, https://doi.org/10.18745/pb.22217 , 2020. 

Ribeiro, I., Martilli, A., Falls, M., Zonato, A., and Villalba, G.: Highly resolved WRF-BEP/BEM simulations over Barcelona urban area with LCZ, Atmos. Res., 248, 105220, https://doi.org/10.1016/j.atmosres.2020.105220 , 2021. 

Robock, A., Oman, L., and Stenchikov, G. L.: Nuclear winter revisited with a modern climate model and current nuclear arsenals: Still catastrophic consequences, J. Geophys. Res., 112, D13107, https://doi.org/10.1029/2006JD008235 , 2007. 

Rodins, V., Lucht, S., Ohlwein, S., Hennig, F., Soppa, V., Erbel, R., Jöckel, K.-H., Weimar, C., Hermann, D. M., Schramm, S., Moebus, S., Slomiany, U., and Hoffmann, B.: Long-term exposure to ambient source-specific particulate matter and its components and incidence of cardiovascular events – The Heinz Nixdorf Recall study, Environ. Int., 142, 105854, https://doi.org/10.1016/j.envint.2020.105854 , 2020. 

Rodriguez, D., Parent, E., Eymard, L., Valari, M., and Payan, S.: NO X and PM 10 Bayesian concentration estimates using high-resolution numerical simulations and ground measurements over Paris, France, Atmos. Environ. X, 3, 100038, https://doi.org/10.1016/j.aeaoa.2019.100038 , 2019. 

Roos, J.: Ermittlung und Bewertung von vermiedenen Gesundheitsschäden im Rahmen der Technikbewertung, Universität Stuttgart, https://doi.org/10.18419/opus-9177 , 2017. 

Russell, W. T.: The relative influence of fog and low temperature on the mortality from respiratory disease, Lancet, 2, 1128, https://doi.org/10.1016/S0140-6736(01)02367-4 , 1926. 

Sakai, R., Sasaki, D., Obayashi, S., and Nakahashi, K.: Wavelet-based data compression for flow simulation on block-structured Cartesian mesh, Int. J. Numer. Meth. Fl., 73, 462–476, https://doi.org/10.1002/fld.3808 , 2013. 

Sakellaris, I., Bartzis, J., Neuhäuser, J., Friedrich, R., Gotti, A., and Sarigiannis, D.: A novel approach for air quality trend studies and its application to European urban environments: The ICARUS project, Atmos. Environ., 273, 118973, https://doi.org/10.1016/j.atmosenv.2022.118973 , 2022. 

Salamanca, F., Martilli, A., Tewari, M., and Chen, F.: A Study of the Urban Boundary Layer Using Different Urban Parameterizations and High-Resolution Urban Canopy Parameters with WRF, J. Appl. Meteorol. Clim., 50, 1107–1128, https://doi.org/10.1175/2010jamc2538.1 , 2011. 

Salamanca, F., Zhang, Y., Barlage, M., Chen, F., Mahalov, A., and Miao, S.: Evaluation of the WRF-Urban Modeling System Coupled to Noah and Noah-MP Land Surface Models Over a Semiarid Urban Environment, J. Geophys. Res.-Atmos., 123, 2387–2408, https://doi.org/10.1002/2018jd028377 , 2018. 

Salthammer, T., Schripp, T., Wientzek, S., and Wensing, M.: Impact of operating wood-burning fireplace ovens on indoor air quality, Chemosphere, 103, 205–211, https://doi.org/10.1016/j.chemosphere.2013.11.067 , 2014. 

Samoli, E., Stafoggia, M., Rodopoulou, S., Ostro, B., Declercq, C., Alessandrini, E., Díaz, J., Karanasiou, A., Kelessis, A. G., Le Tertre, A., Pandolfi, P., Randi, G., Scarinzi, C., Zauli-Sajani, S., Katsouyanni, K., and Forastiere, F.: Associations between Fine and Coarse Particles and Mortality in Mediterranean Cities: Results from the MED-PARTICLES Project, Environ. Health Persp., 121, 932–938, https://doi.org/10.1289/ehp.1206124 , 2013. 

San José, R., Pérez, J. L., Pérez, L., and Gonzalez, R. M.: A multiscale simulation tool to assess the effects of nature-based solutions (NBS) in urban air quality, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 10, https://doi.org/10.18745/pb.22217 , 2020. 

Santiago, J. L., Borge, R., Martin, F., La Paz, D. d., Martilli, A., Lumbreras, J., and Sanchez, B.: Evaluation of a CFD-based approach to estimate pollutant distribution within a real urban canopy by means of passive samplers, Sci. Total Environ., 576, 46–58, https://doi.org/10.1016/j.scitotenv.2016.09.234 , 2017. 

Saraga, D., Maggos, T., Degrendele, C., Klanova, J., Horvat, M., Kocman, D., Kanduc, T., Dos Santos, S. G., Franco, R., Gomez, P. M., Manousakas, M., Bairachtari, K., Eleftheriadis, K., Kermenidou, M., Karakitsios, S., Gotti, A., and Sarigiannis, D.: Multi-city comparative PM 2.5 source apportionment for fifteen sites in Europe: The ICARUS project, Sci. Total Environ., 751, 141855, https://doi.org/10.1016/j.scitotenv.2020.141855 , 2021. 

Sarigiannis, D. and Karakitsios, S.: Report on the methodology for estimating health effects of individuals or population groups and health impact results in the ICARUS participating cities, Deliverable D4.3 of the EU Horizon 2020 project ICARUS, 45 pp., https://icarus2020.eu/wp-content/uploads/2018/06/ICARUS_D4.3.pdf (last access: 25 February 2022), 2018. 

Savolahti, M., Karvosenoja, N., Soimakallio, S., Kupiainen, K., Tissari, J., and Paunu, V.-V.: Near-term climate impacts of Finnish residential wood combustion, Energ. Policy, 133, 110837, https://doi.org/10.1016/j.enpol.2019.06.045 , 2019. 

Schade, S., Herding, W., Fellermann, A., and Kotsev, A.: Joint Statement on new opportunities for air quality sensing – lower-cost sensors for public authorities and citizen science initiatives, Research Ideas and Outcomes, 5, e34059, https://doi.org/10.3897/rio.5.e34059 , 2019. 

Schäfer, K., Lande, K., Grimm, H., Jenniskens, G., Gijsbers, R., Ziegler, V., Hank, M., and Budde, M.: High-resolution Assessment of Air Quality in Urban Areas – A Business Model Perspective, Atmosphere, 12, 595, https://doi.org/10.3390/atmos12050595 , 2021. 

Scherer, D., Ament, F., Emeis, S., Fehrenbach, U., Leitl, B., Scherber, K., Schneider, C., and Vogt, U.: Three-Dimensional Observation of Atmospheric Processes in Cities, Meteorol. Z., 28, 121–138, 2019. 

Schieberle, C.: Development of a stochastic optimization approach to determine cost-efficient environmental protection strategies: case study of policies for the future European passenger transport sector with a focus on rail-bound and on-road activities, Institut fuer Energiewirtschaft und Rationelle Energieanwendung, Universitaet Stuttgart, Stuttgart, https://doi.org/10.18419/opus-10473 , 2019. 

Schmid, D.: D2.1 Report and data on emission inventory at EU-wide level for the considered pollutants and GHGs for the years 2015, 2020 and 2030, EU Horizon 2020 Project: 690105 – ICARUS, https://icarus2020.eu/wp-content/uploads/2018/03/ICARUS-Deliverable-D2.1_FINAL_REVISED.pdf (last access: 22 February 2022), 2018. 

Schmid, D., Korkmaz, P., Blesl, M., Fahl, U., and Friedrich, R.: Analyzing transformation pathways to a sustainable European energy system – Internalization of health damage costs caused by air pollution, Energy Strateg. Rev., 26, 100417, https://doi.org/10.1016/j.esr.2019.100417 , 2019. 

Schneider, P., Castell, N., Vogt, M., Dauge, F. R., Lahoz, W. A., and Bartonova, A.: Mapping urban air quality in near real-time using observations from low-cost sensors and model information, Environ. Int., 106, 234–247, https://doi.org/10.1016/j.envint.2017.05.005 , 2017. 

Schraufnagel, D. E., Balmes, J. R., Cowl, C. T., Matteis, S. d., Jung, S.-H., Mortimer, K., Perez-Padilla, R., Rice, M. B., Riojas-Rodriguez, H., Sood, A., Thurston, G. D., To, T., Vanker, A., and Wuebbles, D. J.: Air Pollution and Noncommunicable Diseases, Chest, 155, 409–416, https://doi.org/10.1016/j.chest.2018.10.042 , 2019. 

Schrenk, H. H.: Air pollution in Donora, Pa, Federal Security Agency Public Health Service Bureau of State Services Division of Industrial Hygiene, Washington, Public health bulletin no. 306, 173 pp., 1949. 

Schripp, T., Kirsch, I., and Salthammer, T.: Characterization of particle emission from household electrical appliances, Sci. Total Environ., 409, 2534–2540, https://doi.org/10.1016/j.scitotenv.2011.03.033 , 2011. 

Schripp, T., Markewitz, D., Uhde, E., and Salthammer, T.: Does e-cigarette consumption cause passive vaping?, Indoor Air, 23, 25–31, https://doi.org/10.1111/j.1600-0668.2012.00792.x , 2013. 

Schripp, T., Salthammer, T., Wientzek, S., and Wensing, M.: Chamber studies on nonvented decorative fireplaces using liquid or gelled ethanol fuel, Environ. Sci. Technol., 48, 3583–3590, 2014. 

Schrödner, R., Genz, C., Heinold, B., Baars, H., Henning, S., Madenach, N., Carbajal Henken, C., Costa Surós, M., Sourdeval, O., Hesemann, J., Brueck, M., Cioni, G., Hoose, C., Tegen, I., and Quaas, J.: Aerosol-cloud interaction in 1985 and today, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 61, https://doi.org/10.18745/pb.22217 , 2020. 

Schubert, S. and Grossman-Clarke, S.: Evaluation of the coupled COSMO-CLM/DCEP model with observations from BUBBLE, Q. J. Roy. Meteor. Soc., 140, 2465–2483, https://doi.org/10.1002/qj.2311 , 2014. 

Schwartz, J., Dockery, D. W., and Neas, L. M.: Is Daily Mortality Associated Specifically with Fine Particles?, J. Air Waste Manage., 46, 927–939, https://doi.org/10.1080/10473289.1996.10467528 , 1996. 

Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd edn., John Wiley & Sons, 1152 pp., ISBN: 978-1-118-94740-1, 2016. 

Sevilla, I., Chrobocinski, P., Barmpas, F., Schmidt, F., Kerle, N., Kostaridis, A., Doulamis, A., Russotto, R., and Huang, R.: Improving Resilience of Transport Instrastructure to Climate Change and other natural and Manmande events based on the combined use of Terrestrial and Airbone Sensors and Advanced Modelling Tools, 14 ∘ Congreso Nacional del Medio Ambiente (CONAMA 2018), November 2018, MADRID, Spain, 11 pp., hal-02280917v2, 2018. 

Shaddick, G., Thomas, M. L., Mudu, P., Ruggeri, G., and Gumy, S.: Half the world's population are exposed to increasing air pollution, npj Climate and Atmospheric Science, 3, 23, https://doi.org/10.1038/s41612-020-0124-2 , 2020. 

Shaddick, G., Salter, J. M., Peuch, V.-H., Ruggeri, G., Thomas, M. L., Mudu, P., Tarasova, O., Baklanov, A., and Gumy, S.: Global Air Quality: An Inter-Disciplinary Approach to Exposure Assessment for Burden of Disease Analyses, Atmosphere, 12, 48, https://doi.org/10.3390/atmos12010048 , 2021. 

Sharma, A., Fernando, H. J. S., Hamlet, A. F., Hellmann, J. J., Barlage, M., and Chen, F.: Urban meteorological modeling using WRF: A sensitivity study, Int. J. Climatol., 37, 1885–1900, https://doi.org/10.1002/joc.4819 , 2017. 

Siddika, N., Rantala, A. K., Antikainen, H., Balogun, H., Amegah, A. K., Ryti, N. R. I., Kukkonen, J., Sofiev, M., Jaakkola, M. S., and Jaakkola, J. J. K.: Synergistic effects of prenatal exposure to fine particulate matter (PM 2.5 ) and ozone (O 3 ) on the risk of preterm birth: A population-based cohort study, Environ. Res., 176, 108549, https://doi.org/10.1016/j.envres.2019.108549 , 2019. 

Siddika, N., Rantala, A. K., Antikainen, H., Balogun, H., Amegah, A. K., Ryti, N. R. I., Kukkonen, J., Sofiev, M., Jaakkola, M. S., and Jaakkola, J. J. K.: Short-term prenatal exposure to ambient air pollution and risk of preterm birth – A population-based cohort study in Finland, Environ. Res., 184, 109290, https://doi.org/10.1016/j.envres.2020.109290 , 2020. 

Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L. D., Fagerli, H., Flechard, C. R., Hayman, G. D., Gauss, M., Jonson, J. E., Jenkin, M. E., Nyíri, A., Richter, C., Semeena, V. S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á., and Wind, P.: The EMEP MSC-W chemical transport model – technical description, Atmos. Chem. Phys., 12, 7825–7865, https://doi.org/10.5194/acp-12-7825-2012 , 2012. 

Singh, V., Sokhi, R. S., and Kukkonen, J.: PM 2.5 concentrations in London for 2008 – A modeling analysis of contributions from road traffic, J. Air Waste Manage., 64, 509–518, https://doi.org/10.1080/10962247.2013.848244 , 2014. 

Singh, V., Sokhi, R. S., Beig, G., Biswal, A., Sahu, S. K., Sandeepan, S., Stanley, W., Momoh, K., and Fritz, S. C.: Analysis of air quality in the megacity of Delhi with observations and a multiscale coupled modelling system, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 150, https://doi.org/10.18745/pb.22217 , 2020a. 

Singh, V., Sokhi, R. S., and Kukkonen, J.: An approach to predict population exposure to ambient air PM 2.5 concentrations and its dependence on population activity for the megacity London, Environ. Pollut., 257, 113623, https://doi.org/10.1016/j.envpol.2019.113623 , 2020b. 

Skamarock, W. C., Duda, M. G., Ha, S., and Park, S.-H.: Limited-Area Atmospheric Modeling Using an Unstructured Mesh, Mon. Weather Rev., 146, 3445–3460, https://doi.org/10.1175/mwr-d-18-0155.1 , 2018. 

Skjøth, C. A., Geels, C., Berge, H., Gyldenkærne, S., Fagerli, H., Ellermann, T., Frohn, L. M., Christensen, J., Hansen, K. M., Hansen, K., and Hertel, O.: Spatial and temporal variations in ammonia emissions – a freely accessible model code for Europe, Atmos. Chem. Phys., 11, 5221–5236, https://doi.org/10.5194/acp-11-5221-2011 , 2011. 

Smith, T. W. P., Jalkanen, J. P., Anderson, B. A., Corbett, J. J., Faber, J., Hanayama, S., O'Keeffe, E., Parker, S., Johansson, L., Aldous, L., Raucci, C., Traut, M., Ettinger, S., Nelissen, D., Lee, D. S., Ng, S., Agrawal, A., Winebrake, J. J., Hoen, M., Chesworth, S., and Pandey, A.: Third IMO GHG Study 2014, International Maritime Organization (IMO) London, UK, April 2015. 

Smith, J. D., Mitsakou, C., Kitwiroon, N., Barratt, B. M., Walton, H. A., Taylor, J. G., Anderson, H. R., Kelly, F. J., and Beevers, S. D.: London Hybrid Exposure Model: Improving Human Exposure Estimates to NO 2 and PM 2.5 in an Urban Setting, Environ. Sci. Technol., 50, 11760–11768, https://doi.org/10.1021/acs.est.6b01817 , 2016. 

Smith, J. D., Barratt, B. M., Fuller, G. W., Kelly, F. J., Loxham, M., Nicolosi, E., Priestman, M., Tremper, A. H., and Green, D. C.: PM 2.5 on the London Underground, Environ. Int., 134, 105188, https://doi.org/10.1016/j.envint.2019.105188 , 2020. 

Soares, J., Kousa, A., Kukkonen, J., Matilainen, L., Kangas, L., Kauhaniemi, M., Riikonen, K., Jalkanen, J.-P., Rasila, T., Hänninen, O., Koskentalo, T., Aarnio, M., Hendriks, C., and Karppinen, A.: Refinement of a model for evaluating the population exposure in an urban area, Geosci. Model Dev., 7, 1855–1872, https://doi.org/10.5194/gmd-7-1855-2014 , 2014. 

Sofiev, M., Winebrake, J. J., Johansson, L., Carr, E. W., Prank, M., Soares, J., Vira, J., Kouznetsov, R., Jalkanen, J.-P., and Corbett, J. J.: Cleaner fuels for ships provide public health benefits with climate tradeoffs, Nat. Commun., 9, 406, https://doi.org/10.1038/s41467-017-02774-9 , 2018. 

Sokhi, R. (Ed.): World Atlas of Atmospheric Pollution, Anthem Press, London, https://doi.org/10.7135/upo9780857288448 , 2012. 

Sokhi, R. S., Baklanov, A., and Schlünzen, K. H. (Eds.): Mesoscale modelling for meteorological and air pollution applications, Anthem Press, an imprint of Wimbledon Publishing Company, London, 342 pp., 2018. 

Sokhi, R. S., Singh, V., Querol, X., Finardi. S., Targino, A. C., Andrade, M. F., Pavlovic, R., Garland, R. M., Massagué, J., Kong, S., Baklanov, A., Ren, L., Tarasova, O., Carmichael, G., Peuch, V. H., Anand, V., Arbilla, G., Badali, K., Beig, G., Belalcazar, L. C., Bolignano, A., Brimblecombe, P., Camacho, P., Casallas, A., Charland, J. P., Choi, J., Chourdakis, E., Coll, I., Collins, M., Cyrys, J., da Silva, C. M., Di Giosa, A. D., Di Leo, A., Ferro, C., Gavidia-Calderon, M., Gayen, A., Ginzburg, A., Godefroy, F., Gonzalez, Y. A., Guevara-Luna, M., Haque, S. M., Havenga, H., Herod, D., Hõrrak, U., Hussein, T., Ibarra, S., Jaimes, M., Kaasik, M., Khaiwal, R., Kim, J., Kousa, A., Kukkonen, J., Kulmala, M., Kuula, J., La Violette, N., Lanzani, G., Liu, X., MacDougall, S., Manseau, P. M., Marchegiani, G., McDonald, B., Mishra, S. V., Molina, L. T., Mooibroek, D., Mor, S., Moussiopoulos, N., Murena, F., Niemi, J. V., Noe, S., Nogueira, T., Norman, M., Pérez-Camaño, J. L., Petäjä, T., Piketh, S., Rathod, A., Reid, K., Retama, A., Rivera, O., Rojas, N. Y., Rojas-Quincho, J. P., San José, R., Sánchez, O., Seguel, R. J., Sillanpää, S., Su, Y., Tapper, N., Terrazas, A., Timonen, H., Toscano, D., Tsegas, G., Velders, G. J. M., Vlachokostas, C., von Schneidemesser, E., VPM, R., Yadav, R., Zalakeviciute, R., and Zavala, M.: A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions, Environ Int., 157, 106818, https://doi.org/10.1016/j.envint.2021.106818 , 2021. 

Solazzo, E., Bianconi, R., Vautard, R., Appel, K. W., Moran, M. D., Hogrefe, C., Bessagnet, B., Brandt, J., Christensen, J. H., Chemel, C., Coll, I., Denier van der Gon, H., Ferreira, J., Forkel, R., Francis, X. V., Grell, G., Grossi, P., Hansen, A. B., Jeričević, A., Kraljević, L., Miranda, A. I., Nopmongcol, U., Pirovano, G., Prank, M., Riccio, A., Sartelet, K. N., Schaap, M., Silver, J. D., Sokhi, R. S., Vira, J., Werhahn, J., Wolke, R., Yarwood, G., Zhang, J., Rao, S. T., and Galmarini, S.: Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII, Atmos. Environ., 53, 60–74, https://doi.org/10.1016/j.atmosenv.2012.01.003 , 2012. 

Son, J.-Y., Liu, J. C., and Bell, M. L.: Temperature-related mortality: a systematic review and investigation of effect modifiers, Environ. Res. Lett., 14, 073004, https://doi.org/10.1088/1748-9326/ab1cdb , 2019. 

Sonawane, N. V., Patil, R. S., and Sethi, V.: Health benefit modelling and optimization of vehicular pollution control strategies, Atmos. Environ., 60, 193–201, 2012. 

Soret, A., Guevara, M., and Baldasano, J. M.: The potential impacts of electric vehicles on air quality in the urban areas of Barcelona and Madrid (Spain), Atmos. Environ., 99, 51–63, 2014. 

Soulhac, L., Salizzoni, P., Mejean, P., Didier, D., and Rios, I.: The model SIRANE for atmospheric urban pollutant dispersion; PART II, validation of the model on a real case study, Atmos. Environ., 49, 320–337, https://doi.org/10.1016/j.atmosenv.2011.11.031 , 2012. 

Souza, D. M., Teixeira, R. F. M., and Ostermann, O. P.: Assessing biodiversity loss due to land use with Life Cycle Assessment: are we there yet?, Glob. Change Biol., 21, 32–47, https://doi.org/10.1111/gcb.12709 , 2015. 

Stafoggia, M., Samoli, E., Alessandrini, E., Cadum, E., Ostro, B., Berti, G., Faustini, A., Jacquemin, B., Linares, C., Pascal, M., Randi, G., Ranzi, A., Stivanello, E., and Forastiere, F.: Short-term Associations between Fine and Coarse Particulate Matter and Hospitalizations in Southern Europe: Results from the MED-PARTICLES Project, Environ. Health Persp., 121, 1026–1033, https://doi.org/10.1289/ehp.1206151 , 2013. 

Stafoggia, M., Cesaroni, G., Peters, A., Andersen, Z. J., Badaloni, C., Beelen, R., Caracciolo, B., Cyrys, J., Faire, U. d., Hoogh, K. d., Eriksen, K. T., Fratiglioni, L., Galassi, C., Gigante, B., Havulinna, A. S., Hennig, F., Hilding, A., Hoek, G., Hoffmann, B., Houthuijs, D., Korek, M., Lanki, T., Leander, K., Magnusson, P. K., Meisinger, C., Migliore, E., Overvad, K., Östenson, C.-G., Pedersen, N. L., Pekkanen, J., Penell, J., Pershagen, G., Pundt, N., Pyko, A., Raaschou-Nielsen, O., Ranzi, A., Ricceri, F., Sacerdote, C., Swart, W. J. R., Turunen, A. W., Vineis, P., Weimar, C., Weinmayr, G., Wolf, K., Brunekreef, B., and Forastiere, F.: Long-Term Exposure to Ambient Air Pollution and Incidence of Cerebrovascular Events: Results from 11 European Cohorts within the ESCAPE Project, Environ. Health Persp., 122, 919–925, https://doi.org/10.1289/ehp.1307301 , 2014. 

Stohl, A., Kim, J., Li, S., O'Doherty, S., Mühle, J., Salameh, P. K., Saito, T., Vollmer, M. K., Wan, D., Weiss, R. F., Yao, B., Yokouchi, Y., and Zhou, L. X.: Hydrochlorofluorocarbon and hydrofluorocarbon emissions in East Asia determined by inverse modeling, Atmos. Chem. Phys., 10, 3545–3560, https://doi.org/10.5194/acp-10-3545-2010 , 2010. 

Stojiljkovic, A., Kauhaniemi, M., Kukkonen, J., Kupiainen, K., Karppinen, A., Denby, B. R., Kousa, A., Niemi, J. V., and Ketzel, M.: The impact of measures to reduce ambient air PM 10 concentrations originating from road dust, evaluated for a street canyon in Helsinki, Atmos. Chem. Phys., 19, 11199–11212, https://doi.org/10.5194/acp-19-11199-2019 , 2019. 

Stone, R.: Counting the Cost of London's Killer Smog, Science, 298, 2106–2107, https://doi.org/10.1126/science.298.5601.2106b , 2002. 

Stone, V., Miller, M. R., Clift, M. J. D., Elder, A., Mills, N. L., Møller, P., Schins, R. P. F., Vogel, U., Kreyling, W. G., Alstrup Jensen, K., Kuhlbusch, T. A. J., Schwarze, P. E., Hoet, P., Pietroiusti, A., Vizcaya-Ruiz, A. d., Baeza-Squiban, A., Teixeira, J. P., Tran, C. L., and Cassee, F. R.: Nanomaterials Versus Ambient Ultrafine Particles: An Opportunity to Exchange Toxicology Knowledge, Environ. Health Persp., 125, 106002, https://doi.org/10.1289/ehp424 , 2017. 

Sun, D., Zhang, Y., Xue, R., and Zhang, Y.: Modeling carbon emissions from urban traffic system using mobile monitoring, Sci. Total Environ., 599–600, 944–951, https://doi.org/10.1016/j.scitotenv.2017.04.186 , 2017. 

Suter, I. and Brunner, D.: Influence of boundary conditions and cloud chemistry on sulfate concentrations in a nested model setup, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 55, https://doi.org/10.18745/pb.22217 , 2020. 

Tan, J., Mu, L., Huang, J., Yu, S., Chen, B., and Yin, J.: An initial investigation of the association between the SARS outbreak and weather: with the view of the environmental temperature and its variation, J. Epidemiol. Commun. H., 59, 186–192, https://doi.org/10.1136/jech.2004.020180 , 2005. 

Tan, J., Fu, J. S., Carmichael, G. R., Itahashi, S., Tao, Z., Huang, K., Dong, X., Yamaji, K., Nagashima, T., Wang, X., Liu, Y., Lee, H.-J., Lin, C.-Y., Ge, B., Kajino, M., Zhu, J., Zhang, M., Liao, H., and Wang, Z.: Why do models perform differently on particulate matter over East Asia? A multi-model intercomparison study for MICS-Asia III, Atmos. Chem. Phys., 20, 7393–7410, https://doi.org/10.5194/acp-20-7393-2020 , 2020. 

Tarín-Carrasco, P., Im, U., Geels, C., Palacios-Peña, L., and Jiménez-Guerrero, P.: Reducing future air pollution-related premature mortality over Europe by mitigating emissions: assessing an 80 % renewable energies scenario, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-86 , in review, 2021. 

Teuchies, J., Cox, T. J. S., Van Itterbeeck, K., Meysman, F. J. R., and Blust, R.: The impact of scrubber discharge on the water quality in estuaries and ports, Environmental Sciences Europe, 32, 103, https://doi.org/10.1186/s12302-020-00380-z , 2020. 

Theloke, J. and Friedrich, R.: Compilation of a database on the composition of anthropogenic VOC emissions for atmospheric modeling in Europe, Atmos. Environ., 41, 4148–4160, https://doi.org/10.1016/j.atmosenv.2006.12.026 , 2007. 

Thiruchittampalam, B.: Entwicklung und Anwendung von Methoden und Modellen zur Berechnung von räumlich und zeitlich hochaufgelösten Emissionen in Europa, Institut für Energiewirtschaft und Rationelle Energieanwendung, https://doi.org/10.18419/opus-2325 , 2014. 

Thompson, J. E.: Crowd-sourced air quality studies: A review of the literature & portable sensors, Trends in Environmental Analytical Chemistry, 11, 23–34, https://doi.org/10.1016/j.teac.2016.06.001 , 2016. 

Thunis, P.: On the validity of the incremental approach to estimate the impact of cities on air quality, Atmos. Environ., 173, 210–222, https://doi.org/10.1016/j.atmosenv.2017.11.012 , 2018. 

Thunis, P., Miranda, A., Baldasano, J. M., Blond, N., Douros, J., Graff, A., Janssen, S., Juda-Rezler, K., Karvosenoja, N., Maffeis, G., Martilli, A., Rasoloharimahefa, M., Real, E., Viaene, P., Volta, M., and White, L.: Overview of current regional and local scale air quality modelling practices: Assessment and planning tools in the EU, Environ. Sci. Policy, 65, 13–21, https://doi.org/10.1016/j.envsci.2016.03.013 , 2016. 

Thunis, P., Clappier, A., Tarrason, L., Cuvelier, C., Monteiro, A., Pisoni, E., Wesseling, J., Belis, C. A., Pirovano, G., Janssen, S., Guerreiro, C., and Peduzzi, E.: Source apportionment to support air quality planning: Strengths and weaknesses of existing approaches, Environ. Int., 130, 104825, https://doi.org/10.1016/j.envint.2019.05.019 , 2019. 

Thurston, G. D., Kipen, H., Annesi-Maesano, I., Balmes, J., Brook, R. D., Cromar, K., De Matteis, S., Forastiere, F., Forsberg, B., Frampton, M. W., Grigg, J., Heederik, D., Kelly, F. J., N., K., Laumbach, R., Peters, A., Rajagopalan, S. T., Rich, D., Ritz, B., Samet, J. M., Sandstrom, T., Sigsgaard, T., Sunyer, J., and Brunekreef, B.: A joint ERA/ATS policy statement: What constitutes an adverse health effect of air pollution? An analytical framework, Eur. Respir. J., 49, 1600419, https://doi.org/10.1183/13993003.00419-2016 , 2017. 

Thygesen, M., Holst, G. J., Hansen, B., Geels, C., Kalkbrenner, A., Schendel, D., Brandt, J., Pedersen, C. B., and Dalsgaard, S.: Exposure to air pollution in early childhood and the association with Attention-Deficit Hyperactivity Disorder, Environ. Res., 183, 108930, https://doi.org/10.1016/j.envres.2019.108930 , 2020. 

Tinarelli, G. L. and Trini Castelli, S.: Assessment of the Sensitivity to the Input Conditions with a Lagrangian Particle Dispersion Model in the UDINEE Project, Bound.-Lay. Meteorol., 171, 491–512, https://doi.org/10.1007/s10546-018-0413-z , 2019. 

Toon, O. B., Bardeen, C. G., Robock, A., Xia, L., and Kristensen, H.: Rapidly expanding nuclear arsenals in Pakistan and India portend regional and global catastrophe, Science Advances, 5, eaay5478, https://doi.org/10.1126/sciadv.aay5478 , 2019. 

Torras Ortiz, S.: A hybrid dispersion modelling approach for quantifying and assessing air quality in Germany with focus on urban background and kerbside concentrations, dissertation, Universtät Stuttgart, Stuttgart, https://doi.org/10.18419/opus-1990 , 2012. 

Torras Ortiz, S. and Friedrich, R.: A modelling approach for estimating background pollutant concentrations in urban areas, Atmos. Pollut. Res., 4, 147–156, https://doi.org/10.5094/apr.2013.015 , 2013. 

Trusilova, K., Schubert, S., Wouters, H., Früh, B., Grossman-Clarke, S., Demuzere, M., and Becker, P.: The urban land use in the COSMO-CLM model: a comparison of three parameterizations for Berlin, Meteorol. Z., 25, 231–244, https://doi.org/10.1127/metz/2015/0587 , 2016. 

Tsegas, G., Moussiopoulos, N., Barmpas, F., Akylas, V., and Douros, I.: An integrated numerical methodology for describing multiscale interactions on atmospheric flow and pollutant dispersion in the urban atmospheric boundary layer, J. Wind Eng. Ind. Aerod., 144, 191–201, 2015. 

Turco, R. P., Toon, O. B., Ackerman, T. P., Pollack, J. B., and Sagan, C.: Nuclear Winter: Global Consequences of Multiple Nuclear Explosions, Science, 222, 1283–1992, https://doi.org/10.1126/science.222.4630.1283 , 1983. 

UNECE: Protocols, United Nations Economic Commission for Europe, https://unece.org/protocols (last access: 21 February 2022), 2020. 

Van Dingenen, R., Dentener, F., Crippa, M., Leitao, J., Marmer, E., Rao, S., Solazzo, E., and Valentini, L.: TM5-FASST: a global atmospheric source–receptor model for rapid impact analysis of emission changes on air quality and short-lived climate pollutants, Atmos. Chem. Phys., 18, 16173–16211, https://doi.org/10.5194/acp-18-16173-2018 , 2018. 

van Doremalen, N., Bushmaker, T., and Munster, V. J.: Stability of Middle East respiratory syndrome coronavirus (MERS-CoV) under different environmental conditions, Euro Surveill., 18, 20590, https://doi.org/10.2807/1560-7917.es2013.18.38.20590 , 2013. 

van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., Lloyd-Smith, J. O., de Wit, E., and Munster, V. J.: Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1, New Engl. J. Med., 382, 1564–1567, 2020. 

Vardoulakis, S., Giagloglou, E., Steinle, S., Davis, A., Sleeuwenhoek, A., Galea, K. S., Dixon, K., and Crawford, J. O.: Indoor Exposure to Selected Air Pollutants in the Home Environment: A Systematic Review, Int. J. Env. Res. Pub. He., 17, 8972, https://doi.org/10.3390/ijerph17238972 , 2020. 

Vautard, R., Buitjes, P., Thunis, P., Cuvelier, C., Bedonj, M., Bessagnet, B., Honore, C., Moussiopoulos, N., Pirovano, M., Schaap, M., Stern, R., Tarrason, L., and Wind, P.: Evaluation and intercomparison of Ozone and PM 10 simulations by several chemistry transport models over four European cities within the CityDelta project, Atmos. Environ., 41, 173–188, https://doi.org/10.1016/j.atmosenv.2006.07.039 , 2007. 

Veratti, G., Fabbi, S., Bigi, A., Lupascu, A., Tinarelli, G., Teggi, S., Brusasca, G., Butler, T. M., and Ghermandi, G.: Towards the coupling of a chemical transport model with a micro-scale Lagrangian modelling system for evaluation of urban NO x levels in a European hotspot, Atmos. Environ., 223, 117285, https://doi.org/10.1016/j.atmosenv.2020.117285 , 2020. 

Viana, M., Kuhlbusch, T. A. J., Querol, X., Alastuey, A., Harrison, R. M., Hopke, P. K., Winiwarter, W., Vallius, M., Szidat, S., Prévôt, A. S. H., Hueglin, C., Bloemen, H., Wåhlin, P., Vecchi, R., Miranda, A. I., Kasper-Giebl, A., Maenhaut, W., and Hitzenberger, R.: Source apportionment of particulate matter in Europe: A review of methods and results, J. Aerosol Sci., 39, 827–849, 2008. 

Villeneuve, P. J., Jerrett, M., Su, J., Burnett, R. T., Chen, H., Brook, J., Wheeler, A. J., Cakmak, S., and Goldberg, M. S.: A cohort study of intra-urban variations in volatile organic compounds and mortality, Toronto, Canada, Environ. Pollut., 183, 30–39, https://doi.org/10.1016/j.envpol.2012.12.022 , 2013. 

Vodonos, A., Awad, Y. A., and Schwartz, J.: The concentration-response between long-term PM 2.5 exposure and mortality; A meta-regression approach, Environ. Res., 166, 677–689, https://doi.org/10.1016/j.envres.2018.06.021 , 2018. 

Voss, V., Schlünzen, K.H., and Grawe, D.: Atmospheric model data (ATMODAT) - creation of a model data standard for obstacle resolving models, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis I., Hatfield, UK, p. 128, https://doi.org/10.18745/PB.22217 , 2020. 

Vouitsis, I., Ntziachristos, L., Samaras, C., and Samaras, Z.: Particulate mass and number emission factors for road vehicles based on literature data and relevant gap filling methods, Atmos. Environ., 168, 75–89, https://doi.org/10.1016/j.atmosenv.2017.09.010 , 2017. 

Wang, M., Beelen, R., Stafoggia, M., Raaschou-Nielsen, O., Andersen, Z. J., Hoffmann, B., Fischer, P., Houthuijs, D., Nieuwenhuijsen, M., Weinmayr, G., Vineis, P., Xun, W. W., Dimakopoulou, K., Samoli, E., Laatikainen, T., Lanki, T., Turunen, A. W., Oftedal, B., Schwarze, P., Aamodt, G., Penell, J., Faire, U. d., Korek, M., Leander, K., Pershagen, G., Pedersen, N. L., Östenson, C.-G., Fratiglioni, L., Eriksen, K. T., Sørensen, M., Tjønneland, A., Bueno-de-Mesquita, B., Eeftens, M., Bots, M. L., Meliefste, K., Krämer, U., Heinrich, J., Sugiri, D., Key, T., Hoogh, K. d., Wolf, K., Peters, A., Cyrys, J., Jaensch, A., Concin, H., Nagel, G., Tsai, M.-Y., Phuleria, H., Ineichen, A., Künzli, N., Probst-Hensch, N., Schaffner, E., Vilier, A., Clavel-Chapelon, F., Declerq, C., Ricceri, F., Sacerdote, C., Marcon, A., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Katsoulis, M., Trichopoulou, A., Keuken, M., Jedynska, A., Kooter, I. M., Kukkonen, J., Sokhi, R. S., Brunekreef, B., Katsouyanni, K., and Hoek, G.: Long-term exposure to elemental constituents of particulate matter and cardiovascular mortality in 19 European cohorts: Results from the ESCAPE and TRANSPHORM projects, Environ. Int., 66, 97–106, https://doi.org/10.1016/j.envint.2014.01.026 , 2014. 

Wang, K., Yahya, K., Zhang, Y., Hogrefe, C., Pouliot, G., Knote, C., Hodzic, A., San Jose, R., Perez, J. L., Jiménez-Guerrero, P., Baro, R., Makar, P., and Bennartz, R.: A multi-model assessment for the 2006 and 2010 simulations under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part II. Evaluation of column variable predictions using satellite data, Atmos. Environ., 115, 587–603, https://doi.org/10.1016/j.atmosenv.2014.07.044 , 2015. 

Wang, P., Liu, Y., Qin, Z., and Zhang, G.: A novel hybrid forecasting model for PM10 and SO2 daily concentrations, Sci. Total Environ., 505, 1202–1212, https://doi.org/10.1016/j.scitotenv.2014.10.078 , 2015. 

Wang, Q., Li, B., Benmarhnia, T., Hajat, S., Ren, M., Liu, T., Knibbs, L. D., Zhang, H., Bao, J., Zhang, Y., Zhao, Q., and Huang, C.: Independent and Combined Effects of Heatwaves and PM 2.5 on Preterm Birth in Guangzhou, China: A Survival Analysis, Environ. Health Persp., 128, 017006, https://doi.org/10.1289/ehp5117 , 2020. 

Watkiss, P. and Downing, T.: The social cost of carbon: valuation estimates and their use in UK policy, Integr. Assess., 8, 85–105, 2008. 

Weichenthal, S., Olaniyan, T., Christidis, T., Lavigne, E., Hatzopoulou, M., van Ryswyk, K., Tjepkema, M., and Burnett, R.: Within-city Spatial Variations in Ambient Ultrafine Particle Concentrations and Incident Brain Tumors in Adults, Epidemiology (Cambridge, Mass.), 31, 177–183, https://doi.org/10.1097/ede.0000000000001137 , 2020. 

Weinmayr, G., Romeo, E., De Sario, M., Weiland, S. K., and Forastiere, F.: Short-Term Effects of PM 10 and NO 2 on Respiratory Health among Children with Asthma or Asthma-like Symptoms: A Systematic Review and Meta-Analysis, Environ. Health Persp., 118, 449–457, https://doi.org/10.1289/ehp.0900844 , 2010. 

Werhahn, J., Forkel, R., Emeis, S., Reifeltshammer, R., and Uhrner, U.: Air quality simulations in an urban area within a smart air quality network by the large eddy simulation model PALM-4U, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 126, https://doi.org/10.18745/pb.22217 , 2020. 

Weschler, C. J. and Carslaw, N.: Indoor Chemistry, Environ. Sci. Technol., 52, 2419–2428, https://doi.org/10.1021/acs.est.7b06387 , 2018. 

WHO: Health risks of air pollution in Europe – HRAPIE project; Recommendations for concentration–response functions for cost–benefit analysis of particulate matter, ozone and nitrogen dioxide, WHO Regional Office for Europe, https://www.euro.who.int/en/health-topics/environment-and- health/air-quality/publications/2013/health-risks-of-air- pollution-in-europe-hrapie-project.-recommendations-for-concentrationresponse-pollution-in-europe-hrapie-project.-recommendations-for-concentrationresponse- functions-for-costbenefit-analysis-of-particulate-matter,-ozone-and-nitrogen-dioxide (last access: 22 February 2022), 2013a. 

WHO: Review of evidence on health aspects of air pollution – REVIHAAP Project: Technical Report, Regional Office Europe, Copenhagen, https://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report-final-version.pdf (last access: 25 February 2022), 2013b. 

WHO: Ambient air pollution: A global assessment of exposure and burden of disease, World Health Organization, Geneva, Switzerland, 131 pp., https://apps.who.int/iris/handle/10665/250141 (last access: 25 February 2022), 2016. 

WHO: WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, ISBN 978-92-4-003422-8 (electronic version), https://apps.who.int/iris/handle/10665/345329 (last access: 25 February 2022), 2021. 

Williams, R., Duvall, R., Kilaru, V., Hagler, G., Hassinger, L., Benedict, K., Rice, J., Kaufman, A., Judge, R., Pierce, G., Allen, G., Bergin, M., Cohen, R. C., Fransioli, P., Gerboles, M., Habre, R., Hannigan, M., Jack, D., Louie, P., Martin, N. A., Penza, M., Polidori, A., Subramanian, R., Ray, K., Schauer, J., Seto, E., Thurston, G., Turner, J., Wexler, A. S., and Ning, Z.: Deliberating performance targets workshop: Potential paths for emerging PM 2.5 and O3 air sensor progress, Atmos. Environ. X, 2, 100031, https://doi.org/10.1016/j.aeaoa.2019.100031 , 2019. 

Winnes, H., Fridell, E., and Moldanova, J.: Effects of Marine Exhaust Gas Scrubbers on Gas and Particle Emissions, J. Mar. Sci. Eng., 8, 299, https://doi.org/10.3390/jmse8040299 , 2020. 

WMO: Coupled Chemistry-Meteorology/Climate Modelling (CCMM): Status and Relevance for Numerical Weather Prediction, Atmospheric Pollution and Climate Research, Geneva, Switzerland, 23–25 February 2015, World Meteorological Organization, Geneva, ISBN 978-92-63-11172-2, 165 pp., 2016. 

WMO: WMO Global Atmosphere Watch (GAW) Implementation Plan: 2016–2023, Geneva, Switzerland, 84 pp., ISBN 978-92-63-11156-2, https://library.wmo.int/doc_num.php?explnum_id=10439 (last access: 11 March 2022), 2017. 

WMO: Best Practices and Training Materials for Chemical Weather/Air Quality Forecasting (CW-AQF), WMO Geneva, Chair, Publications Board, World Meteorological Organization 7 bis, avenue de la Paix – P.O. Box 2300 – CH 1211 Geneva 2 – Switzerland, ISBN: 978-92-63-11262-0, 2020. 

WMO: Review on Meteorological and Air Quality Factors Affecting the COVID-19 Pandemic, World Meteorological Organization, WMO-No. 1262, ISBN: 978-92-63-11262-0, 2021. 

Wolf, K., Stafoggia, M., Cesaroni, G., Andersen, Z. J., Beelen, R., Galassi, C., Hennig, F., Migliore, E., Penell, J., Ricceri, F., Sørensen, M., Turunen, A. W., Hampel, R., Hoffmann, B., Kälsch, H., Laatikainen, T., Pershagen, G., Raaschou-Nielsen, O., Sacerdote, C., Vineis, P., Badaloni, C., Cyrys, J., Hoogh, K. d., Eriksen, K. T., Jedynska, A., Keuken, M., Kooter, I., Lanki, T., Ranzi, A., Sugiri, D., Tsai, M.-Y., Wang, M., Hoek, G., Brunekreef, B., Peters, A., and Forastiere, F.: Long-term Exposure to Particulate Matter Constituents and the Incidence of Coronary Events in 11 European Cohorts, Epidemiology, 26, 565–574, https://doi.org/10.1097/ede.0000000000000300 , 2015. 

Wolf, T., Pettersson, L. H., and Esau, I.: A very high-resolution assessment and modelling of urban air quality, Atmos. Chem. Phys., 20, 625–647, https://doi.org/10.5194/acp-20-625-2020 , 2020. 

Wolf-Grosse, T., Esau, I., and Reuder, J.: Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study, Atmos. Chem. Phys., 17, 7261–7276, https://doi.org/10.5194/acp-17-7261-2017 , 2017. 

World Meteorological Congress: Abridged Final Report of the Eighteenth Session, World Meteorological Organization (WMO) – WMO, WMO-No. 1236, ISBN: 978-92-63-11236-1, 2019. 

Wouters, H., Demuzere, M., Blahak, U., Fortuniak, K., Maiheu, B., Camps, J., Tielemans, D., and van Lipzig, N. P. M.: The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modelling: description and application with the COSMO-CLM model for a Belgian summer, Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016 , 2016. 

WWRP: Seamless prediction of the earth system: From minutes to months, WMO, Geneva, No. 1156, 471 pp., ISBN: 9789263111562, 2015. 

Xie, J. and Zhu, Y.: Association between ambient temperature and COVID-19 infection in 122 cities from China, Sci. Total Environ., 724, 138201, https://doi.org/10.1016/j.scitotenv.2020.138201 , 2020. 

Xie, Y., Dai, H. C., Zhang, Y. X., Wu, Y. Z., Hanaoka, T., and Masui, T.: Comparison of health and economic impacts of PM 2.5 and ozone pollution in China, Environ. Int., 130, 104881, https://doi.org/10.1016/j.envint.2019.05.075 , 2019. 

Xu, C., Kan, H.-D., Fan, Y.-N., Chen, R.-J., Liu, J.-H., Li, Y.-F., Zhang, Y., Ji, A.-L., and Cai, T.-J. Acute effects of air pollution on enteritis admissions in Xi'an, China, J. Toxicol. Env. Heal. A, 79, 1183–1189, 2016. 

Yang, L. Q., Chen, G., Zhao, J. L., and Rytter, N. G. M.: Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping, Sustainability, 12, 3649, https://doi.org/10.3390/su12093649 , 2020. 

Yang, Y., Pun, V. C., Sun, S., Lin, H., Mason, T. G., and Qiu, H.: Particulate matter components and health: a literature review on exposure assessment, Journal of Public Health and Emergency, 2, 14, https://doi.org/10.21037/jphe.2018.03.03 , 2018. 

Zacharof, N., Doulgeris, S., Myrsinias, I., Toumasatos, Z., Kolokotronis, D., Dimaratos, A., Mellios, G., and Samaras, Z.: MILE 21: Raising user awareness on on-road fuel consumption, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 36, https://doi.org/10.18745/pb.22217 , 2020. 

Żak, M., Melaniuk-Wolny, E., and Widziewicz, K.: The exposure of pedestrians, drivers and road transport passengers to nitrogen dioxide, Atmos. Pollut. Res., 8, 781–790, https://doi.org/10.1016/j.apr.2016.10.011 , 2017. 

Zanini, P., Chevalier, J., Lebegue, B., Allard, J., and Lascaux, F.: High-resolution mapping of urban air quality based on low-cost sensors and neural network model: application to Grenoble City, in: Proceedings of 12th International Conference on Air Quality, Science and Application, edited by: Moussiopoulos, N., Sokhi, R. S., Tsegas, G., Fragkou, E., Chourdakis, E., and Pipilis, I., Hatfield, UK, p. 112, https://doi.org/10.18745/pb.22217 , 2020. 

Zanobetti, A. and Schwartz, J.: The effect of fine and coarse particulate air pollution on mortality: A national analysis, Environ. Health Persp., 117, 898–903, https://doi.org/10.1289/ehp.0800108 , 2009.  

Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., and Zhang, M.: Spatiotemporal prediction of continuous daily PM 2.5 concentrations across China using a spatially explicit machine learning algorithm, Atmos. Environ., 155, 129–139, 2017. 

Zhang, X., Chen, X., and Zhang, X.: The impact of exposure to air pollution on cognitive performance, P. Natl. Acad. Sci. USA, 115, 9193–9197, https://doi.org/10.1073/pnas.1809474115 , 2018. 

Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, part I: History, techniques, and current status, Atmos. Environ., 60, 632–655, https://doi.org/10.1016/j.atmosenv.2012.06.031 , 2012a. 

Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects, Atmos. Environ., 60, 656–676, https://doi.org/10.1016/j.atmosenv.2012.02.041 , 2012b. 

Zhang, Y., Hong, C., Yahya, K., Li, Q., Zhang, Q., and He, K.: Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States, Atmos. Environ., 138, 162–182, https://doi.org/10.1016/j.atmosenv.2016.05.006 , 2016. 

Zhao, B., Zheng, H., Wang, S., Smith, K. R., Lu, X., Aunan, K., Gu, Y., Wang, Y., Ding, D., Xing, J., Fu, X., Yang, X., Liou, K.-N., and Hao, J.: Change in household fuels dominates the decrease in PM 2.5 exposure and premature mortality in China in 2005–2015, P. Natl. Acad. Sci. USA, 115, 12401–12406, https://doi.org/10.1073/pnas.1812955115 , 2018. 

Zhao, J., Birmili, W., Wehner, B., Daniels, A., Weinhold, K., Wang, L., Merkel, M., Kecorius, S., Tuch, T., Franck, U., Hussein, T., and Wiedensohler, A.: Particle Mass Concentrations and Number Size Distributions in 40 Homes in Germany: Indoor-to-Outdoor Relationships, Diurnal and Seasonal Variation, Aerosol Air Qual. Res., 20, 576–589, https://doi.org/10.4209/aaqr.2019.09.0444 , 2020a. 

Zhao, J. R., Zhang, Y., Patton, A. P., Ma, W. C., Kan, H. D., Wu, L. B., Fung, F., Wang, S. X., Ding, D., and Walker, K.: Projection of ship emissions and their impact on air quality in 2030 in Yangtze River delta, China, Environ. Pollut., 263, 114643, https://doi.org/10.1016/j.envpol.2020.114643 , 2020b. 

Zhu, Y., Xie, J., Huang, F., and Cao, L.: Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China, Sci. Total Environ., 727, 138704, https://doi.org/10.1016/j.scitotenv.2020.138704 , 2020. 

• Introduction
• Scope and structure of the review
• Air pollution sources and emissions
• Air quality observations and instrumentation
• Air quality modelling from local to regional scales
• Interactions between air quality, meteorology, and climate
• Air quality exposure and health
• Air quality management and policy development
• Discussion, synthesis, and recommendations
• Conclusions and future direction
• Data availability
• Author contributions
• Competing interests
• Acknowledgements
• Financial support
• Review statement

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Ambient air pollution and its influence on human health and welfare: an overview

• Review Article
• Published: 03 May 2020
• Volume 27 , pages 24815–24830, ( 2020 )

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• Alsaid Ahmed Almetwally 1 ,
• May Bin-Jumah 2 &
• Ahmed A. Allam 3  

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Human health is closely related to his environment. The influence of exposure to air pollutants on human health and well-being has been an interesting subject and gained much volume of research over the last 50 years. In general, polluted air is considered one of the major factors leading to many diseases such as cardiovascular and respiratory disease and lung cancer for the people. Besides, air pollution adversely affects the animals and deteriorates the plant environment. The overarching objective of this review is to explore the previous researches regarding the causes and sources of air pollution, how to control it and its detrimental effects on human health. The definition of air pollution and its sources were introduced extensively. Major air pollutants and their noxious effects were detailed. Detrimental impacts of air pollution on human health and well-being were also presented.

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Abou Rafee S et al (2017) Contributions of mobile, stationary and biogenic sources to air pollution in the Amazon rainforest: a numerical study with the WRF-Chem model. Atmos Chem Phys 17:7977–7995. https://doi.org/10.5194/acp-17-7977-2017

Article   CAS   Google Scholar  

Altshuller AP (1956) Thermodynamic considerations in the interactions of nitrogen oxides and oxy-acids in the atmosphere. Journal of the Air Pollution Control Association 6:97–100. https://doi.org/10.1080/00966665.1956.10467740

Alves C, Ferraz C (2005) Effects of air pollution on emergency admissions for chronic obstructive pulmonary diseases in Oporto, Portugal. Int J Environ Pollut 23:42–64. https://doi.org/10.1504/IJEP.2005.006395

Appannagari RR (2017) Environmental pollution causes and consequences: a study 3

Arbex MA, de Souza Conceicao GM, Cendon SP, Arbex FF, Lopes AC, Moyses EP, Santiago SL, Saldiva PHN, Pereira LAA, Braga ALF (2009) Urban air pollution and chronic obstructive pulmonary disease-related emergency department visits. J Epidemiol Community Health 63:777–783. https://doi.org/10.1136/jech.2008.078360

Armenta, MdlgaS (2016) The quality of air vol 73. Comprehensive analytical chemistry. Elsevier

Arroyave C, Morcillo M (1995) The effect of nitrogen oxides in atmospheric corrosion of metals. Corros Sci 37:293–305. https://doi.org/10.1016/0010-938X(94)00136-T

Association AL (2013) American Lung Association State of the Air 2013 - Health Effects of Ozone and Particle Pollution American Lung Association

Azari AA, Barney NP (2013) Conjunctivitis: a systematic review of diagnosis and treatment. Jama 310:1721–1729. https://doi.org/10.1001/jama.2013.280318

Article   Google Scholar  

Babayemi J, Ogundiran M, Osibanjo O (2016) Overview of environmental hazards and health effects of pollution in developing countries: a case study of Nigeria. Environmental Hazards and Health Effects of Pollution Environmental Quality Management 26:51–71. https://doi.org/10.1002/tqem.21480

Balmes JR (2019) Household air pollution from domestic combustion of solid fuels and health. J Allergy Clin Immunol 143:1979–1987. https://doi.org/10.1016/j.jaci.2019.04.016

Bao X, Tian X, Yang C, Li Y, Hu Y (2019) Association between ambient air pollution and hospital admission for epilepsy in Eastern China. Epilepsy Res 152:52–58. https://doi.org/10.1016/j.eplepsyres.2019.02.012

Berg AT, Altalib HH, Devinsky O (2017) Psychiatric and behavioral comorbidities in epilepsy: a critical reappraisal. Epilepsia 58:1123–1130. https://doi.org/10.1111/epi.13766

Berglind N (2017) Cardiovascular and respiratory effects of air pollution- application of different observational study designs and analysis approaches. Larolinska Institutet

Bermejo-Orduna R, McBride JR, Shiraishi K, Elustondo D, Lasheras E, Santamaria JM (2014) Biomonitoring of traffic-related nitrogen pollution using Letharia vulpina (L.) hue in the Sierra Nevada, California. Sci Total Environ 490:205–212. https://doi.org/10.1016/j.scitotenv.2014.04.119

Bernstein JA, Alexis N, Barnes C, Bernstein IL, Nel A, Peden D, Diaz-Sanchez D, Tarlo SM, Williams PB, Bernstein JA (2004) Health effects of air pollution. J Allergy Clin Immunol 114:1116–1123. https://doi.org/10.1016/j.jaci.2004.08.030

Bian Y, Huang Z, Ou J, Zhong Z, Xu Y, Zhang Z, Xiao X, Ye X, Wu Y, Yin X, Li C, Chen L, Shao M, Zheng J (2019) Evolution of anthropogenic air pollutant emissions in Guangdong Province, China, from 2006 to 2015. Atmos Chem Phys 19:11701–11719. https://doi.org/10.5194/acp-19-11701-2019

Bloom B, Jones LI, Freeman G (2013) Summary health statistics for U.S. children: National Health Interview Survey, 2012 Vital Health Stat 10:1-81

Brook RD (2008) Cardiovascular effects of air pollution. Clin Sci (Lond) 115:175–187. https://doi.org/10.1042/cs20070444

Brook RD, Jerrett M, Brook JR, Bard RL, Finkelstein MM (2008) The relationship between diabetes mellitus and traffic-related air pollution. J Occup Environ Med 50:32–38. https://doi.org/10.1097/JOM.0b013e31815dba70

Brown TP, Rushton L, Mugglestone MA, Meechan DF (2003) Health effects of a sulphur dioxide air pollution episode. J Public Health 25:369–371. https://doi.org/10.1093/pubmed/fdg083

Cai J, Zhao A, Zhao J, Chen R, Wang W, Ha S, Xu X, Kan H (2014) Acute effects of air pollution on asthma hospitalization in Shanghai, China. Environ Pollut 191:139–144. https://doi.org/10.1016/j.envpol.2014.04.028

Cakmak S, Dales RE, Vidal CB (2010) Air pollution and hospitalization for epilepsy in Chile. Environ Int 36:501–505. https://doi.org/10.1016/j.envint.2010.03.008

Carmichael GR et al (2009) Asian aerosols: current and year 2030 distributions and implications to human health and regional climate change. Environ Sci Technol 43:5811–5817. https://doi.org/10.1021/es8036803

Chang T, Zivin J, Gross T, Neidell M (2019) The effect of pollution on worker productivity: evidence from call center workers in China. Am Econ J Appl Econ 11:151–172. https://doi.org/10.1257/app.20160436

Chen X (2018) A spatial and temporal analysis of the socioeconomic factors associated with breast cancer in Illinois using geographically weighted generalized linear regression. Journal of Geovisualization and Spatial Analysis:2. https://doi.org/10.1007/s41651-017-0011-5

Choe SA, Kauderer S, Eliot MN, Glazer KB, Kingsley SL, Carlson L, Awad YA, Schwartz JD, Savitz DA, Wellenius GA (2018) Air pollution, land use, and complications of pregnancy. Sci Total Environ 645:1057–1064. https://doi.org/10.1016/j.scitotenv.2018.07.237

Chuang KJ, Yan YH, Chiu SY, Cheng TJ (2011) Long-term air pollution exposure and risk factors for cardiovascular diseases among the elderly in Taiwan Occup Environ Med 68:64–68. doi: https://doi.org/10.1136/oem.2009.052704

Combes A, Franchineau G (2019) Fine particle environmental pollution and cardiovascular diseases. Metabolism 100:153944. https://doi.org/10.1016/j.metabol.2019.07.008

Coppola A et al (2016) Psychiatric comorbidities in patients from seven families with autosomal dominant cortical tremor, myoclonus, and epilepsy. Epilepsy Behav 56:38–43. https://doi.org/10.1016/j.yebeh.2015.12.038

Cui L, Conway GA, Jin L, Zhou J, Zhang J, Li X, Zhou L, Li T, Zhang J (2017) Increase in medical emergency calls and calls for central nervous system symptoms during a severe air pollution event, January 2013, Jinan City. China Epidemiology 28(Suppl 1):S67–s73. https://doi.org/10.1097/ede.0000000000000739

D'Amato G et al (2015) Meteorological conditions, climate change, new emerging factors, and asthma and related allergic disorders. A statement of the World Allergy Organization World Allergy Organ J 8:25. https://doi.org/10.1186/s40413-015-0073-0

Dai W, Gao J, Cao G, Ouyang F (2013) Chemical composition and source identification of PM2.5 in the suburb of Shenzhen. China Atmospheric Research 122:391–400. https://doi.org/10.1016/j.atmosres.2012.12.004

Dauchet L, Hulo S, Cherot-Kornobis N, Matran R, Amouyel P, Edme JL, Giovannelli J (2018) Short-term exposure to air pollution: associations with lung function and inflammatory markers in non-smoking, healthy adults. Environ Int 121:610–619. https://doi.org/10.1016/j.envint.2018.09.036

de Prado BP, Mercader EMH, Pujol J, Sunyer J, Mortamais M (2018) The effects of air pollution on the brain: a review of studies interfacing environmental epidemiology and neuroimaging. Curr Environ Health Rep 5:351–364. https://doi.org/10.1007/s40572-018-0209-9

Demetriou CA et al (2012) Biomarkers of ambient air pollution and lung cancer: a systematic review. Occup Environ Med 69:619–627. https://doi.org/10.1136/oemed-2011-100566

Demetriou CA, Vineis P (2015) Carcinogenicity of ambient air pollution: use of biomarkers, lessons learnt and future directions. J Thorac Dis 7:67–95. https://doi.org/10.3978/j.issn.2072-1439.2014.12.31

Ebenstein A, Lavy V, Roth S (2016) The long-run economic consequences of high-stakes examinations: evidence from transitory variation in pollution American Economic Journal: Applied Economics 8. doi: https://doi.org/10.1257/app.20150213 , 65

EEA (2008) EU-27 emission of carbon monoxide, 2006

Europe WHOROf (2013) Health effects of particulate matter. Policy implications for countries in Eastern Europe, Caucasus and Central Asia Copenhagen. W.H. Organization

Ezeh GC (2012) Characterization of PM10 and PM2.5 particulates sizes collected in Ikoyi Lagos, Nigeria Toxicological and environmental chemistry v. 94:pp. 884–894-2012 v.2094 no.2015. doi: https://doi.org/10.1080/02772248.2012.674133

Falcon-Rodriguez CI, Osornio-Vargas AR, Sada-Ovalle I, Segura-Medina P (2016) Aeroparticles, composition, and lung diseases. Front Immunol 7:3. https://doi.org/10.3389/fimmu.2016.00003

Faustini A, Rapp R, Forastiere F (2014) Nitrogen dioxide and mortality: review and meta-analysis of long-term studies. Eur Respir J 44:744–753. https://doi.org/10.1183/09031936.00114713

Fisher RS, van Emde BW, Blume W, Elger C, Genton P, Lee P, Engel J Jr (2005) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46:470–472. https://doi.org/10.1111/j.0013-9580.2005.66104.x

Fluegge K, Fluegge K (2017) Air pollution and risk of hospitalization for epilepsy: the role of farm use of nitrogen fertilizers and emissions of the agricultural air pollutant, nitrous oxide. Arq Neuropsiquiatr 75:614–619. https://doi.org/10.1590/0004-282x20170107

Fujita EM, Campbell DE, Arnott WP, Johnson T, Ollison W (2014) Concentrations of mobile source air pollutants in urban microenvironments. J Air Waste Manage Assoc 64:743–758. https://doi.org/10.1080/10962247.2013.872708

Fuzzi S et al (2015) Particulate matter, air quality and climate: lessons learned and future needs. Atmos Chem Phys 15:8217–8299. https://doi.org/10.5194/acp-15-8217-2015

Garba Ibrahim, Dakasku ISi, Hassan Galadima Kurkushe (2018) Review on the Influence of Primary Air Pollutants upon Human Health. International Research Journal of Engineering and Technology (IRJET) 5

Gauderman WJ et al (2015) Association of improved air quality with lung development in children. N Engl J Med 372:905–913. https://doi.org/10.1056/NEJMoa1414123

Ghorani-Azam A, Riahi-Zanjani B, Balali-Mood M (2016) Effects of air pollution on human health and practical measures for prevention in Iran Journal of research in medical sciences. The Official Journal of Isfahan University of Medical Sciences 21:65. https://doi.org/10.4103/1735-1995.189646

Gourdji S (2018) Review of plants to mitigate particulate matter, ozone as well as nitrogen dioxide air pollutants and applicable recommendations for green roofs in Montreal. Quebec Environmental Pollution 241:378–387. https://doi.org/10.1016/j.envpol.2018.05.053

Greenberg N, Carel RS, Derazne E, Bibi H, Shpriz M, Tzur D, Portnov BA (2016) Different effects of long-term exposures to SO2 and NO2 air pollutants on asthma severity in young adults. J Toxicol Environ Health A 79:342–351. https://doi.org/10.1080/15287394.2016.1153548

Guo H, Chen M (2018) Short-term effect of air pollution on asthma patient visits in Shanghai area and assessment of economic costs. Ecotoxicol Environ Saf 161:184–189. https://doi.org/10.1016/j.ecoenv.2018.05.089

Hamra GB, Laden F, Cohen AJ, Raaschou-Nielsen O, Brauer M, Loomis D (2015) Lung cancer and exposure to nitrogen dioxide and traffic: a systematic review and meta-analysis. Environ Health Perspect 123:1107–1112. https://doi.org/10.1289/ehp.1408882

Hanna R, Oliva P (2015) The effect of pollution on labor supply: Evidence from a natural experiment in Mexico City. J Public Econ 122:68–79. https://doi.org/10.1016/j.jpubeco.2014.10.004

Hao J, Yang Z, Huang S, Yang W, Zhu Z, Tian L, Lu Y, Xiang H, Liu S (2019) The association between short-term exposure to ambient air pollution and the incidence of mumps in Wuhan, China: A time-series study. Environ Res 177:108660. https://doi.org/10.1016/j.envres.2019.108660

Hehua Z, Qing C, Shanyan G, Qijun W, Yuhong Z (2017) The impact of prenatal exposure to air pollution on childhood wheezing and asthma: A systematic review. Environ Res 159:519–530. https://doi.org/10.1016/j.envres.2017.08.038

Hendryx M, Luo J, Chojenta C, Byles JE (2019) Air pollution exposures from multiple point sources and risk of incident chronic obstructive pulmonary disease (COPD) and asthma. Environ Res 179:108783. https://doi.org/10.1016/j.envres.2019.108783

Heyes A, Zhu M (2019) Air pollution as a cause of sleeplessness: social media evidence from a panel of Chinese cities. J Environ Econ Manag 98:102247. https://doi.org/10.1016/j.jeem.2019.07.002

Ho YC, Su BH, Su HJ, Chang HL, Lin CY, Chen H, Chen KT (2015) The association between the incidence of mumps and meteorological parameters in Taiwan. Hum Vaccin Immunother 11:1406–1412. https://doi.org/10.1080/21645515.2015.1029687

Hoek G, Krishnan RM, Beelen R, Peters A, Ostro B, Brunekreef B, Kaufman JD (2013) Long-term air pollution exposure and cardio- respiratory mortality: a review. Environ Health 12:43. https://doi.org/10.1186/1476-069x-12-43

Hu W, Li Y, Han W, Xue L, Zhang W, Ma W, Bi P (2018) Meteorological factors and the incidence of mumps in Fujian Province, China, 2005–2013. Non-linear effects Science of the Total Environment 619:1286–1298. https://doi.org/10.1016/j.scitotenv.2017.11.108

Huang J, Meng X, Zheng Z, Gao Y (2019) Effect of multi-pollutant state of ozone and sulfur dioxide on atmospheric corrosivity map of Guangdong Province. Environ Pollut 251:885–891. https://doi.org/10.1016/j.envpol.2019.05.073

Huebert B, Vitousek P, Sutton J, Elias T, Heath J, Coeppicus S, Howell S, Blomquist B (1999) Volcano fixes nitrogen into plant-available forms. Biogeochemistry 47:111–118. https://doi.org/10.1023/a:1006276011055

Hviid A, Rubin S, Muhlemann K (2008) Mumps. Lancet 371:932–944. https://doi.org/10.1016/s0140-6736(08)60419-5

Janghorbani M, Momeni F, Mansourian M (2014) Systematic review and metaanalysis of air pollution exposure and risk of diabetes. Eur J Epidemiol 29:231–242. https://doi.org/10.1007/s10654-014-9907-2

Janssen NA et al (2011) Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5. Environ Health Perspect 119:1691–1699. https://doi.org/10.1289/ehp.1003369

Jeong CH et al (2011) Receptor model based identification of PM2.5 sources in Canadian cities. Atmospheric Pollution Research 2:158–171. https://doi.org/10.5094/APR.2011.021

Jorcano A et al (2019) Prenatal and postnatal exposure to air pollution and emotional and aggressive symptoms in children from 8 European birth cohorts. Environ Int 131:104927. https://doi.org/10.1016/j.envint.2019.104927

Karimi B, Shokrinezhad B, Samadi S (2019) Mortality and hospitalizations due to cardiovascular and respiratory diseases associated with air pollution in Iran: A systematic review and meta-analysis. Atmos Environ 198:438–447. https://doi.org/10.1016/j.atmosenv.2018.10.063

Kayode S, John aKF (2013) Air Pollution by carbon monoxide (CO) poisonous gas in lagos area southwestern nigeria. Atmospheric and Climate Sciences 3:510–514. https://doi.org/10.4236/acs.2013.34053

Kelly FJ, Fussell JC (2015) Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health 37:631–649. https://doi.org/10.1007/s10653-015-9720-1

Khallaf MK (2011) The impact of air pollution on health, economy, environment and agricultural sources. In Tech Publishing, Rijeka

Khreis H, Ramani T, de Hoogh K, Mueller N, Rojas-Rueda D, Zietsman J, Nieuwenhuijsen MJ (2019) Traffic-related air pollution and the local burden of childhood asthma in Bradford, UK. International Journal of Transportation Science and Technology 8:116–128. https://doi.org/10.1016/j.ijtst.2018.07.003

Kibble A, Harrison R (2005) Point sources of air pollution. Occupational medicine (Oxford, England) 55:425–431. https://doi.org/10.1093/occmed/kqi138

Kim KH, Jahan SA, Kabir E (2013) A review on human health perspective of air pollution with respect to allergies and asthma. Environ Int 59:41–52. https://doi.org/10.1016/j.envint.2013.05.007

Knibbs LD et al (2018) The Australian Child Health and Air Pollution Study (ACHAPS): a national population-based cross-sectional study of long-term exposure to outdoor air pollution, asthma, and lung function. Environ Int 120:394–403. https://doi.org/10.1016/j.envint.2018.08.025

Ko F, Hui D (2011) Air pollution and chronic obstructive pulmonary disease. Respirology (Carlton, Vic) 17:395–401. https://doi.org/10.1111/j.1440-1843.2011.02112.x

Ko FW et al (2007) Effects of air pollution on asthma hospitalization rates in different age groups in Hong Kong. Clin Exp Allergy 37:1312–1319. https://doi.org/10.1111/j.1365-2222.2007.02791.x

Komarnisky LA, Christopherson RJ, Basu TK (2003) Sulfur: its clinical and toxicologic aspects. Nutrition 19:54–61. https://doi.org/10.1016/s0899-9007(02)00833-x

Kulhánová I et al (2018) The fraction of lung cancer incidence attributable to fine particulate air pollution in France: impact of spatial resolution of air pollution models. Environ Int 121:1079–1086. https://doi.org/10.1016/j.envint.2018.09.055

Lang JD, Taylor DC, Kasper BS (2018) Stress, seizures, and epilepsy: patient narratives. Epilepsy Behav 80:163–172. https://doi.org/10.1016/j.yebeh.2018.01.005

Leibowitz HM (2000) The red eye. N Engl J Med 343:345–351. https://doi.org/10.1056/nejm200008033430507

Levy H, Horowitz L, Schwarzkopf M, Ming Y, Golaz JC, Naik V, Ramaswamy V (2013) The roles of aerosol direct and indirect effects in past and future climate. J Geophys Res-Atmos:118, 4532. https://doi.org/10.1002/jgrd.50192

Li D, Wang JB, Yu ZB, Lin HB, Chen K (2019a) Air pollution exposures and blood pressure variation in type-2 diabetes mellitus patients: a retrospective cohort study in China. Ecotoxicol Environ Saf 171:206–210. https://doi.org/10.1016/j.ecoenv.2018.12.069

Li H, Cai J, Chen R, Zhao Z, Ying Z, Wang L, Chen J, Hao K, Kinney PL, Chen H, Kan H (2017a) Particulate matter exposure and stress hormone levels: a randomized. double-blind, crossover trial of air purification circulation 136:618–627. https://doi.org/10.1161/circulationaha.116.026796

Li R et al (2017b) Epidemiological characteristics and spatial-temporal clusters of mumps in Shandong Province, China, 2005–2014. Sci Rep 7:46328. https://doi.org/10.1038/srep46328

Li Z, Xu X, Thompson LA, Gross HE, Shenkman EA, DeWalt DA, Huang IC (2019b) Longitudinal effect of ambient air pollution and pollen exposure on asthma control: The Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Asthma Study. Acad Pediatr 19:615–623. https://doi.org/10.1016/j.acap.2019.03.010

Lichter A, Pestel N, Sommer E (2017) Productivity effects of air pollution: evidence from professional soccer. Labour Econ 48:54–66. https://doi.org/10.1016/j.labeco.2017.06.002

Liu C, Yin P, Chen R, Meng X, Wang L, Niu Y, Lin Z, Liu Y, Liu J, Qi J, You J, Kan H, Zhou M (2018) Ambient carbon monoxide and cardiovascular mortality: a nationwide time-series analysis in 272 cities in China. The Lancet Planetary Health 2:e12–e18. https://doi.org/10.1016/S2542-5196(17)30181-X

Lu JG, Lee JJ, Gino F, Galinsky AD (2018) Polluted morality: air pollution predicts criminal activity and unethical behavior. Psychol Sci 29:340–355. https://doi.org/10.1177/0956797617735807

Lu P, Zhang Y, Xia G, Zhang W, Li S, Guo Y (2019) Short-term exposure to air pollution and conjunctivitis outpatient visits: A multi-city study in China. Environ Pollut 254:113030. https://doi.org/10.1016/j.envpol.2019.113030

Ma Y, Yang S, Zhou J, Yu Z, Zhou J (2018) Effect of ambient air pollution on emergency room admissions for respiratory diseases in Beijing, China. Atmos Environ 191:320–327. https://doi.org/10.1016/j.atmosenv.2018.08.027

Macdonald B, Denmead O, White I, Melville M (2004) Natural sulfur dioxide emissions from sulfuric soils. Atmos Environ 38:1473–1480. https://doi.org/10.1016/j.atmosenv.2003.12.005

Mao M, Zhang X, Yin Y (2018) Particulate matter and gaseous pollutions in three metropolises along the Chinese Yangtze River: situation and implications. Int J Environ Res Public Health 15:1102. https://doi.org/10.3390/ijerph15061102

Marais EA et al (2018) Nitrogen oxides in the global upper troposphere: interpreting cloud-sliced NO2 observations from the OMI satellite instrument. Atmos Chem Phys 18:17017–17027. https://doi.org/10.5194/acp-18-17017-2018

Martinez-Rivera C, Garcia-Olive I, Stojanovic Z, Radua J, Ruiz Manzano J, Abad-Capa J (2019) Association between air pollution and asthma exacerbations in Badalona, Barcelona (Spain), 2008-2016 Med Clin (Barc) 152:333–338. doi:10.1016/j.medcli.2018.06.027

Meng Z, Qin G, Zhang B, Geng H, Bai Q, Bai W, Liu C (2003) Oxidative damage of sulfur dioxide inhalation on lungs and hearts of mice. Environ Res 93:285–292. https://doi.org/10.1016/s0013-9351(03)00045-8

Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman JD (2007) Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med 356:447–458. https://doi.org/10.1056/NEJMoa054409

Mills NL et al (2009) Adverse cardiovascular effects of air pollution. Nat Clin Pract Cardiovasc Med 6:36–44. https://doi.org/10.1038/ncpcardio1399

Morrow GL, Abbott RL (1998) Conjunctivitis. Am Fam Physician 57:735–746

CAS   Google Scholar  

Mukherjee A (2002) Perspectives of the silent majority: air pollution, livelihood and food security. Ashgate Publishing Limited, Hampshire, England

Murphy SR, Schelegle ES, Miller LA, Hyde DM, Van Winkle LS (2013) Ozone exposure alters serotonin and serotonin receptor expression in the developing lung. Toxicol Sci 134:168–179. https://doi.org/10.1093/toxsci/kft090

Murray CJ, Lopez AD (1997) Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study Lancet 349:1498–1504. doi: https://doi.org/10.1016/s0140-6736(96)07492-2

Nriagu JO (2011) Encyclopedia of environmental health

Nriagu JO (2019) Encyclopedia of environmental health

Moses K. Onabowale and Oyediran K. Owoade (2015) Assessment residential indoor outdoor airborne particulate matter in Ibadan, Southwestern Nigeria Donnish J Physical Sci 1:1–7

Organization WH (2014) 7 million premature deaths annually linked to air pollution. WHO

Orok U (2015) Contamination and health risk assessment of suspended particulate matter (SPM) in Uyo, Niger Delta, Nigeria Journal of Scientific Research and Reports 6:276–286. doi: https://doi.org/10.9734/JSRR/2015/16296

Padula AM, Yang W, Lurmann FW, Balmes J, Hammond SK, Shaw GM (2019) Prenatal exposure to air pollution, maternal diabetes and preterm birth. Environ Res 170:160–167. https://doi.org/10.1016/j.envres.2018.12.031

Pangaribuan M, Chuang K-J, Chuang H-C (2019) Association between exposures to air pollution and biomarkers of cardiovascular disease in Northern Taiwan. Atmospheric Pollution Research 10:1250–1259. https://doi.org/10.1016/j.apr.2019.02.008

Park M, Luo S, Kwon J, Stock TH, Delclos G, Kim H, Yun-Chul H (2013) Effects of air pollution on asthma hospitalization rates in different age groups in metropolitan cities of Korea. Air Qual Atmos Health:6, 551. https://doi.org/10.1007/s11869-013-0195-x

Phosri A, Ueda K, Phung VLH, Tawatsupa B, Honda A, Takano H (2019) Effects of ambient air pollution on daily hospital admissions for respiratory and cardiovascular diseases in Bangkok, Thailand. Sci Total Environ 651:1144–1153. https://doi.org/10.1016/j.scitotenv.2018.09.183

Pope CA, 3rd, Burnett RT, Thurston GD, Thun MJ, Calle EE, Krewski D, Godleski JJ (2004) Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease Circulation 109:71–77. doi: https://doi.org/10.1161/01.Cir.0000108927.80044.7f

Puett RC et al (2019) Inflammation and acute traffic-related air pollution exposures among a cohort of youth with type 1 diabetes. Environ Int 132:105064. https://doi.org/10.1016/j.envint.2019.105064

Radovic NI, Bozic K, Duric AP, Vodopic S, Radulovic L, Vujisic S (2017) Health-related quality of life in adolescents with epilepsy in Montenegro. Epilepsy Behav 76:105–109. https://doi.org/10.1016/j.yebeh.2017.07.009

Rahmat M, Maulina W, Rustami E, Azis M, Budiarti DR, Seminar KB, Yuwono AS, Alatas H (2013) Performance in real condition of photonic crystal sensor based NO2 gas monitoring system. Atmos Environ 79:480–485. https://doi.org/10.1016/j.atmosenv.2013.05.057

Rincon M, Irvin CG (2012) Role of IL-6 in asthma and other inflammatory pulmonary diseases. Int J Biol Sci 8:1281–1290. https://doi.org/10.7150/ijbs.4874

Robertson D (2006) Health effects of increase in concentration of carbon dioxide in the atmosphere Current Science 90

Rosa IdMA (2015) The Impact of Transportation Construction Projects and Activities on Emissions., North Carolina State University

Sager L (2019) Estimating the effect of air pollution on road safety using atmospheric temperature inversions. J Environ Econ Manag 98:102250. https://doi.org/10.1016/j.jeem.2019.102250

Samek L, Stegowski Z, Styszko K, Furman L, Fiedor J (2018) Seasonal contribution of assessed sources to submicron and fine particulate matter in a Central European urban area. Environ Pollut 241:406–411. https://doi.org/10.1016/j.envpol.2018.05.082

Schumann U, Huntrieser H (2007) The global lightning-induced nitrogen oxides source. Atmos Chem Phys 7:3823–3907. https://doi.org/10.5194/acp-7-3823-2007

Seok B, Helmig D, Ganzeveld L, Williams MW, Vogel CS (2013) Dynamics of nitrogen oxides and ozone above and within a mixed hardwood forest in northern Michigan. Atmos Chem Phys 13:7301–7320. https://doi.org/10.5194/acp-13-7301-2013

Shahadin MS, Ab Mutalib NS, Latif MT, Greene CM, Hassan T (2018) Challenges and future direction of molecular research in air pollution-related lung cancers. Lung Cancer 118:69–75. https://doi.org/10.1016/j.lungcan.2018.01.016

Shyam Bihari Sharma SJ, Praveen Khirwadkar, and Sunisha Kulkarni (2013) The effects of air pollution on the environment and human health Indian Journal of Research in Pharmacy and Biotechnology 1:391–396

Singh A, Trevick S (2016) The Epidemiology of Global Epilepsy. Neurol Clin 34:837–847. https://doi.org/10.1016/j.ncl.2016.06.015

Skalska K, Miller J, Ledakowicz S (2010) Trends in NOx abatement: a review. Sci Total Environ 408:3976–3989. https://doi.org/10.1016/j.scitotenv.2010.06.001

Song P, Liu Y, Yu X, Wu J, Poon AN, Demaio A, Wang W, Rudan I, Chan KY (2017) Prevalence of epilepsy in China between 1990 and 2015: a systematic review and meta-analysis. J Glob Health 7:020706. https://doi.org/10.7189/jogh.07-020706

Srivastava RK, Hall RE, Khan S, Culligan K, Lani BW (2005) Nitrogen oxides emission control options for coal-fired electric utility boilers. J Air Waste Manage Assoc 55:1367–1388. https://doi.org/10.1080/10473289.2005.10464736

Svensson JE, Johansson LG (1993) A laboratory study of the effect of ozone, nitrogen dioxide, and sulfur dioxide on the atmospheric corrosion of zinc. J Electrochem Soc 140:2210–2216

Tajudin M et al (2019) Risk of concentrations of major air pollutants on the prevalence of cardiovascular and respiratory diseases in urbanized area of Kuala Lumpur. Malaysia Ecotoxicol Environ Saf 171:290–300. https://doi.org/10.1016/j.ecoenv.2018.12.057

Tamayo T, Rathmann W, Krämer U, Sugiri D, Grabert M, Holl RW (2014) Is particle pollution in outdoor air associated with metabolic control in type 2 diabetes? PLoS One 9:–e91639. https://doi.org/10.1371/journal.pone.0091639

Tamayo T, Rathmann W, Stahl-Pehe A, Landwehr S, Sugiri D, Krämer U, Hermann J, Holl RW, Rosenbauer J (2016) No adverse effect of outdoor air pollution on HbA1c in children and young adults with type 1 diabetes. Int J Hyg Environ Health 219:349–355. https://doi.org/10.1016/j.ijheh.2016.02.002

Tian L, Qiu H, Pun VC, Ho KF, Chan CS, Yu IT (2015) Carbon monoxide and stroke: a time series study of ambient air pollution and emergency hospitalizations. Int J Cardiol 201:4–9. https://doi.org/10.1016/j.ijcard.2015.07.099

Tikuisis P, Kane DM, McLellan TM, Buick F (1985) Fairburn SM (1992) Rate of formation of carboxyhemoglobin in exercising humans exposed to carbon monoxide. J Appl Physiol 72:1311–1319. https://doi.org/10.1152/jappl.1992.72.4.1311

Tofield A (2017) Air pollution and traffic noise effect on blood pressure. Eur Heart J 38:71–72. https://doi.org/10.1093/eurheartj/ehw607

Townsend CL, Maynard RL (2002) Effects on health of prolonged exposure to low concentrations of carbon monoxide. Occup Environ Med 59:708–711. https://doi.org/10.1136/oem.59.10.708

Tsangari H, Paschalidou AK, Kassomenos AP, Vardoulakis S, Heaviside C, Georgiou KE, Yamasaki EN (2016) Extreme weather and air pollution effects on cardiovascular and respiratory hospital admissions in Cyprus. Sci Total Environ 542:247–253. https://doi.org/10.1016/j.scitotenv.2015.10.106

Venditti CC, Smith GN (2014) Involvement of the heme oxygenase system in the development of preeclampsia and as a possible therapeutic target. Womens Health (Lond) 10:623–643. https://doi.org/10.2217/whe.14.54

Wang N, Mengersen K, Tong S, Kimlin M, Zhou M, Wang L, Yin P, Xu Z, Cheng J, Zhang Y, Hu W (2019) Short-term association between ambient air pollution and lung cancer mortality. Environ Res 179:108748. https://doi.org/10.1016/j.envres.2019.108748

Weinstock B, Niki H (1972) Carbon monoxide balance in nature. Science 176:290–292. https://doi.org/10.1126/science.176.4032.290

Wong IC, Ng YK, Lui VW (2014) Cancers of the lung, head and neck on the rise: perspectives on the genotoxicity of air pollution Chin J Cancer 33:476–480. doi: https://doi.org/10.5732/cjc.014.10093

World Health Organization O, Environmental Health T (2006) WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide : global update 2005 : summary of risk assessment. World Health Organization, Geneva

World Health Organization. Regional Office for E (2003) Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide : report on a WHO working group, Bonn, Germany 13-15 January 2003. Copenhagen : WHO Regional Office for Europe

Xu C, Fan YN, Kan HD, Chen RJ, Liu JH, Li YF, Zhang Y, Ji AL, Cai TJ (2016) The novel relationship between urban air pollution and epilepsy: a time series study. PLoS One 11:e0161992–e0161992. https://doi.org/10.1371/journal.pone.0161992

Yan Y, Li Y, Sun M, Wu Z (2019) Primary pollutants and air quality analysis for urban air in China: evidence from Shanghai. Sustainability 11:2319

Zhang H, Dong H, Ren M, Liang Q, Shen X, Wang Q, Yu L, Lin H, Luo Q, Chen W, Knibbs LD, Jalaludin B, Wang Q, Huang C (2020) Ambient air pollution exposure and gestational diabetes mellitus in Guangzhou, China: a prospective cohort study. Sci Total Environ 699:134390. https://doi.org/10.1016/j.scitotenv.2019.134390

Zhang X, Zhang X, Chen X (2017) Happiness in the air: how does a dirty sky affect mental health and subjective well-being? J Environ Econ Manag 85:81–94. https://doi.org/10.1016/j.jeem.2017.04.001

Zhao CN, Xu Z, Wu GC, Mao YM, Liu LN, Qian-Wu, Dan YL, Tao SS, Zhang Q, Sam NB, Fan YG, Zou YF, Ye DQ, Pan HF (2019a) Emerging role of air pollution in autoimmune diseases. Autoimmun Rev 18:607–614. https://doi.org/10.1016/j.autrev.2018.12.010

Zhao Y, Hu J, Tan Z, Liu T, Zeng W, Li X, Huang C, Wang S, Huang Z, Ma W (2019b) Ambient carbon monoxide and increased risk of daily hospital outpatient visits for respiratory diseases in Dongguan, China. Sci Total Environ 668:254–260. https://doi.org/10.1016/j.scitotenv.2019.02.333

Zhu F et al (2019) The short-term effects of air pollution on respiratory diseases and lung cancer mortality in Hefei: a time-series analysis. Respir Med 146:57–65. https://doi.org/10.1016/j.rmed.2018.11.019

Zivin JG, Neidell M (2012a) The impact of pollution on worker productivity. Am Econ Rev 102:3652–3673

Zivin JG, Neidell M (2012b) The impact of pollution on worker productivity. Am Econ Rev 102:3652–3673. https://doi.org/10.1257/aer.102.7.3652

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This research was funded by the Deanship of scientific research at Princess Nourah bint Abdulrahman University through the fast-track research funding program.

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Alsaid Ahmed Almetwally

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Almetwally, A.A., Bin-Jumah, M. & Allam, A.A. Ambient air pollution and its influence on human health and welfare: an overview. Environ Sci Pollut Res 27 , 24815–24830 (2020). https://doi.org/10.1007/s11356-020-09042-2

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Air pollution and risk of 32 health conditions: outcome-wide analyses in a population-based prospective cohort in Southwest China

Affiliations.

• 1 West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
• 2 School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China.
• 3 Health Information Center of Sichuan Province, Chengdu, Sichuan, China.
• 4 Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China.
• 5 Chenghua District Center for Disease Control and Prevention, Chengdu, China.
• 6 School of Public Health, Kunming Medical University, Kunming, Yunnan, China.
• 7 School of Medicine, Tibet University, Tibet, China.
• 8 West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China. [email protected].
• 9 West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China. [email protected].
• PMID: 39256817
• PMCID: PMC11389248
• DOI: 10.1186/s12916-024-03596-5

Background: Uncertainty remains about the long-term effects of air pollutants (AP) on multiple diseases, especially subtypes of cardiovascular disease (CVD). We aimed to assess the individual and joint associations of fine particulate matter (PM 2.5 ), along with its chemical components, nitrogen dioxide (NO 2 ) and ozone (O 3 ), with risks of 32 health conditions.

Methods: A total of 17,566 participants in Sichuan Province, China, were included in 2018 and followed until 2022, with an average follow-up period of 4.2 years. The concentrations of AP were measured using a machine-learning approach. The Cox proportional hazards model and quantile g-computation were applied to assess the associations between AP and CVD.

Results: Per interquartile range (IQR) increase in PM 2.5 mass, NO 2 , O 3 , nitrate, ammonium, organic matter (OM), black carbon (BC), chloride, and sulfate were significantly associated with increased risks of various conditions, with hazard ratios (HRs) ranging from 1.06 to 2.48. Exposure to multiple air pollutants was associated with total cardiovascular disease (HR 1.75, 95% confidence intervals (CIs) 1.62-1.89), hypertensive diseases (1.49, 1.38-1.62), cardiac arrests (1.52, 1.30-1.77), arrhythmia (1.76, 1.44-2.15), cerebrovascular diseases (1.86, 1.65-2.10), stroke (1.77, 1.54-2.03), ischemic stroke (1.85, 1.61-2.12), atherosclerosis (1.77, 1.57-1.99), diseases of veins, lymphatic vessels, and lymph nodes (1.32, 1.15-1.51), pneumonia (1.37, 1.16-1.61), inflammatory bowel diseases (1.34, 1.16-1.55), liver diseases (1.59, 1.43-1.77), type 2 diabetes (1.48, 1.26-1.73), lipoprotein metabolism disorders (2.20, 1.96-2.47), purine metabolism disorders (1.61, 1.38-1.88), anemia (1.29, 1.15-1.45), sleep disorders (1.54, 1.33-1.78), renal failure (1.44, 1.21-1.72), kidney stone (1.27, 1.13-1.43), osteoarthritis (2.18, 2.00-2.39), osteoporosis (1.36, 1.14-1.61). OM had max weights for joint effects of AP on many conditions.

Conclusions: Long-term exposure to increased levels of multiple air pollutants was associated with risks of multiple health conditions. OM accounted for substantial weight for these increased risks, suggesting it may play an important role in these associations.

Keywords: Air pollution; Fine particulate matter components; Multiple exposure analysis; Outcome-wide analysis; Prospective cohort study.

© 2024. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

Hazard ratios and corresponding 95%…

Hazard ratios and corresponding 95% CI for associations between AP and 32 health…

Hazard ratios and corresponding 95% CI for associations between mixture pollutant and health…

• WHO. WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva: World Health Organization; 2021. - PubMed
• Al-Kindi SG, Brook RD, Biswal S, Rajagopalan S. Environmental determinants of cardiovascular disease: lessons learned from air pollution. Nat Rev Cardiol. 2020;17(10):656–72. 10.1038/s41569-020-0371-2 - DOI - PMC - PubMed
• Schraufnagel DE, Balmes JR, Cowl CT, De Matteis S, Jung SH, Mortimer K, et al. Air pollution and noncommunicable diseases: a review by the forum of international respiratory societies’ environmental committee, Part 2: air pollution and organ systems. Chest. 2019;155(2):417–26. 10.1016/j.chest.2018.10.041 - DOI - PMC - PubMed
• Liu L, Zhang Y, Yang Z, Luo S, Zhang Y. Long-term exposure to fine particulate constituents and cardiovascular diseases in Chinese adults. J Hazard Mater. 2021;416: 126051. 10.1016/j.jhazmat.2021.126051 - DOI - PubMed
• Wright N, Newell K, Chan KH, Gilbert S, Hacker A, Lu Y, et al. Long-term ambient air pollution exposure and cardio-respiratory disease in China: findings from a prospective cohort study. Environ Health. 2023;22(1):30. 10.1186/s12940-023-00978-9 - DOI - PMC - PubMed
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The impact of data imputation on air quality prediction problem

Roles Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft

Affiliations Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam, Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam

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Affiliation SimulaMet, Oslo, Norway

Roles Formal analysis

Affiliation National Institute of Information and Communications Technology, Tokyo, Japan

Roles Formal analysis, Methodology

Affiliations Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam, University of Information Technology, Ho Chi Minh City, Vietnam

Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

• Van Hua, 
• Thu Nguyen, 
• Minh-Son Dao, 
• Hien D. Nguyen, 
• Binh T. Nguyen

• Published: September 12, 2024
• https://doi.org/10.1371/journal.pone.0306303
• Reader Comments

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing with missing data in time series scenarios for environmental data.

Citation: Hua V, Nguyen T, Dao M-S, Nguyen HD, Nguyen BT (2024) The impact of data imputation on air quality prediction problem. PLoS ONE 19(9): e0306303. https://doi.org/10.1371/journal.pone.0306303

Editor: Abid Rashid Gill, Islamia University of Bahawalpur, PAKISTAN

Received: February 14, 2024; Accepted: June 15, 2024; Published: September 12, 2024

Copyright: © 2024 Hua et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: - The dataset "Frankfurt (German)" is available at the following: URL: https://www.kaggle.com/datasets/avibagul80/air-quality-dataset Author: Avinash Bagul - University of Aberdeen - The dataset "Beijing (China)" is available upon request from the authors of the following article: Du W, Côté D, Liu Y. Saits: Self-attention-based imputation for time series, Expert Systems with Applications. 2023;219:119619. URL: https://www.sciencedirect.com/science/article/abs/pii/S0957417423001203 DOI: https://doi.org/10.1016/j.eswa.2023.119619 Dataset link: https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+Air-Quality+Data Corresponding authors: Yan Liu Email: [email protected] - The dataset " Northern Taiwan (Taiwan)" is available at the following: URL: https://www.kaggle.com/datasets/nelsonchu/air-quality-in-northern-taiwan Author: Open Government Data License, version 1.0 http://data.gov.tw/license - The dataset "Dalat (Vietnam)" is available upon request from the authors of the following article: Dao MS, Dang TH, Nguyen-Tai TL, Nguyen TB, Dang-Nguyen DT. Overview of MediaEval 2022 Urban Air: Urban Life and Air Pollution. In: Proc. of the MediaEval 2022 Workshop; 2023. p. 13–15. URL: https://ceur-ws.org/Vol-3583/paper4.pdf Corresponding authors: Minh-Son Dao Email: [email protected] Dataset link: https://github.com/BinhMisfit/air-pollution-datasets/tree/main/Dalat-air-quality-dataset - The dataset "Cau Giay District (Hanoi, Vietnam)" is available upon request from the authors of the following article: Ton-Thien MA, Nguyen CT, Le QM, Duong DQ, Dao MS, Nguyen BT. Air Pollution Forecasting Using Multimodal Data. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer; 2023. p. 360–371. URL: https://link.springer.com/chapter/10.1007/978-3-031-36822-6_31 DOI: https://doi.org/10.1007/978-3-031-36822-6_31 Corresponding authors: Binh T. Nguyen Email: [email protected] Dataset link: https://github.com/BinhMisfit/air-pollution-datasets/tree/main/Hanoi-air-quality-dataset - The dataset "Minh Khai District (Hanoi, Vietnam)" is available upon request from the authors of the following article: Ton-Thien MA, Nguyen CT, Le QM, Duong DQ, Dao MS, Nguyen BT. Air Pollution Forecasting Using Multimodal Data. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer; 2023. p. 360–371. URL: https://link.springer.com/chapter/10.1007/978-3-031-36822-6_31 DOI: https://doi.org/10.1007/978-3-031-36822-6_31 Corresponding authors: Binh T. Nguyen Email: [email protected] Dataset link: https://github.com/BinhMisfit/air-pollution-datasets/tree/main/Hanoi-air-quality-dataset .

Funding: This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number DS2023-18-01. When working on this research paper, Ms. Van Hua was a Master student at the University of Science, Vietnam National University Ho Chi Minh City. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

Air helps sustain human life, so air tracking and understanding its quality is essential for our health. Air pollutants can pose significant threats to public health, and sources of air pollution can come from nature, such as smoke from volcano eruptions or forest fires, methane from animals’ process of digesting food, or radon gas from radioactive decay in the earth’s crust. In addition, pollution can also come from manufacturing activities such as industry and agriculture. They emit CO 2 , CO , SO 2 , NO 2 , and other organic substances at extremely high concentrations, polluting the air. Besides, burning fossil fuels yields climate change and air pollution. Therefore, air quality has still been a concern in recent years. Consequently, environmental researchers mine air quality data to uncover potential value and information from these data, thereby capturing user behavior, estimating disease causes, discovering gases, detecting individual actions to reduce greenhouse gases, acid rain, etc., and then advising management agencies and local governments to plan related policies. By using machine learning techniques, the local air quality data can be analyzed using sensors that gather real-time humidity and temperature readings. Duong et al. (2021) effectively extracted pertinent features from a dataset. They applied machine learning models to forecast AQI (Air Quality Indexing) values and levels at any user-specified location in Ho Chi Minh City [ 1 ]. The dataset includes data on six atmospheric pollutants: SO 2 , NO 2 , PM 10, PM 2.5, CO , and O 3 , collected by volunteers who traversed predetermined routes to provide ground-truth AQI levels.

Many air quality data are in the form of time series and can contain missing values due to corrupted sensors, loss of electricity, etc. In such cases, data imputation, i.e., filling in missing values with some reasonable value according to some criteria, is a conventional practice to resolve the issue. The quality of imputation can significantly impact the downstream classification or prediction task. One can characterize missing data into three types: missing completely at random (MCAR), where the missing values are independent of any other values; missing at random (MAR), where missing values depend only on observed values; and missing not at random (MNAR), where missing values depends on both observed and unobserved values [ 2 ]. There are many methods to deal with missing values based on the missing data mechanism. This work focuses on the MAR case, as it is prevalent for sensor data related to the environment [ 3 ]. Furthermore, most air quality observation data are time series data. Dealing with missing values in time series data is often difficult, time-consuming, and labor-intensive. In addition, the missing data can significantly affect the processing and analysis of data. Therefore, handling missing values in time-series air quality data is necessary.

People can reveal critical enhancements regarding performance and running time by examining newly introduced approaches for data imputation. Multiple imputations can be further applied with these imputation methods to reduce the uncertainty by repeating the imputation procedure numerous times and averaging the results. Combining the imputation methods with forecasting models often results in a two-step process where imputation and forecasting models are separated. By doing this, the missingness is effectively explored in the forecasting model, thus leading to suboptimal analysis results. In addition, many imputation methods also have other requirements that may not be satisfied in real applications; for example, many of them work on data with low missing rates only, assume the data is missing randomly or completely at random, or can not handle time series data with varying lengths [ 4 ]. Moreover, training and applying these imputation methods are usually computationally expensive.

Various imputation techniques have been proposed to fill in missing values, each using a distinct set of assumptions, algorithms, and performance metrics. Choosing a relevant imputation method can significantly influence the subsequent analysis and the reliability of the results. To give a thorough comparative analysis of various missing data imputation methods for time series air quality of data [ 5 ], we compare several conventional but often used imputation techniques (mean, median, kNNI, and MICE) with several recently developed imputation techniques for temporal data (SAITS, BRITS, MRNN, and Transformer) to examine their impact on air quality data from various places. On the other hand, the rate of missing data can also impact the problem-solving strategy we use since missing values can be handled in the step of data preprocessing. Various works have conducted experiments under a variety of missing rates. For example, [ 6 ] conducts experiments with missing rates from 5% to 50%. Nevertheless, in some other papers, the missing values rate could range from 1% to 80% of data [ 7 – 9 ], or start from 10% to 50% [ 10 ].

While some work [ 11 ] has been done to compare the performance of classical and newly developed time series imputation techniques such as BRITS [ 12 ], SAITS [ 13 ] for health care data, such practical comparisons for air quality has not been conducted yet. In addition, while there have been several works that examine the effects of imputation on air quality [ 14 – 16 ], most of them do not cover state-of-the-art imputation methods for time series that have been developed in recent years. In addition, up to our knowledge, while there have been some surveys on air quality prediction [ 17 , 18 ] or missing data imputation for air quality data [ 19 , 20 ], there has not been any work that reviews both problems and systematically compares state-of-the-art imputation algorithms for air quality data. This motivates us to review recent studies related to the air quality prediction problem, along with missing values handling methods or techniques on time series data with a concentration on air quality data. In addition, we also empirically evaluate various time series imputation techniques, including classical and state-of-the-art methods for air quality data. In summary, the contribution of our work can be described as follows:

• We review existing techniques for air quality prediction and missing data imputation.
• We conduct experiments on various air quality datasets to compare the performance of various time series imputation methods using various measures.
• We provide analysis and evaluation of the performance of techniques.
• We provide practitioners practical guidance on how to deal with missing data in air quality data.

The structure of the paper can be organized as follows. Firstly, Section 1 gives an overview of the current research related to data and missing values and describes the research problem in Section 2. Afterward, we review the prediction methods for air quality data in Section 3. Besides, we also review related techniques imputing missing values in time series data from conventional to modern data in Section 4. Next, in Section 5, we present methods for imputing the missing values in this paper. Experiments compare and evaluate the results and imputation time of the methods and the accuracy of prediction models on air pollutant values and AQI levels on the different datasets in Section 6. Then, we discuss the related problems to impute missing values in Section 7. Finally, the paper ends with our conclusion and future works in Section 8.

2 Problem formulation

Most urban areas worldwide, including Vietnam, are facing increasing air pollution. Among them, the problem of air pollution due to dust is still the most prominent. In some large cities like Hanoi, the number of days with PM 10 and PM 2.5 dust pollution levels exceeding the limits is relatively high. The problem is how to reduce the impact of air pollution on human health. Therefore, to solve the above problem, experts believe that if air pollution is informed early in the form of prediction, it can help people proactively plan their lives, especially on days when air pollution is high, minimizing the effects of air pollution on health. Thereby, people will know and choose how to protect their health and that of their family members. Many countries predict air quality from three to five days in advance based on air and meteorological data (such as temperature, humidity, wind direction, and topography) from air monitoring stations. However, in implementing the problem of collecting data through sensors, the possibility of data loss of information occurs very often and unavoidably. Through this paper, we also present ways to handle missing data and how it will affect the problem of air pollution prediction or similar time series problems.

Missing data can exist in various ways, for example, at individual points or over intervals, where one sensor loses data for a period of time. In this section, we introduce preliminary definitions and formalize the problem of air quality imputation. Air quality data is generally collected from a set of sensors over different periods. We focus on the time series data with missing values. Some notations are defined to describe this problem.

The performance of each imputation model is computed by considering the indicating mask. The missing values in the matrix X will be imputed using traditional imputation techniques (i.e., Mean, Median, MICE, kNNI) and recently developed imputation techniques (i.e., SAITS, BRITS, MRNN, Transformer). In what follows, we will review the current approaches in detail.

3 Air quality prediction: Existing techniques

In the current studies, there is a wealth of research on air quality prediction due to its importance in informing about the pollution level that will allow policy-makers to adopt measures for reducing its impact [ 21 , 22 ]. Methods for air quality prediction can be classified into statistical, machine learning [ 23 , 24 ], and deep learning approaches.

3.1 Statistical methods

3.1.1 vector auto-regression (var)..

One of the most popular statistical models for forecasting multivariate time series is the Vector Auto-Regression (VAR). It is considered an extension of the univariate autoregressive model. The findings of [ 25 ] have revealed that the VAR model is particularly valuable in capturing the dynamic characteristics of economic and financial time series, making it a powerful tool for describing their behavior and making forecasts. In [ 26 ], VAR was used to forecast daily concentrations of air pollutants (i.e., CO , NO 2 , and SO 3 ) in Tehran city for the next 24h. For such a task, the authors have considered the correlations between air pollutants to get more accurate forecasts. Experimental results have indicated the high efficiency of the proposed method.

3.1.2 Autoregressive Integrated Moving (ARIMA).

Aside from VAR, ARIMA algorithms [ 27 ] were applied to forecast air quality. In [ 28 ], authors proposed a hybrid method named ARIMAX by combining the advantage of ARIMA and numerical modeling to forecast real-time air pollutants in Hong Kong (i.e., PM 2.5, O 3 , and NO 2 ). By employing experimental analysis, the proposed method significantly improves the quality of forecast results in multiple evaluation metrics. Similarly, the findings in [ 29 ] have shown the prominent role of ARIMA in forecasting PM10 in Dakar, Senegal. Accordingly, the proposed method combines system observations with multi-agent real-time simulation and evaluates with several simulations.

3.2 Traditional machine-learning methods

Some traditional machine learning algorithms used for air quality prediction can be Support Vector Regression (SVR), Random Forest (RF), and Linear Regression (LR).

3.2.1 Support Vector Regression (SVR).

SVR models were used to forecast PM 2.5 and PM 10 in London [ 30 ]. In that paper, the experimental results indicate the SVR’s efficiency in forecasting air quality parameters (i.e., PM2.5 and PM10). A nonlinear dynamic model based on the SVR technique was proposed to forecast AQI in Oviedo, Spain [ 31 ]. Accordingly, the proposed model first analyzed the relationship between primary and secondary pollutants. Then, it derived vital factors influencing the air quality and recommended potential enhancements for health and lifestyle. Zhu et al. [ 32 ] investigated an application of the SVR algorithm with a quasi-linear kernel for air quality prediction. For such a task, the paper designed a gated linear network to construct the multiple piecewise linear model, and it could be developed through the pre-training of a Winner-Take-All (WTA) autoencoder. This approach could outperform other state-of-the-art methods in the case of complex air quality prediction problems. It is due to the WTA strategy reducing the risk of overfitting and choosing appropriate sparsity parameters.

3.2.2 Random Forest (RF).

Regarding RF [ 33 , 34 ] proposed a parallel approach combined with Spark to forecast PM 2.5 in Beijing. The experimental results revealed the efficiency and scalability of the proposed method in the case of big data. Later, RF was used to select the most important features to improve the quality of real-time air quality prediction [ 35 ]. Concretely, the proposed method provides highly accurate predictions of three air pollutants (i.e., PM 2.5, NO 2 , SO 2 ) and outperforms other state-of-the-art methods.

3.2.3 Linear regression.

Linear regression is also a state-of-the-art model for air quality prediction. Indeed, many linear regression models have been proposed to predict AQI levels in New Delhi [ 36 ]; In Catalonia [ 37 ], authors combined factors including the effect of the surface reflectance capacity of urban surfaces with solar radiation and elevation to predict AQI level in Catalonia. The dataset is collected from 75 different air quality monitoring stations. A clustering technique was applied to cluster these stations based on their similarity. Meanwhile, Multiple Linear Regression (MLR) was used to replicate the annual mean values of AQI in Catalonia. Experimental results illustrated that the proposed model provided highly accurate predictions of AQI. Djuric et al. [ 38 ] proposed a multiple linear regression to forecast air pollution indices (i.e., SO 2 , NO 2 , PM 10, O 3 , and CO ) in Belgrade, Serbia. They collected the training and testing sets from the winters of 2011 and 2012/2013, respectively. In addition, the findings show that the proposed model can be scaled up to forecast long-term air quality.

3.3 Deep learning techniques

3.3.1 long short-term memory..

Apart from the traditional machine learning approaches, most deep learning methods, such as Long short-term memory (LSTM), have shown their superiority over many machine learning techniques. Even though in [ 39 ], the LSTM model has outperformed MLP and RNN models in predicting PM 10 and SO 2 in the Basaksehir district of Istanbul province. In [ 40 ], authors have proposed a bidirectional LSTM (Bi-LSTM) model by considering both past and future information to forecast PM2.5 of three cities in Korea and five cities in China. Accordingly, its performance is superior to GRU and LSTM in terms of the air quality forecast for these cities. Concretely, with short-term prediction, these models have similar performances. Meanwhile, with long-term prediction, Bi-LSTM outperformed GRU and LSTM. Wang and colleagues [ 41 ] developed a combination of the CT (chi-square test) with the LSTM model to analyze the relationship between air pollution variables. The paper identified the factors influencing air quality by using CT for such a task. Then, the AQI level was predicted by the LSTM model using a dataset collected at Shijiazhuang in the Hebei Province of China. In comparison to other competitive methods (i.e., SVR, MLP, BP neural network, Simple RNN), the proposed method provides an accuracy of 93.7% (the highest one). In addition, the proposed method outperforms the baseline methods in terms of MAE, MSE, and RMSE metrics. In [ 42 ], a GRU layer has been added to the LSTM structure to improve the accuracy of the air quality prediction problem. Experimental results with a dataset collected in Delhi show the outperformance of the proposed approach compared to other competitive methods of linear regression, GRU, kNN, and SVM in terms of MAE and R 2 .

3.3.2 Recurrent Neural Networks (RNN).

In [ 43 ], the authors have proposed an RNN algorithm to predict PM2.5 in Japan by employing a dynamic method to pre-train the model based on multi-step-ahead time series prediction. [ 44 ] apply RNN to predict PM 10, O 3 , SO 2 , CO and NO 2 . The dataset is collected from different sensors with intervals of 1 hour. In addition, the authors applied fine-tuning to find the best hyperparameters of neural network structure and optimization function. Moreover, the investigated model can be applied to predict similar pollutants in other neighboring areas.

3.3.3 Gated Recurrent Unit (GRU).

In the current literature, many research results indicate that existing models are best at short-term forecasts. Meanwhile, improving existing approaches to forecasting long-term air quality is necessary. [ 45 ] proposed an algorithm that is considered an enhanced version of GRU (named BiAGRU) by combining bidirectional gated recurrent unit integrated with an attention mechanism. By means of experimental analysis, the proposed model is superior to many traditional machine learning models and modern deep learning models.

Referring to [ 46 ], a model based on Gated Recurrent Units (GRUs) has been proposed to forecast NO 2 pollutant concentration. The proposed model is assessed and fine-tuned for such a task concerning the number of features, look-backs, neurons, and epochs. Also, in Beijing [ 47 ], authors introduced a model based on spatiotemporal CRUs combined with a Geographic Self-Organizing Map (GeoSOM). Concretely, all monitor stations were clustered using time-series features and geographical coordinates. Later, GRU models were proposed for clusters, and Gaussian vector weights were used to weigh different models in predicting the target sequence. Experimental results showed the technique’s efficiency compared to several state-of-the-art ones regarding MAE, MRE, and R2 metrics.

Since existing models do not fully consider the temporal dependencies, spatial correlations, and feature correlations hidden in a given dataset, in [ 48 ], authors examined these correlations by introducing a spatiotemporal deep learning model named Conv1D-LSTM based on 1-D convolutional neural network and LSTM for spatial and temporal correlation feature extraction. In addition, a fully connected network exploits these features for the air quality prediction problem. Furthermore, missing data have been imputed to enhance the quality of air quality prediction. The proposed method outperformed other well-known baseline methods through experimental analysis.

3.4 Data fusion

Besides techniques for the air quality prediction problems as mentioned above, there exist further works solving the problem by using data fusion. In this section, we will provide a brief discussion of these approaches.

3.4.1 Multimodal data.

Air pollution is one of the most worrying issues facing the world today. So, forecasting of particulate matter (PM) is necessary nowadays. Ton et al. [ 49 ] pointed out that combining meteorological features and timestamp information in Hanoi air quality datasets improved the results of PM10 and PM2.5 forecasting. The authors extracted two new features, which were weekend and working hour , from the “Date Time” recorded variable. Then, encoding the time into a vector of 0, 1 to include two new variables, weekend and working hour . First, with the variable weekend , the time vector from Monday to Friday was encoded as 0. On the other hand, during Saturday and Sunday weekends, the time vector was 1. Second, with working hour , the time vector was 1 in the range 7 AM to 7 PM, whereas this was 0. According to the authors, the time steps of the two new variables weekend and working hour were synchronized with other weather and air quality variables. It was highly efficient in 68% of the cases compared to other methods by conducting five deep learning models: MLP, 1D-CNN, LSTM, Bi-LSTM, and Stacked LSTM. Besides, in the long-term forecast of PM concentrations, the Vanilla LSTM model with combined features performed better than the other.

Similarly, to predict the PM 2.5 air pollution level in the short- and medium-term, Tejima and colleagues [ 50 ] also proposed a framework that looks for hidden associations between traffic factors and air pollution. The six steps in their framework can be defined as follows: (1) Use any machine learning algorithm to extract features from the traffic images, (2) Create a new dataset by combining the extracted features dataset and air pollution dataset using time, (3) Use fuzzy rules to convert this new dataset into an uncertain temporal database, and (4) Use uncertain periodic-frequent pattern mining techniques to uncover hidden relationships between various traffic factors and air pollution, (5) estimate air pollution level from a given image using transfer learning on a pre-trained model, and (6) predict air pollution level using estimated air pollution level and mined patterns dataset. Experimental results show that their method can accurately estimate and predict air pollution levels, ranging from 77% to 98%.

3.4.2 Neighbor stations.

Currently, air pollution and urban life influence human health. Therefore, environmental and data science experts always try to find the most accurate way to predict and provide timely warnings to humans. Specifically, Dao et al. [ 51 ] use methods of data imputation for the UrbanAir dataset to predict air pollution at a place without a station by using neighbor stations and predict air pollution of Dalat and discover the correlation/association between air pollution and human activities. The authors divided the article into two tasks that need to be performed: Subtask 1 only used environmental data to predict air pollution and only used traffic data in Subtask 2. Subtask 2 accepts training a prediction model using environmental and traffic data, but only traffic data is used to predict air pollution. While Subtask 2 only accepts AQI levels, Subtask 1 requires predicting both the exact value and AQI level of each pollutant concentration. The paper encouraged researchers to develop a generic framework to discover a correlation among different traffic factors, weather, and air pollution in a locality. By using these correlations, the authors improved the accuracy of AQI prediction and understood the mutual impact between urban life and air pollution.

Besides, Nguyen et al. [ 52 ] also introduced a dataset containing data about personal life and the surrounding environment, collected periodically along predetermined routes in Ho Chi Minh City, Vietnam. They also introduced self-developed devices and system architectures for data collection, storage, access, and visualization. There were interesting research topics and applications, including understanding the correlation between human health, air pollution, and traffic congestion.

3.4.3 Images.

Human health is mostly impacted by air pollution. Over time, there has been an increase in the number of patients and disease reports related to air pollution. By using lifelog data and urban nature similarity, a method was introduced in [ 53 , 54 ] that could predict AQI at a local and individual scale with a few images taken from smartphones and open AQI and weather datasets. Various public datasets pertaining to weather, air pollution, and images are used to develop and evaluate image retrieval and prediction model techniques. The outcomes support their hypothesis regarding the strong correlation between the AQI and snapshots of the surrounding area.

3.4.4 Variable selection.

Currently, several statistical and machine learning methods are used to uncover useful information and patterns for enormous datasets. The common model selection (variable selection) methods include Neural Networks (NN) and RF. The statistical methods like the Least Absolute Shrinkage And Selection Operator (LASSO) [ 55 ] and principal component analysis (PCA). The authors [ 56 ] have proposed combining NN with LASSO or RF for even better results. In addition, they tested these new methods along with classical techniques (ordinary least square and feed-forward NN) using Monte Carlo simulation and real-world air quality data from Italy. The study found that the combined methods achieved lower errors, suggesting they outperform the traditional approaches.

Many methods have been proposed to improve the performance of air quality prediction. However, most investigated methods are based on complete datasets. Therefore, we need to impute missing values to reinforce the prediction models’ performance.

4 Data imputation: Recent techniques

Various statistical and machine learning methods [ 57 – 59 ] have been developed to overcome the problem of missing data for time series, to fill in the missing values in the data, or in other words, imputing the missing values. However, methods have limitations in handling data with high missing rates or changes in available variables. In addition, the performances of these methods vary widely according to the type of data, noise levels, or other factors and show a high dependence on correlations within the data.

In this section, we want to provide an overview of the relationships among the given imputation techniques and comparisons and then discuss them individually.

4.1 Conventional methods

4.1.1 ignoring..

Ignoring [ 60 , 61 ] is a method that completely ignores missing values when conducting the analysis process. Although this is a simple method, if the rate of missing data is high enough to influence the analysis outcomes, it is highly dangerous.

4.1.2 Deletion.

An approach of removing/deleting missing observations from raw data is called Deletion [ 62 , 63 ]. It is also a frequently used method when the missing values of the data are not high, and removing missing values will not affect the analysis results. Nevertheless, when the missing data rate is high, deleting missing values makes the data incomplete and unsuitable for some other analysis applications.

4.1.3 Mean/Median/Mode imputation.

Mean/Median/Mode are simple methods. There, the missing value for a continuous variable is imputed by the mean/median of the observed values. When the missing values for a categorical variable are replaced by the Mode of the observed values, these approaches are quick to compute and simple to implement. Mean/Median/Mode Imputation methods [ 64 ] are a solution for better analysis results when they solve the issue of handling missing data values, whereas Ignoring and Deletion methods are thought to provide poor results in the analysis or data mining process when the missing data rate is high. Furthermore, the limitation of these methods is that the bias created by multiple values on the data has the same value, even if the data are MCAR. As a result, it may bias the estimation of skewed distributions.

4.1.4 Regression imputation.

There are two steps in Regression imputation [ 65 , 66 ]. The first is to estimate a linear regression model using the target variable’s observed values along with the explanatory variables. After that, one can use the model to predict values for the missing cases in the target variable. Missing values of the variable are replaced based on these predictions. There are two types. First is deterministic regression imputation. It means missing values are replaced with the exact prediction of the regression model. The second is stochastic regression imputation, which adds an additional random error term to the predicted value imputed by deterministic regression imputation. Regression imputation is the improvement over Mean/Median/Mode imputation. Besides, it has disadvantages, including the assumptions of error distribution and linear relationship, which are relatively strict and give poor results for heteroscedastic data.

4.1.5 Last Observation Carried Forward (LOCF).

Last Observation Carried Forward [ 67 , 68 ] fills in missing values by using the last observed value of the given features in each sample; if there is no previous observation, 0 will be filled in. LOCF assumes that the missing data is constant or follows a gradual change. However, if the missing values are not stationary or the sensor readings exhibit abrupt changes, this method may introduce bias and inaccuracies.

4.1.6 Multivariate Imputation by Chained Equations (MICE).

Multiple imputation offers numerous benefits compared to the single imputation methods mentioned above. MICE [ 69 , 70 ] is one of the most popular multiple imputation techniques. The process uses an iterative set of regression models to impute missing data from a dataset. It imputes missing values in the dataset’s variables by focusing on one variable at a time. Once the focus is placed on one variable, MICE uses all the other variables in the dataset to predict missingness in that variable. The prediction is based on a regression model, with the form of the model depending on the nature of the focus variable. MICE methods perform better and are more reliable for data with a limited sample size.

On the other hand, MICE has several benefits, such as results in unbiased estimates, being easily interpreted in a Bayesian context, and having a large number of workable algorithms built into the MICE framework. It is worth noting that MICE is especially helpful when missing values are associated with the target variable in a way that causes leakage. Users can also state what they believe to be the likely distribution of the missing value using MICE. However, MICE comes at a high computational cost.

4.1.7 First five last three logistic regression imputation (FTLRI).

Chen et al. [ 71 ] proposed an interesting approach for data imputation, namely FTLRI, for time-series air quality data. The paper is based on the traditional logistic regression and a presented “first Five & last Three” model. These techniques could explain relationships among disparate attributes and then derive highly relevant data, for both time and attributes, to the missing data, respectively. The results showed that FTLRI has a significant advantage over the compared imputation approaches, particularly in short-term and long-term time-series air quality data. Furthermore, FTLRI can perform better on datasets with relatively high missing rates (about 40%) since it only selects highly relevant data to the missing values instead of relying on all other data like other methods.

4.1.8 Autoregressive Distributed Lag (ARDL).

Selecting criteria is considered an important issue in the Autoregressive Distributed Lag (ARDL) model. El et al. [ 72 ] proposed the use of four imputation methods (k-Nearest Neighbors, Expectation-Maximization, Classification, and Regression Tree, and Random Forest) for handling the missing values. Their goal was to improve the accuracy of the model with the optimal order of lags. They compared these methods using real economic data related to foreign direct investment (FDI) in Libya. Their findings suggest that the Expectation-Maximization method performed best compared to the others.

Next, Mohamed et al. [ 73 ] introduced a new imputation technique called EPK. Using the Monte Carlo simulation, they evaluated the effectiveness of nine different imputation methods, including EPK. The simulations focused on a specific type of statistical model (binary logistic regression) when the missingness mechanism is MAR. Additionally, they tested the methods on real data from social network advertising. The results from both simulations and real-world applications showed that EPK outperformed other imputation methods regardless of where the missing data occurred (independent variables only, dependent variable only, or both).

4.2 Machine-learning approaches

The recent methods for imputing missing data in time series led to more accurate and improved imputed data than traditional approaches. Choosing an appropriate imputation method for a specific type of missing data significantly impacts the performance of data imputation.

4.2.1 k-Nearest Neighbor Imputation (kNNI).

kNNI method [ 14 , 69 ] uses the k-nearest neighbor to identify similar samples with normalized Euclidean distances or some other type of distance and impute the missing values with the average value of its neighbors. The k-nearest neighbor method can impute continuous variables (by using the mean or weighted mean among the k-nearest neighbors) and categorical variables (by using the Mode among the k-nearest neighbors). Both quantitative and qualitative features are handled by kNNI with ease. However, it performs computationally intensively for large data since it searches through all the datasets and requires the specification of hyper-parameters that can greatly affect the results.

4.2.2 MissForest.

The Random Forest (RF) algorithm can also applied for multivariate time series data, employing an average of the corresponding full values. Using proximity data points, this algorithm then iteratively improves the imputation of missing data. Generally, missForest is a technique that was proposed by [ 74 ] based on Random Forests. The article showed that RF intrinsically constitutes a multiple imputation scheme by averaging many unpruned classification or regression trees. The imputation error can be estimated without a test set using Random Forest’s built-in out-of-bag error estimates. Furthermore, missForest performs better than K-nearest neighbors and other imputation techniques, giving outstanding results for data containing non-linear relations and/or complex interactions. Additionally, it works well with data containing both qualitative and quantitative features. When using missForest, there is no need to tune parameters, do categorical encoding, or standardize the data. MissForest can be utilized to achieve good imputation results even in high-dimensional datasets with a large number of variables compared to the sample size.

4.3 Deep neural networks

In addition, many deep learning techniques have been developed to solve imputation for missing values in time series data.

4.3.1 GRU-D.

Chen et al. [ 75 ] proposed the GRU-D model, which is a deep learning model based on Gated Recurrent Unit (GRU) that takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it does not only captures the long-term temporal dependencies in time series but also utilizes the missing patterns to achieve better prediction results.

4.3.2 Deep auto-encoder.

One method that can be used for data imputation is the auto-encoder structure. It extracts features from low-dimensional layers using the encoder and decoder structure, and the decoder recovers missing values. As such, it can function as a methodological feature. [ 76 ] presented one technique using deep autoencoders for spatiotemporal challenges involving imputing missing data. The proposed method for capturing temporal and spatial patterns was a convolution bidirectional LSTM. Additionally, the authors analyzed an autoencoder’s latent feature representation in spatiotemporal data and illustrated its performance for missing data imputation. The experimental result illustrated that the convolution recurrent neural network outperforms state-of-the-art methods.

4.3.3 MultiLayer Perceptron (MLP).

Next, [ 77 ] estimated the missing values of a variable in multivariate time series data using a MultiLayer Perceptron. To achieve the best prediction performance for the specified time series, an automated technique was employed to identify the optimal MLP model architecture, filling in a long continuous gap instead of relying on isolated, randomly missing observations. The findings demonstrated that using MLP to fill a big gap produces better outcomes, especially when the data behaves nonlinearly.

4.3.4 Raindrop.

Raindrop [ 78 ] is a Graph Neural Network-based algorithm embedding irregularly sampled and multivariate time series. It is inspired by how raindrops hit a surface at varying time intervals and create ripple effects propagating throughout the surface. Raindrop helps handle missing data with irregular time series. It represents every sample as a separate sensor graph and models time-varying dependencies between sensors with a novel message-passing operator. It estimates the latent sensor graph structure and leverages the structure together with nearby observations to predict misaligned readouts. This model can be interpreted as a graph neural network that sends messages over graphs that are optimized for capturing time-varying dependencies among sensors. Another typical work comes from Festag et al. [ 79 ], where the authors developed a system based on Generative Adversarial Networks that consist of recurrent encoders and decoders with attention mechanisms and can learn the distribution of intervals from multivariate time series conditioned on the periods before and, if available, periods after the values that are to be predicted.

Therefore, it is worthwhile to understand the data types with missing values and propose an effective and robust strategy to fill time-series air quality data with missing values.

5 Data imputation for air quality prediction

A flowchart for the setup of the training process for the Machine Learning and Deep Learning framework proposed in this work is shown in Fig 1 .

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• TIFF original image

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In experiments, to obtain a thorough comparison, we compare some classical by widening traditional imputation methods with some recently developed imputation methods:

• Mean Imputation [ 80 ]: The missing values are replaced with the mean value of the corresponding features.
• Median Imputation [ 81 ]: It is similar to Mean imputation, but the median is utilized instead of the mean.
• Multivariate Imputation by Chained Equations (MICE) [ 69 ]: MICE imputes missing values in the variables of the dataset by focusing on one variable at a time. Once the focus is placed on one variable, MICE uses all the other variables in the dataset to predict missingness in that variable. The prediction is based on a regression model, with the form of the model depending on the nature of the focus variable.
• k-Nearest Neighbor Imputation (kNNI) [ 69 ]: It uses the k-nearest neighbor method to identify similar samples and impute the missing values with the average value of its neighbors. The k-nearest neighbor method can impute continuous variables (the mean or weighted mean among the k-nearest neighbors) and categorical variables (the mode among the k-nearest neighbors).

The main deep learning methods researched for time series imputation are SAITS [ 13 ], BRITS [ 12 ], MRNN [ 82 ], and Tranformer [ 83 , 84 ]. All of them are deep learning approaches published recently for time series imputation.

• Self-attention-based imputation for time series (SAITS) [ 13 ]: a self-attention mechanism for missing value imputation in multivariate time series. Typically, it is trained by a joint-optimization approach. SAITS learns missing values from a weighted combination of two diagonally masked self-attention (DMSA) blocks. DMSA explicitly captures both the temporal dependencies and feature correlations between time steps, which improves imputation accuracy and training speed. Meanwhile, the weighted-combination design enables SAITS to dynamically assign weights to the learned representations from two DMSA blocks according to the attention map and the missingness information.
• Bidirectional Recurrent Imputation for Time Series (BRITS) [ 12 ]: a method for filling the missing values for multiple correlated time series. It learns the missing values in a bidirectional recurrent dynamical system without any specific assumption. The imputed values are treated as variables of the RNN graph and can be effectively updated during the backpropagation.
• Multi-directional Recurrent Neural Network (MRNN) [ 82 ]: is a neural network architecture including two blocks (interpolation and imputation) trained simultaneously. The interpolation process operates within data streams, while the imputation process operates across data streams. The interpolater uses a Bi-directional Recurrent Neural Network (Bi-RNN) to interpolate missing values within each channel along the time dimension. Afterward, using a simple, fully connected neural network, the imputer can compute an estimate for each time step along all channels.
• Transformer [ 83 , 84 ]: Transformer is a self-attention-based model. It uses transformer architecture in an unsupervised manner to perform missing value imputation. Unlike the existing transformer architectures, this model only uses the encoder part of the transformer due to computational benefits. It is a joint-optimization training approach of imputation and reconstruction for self-attention models to perform missing value imputation for multivariate time series.

In the following sections, we will compare different data imputation techniques with various datasets related to air quality prediction.

6.1 Experimental setup

The efficacy of the missing data imputation methods depends heavily on the problem domain, for example, sample size, types of variables, and missingness mechanisms.

We evaluated the methods mentioned in Section 5 on six real datasets. These datasets include cases with small, moderate, and large sample sizes: Air quality in Frankfurt, Germany (Available on: https://www.kaggle.com/datasets/avibagul80/air-quality-dataset ); Beijing Multi-Site Air Quality (Available on: https://archive.ics.uci.edu/dataset/501/beijing+multi+site+air+quality+data ) [ 13 , 85 ]; Air quality in Northern Taiwan (Available on: https://www.kaggle.com/datasets/nelsonchu/air-quality-in-northern-taiwan ); Air Quality in Dalat, Vietnam (Available on: https://github.com/BinhMisfit/air-pollution-datasets/tree/main/Dalat-air-quality-dataset ) [ 51 ] and Air quality dataset in Minh Khai district and Cau Giay district in Hanoi, Vietnam (Available on: https://github.com/BinhMisfit/air-pollution-datasets/tree/main/Hanoi-air-quality-dataset ) [ 49 ].

The descriptions of the six datasets used in this work and their preprocessing details are elaborated on below:

The first dataset is a time-series air quality dataset with categorical contextual information (time and weather); the air pollution PM2.5 values were collected from sense-boxes installed in Frankfurt, Germany. The dataset has been read from 14 different sensors in close spatial proximity. The dataset was efficiently labeled and can be used as a gold-standard dataset for unsupervised problems. Similarly, the test set of this data takes data from 20% original dataset, 20% of the remaining 80% of the original dataset is used for validation, and the remaining is for training. We also chose every 30 minutes of data and every 1-hour consecutive step to generate time series data samples.

The second dataset is Beijing Multi-Site Air-Quality. It includes hourly air pollutant data from 12 monitoring sites in Beijing. This dataset collected data from 01/03/2013 to 28/02/2017 (48 months in total). For each monitoring site, 12 continuous time series variables are measured ( e . g ., PM 2.5, PM 10, SO 2). The test set of the third dataset takes data from 20% original dataset, 20% of the remaining 80% of the original dataset is used for validation, and the remaining is for training. The validation set contains data from the following 05/11/2016. The training set takes from 18/12/2015. In addition, we take every one-hour data to generate a time series of data samples for every 24 consecutive steps.

The third dataset is from the Environmental Protection Administration, Executive Yuan, R.O.C. (Taiwan). It was only collected in Northern Taiwan in 2015, containing air quality and meteorological monitoring data. Besides, this data included 25 air pollution stations and 21 features. The test set takes data from 20% original dataset, 20% of the remaining 80% of the original dataset is used for validation, and the remaining is for training. Specifically, the training set takes place on 15/01/2015, the validation set takes place on 01/09/2015, and the remaining part is used as a test set. We selected every 1-hour data and every 12 consecutive steps in experiments to generate time series data samples.

The fourth dataset is Dalat Air Quality. Urban Air provides a streaming dataset from CCTV and air station networks installed in Dalat City, Vietnam. The system runs 24 × 7 and has several real problems, such as sudden camera/station turn-on/switch-off, noise, and outliers. There are ten air pollution stations (i.e., sensors 01-10), three attached to weather stations (i.e., sensor01, sensor02, sensor03), and fourteen CCTV cameras. Furthermore, the test set of the dataset takes data from 20% original dataset, 20% of the remaining 80% of the original dataset is used for validation, and the remaining is for training. We generate time series data samples by selecting every 1-hour data and every 24 consecutive steps.

The two final datasets were collected hourly at two monitoring stations in Hanoi: Cau Giay district and Minh Khai district. For example, Cau Giay dataset with observation time from 25/2/2019 to 25/11/2020, and Minh Khai dataset from 01/1/2019 to 25/11/2020 record measured features including PM 10, PM 2.5, SO 2 , O 3 , NO 2 , NO , NO x , CO , Temperature, Humidity, Wind speed, Rain, Wind direction, Atmospheric Pressure, Solar Radiation. Besides, 20% of the original dataset is utilized for the test set, 20% of the remaining 80% is used for validation, and the remaining is used for training. Then, we choose one hour’s worth of data per 24 consecutive steps to create time series data samples.

After the preprocessing step, general information about the datasets is described in Table 1 .

https://doi.org/10.1371/journal.pone.0306303.t001

We generate artificial missingness to evaluate all imputation methods used. It is important to note that normalization is applied in the preprocessing of all datasets. For each dataset, the missing ratio p is varied from 10% to 80% with increments of 10% for each dataset to evaluate the models at different missing ratios. For p ∈ {10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%}, we train the model to fill the missing values and then calculate the imputation accuracy. However, with the high missing rates in the original Dalat dataset (greater than 40%) for this dataset, we generate extra artificial missing values with missing rates of 10%–30% only.

Besides, the specific information about the architectures of SAITS, BRITS, MRNN, and Transformer models in this paper can be described in Table 2 .

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For multiple imputation cases with K imputations, we have K values for MAE and RMSE per dataset, and we use the average to evaluate the model performance. We designed each experiment 10 times. We report mean MAE and RMSE, along with their running time, as the performance metrics.

In this paper, all the models were trained/tested on a computer with the following configurations: Intel(R) Xeon,(R) Gold 6254 CPU @3.10GHz/512GB RAM.

Detailed experimental results and the running time of imputation methods on six datasets are recorded in Tables 3 – 8 and described in Figs 2 – 7 .

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Due to the high missing rates in the original Dalat dataset greater than 40%, we generate extra artificial missing values with missing rates of 10%–30% only.

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6.2 Results on different datasets

6.2.1 frankfurt dataset..

Table 3 presents the results of the traditional imputation method compared with other imputation methods regarding the experiment accuracy and running time on the Frankfurt air quality dataset. One can see that when the missing rate of the dataset increases from 10% to 80%, Mean and Median methods have an almost constant MAE error (fluctuating around 0.787–0.788); the MRNN model gives the highest error (greater than 0.904). Moreover, the Transformer model has an MAE error from 0.651 to 0.77; the kNNI model alone has the smallest MAE and RMSE errors among the remaining machine learning models, such as MICE, even lower than the currently used neural-network models, such as SAITS and BRITS. In general, in this dataset, the MAE and RMSE errors of the kNNI model both give the lowest and most stable results among the remaining missing data models when the missing rate of the original data is 0%, and the artificial missing data rate gradually increases from 10% to 80%. As depicted in Fig 2c , it is worth noting that the running time of the models used in this dataset mainly increases when the missing rate of the dataset changes from 10% to 80%. On the other hand, compared to traditional models or basic machine learning models, although models based on neural networks have a long calculation time, MRNN gives relatively positive results and is the most effective among the models using neural networks in terms of Mean, Median, and MICE.

On the other hand, when the original dataset is not missing and the data size is larger than one million records (for example, Frankfurt air quality data), kNNI is considered the model that gives the best results with an artificial missing rate of 10%–80% and time execution time gradually increases from 62.684 × 10 3 milliseconds to 941.832 × 10 3 milliseconds, followed by SAITS with computation time decreasing from 843.803 × 10 3 milliseconds to 391.645 × 10 3 milliseconds.

6.2.2 Beijing dataset.

Table 4 depicts the results of imputation methods compared with other imputation methods on the Beijing air quality dataset. In this dataset, the SAITS model also gives the lowest MAE measure compared to other machine learning models; the model error varies from 0.142 to 0.349, followed by BRITS. Meanwhile, the traditional data-filling models vary from 0.724 to 0.885 as the missing ratio gradually increases from 10% to 80%. On the other hand, the MAE error of the SAITS model when the artificial missing rate of data changes from 10% to 40% is lower than that of the MICE model; on the contrary, the RMSE error of MICE is lower than that of SAITS, and the lowest in the remaining used models such as BRITS, kNNI, Transformer, Mean, Median, and MRNN. The artificial missing rate of data ranges from 50% to 70%, the MAE and RMSE errors of the SAITS model are stable again, and the experimental results obtained are the smallest among the models. The remaining models give a relatively large error, with MRNN having the largest error. When the missing data rate is at 80%, the kNNI model gives the best MAE and RMSE errors compared to traditional data filling or machine learning models. In addition, Fig 3c shows the computational time of models such as Median, MRNN, MICE, SAITS, and BRITS remains almost constant when the missing rate increases from 10% to 80%. Next, kNNI is a model with large fluctuations in calculation time, gradually increasing as the missing rate increases. Moreover, the calculation time of the Transformer gradually decreases and changes sharply as the missing ratio increases. Compared to traditional machine learning models, MRNN has the most stable and fastest calculation time compared to the remaining models in this dataset.

6.2.3 Taiwan dataset.

Table 5 shows that the MAE error between traditional models and current models using neural networks grows larger as the missing rate of input temporal data increases on the Northern Taiwan air quality dataset. Specifically, SAITS is the model with the lowest error (ranging from 0.121 to 0.284), followed by BRITS (error only from 0.136 to 0.29) in this data. Meanwhile, methods such as Mean, Median, kNNI, or MICE give errors when filling in missing values that deviate greatly from the original value. Besides, when the artificial missing rate of the data changes from 10% to 60%, the MAE and RMSE errors of the SAITS model give the lowest results. However, when the missing data rate reaches 70%, the SAITS model’s MAE error is the smallest, but the RMSE error is higher than that of the kNNI model. When the missing rate is from 80%, the MAE and RMSE error results of kNNI are the lowest among the models, followed by BRITS.

On the one hand, in Fig 4c , we can see the computational time of machine learning models like kNNI is the smallest after traditional missing data filling models like Mean and Median. Besides, MRNN is the model with the least computational time among machine learning models, followed by MICE. The remaining models have fluctuating and irregular calculation times, the highest when the missing data rate is 10%–30%, and the lowest when the missing data rate is 40%–50%. By comparing the performance of the Mean, Median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer models, we see that SAITS is the best missing data imputation model on Northern Taiwan and Beijing air quality dataset with an artificial missing rate under 70%. One can see that when the original missing rate of the dataset is less than 30% (or 10%). The dataset only has a few hundred thousand records. SAITS seems to be the model with the lowest error, and model execution time also gradually decreased (from 4223.126 × 10 3 milliseconds to 1117.453 × 10 3 milliseconds with the Northern Taiwan dataset and from 403.223 × 10 3 milliseconds to 259.380 × 10 3 milliseconds with the Beijing dataset) as the missing rate of the dataset increased.

6.2.4 Dalat dataset.

Table 6 presents similar experimental results on the Dalat air quality dataset [ 51 ]. In this table, traditional methods such as Mean and Median give a constant MAE measure (about 0.83–0.86) when the missing rate of data changes from 10% to 30% and almost the result of these measures is the largest compared to the remaining missing data filling models. Meanwhile, the neural network models used in this dataset, such as SAITS and BRITS, give optimal results, which are not much different from traditional machine learning models such as kNNI and MICE (for MAE measurement, the shortest). However, when the artificial missingness ratio of the data is at 10%, MICE gives relatively low MAE and RMSE errors among the models. Furthermore, when increasing the artificial missing rate to 20%, although the MAE error of MICE is the lowest, the RMSE error of the BRITS model is the smallest. Next, when continuing to increase the artificial missing rate of the model to 30%, the experimental results, MICE is the model with the smallest MAE, and kNNI is the model with the lowest RMSE of all. On the other hand, the calculation time of most models increases when the data’s artificial missing rate increases. Accordingly, MRNN is the model with the fastest computation time, followed by SAITS, Transformer, BRITS, and MICE in Fig 5c .

6.2.5 Cau Giay dataset.

The experimental results on the Cau Giay District air quality dataset [ 49 ] are presented in Table 7 . One can see that the MAE errors of MICE showed the best results among the used models (only from 0.241–0.424) when the artificial missing rate of the data gradually increased from 10% to 80%. The second best is SAITS (from 0.289–0.578), and the third one is BRITS (from 0.337–0.666). However, the RMSE error of SAITS is the lowest with artificial missing rates of 10%–30% and 50%–60%. Meanwhile, when the missing rate is 40% and increases to 70%–80%, MICE almost always gives relatively good results compared to the remaining models. Besides, the MAE and RMSE errors of the models become larger when the missing rate changes from 10% to 80%, especially the MAE and RMSE errors of the two methods Mean and Median are large, only fluctuating around 0.8. In addition, the running time of machine learning and neural network models is significantly slower than Mean imputation (0.379–0.509 milliseconds) and Median imputation (0.933–0.582 milliseconds). Also, the slowest is MICE, with a relatively large running time, ranging from 71.192 × 10 3 to 71.527 × 10 3 milliseconds.

6.2.6 Minh Khai dataset.

Table 8 presents experimental results on the Minh Khai air quality dataset [ 49 ]. Although the experimental time of MICE is the largest, the model gives the most optimal MAE and RMSE error results among the models used, followed by SAITS, BRITS, kNNI, and Transformer. The MAE and RMSE errors of the Mean and Median methods hardly change much when increasing the missing data rate from 10% to 80%; MAE and RMSE errors of the remaining models gradually increase as the missing data rate increases.

Furthermore, one can also see that when the original missing rate of data is less than 5%, MICE is the method that gives the most optimal results among the missing data imputation methods used in this article (specifically with the Cau Giay district and Minh Khai district air quality dataset). Besides, the running time of MICE is high and increases the fastest when the missing rate of data gradually increases with the Minh Khai dataset. On the other hand, the Cau Giay dataset does not change much over time. However, when considering the performance of filling in missing values using neural networks, SAITS is the model with the most optimal performance, followed by BRITS. We knew that Transformer is a deep-learning model designed to solve many problems. However, in this study, we can see that Transformer hardly promotes its strengths, and experimental results on different data all give much larger MAE and RMSE errors than SAITS and BRITS.

6.3 The impact of different numbers of layers

Based on the experimental results presented above, although MICE gives a better MAE measure than SAITS in some cases (specifically in datasets in Vietnam), the running time of MICE is many times longer than that of SAITS. Therefore, we propose SAITS as the model to fill in missing values for the air quality data for the multivariate time series of those tested in this paper. We now perform another test and compare the results when performing missing data filling on air quality data of SAITS with the Transformer model with the different number of layers cases (i.e., two layers, four layers, and six layers) and the BRITS model. We then propose the best model to fill in the last missing value before predicting air quality for the following year.

The result of evaluating Transformer and SAITS with two-layer, four-layer, and six-layer for both datasets is presented accordingly in Fig 8 . From there, one can see that SAITS with two, four, and six layers do not show as clearly as Transformer with two, four, and six layers in the six datasets, including Frankfurt, Beijing, and Taiwan. However, for the Dalat, Cau Giay, and Minh Khai datasets, we can see the results of both SAITS and Transformer with two, four, and six layers clearly shown. As a result, SAITS with two layers performs better than Transformers with two, four, and six layers. Besides, the RMSE performance of SAITS and Transformer with layers of all datasets in Fig 9 is unclear and changes frequently. In contrast, the running time of SAITS and Transformer with two layers in Fig 10 for both datasets is also a more effective model with four layers and six layers.

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6.4 Predict air quality in Vietnam combined AQI indexes

Based on the results and evaluating the performance of the methods with different numbers of layers above, we propose the SAITS model with two layers as the most optimal missing value estimation technique with a small sample size on the three air quality datasets in Vietnam.

We started predicting factors affecting air pollution in the following 24 hours based on Vector Auto-Regression (VAR) and related features. Next, we analyzed daily air pollution levels across countries on three datasets in Vietnam, which were mentioned based on the Air Quality Index (AQI). The AQI level of each pollutant is calculated according to the instruction (Available on: https://www.airnow.gov/sites/default/files/2020-05/aqi-technical-assistance-document-sept2018.pdf ) and divided into seven levels (i.e., Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous, and Extreme Hazardous). It only includes six air pollutants (i.e., PM 2.5, PM 10, CO , NO 2 , SO 2 , and O 3 ) that are required to predict their values and related AQI levels. This index tells us how clean or polluted the air is and what it means to human health. The higher the AQI index value, the greater the level of air pollution and the more negative impact it has on human health.

Additionally, we also perform the outlier detection for the datasets using Mahalanobis distance, where points with large Mahalanobis distance (greater than 97.5%) are considered to be outliers. However, we didn’t remove the outliers from the datasets because deleting them will make the data become irregularly sampled time series data, and VAR is not designed for that type of data.

We analyze indexes such as SO 2 ; CO ; PM 10; PM 2.5; O 3 recorded by sensors in Dalat, Vietnam. The concentration of PM 2.5 collected at some stations is large (ranging from 50–100 μg / m 3 ), but the air quality collected from stations in this place is at an acceptable average level, some stations exceeding the threshold range from 100–106 μg / m 3 but at a poor level and affecting sensitive groups of people. (Similar to the concentration of fine particles PM 10). Meanwhile, the levels of CO , SO 2 , and O 3 in the air are low, only from 1.09–10.7 μg / m 3 , completely at a good level and do not have much impact on human health. In general, from the end of 2019 to the beginning of 2020, the concentration of these indicators increased and posed serious harm to human health. Besides, some other factors can interfere with the fluctuation of air pollution in Dalat City, known as the city of thousands of flowers, and attract many people, especially young people, to visit and relax during festivals or on the weekend. Next, Dalat is geographically located in a mountain valley and has cool weather that may impact air pollution and some activities.

Next, we also analyzed factors such as PM 10, PM 2_5, CO , SO 2 , O 3 recorded by two districts of Cau Giay and Minh Khai in Hanoi, Vietnam. Fine dust concentrations PM 10 and PM 2.5 in the Cau Giay district are mostly at moderate levels according to the AQI categories table, and they do not cause serious effects on human health. However, on some days when the concentration of these indicators is recorded at a high level, greater than 100 μg / m 3 , and even on some days, the concentration of fine dust in some time frames exceeds 300 μg / m 3 , seriously affecting the health of the people of the Capital. In addition, the number of motorbikes and vehicles circulating in Hanoi is quite large, which is also the cause of air pollution here; CO concentration is greater than 1000 μg / m 3 . For example, from 22/2/2023-23/2/2023, CO concentration was recorded above 10, 000 μg / m 3 at an alarming level. In addition, the concentration of SO 2 is relatively low, possibly because this area does not have many manufacturing plants, so the amount of toxic chemicals such as SO 2 released into the environment is small, at a good level, and does not affect the human health. From the end of 03/2019 to mid-04/2019, the concentration of O 3 gradually increased, from 100–350 μg / m 3 , changing from a level that is not harmful to human health to a seriously harmful level. Besides, the concentration of fine dust particles PM 10, PM 2.5 in the air recorded in the Minh Khai district dataset is also quite high, which is not good for human health. Some days, the fine dust concentration of these particles exceeds 479 μg / m 3 and 255 μg / m 3 , respectively PM 10 and PM 2.5. Furthermore, the amount of SO 2 and O 3 in the Minh Khai district dataset is quite low, at a normal level according to the AQI categories scale, so it does not affect human health. However, the concentration of CO in the air is very high; most days, the concentration is recorded to be greater than 1000 μg / m 3 , which is considered “Exceeding AQI.” Recommendations for hazard classification should be implemented. In summary, Hanoi is known to domestic and foreign friends as the Capital of Vietnam, a place to work and welcome heads of state. Based on the air pollution problem in the Cau Giay and Minh Khai districts, we need to take many measures to reduce air pollution and improve the environment to attract tourists, creating economic development and international cooperation conditions.

7 Discussion

There are many estimation techniques to handle missing data. A lot of them focus on the missing values of the time series. However, these techniques might not be able to capture time information and produce reliable imputation results if timestamps are missing. Therefore, expanding on current techniques to impute missing timestamps may be suitable for handling such situations. Research on deep learning modeling is a being cared area. Numerous novel deep-learning models with practical applications have been proposed in recent years. There is a growing number of research papers on deep learning for imputing missing data, and these new approaches seem promising. However, in practical applications, the validity and strength of these models must be carefully assessed. In many cases, reliable and reproducible code is frequently unavailable or incomplete. Compared to conventional statistical methods, the number of hyperparameters for deep learning models typically requires much more tuning. In some applications, the hyperparameter search on big data may be prohibitively expensive due to the required training time or memory size. However, our research showed that for data with small, moderate, or even large sample sizes (i.e., when the sample size is less than 30,000), the stability and convergence of the deep learning models needed to be revised.

Numerous factors, including the sample size of the data, the distribution of the variables, the number of missing values in the data, the correlation structure of the data, and potential missingness mechanisms, affect how effective different missing data imputation techniques are.

While this work presents a thorough investigation of time series imputation techniques and provides practical implications for practitioners, it also has some limitations. For example, different types of geographic locations, such as mountains and plains, may affect air quality. Collecting more datasets and examining the patterns for various types of geographic locations may help draw out common patterns and insights to improve the imputation quality. However, due to limited data available, we have not achieved that goal yet. In addition, this work so far has only concentrated on examining the effect of missing data for the missing at random pattern. However, it is also possible that air quality data is missing, not at random. These will be topics for our future research.

8 Conclusions

We have presented an investigation of the impact of data imputation techniques on the air quality prediction problem. In general, SAITS gives the lowest error, and the difference in running time is negligible compared to the missing value imputation efficiency that SAITS provides when the missing rate increases. Besides, BRITS is the model that gives the second-best error among deep learning models, only after SAITS. kNNI running time can increase significantly as the missing rate increases. However, this may not be true for other methods. Also, kNNI proves itself to remain a promising imputer for the dataset. In addition, for three datasets (Northern Taiwan, Beijing, and Frankfurt) which have a large sample size, at the high missing rate of 80%, kNNI outperforms other techniques, including the state-of-the-art such as SAITS, BRITS, and Transformer. Meanwhile, other cases were varied using limited sample size and ratios of missing data. The experiment results show the conventional method, MICE, outperforms the recently proposed deep learning methods, such as SAITS and BRITS, in these experiments.

The outcomes of this article can open a new direction for predicting air pollution. However, as discussed, it also has some limitations, such as the experiments concentrated only on missingness at random, and the paper has not been able to draw insights by grouping datasets based on geological characteristics due to limited data. Therefore, in the future, we will collect more datasets to examine the imputation quality based on types of geographic locations or some other characteristics and consider the imputation effects for nonrandomly missing data. Also, we will develop an ensemble technique combining the latest missing techniques and SAITS to enhance the imputation performance. Moreover, the process can integrate the knowledge in a particular domain to support extracting helpful information from datasets [ 86 ]. In addition, in the future, we plan to empirically evaluate the performance of imputation techniques for other types of environmental data, such as imbalanced missing data. Last but not least, we want to investigate if methods to combine datasets such as ComImp [ 87 ] can be used to combine air quality datasets to improve the imputation and prediction quality.

Acknowledgments

We want to thank the University of Science, Vietnam National University in Ho Chi Minh City, and AISIA Research Lab in Vietnam for supporting us throughout this paper.

• 1. Duong DQ, Le QM, Nguyen-Tai TL, Nguyen HD, Dao MS, Nguyen BT. An effective AQI estimation using sensor data and stacking mechanism. In: Proceedings of the 20th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT 21). vol. 337. IOS Press; 2021. p. 405–418.
• 2. Vu MA, Nguyen T, Do TT, Phan N, Halvorsen P, Riegler MA, et al. Conditional expectation for missing data imputation. arXiv preprint arXiv:230200911. 2023;.
• 3. Shaadan N, Rahim N. Imputation analysis for time series air quality (PM10) data set: A comparison of several methods. In: Journal of Physics: Conference Series. vol. 1366. IOP Publishing; 2019. p. 012107.
• View Article
• Google Scholar
• 6. Sridevi S, Rajaram S, Parthiban C, SibiArasan S, Swadhikar C. Imputation for the analysis of missing values and prediction of time series data. In: 2011 international conference on recent trends in information Technology (ICRTIT). IEEE; 2011. p. 1158–1163.
• 7. Sitaram D, Dalwani A, Narang A, Das M, Auradkar P. A measure of similarity of time series containing missing data using the mahalanobis distance. In: 2015 second international conference on advances in computing and communication engineering. IEEE; 2015. p. 622–627.
• 9. Dhevi AS. Imputing missing values using Inverse Distance Weighted Interpolation for time series data. In: 2014 Sixth international conference on advanced computing (ICoAC). IEEE; 2014. p. 255–259.
• 11. Le Lien P, Do TT, Nguyen T. Data imputation for multivariate time-series data. In: 2023 15th International Conference on Knowledge and Systems Engineering (KSE). IEEE; 2023. p. 1–6.
• PubMed/NCBI
• 19. Peña M, Ortega P, Orellana M. A novel imputation method for missing values in air pollutant time series data. In: 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE; 2019. p. 1–6.
• 21. Nguyen DH, Nguyen-Tai TL, Nguyen MT, Nguyen TB, Dao MS. MNR-Air: An economic and dynamic crowdsourcing mechanism to collect personal lifelog and surrounding environment dataset. A case study in Ho Chi minh city, Vietnam. In: MultiMedia Modeling: 27th International Conference, MMM 2021, Prague, Czech Republic, June 22–24, 2021, Proceedings, Part II 27. Springer; 2021. p. 206–217.
• 23. Le DD, Tran AK, Dao MS, Nazmudeen MSH, Mai VT, Su NH. Federated Learning for Air Quality Index Prediction: An Overview. In: 2022 14th International Conference on Knowledge and Systems Engineering (KSE). IEEE; 2022. p. 1–8.
• 26. Gholamzadeh F, Bourbour S. Air pollution forecasting for Tehran city using vector auto regression. In: 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE; 2020. p. 1–5.
• 27. Wijesekara W, Liyanage L. Comparison of imputation methods for missing values in air pollution data: Case study on Sydney air quality index. In: Advances in Information and Communication: Proceedings of the 2020 Future of Information and Communication Conference (FICC), Volume 2. Springer; 2020. p. 257–269.
• 29. Ngom B, Diallo M, Seyc MR, Drame MS, Cambier C, Marilleau N. PM10 data assimilation on real-time agent-based simulation using machine learning models: case of dakar urban air pollution study. In: 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications (DS-RT). IEEE; 2021. p. 1–4.
• 30. Sotomayor-Olmedo A, Aceves-Fernandez MA, Gorrostieta-Hurtado E, Pedraza-Ortega JC, Vargas-Soto JE, Ramos-Arreguin JM, et al. Evaluating trends of airborne contaminants by using support vector regression techniques. In: CONIELECOMP 2011, 21st International Conference on Electrical Communications and Computers. IEEE; 2011. p. 137–141.
• 32. Zhu H, Hu J. Air quality forecasting using SVR with quasi-linear kernel. In: 2019 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE; 2019. p. 1–5.
• 33. Zhang C, Yuan D. Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom). IEEE; 2015. p. 929–934.
• 34. Duong DQ, Le QM, Nguyen-Tai TL, Bo D, Nguyen D, Dao MS, et al. Multi-source machine learning for aqi estimation. In: 2020 IEEE International Conference on Big Data (Big Data). IEEE; 2020. p. 4567–4576.
• 35. Li J, Shao X, Zhao H. An online method based on random forest for air pollutant concentration forecasting. In: 2018 37th Chinese Control Conference (CCC). IEEE; 2018. p. 9641–9648.
• 36. Barthwal A, Acharya D. An internet of things system for sensing, analysis & forecasting urban air quality. In: 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE; 2018. p. 1–6.
• 44. Lim YB, Aliyu I, Lim CG. Air pollution matter prediction using recurrent neural networks with sequential data. In: Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence; 2019. p. 40–44.
• 49. Ton-Thien MA, Nguyen CT, Le QM, Duong DQ, Dao MS, Nguyen BT. Air Pollution Forecasting Using Multimodal Data. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer; 2023. p. 360–371.
• 50. Tejima K, Dao MS, Zettsu K. Mm-aqi: A novel framework to understand the associations between urban traffic, visual pollution, and air pollution. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer; 2022. p. 597–608.
• 51. Dao MS, Dang TH, Nguyen-Tai TL, Nguyen TB, Dang-Nguyen DT. Overview of MediaEval 2022 Urban Air: Urban Life and Air Pollution. In: Proc. of the MediaEval 2022 Workshop; 2023. p. 13–15.
• 52. Nguyen-Tai TL, Nguyen DH, Nguyen MT, Nguyen TD, Dang TH, Dao MS. Mnr-hcm data: A personal lifelog and surrounding environment dataset in ho-chi-minh city, viet nam. In: Proceedings of the 2020 on Intelligent Cross-Data Analysis and Retrieval Workshop; 2020. p. 21–26.
• 53. La TV, Dao MS, Tejima K, Kiran RU, Zettsu K. Improving the awareness of sustainable smart cities by analyzing lifelog images and IoT air pollution data. In: 2021 IEEE International Conference on Big Data (Big Data). IEEE; 2021. p. 3589–3594.
• 54. Dao MS, Zettsu K, Rage UK. Image-2-aqi: Aware of the surrounding air qualification by a few images. In: Advances and Trends in Artificial Intelligence. From Theory to Practice: 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021, Proceedings, Part II 34. Springer; 2021. p. 335–346.
• 64. Aljuaid T, Sasi S. Proper imputation techniques for missing values in data sets. In: 2016 International Conference on Data Science and Engineering (ICDSE). IEEE; 2016. p. 1–5.
• 68. Zhou H, Yu KM, Lee MG, Han CC. The application of last observation carried forward method for missing data estimation in the context of industrial wireless sensor networks. In: 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP). IEEE; 2018. p. 1–2.
• 69. Zainuddin A, Hairuddin MA, Yassin AIM, Abd Latiff ZI, Azhar A. Time Series Data and Recent Imputation Techniques for Missing Data: A Review. In: 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST). IEEE; 2022. p. 346–350.
• 76. Asadi R, Regan A. A convolution recurrent autoencoder for spatio-temporal missing data imputation. arXiv preprint arXiv:190412413. 2019;.
• 78. Zhang X, Zeman M, Tsiligkaridis T, Zitnik M. Graph-guided network for irregularly sampled multivariate time series. arXiv preprint arXiv:211005357. 2021;.
• 85. Chen S. Beijing Multi-Site Air Quality; 2019. UCI Machine Learning Repository.
• 87. Nguyen T, Khadka R, Phan N, Yazidi A, Halvorsen P, Riegler MA. Combining datasets to improve model fitting. In: 2023 International Joint Conference on Neural Networks (IJCNN); 2022.

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• Published: 02 September 2024

Transforming air pollution management in India with AI and machine learning technologies

• Kuldeep Singh Rautela 1 &
• Manish Kumar Goyal 1  

Scientific Reports volume  14 , Article number:  20412 ( 2024 ) Cite this article

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A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM 2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM 2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM 2.5 concentrations across India. The results reveal its exceptional precision in PM 2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m 3 . However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM 2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

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Introduction.

Air pollution has emerged as a critical global environmental health issue, with 92% of the world's population exposed to pollutant levels exceeding air quality guidelines 1 , 2 . This widespread exposure poses significant health risks, including increased incidence of respiratory diseases, cardiovascular problems, and premature mortality 3 , 4 . In India specifically, ambient particulate matter (PM) exposure has been linked to an estimated 1.1 million premature deaths annually, with air pollution becoming the fourth leading cause of mortality nationwide 5 , 6 . The economic impact is also substantial, with the World Bank estimating that air pollution costs India 3–8% of its GDP due to healthcare expenses, reduced productivity, and premature deaths 7 .

Atmospheric aerosols, particularly black carbon, organic carbon, dust, sea salt, and sulfates, have been extensively researched in South and Southeast Asia over the past two decades 8 . However, the magnitude of these impacts is largely influenced by spatio-temporal variability and the composition of these aerosols 9 . Aerosols, including, are significant constituents of atmospheric PM and account for approximately 30–70% of the fine aerosol mass over urban areas in India 5 , 10 . However, in recent decades, this concern has increased notably, primarily attributed to the rapid surge in population, unplanned urban development, and the expansion of industries 11 , 12 . India, home to the world's largest population share at 17.76%, faces a significant environmental challenge, with many of its cities (eg; Delhi, Mumbai, Kolkata) ranking among the most polluted on the global scale 13 , 14 . An investigation based on World Health Organization (WHO) data from 2008–2013 brought attention to India's status among the most polluted nations 15 . India has faced alarming and extensive air pollution incidents in the last twenty years, prompting substantial concern among regulatory authorities. The Indo-Gangetic Plain (IGP) is highly susceptible to severe pollution incidents, notably prevalent in the post-monsoon and winter period 16 . Similarly, in many metropolitan cities across India, such as Delhi, air quality has deteriorated to hazardous levels. Concentrations of particulate matter (PM 2.5 and PM 10 ) have surged beyond 500 µg/m 3 , while nitrogen oxides (NO 2 ) have exceeded 10 µg/m 3 . Additionally, ozone (O 3 ) and sulfur dioxide (SO 2 ) levels have surpassed 5 µg/m 3 , alongside other pollutants 17 . The concentration of these pollutants, often surpassing 500 µg/m 3 , far exceeds WHO's safe annual limit of 10 µg/m 3 and India’s national ambient air quality standards (NAAQS) of 40 µg/m 3 during winters 18 . According to the Economic Times, 12.25 million vehicles are registered in Delhi, growing at a rate of 7% per annum, and they account for 67% of the total pollution 19 , 20 . Additionally, Coal-based thermal power plants and small-scale industries each contribute 12% to the pollution, including emissions from various industrial units followed by the agricultural and biomass burning in Delhi and surrounding areas 20 . This increased pollution level has raised considerable concern among authorities and stakeholders, prompting focused efforts towards addressing this critical issue 9 . The urgency of addressing air pollution in India is evident through compelling data illustrating its significant impact across various sectors.

AI & ML have become pivotal in addressing air pollution by harnessing big data analytics, utilising advanced computing systems, scalable storage, and parallel processing technologies 21 , 22 , 23 . These innovations enable comprehensive management and mitigation strategies for various air pollutants, bridging the gap between atmospheric and climate sciences through sophisticated data-driven approaches. Previous studies have proposed various AI&ML-based models as pivotal components for air pollution and aerosol transport 5 , 8 , 24 , 25 , 26 . Initially, researchers have introduced succinct and efficient statistical models for practical applications. These statistical models primarily encompass multiple linear regression (MLR) 27 and autoregression moving average (ARMA) 28 methods. The predominant use of linear hypotheses in developing statistical models contrasts with the inherent nonlinear properties exhibited by pollutant concentrations. Consequently, researchers have advocated for integrating data mining methods 29 and machine learning models 30 , 31 , 32 designed to accommodate nonlinear predictions in studying air pollutants. However, the notably nonlinear and non-stationary nature of pollutants poses challenges for achieving high prediction accuracy with these models. As a result, several studies have turned to various deep learning techniques 8 , 33 , 34 , 35 , 36 to enhance the prediction of air pollutant levels.

Despite numerous efforts to forecast concentration of major pollutants, comprehending the complex relationship among diverse influencing factors remains a persistently challenging task. Studies exploring the relevance of these factors in predicting pollutants have been scarce and constrained in scope 37 , 38 . Typically, researchers tend to utilize all accessible features and input them into prediction models. While it holds true that AI&ML models exhibit superior performance in scenarios with abundant data availability, the effectiveness of these models in pollutant prediction hinges on understanding and incorporating the most influential factors. Figure  1 illustrates the comprehensive AI/ML model development workflow for environmental or traffic-related predictions. The process includes data collection across various domains, preprocessing, algorithm selection, model development, training, testing, and validation. The process completes with prediction, incorporating a feedback loop for model refinement if needed, ensuring adaptability and continuous improvement in predictive accuracy.

Charting the sequential steps of AI and ML involvement in predicting air pollution concentrations.

Previous studies have conducted comparative analyses between AI&ML-based methodologies for forecasting concentrations of various pollutants. Initially, Mc Kendry 39 evaluated Artificial Neural Networks (ANN) with MLR for simulating the concentrations of PM 2.5 and PM 10 . Similarly, Dutta and Jinsart 40 compared the performances of decision tree and ANN algorithms in estimating PM 10 concentrations. Other comparisons include Turias et al. 41 pitting back-propagation based ANN against ARIMA for predicting the Sulfur Dioxide (SO 2 ), concentrations of Carbon Monoxide (CO) and Suspended Particulate Matter (SPM), over an industrialized region. Shang and He 42 formulated an innovative prediction method by coupling of ANN and Random forest (RF) to forecast hourly PM 2.5 concentrations. Bozdağ et al. 43 presented a comprehensive analysis for the simulation of PM 10 concentrations by comparing various modelling approaches—ANN, KNN (K-Nearest Neighbour Algorithm), SVM (Support Vector Machine) , LASSO (Least Absolute Shrinkage and Selection Operator), RF, and xGBoost.

This study systematically explores the consequences of severe air pollution in India, focusing on contributors like PM, Organic Aerosols (OAs), BC, Water-Soluble Brown Carbon (WS-BrC), and Volatile Organic Compounds (VOCs). Remediation techniques, including legislation, NAAQS, and an Air Quality Index (AQI), are inspected alongside the evolution of emission load studies and management strategies. Additionally, the study investigates the integration of AI&ML in mitigating and predicting air pollution. It details the application of AI&ML models and underscores the potential of deep learning algorithms, exemplified through a case study predicting PM 2.5 concentrations over India. Identifying challenges like technological barriers, regulatory hurdles, public awareness gaps, agricultural practices, urbanization impacts, cross-border pollution, climate change interlinkages, and socio-economic disparities, the study emphasizes the urgency of comprehensive solutions. Looking forward, the study discusses prospects involving emerging technologies and global collaborations. The study emphasizes the imperative to address air pollution in India holistically, leveraging AI&ML advancements, global cooperation, and technological innovations to formulate effective strategies for combatting the multifaceted challenges posed by air pollution in the region.

Results and discussion

Consequences of air pollution in india.

Air pollution in India specially in metropolitan cities has dire consequences for public health, stemming from increased levels of particulate matter, nitrogen oxides, and various pollutants. This increase pollution level is consistently linked to increased respiratory diseases, particularly asthma, chronic obstructive pulmonary disease (COPD), and bronchitis 7 , 44 . Children, with developing respiratory systems, are particularly vulnerable to irreversible health issues upon prolonged exposure, while the elderly, with compromised immune systems, face pre-eminent risks, including deep lung penetration, inflammation, and enduring damage caused by PM 2.5 . Beyond respiratory implications, air pollution has severe cardiovascular consequences, with nitrogen oxides significantly contributing to an increased risk of heart attacks and strokes, leading to heightened cardiovascular mortality with prolonged exposur 7 . The significant study conducted by the CPCB in Delhi highlighted robust correlations between air quality levels and negative health effects. Comparative analysis against a rural control population in West Bengal indicated a 1.7-fold higher occurrence of respiratory symptoms in Delhi, emphasizing the direct impact of air quality on public health 20 , 45 , 46 , 47 . Odds ratios for upper and lower respiratory symptoms were 1.59 and 1.67, respectively, emphasizing the profound impact of air pollution. The study also highlighted a significantly higher prevalence of current and physician-diagnosed asthma in Delhi, with lung function notably reduced in 40.3% of Delhi's participants compared to 20.1% in the control group 20 .

In addition to respiratory effects, non-respiratory impacts were observed in the cities as compared to rural controls. The prevalence of hypertension was notably higher in cities (36% vs. 9.5% in controls), correlating positively with respirable suspended particulate matter (PM 10 ) levels in ambient air 48 . Chronic headaches, eye irritation, and skin irritation were significantly more pronounced in most of the cities. Community-based studies consistently affirm the association between air pollution and respiratory morbidity. Studies focusing on indoor air pollution reveal similar correlations with respiratory morbidity, extending to conditions such as attention-deficit hyperactivity disorder in children, increased blood levels of lead, and decreased serum concentration of vitamin D metabolites 49 . Beyond health impacts, the environmental consequences of air pollution are profound. Pollutants harm plants and animals, disrupt ecosystems, and lead to biodiversity loss 50 . The issue extends beyond health and the environment, impacting economics and society, straining healthcare, productivity, and social equity, demanding holistic strategies spanning economic, social, and environmental facets making it imperative, in this crisis, to understand the existing and potential remediation techniques 51 .

The economic and social ramifications are substantial, with healthcare costs soaring as the incidence of pollution-related illnesses rises 7 . Treating respiratory and cardiovascular diseases places a significant burden on the healthcare system, affecting both public and private healthcare expenditures 44 . Air pollution in India incurred an estimated economic toll of $95 billion in 2019, amounting to 3% of the country's GDP, attributable to decreased productivity, increased work absences, and premature fatalities 52 . The economic implications of air pollution extend beyond direct healthcare costs, affecting labor markets and overall productivity 53 . Social disparities are accentuated by air pollution, with vulnerable communities facing disproportionate exposure to pollutants. Factors such as socio-economic status, access to healthcare, and geographic location contribute to disparities in exposure and health outcomes 54 . Addressing these social dimensions is crucial for devising equitable solutions that prioritize environmental justice. As India grapples with the immediate consequences of air pollution, emerging challenges require attention. Also, climate change exacerbates existing issues, influencing weather patterns and contributing to the persistence of stagnant air masses that trap pollutants and their transportation mechanism 8 . The increasing frequency of extreme weather events further complicates pollution dynamics 55 . Moreover, the complex interplay of indoor and outdoor air pollution adds another layer of complexity, with indoor air pollution often stemming from household activities such as cooking with solid fuels, compounding the overall burden on public health 49 . However, government policies and initiatives take center stage in this exploration, with regulatory measures, such as emission standards and vehicle restrictions, scrutinized for their effectiveness and implementation challenges 12 . Sustainable urban planning, including the creation of green spaces and transportation planning for pollution reduction, is examined as a proactive approach to mitigate pollution at its source 56 . Technological solutions, ranging from air purifiers to pollution monitoring devices, are also evaluated 57 . The challenges of scalability, accessibility, and integration into existing infrastructure are dissected to discern the practicality and potential impact of these technologies. Emerging technologies and global collaborations are explored as potential catalysts for change 57 , 58 .

Contributors to air pollution in India

Air pollution in India is a complex issue with multiple sources and contributors, as highlighted by various studies conducted by Lalchandani et al. 59 , Tobler et al. 60 , Rai et al. 61 , Talukdar et al. 62 and Wang et al. 63 . The sources and contributors to air pollution can be broadly categorized into particulate matter (PM 2.5 and PM 10 ), organic aerosols (OAs) including black carbon (BC), water-soluble brown carbon (WS-BrC), and volatile organic compounds (VOCs). Each of these components plays a signifsicant role in the overall air quality of the region.

Particulate matter (PM)

Particulate matter is a key component of air pollution, and Lalchandani et al. 59 conducted studies using the Positive Matrix Factorization (PMF) model to identify and apportion different sources of PM. The sources identified included traffic-related emissions, dust transportation, solid-fuel burning emissions, and secondary factors 62 , 64 . Traffic-related emissions in metropolitan cities were found to be the significant contributor to the total concentration of PM, for example, at the IIT Delhi site, emphasizing the impact of vehicular activities on air quality. Additionally, solid fuel burning emissions, often associated with residential cooking and heating, were identified as a major contributor to PM, particularly at night 62 . Rai et al. 61 conducted source apportionment of elements in PM 10 and PM 2.5 , identifying nine source profiles/factors, including dust, non-exhaust sources, solid fuel combustion, and industrial/combustion aerosol plume events. The contribution of anthropogenic sources to elements associated with health risks, such as carcinogenic elements. The geographical origins of these sources were also determined, emphasizing the regional and local influences on element concentrations in atmosphere 65 .

Organic aerosols (OAs)

Organic aerosols are another crucial component of air pollution, and studies by Tobler et al. 60 and Lalchandani et al. 62 revealed three main components of OAs: solid fuel combustion OAs (SFC OAs), hydrocarbon-like OA (HOAs) from vehicular emissions, and oxygenated OAs (OOAs). Lalchandani et al. 65 further categorized these components into sub-factors, providing a detailed understanding of the OA composition. Emissions stemming from traffic emerged as the primary contributor to the overall OA mass, underscoring the profound influence of vehicular pollution 59 .

Black carbon (BC)

BC, a product of incomplete combustion, was studied by Using the Absorption Ångström Exponent (AAE) method, contributions from biomass burning and vehicular emissions were apportioned 66 . Vehicular emissions were found to be a dominant source of BC, contributing around 67.5% 62 , 67 . The distinction between BC and brown carbon (BrC), which absorbs light in the near-UV to visible region, was also discussed, highlighting the need to consider multiple light-absorbing aerosols in air quality assessments.

Water-soluble brown carbon (WS-BrC)

Rastogi et al. 68 performed a PMF analysis of WS-BrC spectra, identifying six factors representing specific sources of BrC. The study revealed diurnal variability in BrC absorption, with factors associated with different emission sources. The presence of secondary BrC was indicated, suggesting the importance of atmospheric processes in the formation of brown carbon. This finding adds another layer of complexity to the sources of light-absorbing aerosols in the atmosphere 69 .

Volatile organic compounds (VOCs)

Wang et al. 63 investigated the characteristics and sources of VOCs, identifying six factors related to traffic, solid fuel combustion, and secondary sources. Traffic-related emissions were found to be the dominant source of VOCs at the urban site, while at the suburban site (MRIIRS), contributions from secondary formation and solid fuel combustion were more significant. The study highlighted the major role of anthropogenic sources in VOC pollution 70 .

Current remediation techniques

India has faced escalating challenges in managing air pollution over the years, necessitating the implementation of diverse remediation techniques. Figure  2 illustrates the legislative evolution of air quality management in India across three eras: Pre-Internet (1905–89), Transition (1990–99), and Internet Era (2000 onwards). This timeline showcases key acts and regulations implemented over time to address air pollution. The bottom timeline highlights the progression of NAAQS in India, from monitoring just 3 pollutants in 1982 to 7 in 1994, and 12 in 2009. The latest phase (2019–24) involves a comprehensive review of air quality standards under the National Clean Air Programme (NCAP) in 2019, demonstrating India's ongoing commitment to improving air quality management.

Legalisation and Evaluation of NAAQS in India 12 .

Legislation and regulatory measures

India's legislative landscape has evolved significantly to address air pollution. The introduction of key acts such as the Air (Prevention and Control of Air Pollution) Act in 1981 and subsequent amendments empowered central and state pollution control boards to handle severe air pollution emergencies 71 . The Environment (Protection) Act of 1986 served as an umbrella act for environmental protection, while the Motor Vehicles Act has been periodically amended to regulate vehicular pollution 72 . Recent developments include the Motor Vehicles (Amendment) Bill of 2019, allowing the government to recall vehicles causing environmental harm 73 . The establishment of institutions like the National Green Tribunal (NGT) and the National Environment Tribunal reflects a commitment to environmental accountability 74 .

National ambient air quality standards (NAAQS) and air quality index (AQI)

The formulation and periodic revision of National Ambient Air Quality Standards (NAAQS) have been pivotal in regulating air quality 18 . Beginning in 1982, the Central Pollution Control Board (CPCB) introduced NAAQS, initially covering SO 2 , NO 2 , and SPM 47 . Subsequent amendments expanded the list to include RSPM, Pb, NH 3 , and CO 75 . The National Air Quality Index (NAQI) was introduced to enhance public awareness, categorizing air quality into six levels from 'Good' to 'Severe' 76 . This index, based on the concentration of eight pollutants, guides interventions for improved air quality.

Air pollution monitoring network

India's air quality monitoring network has witnessed substantial growth. The initiation of the National Ambient Air Quality Monitoring (NAAQM) Network in 1984, expanded to the National Air Quality Monitoring Programme (NAMP), marked a critical step 77 . The network, comprising both manual and Continuous Ambient Air Quality Monitoring System (CAAQMS) stations, now stands at 1082 locations 78 , 79 . Real-time monitoring, as exemplified by CAAQMS, provides valuable data for prompt decision-making. The introduction of the System of Air Quality and Weather Forecasting and Research (SAFAR) further enhances forecasting capabilities 80 .

Evolution of studies on emission load

Emission inventories, critical for formulating air pollution control policies, have evolved over time. Initiatives by CSIR-NEERI and CPCB in the late twentieth century laid the foundation 12 . Emission inventory data, collected through GIS, has become integral in mapping pollution sources and understanding spatial distribution 81 . The Air Pollution Knowledge Assessments (APnA) city program and organizations like TERI contribute to city-specific inventories 82 . The emphasis on utilizing secondary data streamlines the process, enabling the creation of comprehensive databases for national and urban pollution inventories. The secondary data refers to datasets that include emission loads from various sources such as vehicular emissions, industrial outputs, construction activities, residential heating, and biomass burning 83 .

Management strategies and control policies

India's air pollution management strategies encompass a multifaceted approach, with a blend of judicial interventions and executive actions.

Judicial interventions

The judiciary, particularly through petitions filed by M.C. Mehta, has been instrumental in setting guidelines and policies 84 . For instance, interventions in the Taj Trapezium Zone and the oversight of air quality management plans for non-attainment cities by the National Green Tribunal (NGT) are notable 74 . The judiciary has played a significant role in shaping policies for better governance and legislation.

Executive actions

Several executive measures contribute to air pollution control. The Auto Fuel Policy, initiated in 2003 and updated in 2014, addresses vehicular emissions 85 . Emphasis on alternative fuels, as seen in the National Auto Fuel Policy and the Pradhan Mantri Ujwala Yojana (PMUY) for subsidized LPG connections, aligns with cleaner fuel initiatives 86 . Stricter emission standards for thermal power plants and the push for Hybrid and Electric Vehicles (EVs) under schemes like Faster Adoption and Manufacturing of Hybrid & Electric Vehicles (FAMHE) contribute to pollution reductions 87 .

AI&ML Techniques for addressing and forecasting air pollution

Overview of ai&ml models.

Various AI&ML techniques, such as ANN, Fuzzy logic (FL), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Recurrence Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Autoencoder (CA) etc., are commonly used in previous studies to predict and forecast earth and atmospheric variables 8 , 25 , 88 , 89 , 90 , 91 (Table 1 ). AI&ML models have become pivotal in processing and simulating non-linear information, with a notable focus on ANNs 92 . ANNs emulate the human nervous system, comprising interconnected neurons that collectively address a spectrum of challenges, from function approximation to clustering and optimization 93 . The three-stage process involved in ANN modelling, encompassing design, training, and validation, underscores its versatility 92 . During the design phase, crucial parameters such as architecture, layers, neurons, and learning algorithms are thoroughly chosen 94 . Training involves iterative adjustments of synaptic weights to minimize errors, while validation gauges the network's generalization performance for unknown data.

Multilayer Perceptron (MLPs), a prominent type of ANN, have proven effective in predicting atmospheric pollution events. Typically featuring input, hidden, and output layers, MLPs can adapt to complex patterns by incorporating multiple hidden layers 92 . Configuring neurons in the hidden layers is of utmost importance, as an incorrect count can lead to over-fitting or under-fitting. Techniques like thumb rule and trial and error, network reduction offer solutions to optimize neuron numbers. FL, another AI technique, operates on a different paradigm by assigning truth values in a range. Developed from fuzzy set theory, it accommodates linguistic variables, making it adept at handling uncertainty in natural language statements. Fuzzy logic's three main phases—fuzzification, inference, and defuzzification—form a robust modelling system capable of addressing nuanced problems. SVM are popular for supervised learning, excelling in classification, prediction, density estimation, and pattern recognition. SVM seeks an optimal hyperplane to segregate data into predefined classes, with kernel functions playing a pivotal role in introducing non-linearity.

Deep Neural Networks (DNNs) represent an advanced version of ANNs, characterized by structural depth and scalability 8 . DNNs, with more than three layers, can automatically extract features from raw inputs, known as feature learning. Notable architectures within DNNs, such as CA, LSTM, CNNs and RNNs have demonstrated superior performance, especially in air pollution forecasting. The training of DNNs demands significant computational power, leading to advancements in processing capabilities and the development of sophisticated algorithms. Overcoming challenges like vanishing gradient and overfitting has prompted the application of advanced algorithms like SVM, RF, Greedy layer-wise, and Dropout. The application of these models extends across various domains due to their versatility and robust performance. The modelling of complex atmospheric variables such as air pollution forecasting, LSTM, CA, and CNNs emerge as particularly effective and popular architectures.

Application of AI&ML in addressing and forecasting air pollution

The application of AI&ML models, particularly ANNs, FL, SVM and DL models, have emerged as a crucial tool in addressing and forecasting air pollution. ANNs have helped in a transformative era in air pollution forecasting, with a diverse range of applications capturing the attention of researchers. Numerous studies attest to the success of ANNs in predicting both particulate and gaseous pollutants with desired accuracy over various spatio-temporal resolution. The early forays into air pollution forecasting by Mlakar et al. 95 marked a significant milestone, employing a trained nonlinear three-layered back propagation feed forward network. This model successfully predicted the concentration of SO 2 over a thermal power plant, showcasing the potential of ANNs. Subsequent research expanded the scope and sophistication of ANN applications. Similarly, Arena et al. 96 demonstrated the efficacy of multi-layer perceptron in predicting concentration of SO 2 over an industrial area, emphasizing the model's accuracy across diverse weather conditions. Sohn et al. 97 extended the ANN approach to model multiple pollutants, including NO, SO 2 , NO 2 , CO, O 3 , CH 4 and total hydrocarbons. The results indicated reasonable accuracy within a limited prediction range, highlighting the need for further optimization by incorporating additional weather-related input parameters. The application of ANNs in gaseous pollutants forecasting continued with studies by Slini et al. 98 and Kandya 99 both emphasizing the importance of optimizing input parameters for improved accuracy. Comparative assessments with other forecasting techniques consistently positioned ANNs as superior for gaseous pollutants. Chaloulakou et al. 100 found that ANN outperformed Multiple Linear Regression (MLR) in predicting ozone concentrations, showcasing the model's superior accuracy. Similar findings were reported by Mishra and Goyal 101 , compared Principal Component Analysis (PCA)-based ANN model with MLR for estimating the concentrations of NO 2 . In the realm of particulate matter forecasting, ANNs have proven equally effective. Fernando et al. 102 successfully used multi-layered MLP to predict PM 10 concentrations, considering parameters such as hourly meteorological data, particulate, matter with statistical indicators. Grivas and Chaloulakou 103 employed an ANN model for hourly PM 10 predictions, showcasing consistent accuracy even in the presence of noisy datasets. The versatility of ANNs extends to predicting roadside contributions to PM 10 concentrations, as demonstrated by Suleiman et al. 104 . Comparative studies with other models have affirmed the efficacy of ANNs in particulate matter forecasting. Zhang et al. 105 utilized BPANN to forecast the concentrations of PM 10 and found BPANN outperforming other models in predictive accuracy. Paschalidou et al. 106 evaluated the multi-layer perceptron-based ANN those models provided superior results compared to Radial Basis Function models, establishing the former's dominance in terms of forecasting capability. Contrasting trends were observed in certain studies, such as those by Mishra et al. 107 and Moisan et al. 108 , where alternative models outperformed ANN during extreme events. This highlights the nuanced nature of model performance, with specific conditions favouring different approaches. However, recent progress has witnessed researchers utilizing ensemble methods to improve both the stability and accuracy of ANN models. Liu et al. 109 combined Wavelet Packet Decomposition (WPD), Particle Swarm Optimization (PSO), and BPNN to create an ensemble model for PM 2.5 forecasting, demonstrating superior precision compared to individual models.

FL, renowned for its capacity to manage uncertainty, enhanced fault tolerance, and adeptness in handling highly complex nonlinear functions, has garnered extensive adoption in the realm of air pollution prediction. The advantages of FL are exemplified in various studies. For example, Chen et al. 110 innovatively introduced a novel fuzzy time series model specifically for O 3 prediction, showcasing its superior performance when compared to traditional fuzzy time series models. Jain and Khare 111 applied a neuro-fuzzy model to predicts the concentration of CO in Delhi, achieving accurate estimates at complex urban levels. Carbajal-Hernández et al. 112 predicts air quality in Mexico City by utilising FL model alongside autoregression model and signal processing. The introduction of a novel algorithm, the "Sigma operator," allowed for precise evaluation of air quality variables, showcasing the effectiveness of fuzzy-based models. Moreover, Al-Shammari et al. 113 , evaluates stochastic and FL-driven models to estimate the daily maximum concentrations of O 3 . The findings indicated that the FL-based model exhibited a marginal superiority over the statistical model particularly in instances of severe pollution events. Innovative approaches like the Fuzzy Inference Ensemble (FIE), as proposed by Bougoudis et al. 114 , demonstrated high accuracy in air pollution forecasting for Athens. Another significant application was presented by Song et al. 115 , where different probability density functions were employed to enhance particulate matter (PM) forecasting. They developed an adaptive neuro-fuzzy model, emphasizing the importance of density functions in addressing uncertainty associated with future PM trends. Furthermore, Wang et al. 116 presented a hybrid model for forecasting air pollution. This model merges uncertainty analysis with fuzzy time series, demonstrating precision in predicting PM and NO 2 concentrations. Behal and Singh 117 leveraged FL within an intelligent IoT sensor framework to monitor and simulate benzene, demonstrating satisfactory statistical efficacy in recent advancements. The versatility of fuzzy logic extends to unconventional pollutants as demonstrated by Arbabsiar et al. 118 , who modelled the leakage of CH 4 and H 2 S using a fuzzy inference technique. The suggested model demonstrated satisfactory performance when evaluating these contaminants.

Support Vector Machines (SVM), when combined with other machine learning algorithms, have been helpful in forecasting diverse types of pollutants. Feng et al. 119 compared SVM with other models for forecasting daily maximum concentrations of O 3 in Beijing, highlighting its stable and accurate performance. Yeganeh et al. 120 assessed the efficacy of a forecasting model utilizing SVM integrated with Partial Least Squares (PLS) for the prediction of CO concentrations, demonstrating positive outcomes. García Nieto et al. 121 conducted a comparative analysis of various prediction models for PM 10 concentrations, determining that the SVM method exhibited superior accuracy and robustness. Luna et al. 122 utilized Principal PCA in combination with SVM and ANN for the prediction of O 3 levels in Rio de Janeiro. Their study specifically investigated the influence of meteorological parameters on the concentrations of O 3 . Wang et al. 123 proposed hybrid adaptive forecasting models combining SVM and ANN for predicting PM 10 and SO 2 , demonstrating superior performance compared to individual models. FL and SVM in the forecasting air pollution levels have proven to be highly effective in addressing the complexities and uncertainties associated with predicting pollutant concentrations.

While still in its early stages, the potential of DNNs in this domain is evident from a review of various applications such as forecasting of variables in earth and atmospheric sciences. Early on, Freeman et al. (2018) employed a combination of Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to predict ozone concentrations in an urban area. While showing strong predictability in 8 h average ozone concentrations, various model runs revealed overfitting concerns, underscoring the necessity for further refinement. Wang and Song 125 introduced an ensemble method using a deep LSTM network with fuzzy c-means clustering for air quality forecasting. This ensemble approach outperformed individual models, showcasing its efficacy in both short-term and long-term predictions. Zhou et al. 126 explored the application of LSTM and deep learning algorithms for multi-step ahead forecasting of PM 2.5 , PM 10 , and NO x . Their deep learning architecture, integrating dropout neurons and L 2 regularization, demonstrated exceptional capabilities in capturing variations in the processes of air pollutant generation. Recent research highlights the growing preference for employing deep neural networks to capture dynamic spatiotemporal features from historical air quality and climatological datasets. Fan et al. 91 introduced stacked LSTM (LSTME), spatiotemporal deep learning (STDL), time delay neural network (TDNN), autoregressive moving average (ARMA), and support vector regression (SVR) for modelling of air pollutants over different spatiotemporal resolutions. The inclusion of auxiliary inputs resulted in a model with exceptional performance, outshining other machine learning techniques. Soh et al. 127 proposed a STDL integrating ANN, CNN, and LSTM for PM 2.5 prediction. The model exhibited stability over extended time periods, with noise reduction achieved through Airbox sensor source models, further enhancing prediction accuracy. Qi et al. 128 presented a novel forecasting approach employing a fusion of Graph Convolutional and LSTM (GC-LSTM) neural networks, aiming to investigate spatial interdependence within air quality data. The spatial correlation modelling highlighted the consistency of the GC-LSTM model for short-term forecasting, suggesting potential improvements for long-term predictions with enhanced spatiotemporal considerations. Fan et al. 91 developed a LSTM-based deep–RNN for predicting PM 2.5 for different spatiotemporal frames showcasing superior specificity measures compared to baseline models. In a novel approach, Li et al. 129 and Zhang et al. 130 incorporated large-scale datasets of graphical images for air pollution estimation, utilizing CNN. The models, trained on images capturing various atmospheric conditions, demonstrated improved prediction accuracy, emphasizing the adaptability of deep learning to diverse data types. These models offer robust solutions, demonstrating superior performance in various studies and showcasing their potential to contribute significantly to the field of environmental monitoring and public health.

Performance analysis

The evaluation is based on the comparison of their performances using statistical measures such as RMSE and R 2 , widely accepted metrics in air pollution forecasting studies. Previous research, utilizing a range of datasets, has yielded disparate results 134 . While certain studies advocate for ensemble methods, others find negligible disparities in the overall accuracy of the outcomes. The efficacy of AI and ML-driven methodologies relies heavily on the precise curation of influential parameters, especially when addressing various pollutants such as PM, O 3 , NO 2 , SO 2 , and CO 29 . For example, for PM forecasting, critical elements such as precipitation, pressure, humidity, land utilization, wind speed and direction, traffic flow on roads, and population density exert significant influence. Similarly, different influential parameters are identified for SO 2 , NO 2 , O 3 , and CO, emphasizing the importance of tailoring models to specific pollutants. The precision of the methods is notably impacted by the direct correlation between these factors and forecasted levels of pollutants. Additionally, the efficacy of AI&ML models hinges upon variables including network structure, intricacy, learning algorithms, correspondence between input and output information, and the presence of data interference. A comprehensive analysis shows the varying performances of DNN, SVM, ANN, and Fuzzy techniques across different pollutants. DNNs emerge as particularly effective in forecasting PM concentrations, outperforming other techniques with R 2 and mean RMSE values of 0.96 and 7.27 μg/m 3 , respectively 91 , 126 , 133 . In O 3 prediction, SVM, FL and DNN exhibit superior accuracy, with DNNs once again leading with R 2 and mean RMSE values of 0.92 and 3.51 μg/m 3 , respectively 119 , 120 . SVM excels in forecasting NO 2 concentrations, although Fuzzy and DNN techniques also demonstrate reasonable accuracy 116 , 118 , 131 . Notably, the DNN approach consistently stands out, showcasing the best statistical performance for O 3 and CO categories. For CO, DNN achieves an exceptional RMSE of 0.69 × 10 –5  ppm and an R 2 of 0.95 119 , 120 , 124 , 125 . The overall analysis represents the superiority of DNN across all pollutants, with the lowest overall RMSE score of 5.68. However, despite DNN's dominance, it is crucial to note the underdeveloped application of ensemble methodologies based on DL models for the forecasting of air pollution 131 , 135 , 136 . These approaches, involving multiscale spatiotemporal predictions, have untapped potential to further advance the field, incorporating more explanatory variables to represent air pollution episodes with robust dynamical forcing. The DNN emerges as the leading AI&ML system for the forecasting and prediction of air pollution based on statistical evidence, the exploration of ensemble approaches presents an avenue for future developments in enhancing predictive accuracy.

Prediction of PM 2.5 concentrations

The study used a convolutional autoencoder (CA) for analysing PM 2.5 concentrations. The dataset was divided into training (70%), testing (20%), and validation (10%) sets, trained over 30 epochs (Fig.  3 ). This PM 2.5 -focused CA processes sequences of ten consecutive images, using acquired features to reconstruct subsequent images. The visual representation of the model's capabilities includes sequences of 10 input images, their corresponding 11 th ground truth, and the model's predictions (Fig.  4 ). The model demonstrates promising performance in predicting PM 2.5 concentration patterns across India. Comparing the actual 11 th image with the predicted one reveals that the model successfully captures the broad spatial distribution of PM 2.5 concentrations. Key findings show that the model accurately predicts high concentration areas in the northern regions, particularly in the IGP (Fig.  4 ). It also effectively represents lower concentrations in southern and eastern coastal areas. The model captures the general gradient from northwest to southeast quite effectively. The prediction tends to slightly overestimate PM 2.5 levels in the northwestern region. Additionally, some localized high-concentration areas in central India are not fully captured in the prediction. Furthermore, the model's prediction shows a smoother distribution compared to the more granular actual data. (Fig.  4 ). Performance evaluation employed established image quality metrics: Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) (Fig.  5 ). SSIM, which assesses image similarity, predominantly ranged from 0.50 to 0.70 during training, slightly lowering to 0.45 to 0.55 during testing, and stabilizing at 0.50 to 0.60 in validation. PSNR peaked at 25 to 30 dB during training, followed by 24 to 28 dB in testing, and 28 to 30 dB in validation. Lower MSE values (10 to 15 µg/m 3 in training, 10 to 20 µg/m 3 in testing, and 8 to 11 µg/m 3 in validation) signify improved accuracy at the pixel level.

RMSE loss during the training, testing and validation phase.

Example set for predicting the 11th image of PM 2.5 by providing a batch of 10 images of concentration and comparing with the 11th actual image. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries ( https://matplotlib.org/ and https://matplotlib.org/basemap/ ).

Model evaluation parameters used for prediction the PM 2.5 concentrations.

These metrics offer insights into image quality, indicating some variation between training, testing, and validation, yet within acceptable ranges. Consistently higher SSIM and PSNR values and lower MSE values highlight the model's exceptional precision compared to benchmarks. The model's excellence traces back to its ability to capture complex spatio-temporal features through Autoencoder-based models and strategic integration of Conv2d, Batch Normalization, and Upsampling layers. The model outperforms prior methodologies in predicting PM 2.5 concentrations, achieving precise and high-quality predictions across phases. Attempting to forecast PM 2.5 levels for the next 4 days led to efficiency parameter decreases (SSIM, PSNR, MSE) with increased time frames, suggesting the need for more parameters for model efficiency improvement (Fig.  6 ). Predicting PM 2.5 concentrations remains challenging due to intricate spatiotemporal features, where DL models offer promise. Leveraging deep learning architectures and transfer learning, this study fine-tuned models, achieving promising PM 2.5 prediction results. Despite ongoing challenges in precise location predictions due to PM 2.5 's dynamic nature, the model demonstrated spatial distribution prediction abilities, evident in visual comparisons between predicted and actual PM 2.5 concentration maps.

Example set of predictions of PM 2.5 for next 4 days compared with their actual images. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries ( https://matplotlib.org/ and https://matplotlib.org/basemap/ ).

Challenges and limitations

Technological barriers.

One of the primary challenges lies in overcoming technological barriers. While advanced pollution control technologies exist, their widespread adoption is hindered by factors such as high costs and limited access to cutting-edge solutions. Many regions, particularly in rural areas, lack the infrastructure necessary to deploy and maintain sophisticated air quality monitoring and purification systems. Bridging this technological divide is essential for comprehensive pollution control.

Regulatory and enforcement challenges

India grapples with the challenge of implementing and enforcing air quality regulations consistently. While the country has established regulatory frameworks to curb emissions from industries, vehicles, and other pollution sources, enforcement remains uneven. This inconsistency is often compounded by resource constraints, bureaucratic hurdles, and the need for stronger mechanisms to penalize non-compliance. Strengthening regulatory frameworks and enhancing enforcement mechanisms are critical steps in addressing this challenge.

Public awareness and participation

Creating widespread awareness and fostering public participation are essential components of any successful pollution control strategy. However, there is a considerable gap in public awareness regarding the causes and consequences of air pollution. Engaging citizens in proactive measures, such as adopting sustainable practices and reducing individual carbon footprints, requires comprehensive educational campaigns and community involvement. Overcoming societal inertia and instigating behavioral change are significant challenges in this regard.

Agricultural practices and crop burning

Agricultural practices, particularly the prevalent practice of crop burning, contribute significantly to air pollution. The burning of crop residues releases substantial amounts of particulate matter and pollutants into the air. Farmers resort to this practice due to a lack of viable alternatives and time constraints between harvest seasons. Developing and promoting sustainable agricultural practices, coupled with providing farmers with effective alternatives to crop burning, is a complex challenge that requires a holistic approach.

Urbanization and infrastructure development

Rapid urbanization and infrastructure development, while essential for economic growth, often contribute to increased pollution levels. The construction industry, in particular, releases pollutants into the air. Balancing the need for development with sustainable and environmentally conscious practices poses a significant challenge. Implementing green building technologies, stringent emission norms for construction activities, and incorporating urban planning strategies that prioritize air quality are vital steps in addressing this challenge.

Cross-border pollution

Air pollution knows no boundaries, and India contends with the impact of cross-border pollution. Transboundary movement of pollutants, especially during crop burning seasons, contributes to elevated pollution levels in various regions. Collaborative efforts with neighbouring countries are necessary to address this challenge effectively. Developing joint strategies, sharing data, and fostering regional cooperation are imperative for tackling the transboundary dimension of air pollution.

Climate change interlinkages

The interlinkages between air pollution and climate change present a complex challenge. Mitigating air pollution often aligns with climate action goals, but there are trade-offs and synergies that need careful consideration. Striking a balance between addressing immediate air quality concerns and contributing to long-term climate resilience requires integrated policies and strategic planning.

Socio-economic disparities

Air pollution disproportionately affects vulnerable communities, exacerbating existing socio-economic disparities. The challenge lies in designing interventions that address environmental concerns and promote social equity. Ensuring that pollution control measures do not inadvertently burden marginalized communities and providing equitable access to clean technologies are critical to overcoming this challenge.

Future prospects

India stands at the cusp of a pivotal moment in its battle against air pollution, with promising avenues emerging on both technological and collaborative fronts.

Emerging technoloagies

The integration of cutting-edge technologies offers hope for India's future in pollution control. Advancements in AI&ML, when coupled with sophisticated numerical weather prediction models, present a potent toolset for predicting and managing air pollution. These technologies can enhance real-time monitoring, improve predictive capabilities, and facilitate data-driven decision-making, allowing for more precise and targeted interventions. Additionally, the fusion of AI&ML with numerical weather prediction (NWP) models can refine pollution control strategies by providing a deeper understanding of atmospheric dynamics and pollutant dispersion patterns. Furthermore, exploring potential breakthroughs in sustainable energy sources offers a transformative pathway. Shifting from traditional, pollutant-intensive energy sources to sustainable alternatives is crucial for reducing the overall carbon footprint. Investments in research and development, coupled with policy incentives, can accelerate the adoption of clean and renewable energy solutions, fostering a paradigm shift in India's energy landscape.

Global collaborations

Recognizing that air pollution transcends national boundaries, India looks toward global collaborations as a key driver for progress. International efforts in combating air pollution gain significance as countries join forces to address shared challenges. Collaborative platforms provide opportunities for knowledge sharing, exchange of best practices, and collective research initiatives. India's participation in these global endeavours not only enriches its own understanding of air pollution dynamics but also contributes to the global pool of knowledge. By fostering partnerships with other nations, India can access expertise, technologies, and resources that augment its capacity to implement effective pollution control measures. Knowledge sharing and collaborative research initiatives form the cornerstone of global efforts. Platforms that facilitate the exchange of data, research findings, and innovative solutions enable nations to collectively tackle the intricate and interconnected challenges of air pollution. As India engages in these collaborative endeavours, it not only benefits from the collective wisdom of the global community but also contributes its unique insights and experiences, enriching the collective understanding of air pollution dynamics.

India's strategic focus on emerging technologies and global collaborations holds immense promise in navigating the future. By harnessing the power of advanced technologies and participating in international initiatives, India can chart a course toward a cleaner, more sustainable future where the skies are clear, and the air is a testament to the collective commitment to environmental well-being.

Materials and methods

Maintaining fresh air quality is a complex undertaking influenced by various factors over time. These elements encompass air pollutant emissions, deposition, weather patterns, traffic dynamics, and human activities, among others 8 , 64 . The complexity of these interrelated factors makes it challenging for traditional shallow models to offer precise portrayals of air quality attributes. Based on the above review, deep learning algorithms were found most suitable for predicting air quality variables without needing prior knowledge. This capability enhances the potential for more accurate predictions regarding air quality, signifying a valuable contribution to addressing the intricacies associated with sustaining optimal air quality levels.

The case study utilized MERRA-2 reanalysis data from the NASA GESDISC DATA ARCHIVE application 137 , 138 . This dataset, spanning from January 1, 2015, to December 31, 2022, features a spatial resolution of 0.5° × 0.625° and a temporal resolution of 1 h (Fig.  7 ). It includes five key variables: black carbon surface mass concentration (BCSMASS), dust surface mass concentration—PM 2.5 (DUSMASS25), organic carbon surface mass concentration (OCSMASS), sea salt surface mass concentration—PM 2.5 (SSSMASS25), and SO 4 surface mass concentration (SO 4 SMASS). These variables are analysed across three dimensions: latitude, longitude, and time. The concentration of the PM 2.5 (µg/m 3 ) for each grid cell was computed as 139 , 140 :

Surface PM 2.5 concentration over India during ( a ) Winters and ( b ) Summers; Maps were generated using R Studio (v4.3.3, https://www.rstudio.com/ ).

Convolutional Autoencoder model

Air quality monitoring and predicting PM 2.5 concentrations accurately stands crucial for public health and environmental management 8 . The case study explores an innovative approach employing an Autoencoder-based DL model for forecasting PM 2.5 concentrations from spatiotemporal data over India. The study begins by complexly handling the datasets, leveraging PyTorch's Dataset and data loader classes. The ATMriver Dataset class is crafted to capture the dataset, enabling sequential data handling 9 . The data, formatted into tensors and split into training, testing, and validation subsets in a ratio of 70, 20 and 10, respectively, undergoes a custom transformation via the tensor class, ensuring compatibility with the neural network model 8 , 141 . The core of this methodology lies in the architecture of the Autoencoder, a neural network comprising convolutional and transposed convolutional layers. Specifically, the model comprises convolutional layers (conv1, conv2, conv3) responsible for feature extraction and transposed convolutional layers (conv1_d, conv2_d, conv3_d) for data reconstruction (Fig.  8 ). Each convolutional layer is paired with batch normalization and dropout (set at 25%) to regularize the network and prevent overfitting. The use of five layers in this Autoencoder architecture allows for hierarchical feature extraction and reconstruction, enhancing the model's ability to learn complex representations. The learning rate, a critical hyperparameter governing the magnitude of parameter updates during optimization, is set to 0.0025 for the Adam optimizer. This value influences the convergence speed and stability of the training process. A higher learning rate might lead to faster convergence but risks overshooting the optimal parameters, while a lower rate might result in slower convergence. The chosen learning rate balances the trade-off between convergence speed and stability, aiming to facilitate efficient model training while preventing divergence or oscillation in the optimization process.

Convolution autoencoder architecture for PM 2.5 data processing with model features an encoding phase with three autoencoder stages, followed by a decoding phase with two transpose convolution stages; structure enables dimensionality reduction and subsequent reconstruction of PM 2.5 concentration maps.

To train the Autoencoder, a custom root mean squared error (RMSE) loss function is defined. This loss function quantifies the disparity between predicted and actual PM 2.5 concentrations, guiding the model toward more accurate predictions. The training process iterates through the dataset multiple times (epochs), optimizing the model parameters using the Adam optimizer. The evaluation phase of the model involves assessing its predictive capabilities on separate testing and validation sets. The model's outputs are compared against the original images PM 2.5 concentrations, and the RMSE loss is computed. The best-performing model, based on its performance on the testing set, is identified and saved for the prediction. Further the records and reports the losses incurred during training, testing, and validation across epochs, providing insights into the model's loss curve and performance stability. Additionally, the best model's loss metric is highlighted, signifying its capability to accurately predict PM 2.5 concentrations. The evaluation of the trained model's predictive capability in this study primarily relied on two widely accepted image quality metrics: Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). The Structural Similarity Index (SSIM) serves as a measure to assess the similarity between the predicted and actual images 142 . SSIM evaluates the perceived change in structural information, including luminance, contrast, and structure, between the predicted and actual images. A higher SSIM score, closer to 1, indicates a greater similarity between the two images, implying better predictive performance of the model. Peak Signal-to-Noise Ratio (PSNR) is another commonly used metric for quantifying the quality of reconstructed or predicted images. PSNR measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR values signify lower image distortion or higher image fidelity, implying better prediction accuracy in capturing the details of the actual images.

Addressing the complex challenges of air pollution in India necessitates a multifaceted and technically informed approach. The existing impediments, including technological barriers and limited access to advanced pollution control technologies, underline the urgency of bridging the technological divide, particularly in rural areas. While regulatory frameworks are in place, inconsistent enforcement due to resource constraints and bureaucratic hurdles requires strategic strengthening. Public awareness and participation, integral components of effective pollution control, demand targeted educational campaigns to instigate behavioural change. Agricultural practices, notably crop burning, pose a significant challenge, and resolving this requires not only viable alternatives but a holistic approach that integrates sustainable agricultural practices. Rapid urbanization and infrastructure development, while essential for economic growth, necessitate the incorporation of green building technologies, stringent emission norms, and urban planning strategies prioritizing air quality. Cross-border pollution adds a transboundary dimension, demanding collaborative efforts with neighbouring countries. The intricate interlinkages between air pollution and climate change underscore the need for carefully balanced policies that address immediate air quality concerns while contributing to long-term climate resilience. Moreover, the disproportionate impact of air pollution on vulnerable communities emphasizes the importance of interventions that promote social equity alongside environmental considerations. Looking towards the future, the convergence of emerging technologies offers a beacon of hope. The integration of AI&ML with numerical weather prediction models presents a potent toolset for real-time monitoring, precise predictive capabilities, and data-driven decision-making. This amalgamation not only enhances our understanding of atmospheric dynamics and pollutant dispersion patterns but also refines pollution control strategies. Exploring breakthroughs in sustainable energy sources becomes imperative for reducing the overall carbon footprint. Shifting from traditional, pollutant-intensive energy sources to clean and renewable alternatives require concerted efforts through research, development, and policy incentives.

Furthermore, global collaborations stand out as a key driver for progress, given the transboundary nature of air pollution. Participating in international efforts fosters knowledge sharing, exchange of best practices, and collective research initiatives. By engaging in these collaborative activities, India not only enriches its understanding of air pollution dynamics but contributes to the global pool of knowledge. Platforms facilitating data exchange, research findings, and innovative solutions enable nations to collectively tackle the complex challenges of air pollution. In navigating the future, India's strategic focus on emerging technologies and global collaborations holds immense promise. The careful harnessing of advanced technologies and participation in international initiatives can chart a course toward a cleaner, more sustainable future. The fusion of AI&ML with numerical weather prediction (NWP) models positions India to proactively manage air quality, with the skies serving as a testament to the collective commitment to environmental well-being. As India progresses, the synergy of technological advancements and global cooperation emerges as the cornerstone for effective, informed, and sustainable solutions to combat air pollution.

Data availability

Data will be made online on a reasonable request to the corresponding author.

Masood, A. & Ahmad, K. A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance. J. Clean. Prod. 322 , 129072 (2021).

Article   CAS   Google Scholar  

WHO. Atlas on Children’s Health and the Environment . (2017).

Jiang, X. Q., Mei, X. D. & Feng, D. Air pollution and chronic airway diseases: what should people know and do?. J. Thorac. Dis. 8 , E31–E40 (2016).

PubMed   PubMed Central   Google Scholar  

Hayes, R. B. et al. PM25 air pollution and cause-specific cardiovascular disease mortality. Int. J. Epidemiol. 49 , 25–35 (2020).

Article   PubMed   Google Scholar  

Shakya, D., Deshpande, V., Goyal, M. K. & Agarwal, M. PM2.5 air pollution prediction through deep learning using meteorological, vehicular, and emission data: A case study of New Delhi. India. J. Clean. Prod. 427 , 139278 (2023).

Brown, P. E. et al. Mortality Associated with Ambient PM2.5 Exposure in India: Results from the Million Death Study. Environ. Health Perspect. 130 , 097004 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Pandey, A. et al. Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. Lancet Planet. Heal. 5 , e25–e38 (2021).

Article   Google Scholar  

Rautela, K. S., Singh, S. & Goyal, M. K. Characterizing the spatio-temporal distribution, detection, and prediction of aerosol atmospheric rivers on a global scale. J. Environ. Manage. 351 , 119675 (2024).

Article   CAS   PubMed   Google Scholar  

Rautela, K. S., Singh, S. & Goyal, M. K. Resilience to Air Pollution: A Novel Approach for Detecting and Predicting Aerosol Atmospheric Rivers within Earth System Boundaries. Earth Syst. Environ. https://doi.org/10.1007/s41748-024-00421-0 (2024).

Chakraborty, S. et al. Extending the Atmospheric River Concept to Aerosols: Climate and Air Quality Impacts. Geophys. Res. Lett. 48 (9), e2020GL091827 (2021).

Kapoor, M. Managing Ambient Air Quality Using Ornamental Plants-An Alternative Approach. Univers. J. Plant Sci. 5 , 1–9 (2017).

Article   MathSciNet   Google Scholar  

Gulia, S. et al. Evolution of air pollution management policies and related research in India. Environ. Challenges 6 , 100431 (2022).

UN. Two ‘Population Billionaires’, China and India, Face Divergent Demographic Futures. Dep. Econ. Soc. Affiars 1–10 (2023).

IQAir. (Report) World Air Quality Report. 2020 World Air Qual. Rep. 1–35 (2020).

WHO. World Health Statistics . (2014).

Kumari, S., Verma, N., Lakhani, A. & Kumari, K. M. Severe haze events in the Indo-Gangetic Plain during post-monsoon: Synergetic effect of synoptic meteorology and crop residue burning emission. Sci. Total Environ. 768 , 145479 (2021).

Delhi Air Pollution: Real-time Air Quality Index. https://aqicn.org/city/delhi .

CPCB. National Ambient Air Quality Status & Trends 2019. Cent. Pollut. Control Board 53 , 1689–1699 (2020).

Google Scholar  

IITK. Comprehensive study on air pollution and green house Google Scholar. A Rep. Submitt. to Gov. NCT Delhi DPCC Delhi 1–334 (2016).

Rizwan, S., Nongkynrih, B. & Gupta, S. K. Air pollution in Delhi: Its Magnitude and Effects on Health. Indian J. Community Med. 38 , 4 (2013).

Bai, L., Wang, J., Ma, X. & Lu, H. Air Pollution Forecasts: An Overview. Int. J. Environ. Res. Public Health 15 , 780 (2018).

Masood, A. & Ahmad, K. A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches. Procedia Comput. Sci. 167 , 2101–2110 (2020).

Mo, X., Zhang, L., Li, H. & Qu, Z. A Novel Air Quality Early-Warning System Based on Artificial Intelligence. Int. J. Environ. Res. Public Health 16 , 3505 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Krishan, M. et al. Air quality modelling using long short-term memory (LSTM) over NCT-Delhi. India. Air Qual. Atmos. Heal. 12 , 899–908 (2019).

Singh, S. & Goyal, M. K. An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder. Atmos. Res. 289 , 106754 (2023).

Singh, S. & Goyal, M. K. Enhancing climate resilience in businesses: The role of artificial intelligence. J. Clean. Prod. 418 , 138228 (2023).

Li, C., Hsu, N. C. & Tsay, S.-C. A study on the potential applications of satellite data in air quality monitoring and forecasting. Atmos. Environ. 45 , 3663–3675 (2011).

Article   ADS   CAS   Google Scholar  

Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. Journal of time series analysis book review time series analysis: forecasting and control, 5th edition, by. J. Time. Ser. Anal 37 , 709–711 (2016).

Siwek, K. & Osowski, S. Data mining methods for prediction of air pollution. Int. J. Appl. Math. Comput. Sci. 26 , 467–478 (2016).

Fu, M., Wang, W., Le, Z. & Khorram, M. S. Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model. Neural Comput. Appl. 26 , 1789–1797 (2015).

Sekar, C., Gurjar, B. R., Ojha, C. S. P. & Goyal, M. K. Potential Assessment of Neural Network and Decision Tree Algorithms for Forecasting Ambient PM2.5 and CO Concentrations: Case Study. J. Hazardous, Toxic, Radioact. Waste 20 , (2016).

Akhtar, A., Masood, S., Gupta, C. & Masood, A. Prediction and Analysis of Pollution Levels in Delhi Using Multilayer Perceptron (Springer, 2018).

Book   Google Scholar  

Li, X., Peng, L., Hu, Y., Shao, J. & Chi, T. Deep learning architecture for air quality predictions. Environ. Sci. Pollut. Res. 23 , 22408–22417 (2016).

Huang, C.-J. & Kuo, P.-H. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors 18 , 2220 (2018).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F. & Wan, Z. A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2.5. J. Clean. Prod. 237 , 117729 (2019).

Chang, Y.-S. et al. An LSTM-based aggregated model for air pollution forecasting. Atmos. Pollut. Res. 11 , 1451–1463 (2020).

Biancofiore, F. et al. Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmos. Pollut. Res. 8 , 652–659 (2017).

Konovalov, I. B., Beekmann, M., Meleux, F., Dutot, A. & Foret, G. Combining deterministic and statistical approaches for PM10 forecasting in Europe. Atmos. Environ. 43 , 6425–6434 (2009).

McKendry, I. G. Evaluation of Artificial Neural Networks for Fine Particulate Pollution (PM 10 and PM 2.5) Forecasting. J. Air Waste Manage. Assoc. 52 , 1096–1101 (2002).

Dutta, A. & Jinsart, W. Air Pollution in Indian Cities and Comparison of MLR, ANN and CART Models for Predicting PM10 Concentrations in Guwahati. India. Asian J. Atmos. Environ. 15 , 68–93 (2021).

Turias, I. J., González, F. J., Martin, M. L. & Galindo, P. L. Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy. Environ. Monit. Assess. 143 , 131–146 (2008).

Shang, Z. & He, J. Predicting Hourly <tex>$\mathbf{PM}_{2.5}$</tex> Concentrations Based on Random Forest and Ensemble Neural Network. in 2018 Chinese Automation Congress (CAC) 2341–2345 (IEEE, 2018). https://doi.org/10.1109/CAC.2018.8623175 .

Bozdağ, A., Dokuz, Y. & Gökçek, Ö. B. Spatial prediction of PM10 concentration using machine learning algorithms in Ankara. Turkey. Environ. Pollut. 263 , 114635 (2020).

Murray, C. J. L. et al. Five insights from the Global Burden of Disease Study 2019. Lancet 396 , 1135–1159 (2020).

Haque, M. & Singh, R. Air Pollution and Human Health in Kolkata, India: A Case Study. Climate 5 , 77 (2017).

Rajak, R. & Chattopadhyay, A. Short and Long Term Exposure to Ambient Air Pollution and Impact on Health in India: A Systematic Review. Int. J. Environ. Health Res. 30 , 593–617 (2020).

CPCB. Epidemiological Study on Effect of Air Pollution on Human Health (Adults) in Delhi CENTRAL POLLUTION CONTROL BOARD MINISTRY OF ENVIRONMENT & FORESTS. (2012).

‘India needs to address challenge of adult immunisation†TM - Elets eHealth. https://ehealth.eletsonline.com/2018/10/india-needs-to-address-challenge-of-adult-immunisation/ .

Raju, S., Siddharthan, T. & McCormack, M. C. Indoor Air Pollution and Respiratory Health. Clin. Chest Med. 41 , 825–843 (2020).

Manisalidis, I., Stavropoulou, E., Stavropoulos, A. & Bezirtzoglou, E. Environmental and Health Impacts of Air Pollution: A Review. Front. Public Heal. 8 , 14 (2020).

Fang, Z., Wu, P.-Y., Lin, Y.-N., Chang, T.-H. & Chiu, Y. Air Pollution’s Impact on the Economic, Social, Medical, and Industrial Injury Environments in China. Healthcare 9 , 261 (2021).

Economy and air pollution - Clean Air Fund. https://www.cleanairfund.org/theme/economics/ .

OECD. Climate-resilient Infrastructure. Policy Perspectives. OECD Environ. Policy Pap. 1–46 (2018).

EPA Research: Environmental Justice and Air Pollution | US EPA. https://www.epa.gov/ej-research/epa-research-environmental-justice-and-air-pollution .

Chakraborty, S., Fu, R., Massie, S. T. & Stephens, G. Relative influence of meteorological conditions and aerosols on the lifetime of mesoscale convective systems. Proc. Natl. Acad. Sci. 113 , 7426–7431 (2016).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Sturiale & Scuderi. The Role of Green Infrastructures in Urban Planning for Climate Change Adaptation. Climate 7 , 119 (2019).

Gulia, S. et al. Performance evaluation of air pollution control device at traffic intersections in Delhi. Int. J. Environ. Sci. Technol. 19 , 785–796 (2022).

Allioui, H. & Mourdi, Y. Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey. Sensors 23 , 8015 (2023).

Lalchandani, V. et al. Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter. Sci. Total Environ. 770 , 145324 (2021).

Tobler, A. et al. Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi. India. Sci. Total Environ. 745 , 140924 (2020).

Rai, P. et al. Real-time measurement and source apportionment of elements in Delhi’s atmosphere. Sci. Total Environ. 742 , 140332 (2020).

Talukdar, S. et al. Air Pollution in New Delhi during Late Winter: An Overview of a Group of Campaign Studies Focusing on Composition and Sources. Atmosphere (Basel). 12 , 1432 (2021).

Wang, T., Wei, K. & Ma, J. Atmospheric Rivers and Mei-yu Rainfall in China: A Case Study of Summer 2020. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-021-1096-9 (2020).

Sarkar, S., Chauhan, A., Kumar, R. & Singh, R. P. Impact of Deadly Dust Storms (May 2018) on Air Quality, Meteorological, and Atmospheric Parameters Over the Northern Parts of India. GeoHealth 3 , 67–80 (2019).

Wei, W. et al. Comprehensive Assessment of Pollution Sources and Health Impacts in Suburban Area of Shanghai. Toxics 11 , 552 (2023).

Blanco-Donado, E. P. et al. Source identification and global implications of black carbon. Geosci. Front. 13 , 101149 (2022).

Mangaraj, P., Sahu, S. K., Beig, G. & Yadav, R. A comprehensive high-resolution gridded emission inventory of anthropogenic sources of air pollutants in Indian megacity Kolkata. SN Appl. Sci. 4 , 117 (2022).

Rastogi, N. et al. Diurnal variability in the spectral characteristics and sources of water-soluble brown carbon aerosols over Delhi. Sci. Total Environ. 794 , 148589 (2021).

Mukherjee, A. et al. Sources and atmospheric processing of brown carbon and HULIS in the Indo-Gangetic Plain: Insights from compositional analysis. Environ. Pollut. 267 , 115440 (2020).

Tripathi, N. et al. Characteristics of VOC Composition at Urban and Suburban Sites of New Delhi, India in Winter. J. Geophys. Res. Atmos. https://doi.org/10.1029/2021JD035342 (2022).

Act, A. (Prevention and C. of A. P. Air_Act_1981. (1981).

Environment (Protection) Act. The Environment (Protection) Act, 1986 Act No. 29 OF 1986. 1–9 (1986).

Bill, M. V. Amendment. THE GAZETTE OF INDIA EXTRAORDINARY. 1988 , 4–6 (2019).

Rengarajan, S., Palaniyappan, D., Ramachandran, P. & Ramachandran, R. National Green Tribunal of India—an observation from environmental judgements. Environ. Sci. Pollut. Res. 25 , 11313–11318 (2018).

CPCB. Pollution Control Acts, Rules & Notifications Issued Thereunder. Central Pollution Control Board, Ministry of Environment, Forest and Climate Change, Government of India. https://cpcb.nic.in/7thEditionPollutionControlLawSeries2021.pdf (2021).

CPCB. National Air Quality Index. Cent. Pollut. Control Board 1–58. https://app.cpcbccr.com/ccr_docs/About_AQI.pdf (2014).

National Ambient Air Quality Monitoring. Air Quality Trends and Action For Plan. Naaqms 5. http://cpcb.nic.in/upload/NewItems/NewItem_104_airquality17cities-package-.pdf (2006).

Roychowdhury, A. & Somvanshi, A. Breathing Space: How to track and report air pollution under the National Clean Air Programme. Cent. Sci. Environ. (New Delhi, 2020).

Roychowdhury, A., Somvanshi, A. & Kaur, S. Urban Lab-Centre for Science and Environment Analysis Status of air quality monitoring in India: Spatial spread, population coverage and data completeness. https://www.cseindia.org/Note-AQM-Network-analysis.pdf (2023).

Yadav, R. et al. COVID-19 lockdown and air quality of SAFAR-India metro cities. Urban Clim. 34 , 100729 (2020).

Lestari, P., Arrohman, M. K., Damayanti, S. & Klimont, Z. Emissions and spatial distribution of air pollutants from anthropogenic sources in Jakarta. Atmos. Pollut. Res. 13 , 101521 (2022).

Guttikunda, S. K., Nishadh, K. A. & Jawahar, P. Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Clim. 27 , 124–141 (2019).

Gargava, P. & Rajagopalan, V. Source apportionment studies in six Indian cities—drawing broad inferences for urban PM10 reductions. Air Qual. Atmos. Heal. 9 , 471–481 (2016).

M.C. Mehta And Anr vs Union Of India & Ors on 20 December, 1986. https://indiankanoon.org/doc/1486949/ .

AFVP 2025. Report of the Expert Committee on Auto Fuel Vision & Policy 2025. Press Inf. Bur. 221 , 174. https://cdn.climatepolicyradar.org/navigator/IND/2014/national-auto-fuel-policy-and-auto-fuel-vision-and-policy-2025_c53488e9acdfd8095d576abd64e15892.pdf (2014).

Sahu, V. et al. Assessment of a clean cooking fuel distribution scheme in rural households of India – “Pradhan Mantri Ujjwala Yojana (PMUY)”. Energy Sustain. Dev. 81 , 101492 (2024).

Das, P. K. & Bhat, M. Y. Global electric vehicle adoption: implementation and policy implications for India. Environ. Sci. Pollut. Res. 29 , 40612–40622 (2022).

Gimeno, L. et al. Major Mechanisms of Atmospheric Moisture Transport and Their Role in Extreme Precipitation Events. Annu. Rev. Environ. Resour. 41 , 117–141. https://doi.org/10.1146/annurev-environ-110615-085558 (2016).

Thayyib, P. V. et al. State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary. Sustainability 15 , 4026 (2023).

Alzubaidi, L. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8 , 53 (2021).

Fan, J. et al. A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. IV - 4/W2 , 15–22 (2017).

Rautela, K. S., Kumar, D., Gandhi, B. G. R., Kumar, A. & Dubey, A. K. Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya. Uttarakhand. RBRH https://doi.org/10.1590/2318-0331.272220220045 (2022).

Sofi, M. S. et al. Modeling the hydrological response of a snow-fed river in the Kashmir Himalayas through SWAT and Artificial Neural Network. Int. J. Environ. Sci. Technol. https://doi.org/10.1007/s13762-023-05170-7 (2023).

Karagulian, F. et al. Contributions to cities’ ambient particulate matter (PM): A systematic review of local source contributions at global level. Atmos. Environ. 120 , 475–483 (2015).

Mlakar, P., Božnar, M. & Lesjak, M. Neural Networks Predict Pollution. In Air Pollution Modeling and Its Application X (ed. Millán, M. M.) (Springer, 1994).

Arena, P., Fortuna, L., Gallo, A., Nunnari, G. & Xibilia, M. G. Air pollution estimation via neural networks. IFAC Proc. 28 , 787–792 (1995).

Sohn, S. H., Oh, S. C. & Yeo, Y.-K. Prediction of air pollutants by using an artificial neural network. Korean J. Chem. Eng. 16 , 382–387 (1999).

Slini, T., Karatzas, K. & Moussiopoulos, N. Correlation of air pollution and meteorological data using neural networks. Int. J. Environ. Pollut. 20 , 218 (2003).

Kandya, A. Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras. India. J. Civ. Environ. Eng. 01 , 2 (2013).

Chaloulakou, A., Saisana, M. & Spyrellis, N. Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Sci. Total Environ. 313 , 1–13 (2003).

Article   ADS   CAS   PubMed   Google Scholar  

Mishra, D. & Goyal, P. Development of artificial intelligence based NO 2 forecasting models at Taj Mahal. Agra. Atmos. Pollut. Res. 6 , 99–106 (2015).

Fernando, H. J. S. et al. Forecasting PM10 in metropolitan areas: Efficacy of neural networks. Environ. Pollut. 163 , 62–67 (2012).

Grivas, G. & Chaloulakou, A. Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens. Greece. Atmos. Environ. 40 , 1216–1229 (2006).

Suleiman, A., Tight, M. R. & Quinn, A. D. Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter. Environ. Model. Assess. 21 , 731–750 (2016).

Zhang, H., Liu, Y., Shi, R. & Yao, Q. Evaluation of PM 10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City. China. J. Air Waste Manage. Assoc. 63 , 755–763 (2013).

Paschalidou, A. K., Karakitsios, S., Kleanthous, S. & Kassomenos, P. A. Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management. Environ. Sci. Pollut. Res. 18 , 316–327 (2011).

Mishra, D., Goyal, P. & Upadhyay, A. Artificial intelligence based approach to forecast PM 2.5 during haze episodes: A case study of Delhi India. Atmos. Environ. 102 , 239–248 (2015).

Moisan, S., Herrera, R. & Clements, A. A dynamic multiple equation approach for forecasting PM 2.5 pollution in Santiago. Chile. Int. J. Forecast. 34 , 566–581 (2018).

Liu, H., Jin, K. & Duan, Z. Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China. Atmos. Pollut. Res. 10 , 1588–1600 (2019).

Chen, J. et al. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environ. Int. 130 , 104934 (2019).

Jain, S. & Khare, M. Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways. Air Qual. Atmos. Heal. 3 , 203–212 (2010).

Carbajal-Hernández, J. J., Sánchez-Fernández, L. P., Carrasco-Ochoa, J. A. & Martínez-Trinidad, J. F. Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmos. Environ. 60 , 37–50 (2012).

Article   ADS   Google Scholar  

Al-Shammari, E. T. Public warning systems for forecasting ambient ozone pollution in Kuwait. Environ. Syst. Res. 2 , 2 (2013).

Bougoudis, I., Demertzis, K. & Iliadis, L. HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. Neural Comput. Appl. 27 , 1191–1206 (2016).

Song, Y., Qin, S., Qu, J. & Liu, F. The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region. Atmos. Environ. 118 , 58–69 (2015).

Wang, J., Li, H. & Lu, H. Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China. Appl. Soft Comput. 71 , 783–799 (2018).

Behal, V. & Singh, R. Personalised healthcare model for monitoring and prediction of airpollution: machine learning approach. J. Exp. Theor. Artif. Intell. 33 , 425–449 (2021).

Arbabsiar, M. H., Ebrahimi Farsangi, M. A. & Mansouri, H. Fuzzy logic modelling to predict the level of geotechnical risks in rock tunnel boring machine (TBM) tunnelling. Rud. Zb. 35 , 1–14 (2020).

Feng, Y., Zhang, W., Sun, D. & Zhang, L. Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmos. Environ. 45 , 1979–1985 (2011).

Yeganeh, B., Motlagh, M. S. P., Rashidi, Y. & Kamalan, H. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmos. Environ. 55 , 357–365 (2012).

García Nieto, P. J., Combarro, E. F., del Coz Díaz, J. J. & Montañés, E. A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study. Appl. Math. Comput. 219 , 8923–8937 (2013).

Luna, A. S., Paredes, M. L. L., de Oliveira, G. C. G. & Corrêa, S. M. Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro. Brazil. Atmos. Environ. 98 , 98–104 (2014).

Wang, P., Liu, Y., Qin, Z. & Zhang, G. A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Sci. Total Environ. 505 , 1202–1212 (2015).

Freeman, B. S., Taylor, G., Gharabaghi, B. & Thé, J. Forecasting air quality time series using deep learning. J. Air Waste Manage. Assoc. 68 , 866–886 (2018).

Wang, J. & Song, G. A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction. Neurocomputing 314 , 198–206 (2018).

Zhou, Y., Chang, F.-J., Chang, L.-C., Kao, I.-F. & Wang, Y.-S. Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J. Clean. Prod. 209 , 134–145 (2019).

Soh, P.-W., Chang, J.-W. & Huang, J.-W. Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations. IEEE Access 6 , 38186–38199 (2018).

Qi, Y., Li, Q., Karimian, H. & Liu, D. A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Sci. Total Environ. 664 , 1–10 (2019).

Li, Y., Huang, J. & Luo, J. Using user generated online photos to estimate and monitor air pollution in major cities. Proceedings of the 7th International Conference on Internet Multimedia Computing and Service 1–5 (ACM, New York, NY, USA). https://doi.org/10.1145/2808492.2808564 . (2015).

Zhang, L., Nan, Z., Xu, Y. & Li, S. Hydrological impacts of land use change and climate variability in the headwater region of the Heihe River Basin, northwest China. PLoS One 11 , 1–25 (2016).

Li, X. et al. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environ. Pollut. 231 , 997–1004 (2017).

Zhang, C. et al. On Estimating Air Pollution from Photos Using Convolutional Neural Network. in Proceedings of the 24th ACM international conference on Multimedia 297–301 (ACM, New York, NY, USA). https://doi.org/10.1145/2964284.2967230 . (2016).

Qiu, Y. et al. Regional aerosol forecasts based on deep learning and numerical weather prediction. Npj Clim. Atmos. Sci. 6 , 71 (2023).

Goyal, M. K. & Rautela, K. S. Aerosol Atmospheric Rivers: Detection and Spatio-Temporal Patterns. https://doi.org/10.1007/978-3-031-66758-9_2 (2024).

Du, S., Li, T., Yang, Y. & Horng, S.-J. Deep Air Quality Forecasting Using Hybrid Deep Learning Framework. IEEE Trans. Knowl. Data Eng. 33 , 2412–2424 (2021).

Araujo, L. N., Belotti, J. T., Alves, T. A., de Tadano, Y. S. & Siqueira, H. Ensemble method based on Artificial Neural Networks to estimate air pollution health risks. Environ. Model. Softw. 123 , 104567 (2020).

Randles, C. A. et al. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation. J. Clim. 30 , 6823–6850 (2017).

Rautela, K. S., Singh, S. & Goyal, M. K. Aerosol atmospheric rivers: patterns, impacts, and societal insights. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-024-34625-8 (2024).

Buchard, V. et al. Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States. Atmos. Environ. 125 , 100–111 (2016).

Provençal, S., Buchard, V., da Silva, A. M., Leduc, R. & Barrette, N. Evaluation of PM surface concentrations simulated by Version 1 of NASA’s MERRA Aerosol Reanalysis over Europe. Atmos. Pollut. Res. 8 , 374–382 (2017).

Singh, S., Goyal, M. K. & Jha, S. Role of large-scale climate oscillations in precipitation extremes associated with atmospheric rivers: nonstationary framework. Hydrol. Sci. J. https://doi.org/10.1080/02626667.2022.2159412 (2023).

Cheggoju, N. & Satpute, V. R. Blind quality scalable video compression algorithm for low bit-rate coding. Multimed. Tools Appl. 81 , 33715–33730 (2022).

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We would like to express our sincere gratitude to the Department of Civil Engineering, Indian Institute of Technology, Indore for their support and resources, which have been instrumental in the successful completion of the present study.

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Rautela, K.S., Goyal, M.K. Transforming air pollution management in India with AI and machine learning technologies. Sci Rep 14 , 20412 (2024). https://doi.org/10.1038/s41598-024-71269-7

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• Long term exposure to...

Long term exposure to road traffic noise and air pollution and risk of infertility in men and women: nationwide Danish cohort study

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• Peer review
• Aslak Harbo Poulsen , staff scientist 1 ,
• Bugge Nøhr , consultant 3 ,
• Jibran Khan , assistant professor 4 5 ,
• Matthias Ketzel , professor 4 6 ,
• Jørgen Brandt , professor 4 ,
• Ole Raaschou-Nielsen , professor 1 4 ,
• Allan Jensen , senior scientist 7
• 1 Work, Environment and Cancer, Danish Cancer Institute, Copenhagen, Denmark
• 2 Department of Natural Science and Environment, Roskilde University, Roskilde, Denmark
• 3 Department of Obstetrics and Gynaecology, University Hospital of Herlev and Gentofte, Herlev, Denmark
• 4 Department of Environmental Science, Aarhus University, Roskilde, Denmark
• 5 Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, Roskilde, Denmark
• 6 Global Centre for Clean Air Research (GCARE), University of Surrey, Guildford, UK
• 7 Virus, Lifestyle and Genes, Danish Cancer Institute, Copenhagen, Denmark
• Correspondence to: M Sørensen mettes{at}cancer.dk
• Accepted 8 July 2024

Objective To investigate associations between long term residential exposure to road traffic noise and particulate matter with a diameter <2.5 µm (PM 2.5 ) and infertility in men and women.

Design Nationwide prospective cohort study.

Setting Denmark.

Participants 526 056 men and 377 850 women aged 30-45 years, with fewer than two children, cohabiting or married, and residing in Denmark between 2000 and 2017.

Main outcome measure Incident infertility in men and women during follow-up in the Danish National Patient Register.

Results Infertility was diagnosed in 16 172 men and 22 672 women during a mean follow-up of 4.3 years and 4.2 years, respectively. Mean exposure to PM 2.5 over five years was strongly associated with risk of infertility in men, with hazard ratios of 1.24 (95% confidence interval 1.18 to 1.30) among men aged 30-36.9 years and 1.24 (1.15 to 1.33) among men aged 37-45 years for each interquartile (2.9 µg/m 3 ) higher PM 2.5 after adjustment for sociodemographic variables and road traffic noise. PM 2.5 was not associated with infertility in women. Road traffic noise (Lden, most exposed facade of residence) was associated with a higher risk of infertility among women aged 35-45 years, with a hazard ratio of 1.14 (1.10 to 1.18) for each interquartile (10.2 dB) higher five year mean exposure. Noise was not associated with infertility among younger women (30-34.9 years). In men, road traffic noise was associated with higher risk of infertility in the 37-45 age group (1.06, 1.02 to 1.11), but not among those aged 30-36.9 years (0.93, 0.91 to 0.96).

Conclusions PM 2.5 was associated with a higher risk of an infertility diagnosis in men, whereas road traffic noise was associated with a higher risk of an infertility diagnosis in women older than 35 years, and potentially in men older than 37 years. If these results are confirmed in future studies, higher fertility could be added to the list of health benefits from regulating noise and air pollution.

Introduction

Infertility is a major global health problem affecting one in seven couples trying to conceive. 1 Infertility affects all geographical areas of the world, with some of the highest rates observed in south and central Asia, sub-Saharan Africa, the Middle East, north Africa, and central and eastern Europe. 2 Infertility is defined as lack of conception after one year of regular, unprotected sexual intercourse. 3 The use of various assisted reproductive technologies has increased noticeably since the 1980s, and more than 10 million children have been conceived using such technologies worldwide. 4 Infertility in both men and women is associated with various long term adverse health effects, including shorter life expectancy and increased risk of various psychiatric disorders and somatic diseases. 5 6 Furthermore, infertility is often a harsh experience, with a high level of physical and psychological strain, including high stress levels, anxiety, and symptoms of depression. 7 8

Many of the established risk factors for infertility are similar for men and women and include advanced age (especially for women, where fertility drops rapidly after the late 30s), tobacco and alcohol use, sexually transmitted infections, various chronic conditions and diseases, obesity, and severe underweight. 9 In addition, exposure to environmental factors, such as air pollution, pesticides, and ionising radiation, are suspected risk factors for infertility. 10 Ambient air pollution is a major environmental pollutant causing cardiometabolic and respiratory morbidity and mortality. 11 12 Furthermore, during the past decade, epidemiological studies have found particulate air pollution to be negatively associated with sperm quality, specifically lower sperm motility and count and changes in sperm morphology. 13 14 15 A growing number of studies have indicated that air pollution is also associated with a reduced success rate after fertility treatment in women, 16 17 18 19 20 although results are inconsistent. 21 22 23 In contrast, only a few studies have studied the effects of air pollution on infertility in women, with inconsistent results. 24 25 26 27 Also, these studies mainly investigated effects on fecundability, thus not capturing infertility in women directly, as fecundability can be influenced by infertility in both men and women.

Road traffic noise is another prevalent environmental pollutant that has been linked with various chronic diseases. 28 29 30 Health effects of noise are suggested to be mediated through the triggering of a stress response, with activation of the autonomic nervous system and the hypothalamic-pituitary-adrenal axis, 31 as well as through sleep disturbance. 32 Both stress and sleep disturbance have been suggested to be associated with impaired reproductive function, including reduced sperm count and quality, menstrual irregularity, and impaired oocyte competence. 33 34 35 A main suggested biological pathway is activation of the hypothalamic-pituitary-adrenal axis, with release of stress hormones and inhibition of the hypothalamic-pituitary-gonadal axis, resulting in decreased levels of male and female sex hormones. 33 34 35 Only one study has investigated the effects of noise on fertility, specifically self-reported time to pregnancy in a cohort of ≈65 000 pregnant women, and the results indicated that road traffic noise was associated with an increased time to pregnancy. 36

We investigated if long term exposure to road traffic noise and pollution from particulate matter air with a diameter <2.5 µm (PM 2.5 ) in the Danish population was associated with a higher risk of infertility in men and women, using individual level, time varying information on noise, air pollution, and socioeconomic variables and follow-up for infertility in the Danish National Patient Register.

Study population

Our study was based on all people residing in Denmark. Since 1968, all Danish inhabitants have been assigned a unique identification number, enabling linkage between administrative and health registers. 37 We used the Civil Registration System with exact address data for people in Denmark, including moving and migration dates, to find the address history from 1995 onwards. 37 We generated a study population for women and a study population for men and both study populations included people aged 30-45 years who were cohabiting or married, had fewer than two children, and lived in Denmark between 1 January 2000 and 31 December 2017 (n=377 850 women; 526 056 men). These inclusion criteria were implemented to obtain study populations with a high proportion of individuals who were actively trying to become pregnant, and thus under risk of receiving an infertility diagnosis.

Estimation of road traffic noise

We used the Building and Housing Register to obtain geocode and floor (for multistorey buildings) for all addresses in Denmark, and estimated road traffic noise at these addresses for 1995, 2000, 2005, 2010, and 2015 based on the validated Nordic prediction method. 38 39 Main traffic variables for the model were road type (motorway, express road, road wider than 6 m, road 3-6 m wide, and other road) and data on distributions of light and heavy vehicles, travel speed, and annual average daily traffic for all Danish roads. 40 We accounted for screening effects from all Danish buildings, noise barriers and terrain, reflections, and ground absorption. Noise was calculated as the equivalent A weighted sound pressure level for the day, evening, and night, and expressed as Lden. We estimated noise at the most and the least exposed facades of the residence at each address. Values <35 dB were set to 35 dB because noise below this level is unlikely to be discernible from background noise. We estimated yearly means for all addresses at all years between 1995 and 2017 using linear interpolation.

Estimation of air pollution

We assessed PM 2.5 at all addresses (ground level) using a validated modelling system comprising the Danish eulerian hemispheric model, the urban background model, and the operational street pollution model. 41 42 43 This system calculated PM 2.5 at all Danish addresses as the sum of air pollution at three different spatial scales: the regional background, estimated by a long range chemistry-transport model at 5.6-150 km 2 resolution (the Danish eulerian hemispheric model) 41 ; local background, estimated in the urban background model covering Denmark in 1 km 2 resolutions 42 ; and local street, calculated in the operational street pollution model, which takes into account traffic, street configurations, and emission factors. 43 All models include weather conditions calculated using the weather research and forecasting model. 44 The model system estimated hourly address specific concentrations of PM 2.5 during 2000, 2010, and 2015, which were summarised to yearly means for each of the three years. We subsequently calculated yearly means for each address for the period 1995-2017, based on yearly changes in urban background PM 2.5 estimated using the Danish eulerian hemispheric model and the urban background model.

Covariates were selected based on availability in the Danish registers and plausibility to act as potential confounders (see supplementary figure S1). We collected yearly individual level information from 2000 to 2017 using national registers on individual income (sex and year standardised fifths), highest attained education (mandatory, secondary or vocational, or medium or long), occupational status (manual worker, professional, or unemployed or retired), number of children (0 or 1), and country of birth (Denmark or other). We obtained yearly information on five neighbourhood level variables: Proportion of inhabitants in each parish (on average 16.2 km 2 and 1032 residents) with only mandatory education, low income, manual labour, and a criminal record, and as sole providers. We estimated population density in each parish (0-100, 100-5000, and >5000 individuals/km 3 ) and received information on house type for all addresses (single family house, semidetached house, apartment, or other).

Ascertainment of infertility

To assess infertility, we used personal identification numbers to link the two study populations of men and women with the Danish National Patient Register (valid since 1977), using ICD-8 and ICD-10 (international classification of diseases, eighth and 10th revisions, respectively) codes. 45 Infertility in women was registered as ICD-8 code 628 and ICD-10 code N97 (excluding N974: infertility in women due to male factors), and infertility in men was registered as ICD-8 code 606 (excluding 606.59, 606.80-89) and ICD-10 code N46 (excluding N469E: infertility in men after sterilisation). We only included the first registered infertility diagnosis. All individuals with a diagnosis of infertility before baseline were excluded. We also excluded women with tubal ligation, bilateral oophorectomy, or hysterectomy before baseline and men who were sterilised before baseline (see supplementary table S1 for operation codes). Furthermore, people undergoing any of these procedures during follow-up were censored at the date of the operation.

For analyses of infertility subtypes, we investigated anovulation (N970), tubal factor (N971), unspecified (N979), and a joint group of other causes of infertility in women (N972, N973, and N978) as subtypes of infertility in women, whereas azoospermia (N469B), oligospermia (N469C), and unspecified (N469) were included as subtypes of infertility in men. Low numbers for other infertility subtypes in men and women precluded meaningful analyses.

Statistical analyses

We analysed data using Cox proportional hazards models, with age as the underlying timescale, to calculate hazard ratios and 95% confidence intervals (CIs) for infertility in men and women (overall and for subtypes of infertility) for each interquartile range as well as for each 10 dB and 5 µg/m 3 increase in road traffic noise and PM 2.5 , respectively. Exposure to both pollutants was modelled as time weighted five year running means, taking exposure at all addresses in the period into account (including moving), and entered as time varying variables into the Cox model, thus for each individual with infertility comparing with the five year mean exposure for all people without infertility at the same age as the individual with infertility at the time of diagnosis. Start of follow-up was defined as 30 years of age, 1 January 2000, or date of marriage or cohabiting, whichever came last, and the study populations were followed until date of infertility diagnosis, death, emigration, unknown address, bilateral oophorectomy (women only), tubal ligation (women only), hysterectomy (women only), sterilisation (men only), 45 years of age, divorce or end of cohabitation, birth of second child, or 31 December 2017, whichever came first.

We analysed data using three adjusted models. Model 1 included adjustment for calendar year (two year categories). In model 2, we further adjusted for highest attained education, individual level income, country of origin, occupation, and area level proportion of inhabitants with low income, only mandatory education, manual labour, and a criminal record, and as sole provider. In model 3, we additionally applied mutual adjustment for PM 2.5 and noise. All individual and area level covariates except country of origin were entered into the Cox models as yearly time varying variables (area level variables also changed with change of address).

We evaluated the assumption of proportional hazards for the three exposures by a correlation test between the scaled Schoenfeld residuals and the rank order of event time. We observed a strong deviation from the assumption for noise (noise at both the most and the least exposed facade) in the men and women study populations. To investigate this further, we calculated associations between the two noise exposures and infertility in men and women in the following age groups: 30-30.9, 31-31.9, 32-32.9, 33-33.9, 34-34.9, 35-35.9, 36-36.9, 37-37.9, 38-38.9, 39-39.9, 40-41.9, and 42-45 years (see supplementary figure S2 and tables S2 and S3). We observed that the hazard ratios differed across age groups, indicating a shift in hazard ratio levels around age 35 years for women and 37 years for men. Subsequent analyses were therefore conducted in the following age groups: 30-34.9 and 35-45 years for women and 30-36.9 and 37-45 years for men.

To investigate the shape of the exposure-response associations, we used natural cubic splines with three degrees of freedom. We furthermore analysed associations (model 3) in categories of noise at the most exposed facade (≤50, 50.01-55, 55.01-60, 60.01-65, and >65 dB) and PM 2.5 (≤12, 12.01-14, 14.01-16, and >16 µg/m 3 ).

For men aged 37-45 years and women aged 35-45 years we analysed associations separately among people: living at low (<100 people/km 2 ), medium (101-5000 people/km 2 ), or high (≥5000 people/km 2 ) population density; with a low, medium, or high level of education; with a personal income in the first, second, third, and fourth income group; and with 0 or 1 child, by including an interaction term in the model. Also, in analyses of noise at the most exposed facade, we investigated associations among people who had access to a silent facade with substantially lower noise levels than at the most exposed facade compared with people without a more silent facade (defined as a difference between noise at the most and least exposed facades above and below 10.8 dB, corresponding to the median). In sensitivity analyses, we further adjusted for population density and type of residence.

All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC), except tests for proportional hazards and the splines, which were done in R version 4.3.2.

Patient and public involvement

No patients were directly involved in defining the research question, in the study design, or in the analyses and reporting. A main reason was that the present study was conducted without any external funding, thus with limited resources to engage in patient and public involvement. However, discussions with citizens concerned about the effects of the environmental pollutants studied and with patients experiencing infertility and worrying about the causes helped to motivate initiation of the study.

Of 1 133 142 men and 1 090 344 women aged between 30 and 45 years (2000-17) identified, we excluded 12 654 men with an infertility diagnosis or sterilisation before baseline, and 85 700 women with an infertility diagnosis, bilateral oophorectomy, tubal ligation, or hysterectomy before baseline. We further excluded 310 940 men and 458 002 women who had two or more children (or with missing information) at time of enrolment, and 222 796 men and 123 188 women who did not live with a partner at any time during follow-up or were in a same sex registered partnership (or with missing information). We also excluded 30 395 men and 21 872 women with an incomplete address history five years before baseline, and 30 301 men and 23 732 women lacking information on any covariates. This resulted in study populations of 526 056 men and 377 850 women of whom 16 172 men and 22 671 women had an infertility diagnosis during a mean follow-up of 4.3 years and 4.2 years, respectively.

Table 1 shows the baseline sociodemographic and exposure characteristics of the two study populations. Distributions of exposure and median levels of noise and PM 2.5 as well as correlations between exposures were similar among men and women ( table 1 , also see supplementary figure S3). Noise at the most and least exposed facades were moderately correlated, with Spearman correlation coefficient (Rs) for women of 0.38, whereas correlations between noise and PM 2.5 were low, with Rs between 0.05 and 0.16 (see supplementary table S4). Median five year mean PM 2.5 levels decreased in the study population during the follow-up period, from 17.3 µg/m 3 in 2000 to 12.1 µg/m 3 in 2017, whereas five year mean noise levels increased slightly from 57.7 dB in 2000 to 59.1 dB in 2017 (see supplementary table S5). Owing to highly non-linear associations between noise at the least exposed facade and infertility in both men and women, we did not conduct further analyses with this noise measure as a continuous variable (see supplementary figure S4).

Baseline sociodemographic and exposure characteristics among men and women in the study population. Values are number (percentage) unless stated otherwise

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Among men, we observed that exposure to PM 2.5 was associated with a higher risk of infertility, with similar sized hazard ratios in the two investigated age groups (30-36.9 and 37-45 years) in the fully adjusted model 3 ( table 2 ). The categorical analyses ( table 3 ) and splines ( fig 1 ) showed that the associations followed linear exposure-response associations throughout the exposure range. For noise, we observed no association with infertility in men in the youngest age group (30-36.9 years) before adjustment for PM 2.5 (model 2), whereas after adjustment, noise was associated with a hazard ratio of <1 (model 3, table 2 ). In the oldest age group (37-45 years), noise was associated with a slightly higher risk of infertility in men both before and after adjustment for PM 2.5 . For both exposures, further adjustment for population density and type of residence resulted in only small changes in hazard ratios (see supplementary table S6). After further adjustment for children (0 or 1) the hazard ratio for noise was reduced in the 37-45 age group, from 1.06 (95% CI 1.02 to 1.11) to 1.02 (0.98 to 1.07), whereas the association with PM 2.5 remained unchanged (see supplementary table S6).

Associations between an interquartile range higher five year mean road traffic noise at the most exposed facade and air pollution (PM 2.5 ) in relation to infertility in men and women in two age groups

Association between five year exposure to road traffic noise and PM 2.5 and risk of infertility in men and women in categories of exposure and per 10 dB and 5 µg/m 3 higher noise and PM 2.5 , respectively

Splines showing association between five year mean residential exposure to road traffic noise at the most exposed façade at home and PM 2.5 and risk of infertility in men and women in groups according to age in the fully adjusted model 3. dB=decibel; PM 2.5 =fine particulate matter with a diameter <2.5 μm

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Among women, noise was associated with a higher risk of infertility in the 35-45 age group, whereas no association was observed in the 30-34.9 age group ( table 2 ). The association with noise in the oldest age group followed a close to linear exposure-response association, although at high exposures (>65 dB) the association levelled off ( table 3 , fig 1 ). Exposure to PM 2.5 was not associated with higher risk of infertility in women in any of the investigated age groups. For both exposures, further adjustment for population density, type of residence, and number of children resulted in only slight changes in hazard ratios (see supplementary table S6).

When investigating the effects of the two exposures on infertility subtypes in women, we found that noise was associated with a higher risk of all three subtypes investigated (anovulation, tubal factor, and unknown cause) in the 35-45 age group but not in the 30-34.9 age group, whereas PM 2.5 was associated with higher risk of unknown infertility in both age groups ( table 4 ). For subtypes of infertility in men, we observed positive associations between PM 2.5 and the three subtypes investigated (oligospermia, azoospermia, and unknown infertility) in both age groups. Noise seemed to be associated with a reduced risk of azoospermia (although based on only 273 people) and unknown infertility in men in the 30-36.9 age group and a higher risk of unknown infertility in men in the 37-45 age group.

Associations between an interquartile range higher five year mean road traffic noise at the most exposed facade and PM 2.5 in relation to subtypes of infertility in men and women

We found similar hazard ratios between the two exposures and infertility in women and between PM 2.5 and infertility in men across areas of low, median, and high population density; low, median, and high individual level education; and fourths of personal income, whereas for noise and infertility in men, associations were only observed among men living in low and median population densities, with low or medium educational level, or with income above the lowest fourth, or a combination of these ( fig 2 , see supplementary table S7). When comparing hazard ratios across people with no children or one child, we observed similar hazard ratios for both exposures in relation to infertility in men, whereas for infertility in women, noise was only associated with higher risk for primary infertility (for secondary infertility we observed a hazard ratio <1) and PM 2.5 was only associated with a higher risk of secondary infertility ( fig 2 , see supplementary table S7).

Associations between an interquartile range higher five year mean road traffic noise and PM 2.5 and risk of infertility among men aged 37-45 years and women aged 35-45 years, according to population density, education, personal income, number of children, and access to a silent façade at home. CI=confidence interval; dB=decibel; PM 2.5 =fine particulate matter with a diameter <2.5 μm

When investigating the association between noise at the most exposed facade and infertility among people with a large versus a small difference between noise level at the most and least exposed facades, we observed stronger associations only when a small difference in noise existed between the two facades, corresponding to having “no silent facade” ( fig 2 , see supplementary table S7).

Based on a large nationwide, prospective cohort, designed to include a high proportion of people actively trying to achieve pregnancy, we found that mean five year exposure to noise was associated with a higher risk of infertility among women aged between 35 and 45 years, whereas no associations were observed between PM 2.5 and infertility in women. The association between noise and infertility in women seemed confined to those without children (primary infertility). For men, we observed that five year exposure to PM 2.5 was associated with a higher risk of infertility across the investigated age range (30-45 years), and noise seemed weakly associated with infertility among men aged 37-45 years. The higher risk of noise related infertility in women and PM 2.5 related infertility in men was consistent across people living in rural, suburban, and urban areas as well as across people with low, medium, and high socioeconomic status. For noise, we observed stronger associations with infertility among people without a silent facade at home.

Strengths and limitations of this study

Strengths of this study include the nationwide design, with low risk of selection bias, together with a high number of people with incident infertility identified from high quality registers during a follow-up period of 18 years. To optimise the likelihood of obtaining valid and unbiased results, we restricted the study population to include a high proportion of people who were at risk of an infertility diagnosis—that is, those who were actively trying to become pregnant. Accordingly, our study population included men and women aged 30-45 years who were married or cohabiting. Furthermore, all study participants were censored at the time they had their second child. This censoring criterion was applied because Danish women on average gave birth to 1.8 children during the study period and therefore it is likely that after the birth of a second child, many couples no longer try for pregnancy. Although applying these restriction criteria increased the probability that a large proportion of our study population were trying to become pregnant, it was inevitable that our cohort also included couples who were not—for example, couples prioritising their career before children. This is a limitation of the study design.

People with infertility were identified using the high quality Danish National Patient Register, which has high validity and completeness of diagnoses in Denmark. 46 Thus, only infertile couples actively seeking infertility counselling were identified as infertile participants in the study. In Denmark, however, all inhabitants can seek infertility counselling and fertility treatment free of charge, and the procedures are standardised across Denmark, starting with a visit to the general practitioner who, if infertility is suspected, refers individuals to a fertility clinic. As Denmark is a small country, the distance between home and a fertility clinic is not expected to be an obstacle to seeking fertility treatment, and we did not expect major differences in the likelihood of obtaining an infertility diagnosis according to geographical location alone.

Another important strength was that we had access to an exact history of residential address for all participants from five years before baseline until end of follow-up, linked with exposure to both road traffic noise and PM 2.5 estimated used validated exposure models and high quality input data. 43 47 As these two exposures are correlated and found to be associated with many of the same diseases, mutual adjustment was crucial.

As the present study was based entirely on register data, we did not have information on lifestyle factors, such as alcohol use, smoking, and body mass index, which is a limitation. We did, however, have access to detailed time varying register based information on individual and neighbourhood level sociodemographic variables, enabling us to adjust for key socioeconomic covariates, thereby indirectly adjusting for lifestyle. That our adjustment strategy may sufficiently capture lifestyle confounding is supported by results from our previous studies on noise and air pollution and risk of cardiovascular disease, diabetes, and mortality, which were based on large Danish questionnaire based cohorts with detailed information on lifestyle. 48 49 50 These studies showed that after adjusting for the socioeconomic variables included in the present study, further adjustment for lifestyle had only a minimal effect on the risk estimates. Another limitation is lack of information on exposure to noise and PM 2.5 at work and at leisure time activities away from home. This may affect the size and statistical precision of risk estimates owing to a mixture of classic and Berkson error.

Comparison with other studies

In support of our results on PM 2.5 and infertility in men, particulate air pollution (PM 2.5 and PM 10 ) has in recent studies been found to be negatively associated with factors defining sperm quality, including sperm motility and count as well as changes in sperm morphology. 13 14 15 Our study therefore adds to these findings, showing that the effects of air pollution on sperm quality will potentially result in a higher risk of requiring assistance from a fertility clinic to achieve pregnancy. Interestingly, we found that the association between air pollution and infertility in men followed a linear exposure-response association, starting from around ≥8.5 µg/m 3 in both investigated age groups, indicating that even at the relatively low levels of PM 2.5 found in Denmark, particulate air pollution can reduce fertility in men.

For women, most previous studies have focused on investigating effects of air pollution on success of fertility treatment among couples referred to fertility clinics. 16 17 18 19 20 21 22 23 Although most studies found particulate matter air pollution to be associated with, for example, a reduced likelihood of clinical pregnancy, live birth after fertility treatment, and odds of receiving fertility treatment, 16 17 18 19 20 others found no association. 21 22 23 Also, the few studies investigating the effects of short term or long term, or both, exposure to air pollution on fecundability (assessed as time to pregnancy) have provided inconsistent results. 25 26 27 However, fecundability can be influenced by infertility in both men and women, and therefore results are difficult to interpret in the context of infertility in women, as a positive association can potentially be driven by effects of air pollution on semen quality. The results from these previous studies can therefore not be directly compared with the present study, where we have direct and differentiated measures of infertility in men and women. However, in a study based on 36 000 women from the Nurses’ Health Study II with self-reported follow-up for infertility (defined as attempting conception for ≥12 months), the authors were able to distinguish between infertility in men and women in 27% of women with fertility problems. 24 In both main analyses (couple based infertility) and analyses restricted to infertility in women, the authors reported that long term exposure to PM 2.5 (four year mean) was not associated with higher risk of infertility, which agrees with the results of the present study.

A potential explanation as to why we found PM 2.5 exposure associated with infertility in men and not women is that while female follicle development begins in utero, new sperm cells are produced continuously in the testis (after puberty), with an overall lifespan of three months. Therefore, particulate air pollution may act directly on the sperm cells during the vulnerable spermatogenesis phase—for example, through direct toxic effects of particles translocated from the lungs into the blood, oxidative stress, inflammatory processes, and genotoxicity. 51 52 In contrast, the potential biological mechanisms underlying an association between air pollution and infertility in women are less established but have been hypothesised to involve some of the same pathogenetic mechanisms as described for infertility in men as well as endocrine disrupting properties caused by air pollutants mimicking the effect of androgens and oestrogens. 53

The only previous study on traffic noise and a fertility related outcome indicated that among 65 000 pregnant women, road traffic noise was associated with a higher risk of trying for six months or more to achieve pregnancy (self-reported) compared with getting pregnant within six months. 36 This indicates that noise may impact fecundity, which supports the findings of the present study—although the two studies are not directly comparable, as the previous study focused on self-reported time to pregnancy among pregnant women, whereas the present study investigated risk of receiving a diagnosis of infertility. A potential explanation as to why we only observed an association with noise among women older than 35 years is that many who are trying to become pregnant in this age group are likely to be in a more stressful state than women in a younger age group if pregnancy is not achieved immediately, as it is well known that fertility drops steeply in women in their late 30s. 54 Therefore, women in this age group may be more susceptible to noise induced stress and sleep disturbance, 31 32 as they are potentially already in a state of distress. In support, we only observed a positive association with noise among women with primary infertility, who are expected to be in a more stressful state than women who already have one child (secondary infertility). It is established that infertility is associated with psychological symptoms, such as depression and distress, especially among women. 33 55 It is still, however, unclear whether stress is a risk factor for infertility. 33 Our finding of an association between noise and infertility only among women older than 35 years may also be partly explained by different underlying causes of infertility across age groups. For example, somatic disorders known to be important causes of infertility in women, such as endometriosis and polycystic ovary syndrome, are often diagnosed at a relatively early age and people with these disorders are thus more likely to contact fertility clinics for counselling at an earlier age. As we hypothesised that noise would have only a minor or no impact on the risk of infertility among individuals with a definite somatic cause of infertility, this may at least partly explain why we observed no association between noise and infertility in women in the 30-35 age group.

Among men, we observed that noise was associated with a lower risk of infertility in the 30-36.9 age group and a higher risk in the 37-45 age group. The biologically implausible lowering of risk in the youngest age group was, however, driven by adjustment for PM 2.5 , suggesting that this was an artefact. In the 37-45 age group, the association was robust to adjustment for PM 2.5 as well as to adjustment for population density, suggesting that noise may be a risk factor for infertility in men. After adjustment for number of children, however, the association was no longer present. More studies are needed to establish whether noise is a risk factor for infertility in men—for example, studies on noise and semen quality.

To investigate the robustness of our results, we examined whether our main findings were consistent across urban, suburban, and rural areas. In Denmark, couples who are considering starting a family are likely to move from apartments in larger cities to single family houses in suburban or rural areas, which in most instances will result in reduction of exposure to air pollution and noise. Although we had detailed information on changes of address (and exposure) for all participants, this could have potentially biased our results. However, the observed associations between PM 2.5 and infertility in men and noise and infertility in women were present regardless of the degree of urbanisation, suggesting that the high mobility of our population did not affect the results. In Denmark, people of high socioeconomic status are more likely to live in urban areas than in more rural areas, and although infertility treatment is free of charge in Denmark, Danish couples with high education and high income are more likely to seek infertility treatment than couples with low education and low income. 56 One could also speculate that people in urban areas might have different healthcare seeking behaviour than people in more rural areas. However, we observed comparable risk estimates for noise and air pollution among people with low, medium, and high educational level as well as among fourths of personal income, indicating that the results were not driven by socioeconomic differences in exposure levels or health seeking behaviour, or both.

Noise at the least exposed facade of a home is hypothesised to be a proxy for exposure to noise during nighttime sleep, as many people prefer their bedroom away from a busy street. 57 We observed that associations between this noise estimate and infertility across both men and women and age groups followed a linear association from 35 dB to around 45 dB, after which the association either levelled off or became negative. This makes it difficult to draw conclusions about the effects of noise at the least exposed facade on infertility. Therefore, to further explore whether noise at the least exposed facade had an impact on infertility risk, we investigated the effects of noise at the most exposed facade among people who had access to a “silent side,” which we defined as having a facade with substantially lower levels of road traffic noise than at the most exposed facade, compared to people with no silent side. Interestingly, we found that hazard ratios for noise at the most exposed facade and infertility among women with a silent side were markedly lower than among people without a silent side. This suggests that having access to a more silent facade may protect against the stressful effects of noise.

Conclusions

Based on a nationwide cohort, designed to include a high proportion of people actively trying to achieve pregnancy, we found that PM 2.5 was associated with a higher risk of an infertility diagnosis among men and road traffic noise was associated with a higher risk of an infertility diagnosis among women older than 35 years, and possibly among men older than 37 years. As many western countries are facing declining birth rates and increasing maternal age at the birth of a first child, knowledge on environmental pollutants affecting fertility is crucial. If our results are confirmed in future studies, it suggests that political implementation of air pollution and noise mitigations may be important tools for improving birth rates in the western world.

What is already known on this topic

Particulate air pollution and transportation noise are the two largest environmental causes of disease and death

Particulate air pollution has been associated with reduced sperm quality and reduced success of fertility treatment, whereas results on fecundability are inconsistent

Although one study found road traffic noise to increase time to pregnancy, no studies have investigated the effects of transportation noise on incident infertility in men and women

What this study adds

Exposure to particulate air pollution was associated with an increased risk of an infertility diagnosis in men

Road traffic noise was associated with a higher risk of an infertility diagnosis among women older than 35 years, and potentially among men older than 37 years

If these findings are confirmed in future studies, they may prove important in guiding decision makers responsible for setting priorities and implementing mitigations strategies to protect the general population from these exposures

Ethics statements

Ethical approval.

Not required.

Data availability statement

The study is based on data from the Danish national registers, which belong to the Danish Ministry of Health and Statistics Denmark. The authors are thus not allowed to share them in their raw form.

Contributors: MS and AJ conceived and designed the study. MS, AHP, ORN, and AJ defined and generated the study population. AJ and BN defined the outcome. JK, MK, and JB developed the method for, and estimated, particulate air pollution. MS conducted the statistical analyses. MS and AJ drafted the manuscript, which was subsequently revised and approved by all authors. MS is the guarantor. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: None received.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (MS) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: The involved research institutions will disseminate the study findings through press releases and patient organisations. Also, we plan to disseminate the results directly to national and international decision makers and officials within the area of environmental pollution.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• Vander Borght M ,
• Mascarenhas MN ,
• Flaxman SR ,
• Vanderpoel S ,
• Zegers-Hochschild F ,
• Adamson GD ,
• de Mouzon J ,
• International Committee for Monitoring Assisted Reproductive Technology ,
• World Health Organization
• ↵ European Society of Human Reproduction and Embryology. ART fact sheet 2023. https://www.eshre.eu/Europe/Factsheets-and-infographics
• Eisenberg ML
• Johnstone E ,
• Peterson CM ,
• Fallahzadeh H ,
• Zareei Mahmood Abadi H ,
• Momayyezi M ,
• Malaki Moghadam H ,
• Keyghobadi N
• Abusief M ,
• Buck Louis GM
• de Bont J ,
• Jaganathan S ,
• Dahlquist M ,
• Persson Å ,
• Stafoggia M ,
• Dominski FH ,
• Lorenzetti Branco JH ,
• Buonanno G ,
• Stabile L ,
• Gameiro da Silva M ,
• González-Comadran M ,
• Jacquemin B ,
• Boulet SL ,
• Shriber J ,
• Kissin DM ,
• Strosnider H ,
• Tartaglia M ,
• Chansel-Debordeaux L ,
• Rondeau V ,
• Mahalingaiah S ,
• Wesselink AK ,
• Nobles CJ ,
• Schisterman EF ,
• Buck Louis GM ,
• Sherman S ,
• Bottagisi S ,
• Solansky I ,
• Lepeule J ,
• Giorgis-Allemand L ,
• Kempen EV ,
• Pershagen G ,
• Sørensen M ,
• Poulsen AH ,
• Hvidtfeldt UA ,
• Cordoza M ,
• Rooney KL ,
• Lateef OM ,
• Akintubosun MO
• Christensen JS ,
• Raaschou-Nielsen O ,
• Schmidt M ,
• Pedersen L ,
• Sørensen HT
• ↵ Ström T. Road traffic noise - an estimate of prediction uncertainty. Swedish National Testing and Research Institute, 1997; Report No 24. [In Swedish.]
• ↵ Jensen SS, Plejdrup MS, Hillig K. GIS-based National Road and Traffic Database 1960-2020. Aarhus University, Danish Centre for Environment and Energy, 2019;Technical Report No 151.
• Silver JD ,
• Christensen JH ,
• Berkowicz R
• Kakosimos KE ,
• Skamarock WC ,
• Sandegaard JL ,
• Schmidt SA ,
• Ehrenstein V ,
• ↵ Levin G, Iosub CI, Jepsen MR. Basemap02. Technical documentation of a model for elaboration of a land-use and land-cover map for Denmark. Aarhus University, Danish Centre for Environment and Energy, 2017; Technical Report No 95. https://dce2.au.dk/pub/TR95.pdf
• Thacher JD ,
• Hammoud A ,
• Carrell DT ,
• Sanderson M ,
• Parker-Jones K ,
• Peterson CM
• Jurewicz J ,
• Conforti A ,
• American College of Obstetricians and Gynecologists Committee on Gynecologic Practice and Practice Committee
• Yamanaka-Altenstein M ,
• Rauch-Anderegg V ,
• Heinrichs N
• Brautsch LAS ,
• Schmidt L ,

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The Public’s Perceptions of Air Pollution. What’s in a Name?

Air pollution is a major global health threat. There is growing evidence for a negative effect of air pollution on health and well-being. Relationships between air pollution and health are mediated by health risk perceptions and play a crucial role in public response to it. Air pollution in the public’s mind is often different from air pollution defined by the scientific community. Therefore, in order to develop successful prevention and alleviation strategies, an understanding of public risk perceptions is key. The central question of this paper is: ‘How does “the public” (in Brussels) perceive air pollution?’ This research is an attempt to enrich the limited body of qualitative research in the field, approaching the topic of perception from 4 different, complementary angles: definition, association, categorisation and problematisation. About 51 interviews were conducted in the Brussels-Capital Region. Consistent with earlier research, this research illustrates that perceptions of air pollution are diverse, subjective, context-dependent and often deviate from conceptualisations and definitions in the scientific community. Respondents underestimate the potential harm and problematisation depends on comparative strategies and perceived avoidability. The novel aspect of this paper is the identification of 5 mental schemes by which specific elements are categorised as being air pollution: (1) the source of the element, (2) its health impact, (3) its climate impact, (4) its functionality and (5) sensory perceptions. The insights gained from this research contribute to the field of environmental epidemiology through a better understanding of how ‘the public’ perceives air pollution and in what way this may deviate from how it is perceived by experts. We hope to raise the awareness among experts and policy makers that air pollution perceptions are far from universal and consensual but on the contrary individual and contested.

Introduction

Air pollution is alongside climate change one of the biggest environmental threats to human health. 1 According to the WHO, 91% of the world’s population lives in places where ambient air pollution levels exceed WHO guideline limits. 2 Despite improvements in air quality over the past 3 decades, exposure to air pollution is estimated to cause 7 million premature deaths, and results in the loss of millions more healthy years of life. 1

There is indeed growing evidence for a negative effect of air pollution on health and well-being. Many studies provide solid evidence of an association between high concentrations of air pollution and mortality 3 or other health outcomes, such as increased ischaemic heart disease, strokes, infections of the lower respiratory tract, asthma or chronic obstructive pulmonary disease 4 and mental health indicators, such as psychological stress, symptoms of depression or suicide. 5 - 9 Brain damage caused by air pollution seems to be associated with dementia and with weakened cognitive functioning throughout the life course. 10 , 11 Exposure to air pollutants has potentially harmful effects from the earliest stages of life with negative effects on pregnancies as well as long-term effects that affect susceptibility to disease later in life. 12

Given this growing evidence of a negative impact on health and quality of life, there is generally an increasing interest in fighting air pollution at the global, regional and local level. It is therefore important to figure out what air pollution is exactly about.

Air pollution obviously has an ontologically objective existence, but the way in which people come to know and make sense of it, is highly contextual, subjective and therefore far from universal. 13 Air pollution in the public’s mind is often different from air pollution as defined by the scientific community. Truth claims of scientists are evidence-based and therefore more convincing for policy makers. However, from a policy perspective, definitions and perceptions of the public need to be considered as well as they define the margins for possible policy action to a large extent. Perceptions being influenced by the social, economic and political context, by knowledge and evolving insights, they will differ between people and contexts. The ambition of this paper is to make a taxonomy of definitions, perceptions and associations that go along with air pollution among Brussels’ citizens.

Why does one’s perception about air pollution matter? Relationships between environmental exposure (eg, air pollution) and physical and mental health (eg, respiratory effects) are mediated by perceptions of the ‘exposure’ (eg, air quality). 14 Risk perceptions – or more exactly the there out resulting attitudes – play thus a crucial role in the public’s response to environmental exposure 15 and in its response to the sources of the exposure. These attitudes impact health both in a direct and an indirect way. In a direct way, high risk perceptions might constitute a cognitive antecedent of a stress reaction negatively impacting upon mental health 16 , 17 or on the other hand, when risks are underestimated, people might not take appropriate measures to protect themselves which impacts on their physical health. Attitudes resulting from risk perceptions also mediate the potentially harmful human health effects of air pollution in a more indirect way since they might result in behavioural changes and support measures aiming to decrease air pollution thereby mitigating air pollution and its negative health impact. Public awareness and realistic perceptions of the health risks associated with air pollution are therefore key in improving public health and in creating public support for policy measures aimed at reducing air pollution.

In order to develop successful prevention and alleviation strategies, understanding risk perceptions is key. Risk perceptions can be defined as involving ‘ people’s beliefs, attitudes, judgements and feelings, as well as the wider cultural and social dispositions they adopt towards hazards and their benefits ’. 18 Key in shaping a health risk perception, is the definition and identification of air pollution. Indeed, if air pollution is not recognised as such, one will not act upon it. 14 These reactions might consist of (individual) behavioural changes, impacting heath directly through protective measures or indirectly through behaviours that reduce levels of air pollution at a personal level (eg, changes in car use). Risk awareness is also crucial for citizens to engage in collective action (eg, through different forms or degrees of activism and to support/call for policy initiatives initiated by local, regional or national governments). 7 , 13 Therefore, understanding how individuals perceive air pollution, is crucial for combating it and to improve public health.

From a review of qualitative research on air pollution perceptions we learn that qualitative research about the topic remains fairly scarce and most often neglects how air pollution is defined by the public and which mental schemes are employed to categorise an element as being air pollution or not. 13

What we learn from the existing body of research on the topic, is that the public and scientists define air pollution differently. The scientific community focuses on specific pollutants derived from multiple sources; the public rarely refers to specific pollutants and rather emphasises the sources of air pollution. In their study on pupils’ knowledge of air pollution in Greece, Dimitriou and Christidou 19 observed that the majority of respondents referred to specific air pollutants as ‘smoke’, ‘exhaust-gases’ or ‘harmful substances’, without making any distinction between the different substances found in the air.

Knowledge about air pollution sources differs between experts and the public. The public often associates air pollution sources with odour. In the Nairobi slums, for instance, smelly drainage channels and toilets were frequently cited as a source of air pollution. 20 Similarly, respondents in Beijing 21 mentioned garbage as a source of air pollution thereby considering odour as the clue connecting garbage with air pollution.

What people categorise as being air pollution is very much culturally defined. In a community in California, smoke caused by wildland fire was perceived as air pollution. 22 On the contrary, in a study on open burning of municipal solid waste (MSW-burning) in India, respondents expressed the belief that smoke from ceremonial fire is a purifier when good fuel is used. 23 When asked explicitly if smoke from MSW-burning also purifies, there was consensus that it was not purifying, but polluting. The ‘pure’ character of ceremonial fire smoke relative to MSW-burning smoke was explained through the fuel used for the burning. In a community in Australia, the presentation of wood smoke as natural and the idea that wood heating is a traditional source of warmth counteracts the strong association of pollution with modernity and ‘artificial’ sources of energy (Reeve, Scott, Hine and Bhullar, 2013). 24 Obviously, the classification of elements as contributing to air pollution is context-dependent. People refer to sources of pollution that are part of their daily lives and the society they live in. Respondents from a London study for instance indicated cars, buses, heavy goods vehicles and pollen as the most significant causes of air pollution, 25 while respondents in a poor neighbourhood in Nairobi mostly pointed to road dust, industrial areas and burning trash. 26 In sum, definitions of air pollution and elements identified as air pollution are not universal: they differ between experts and the public and between different populations in different contexts.

Study aim and research questions

In this qualitative study, we aim at identifying the beliefs, attitudes, judgements and feelings that the public in the Brussels-Capital Region has about ambient air pollution. Our main research question is: How does the public (in Brussels) perceive air pollution?

This question (see Figure 1 ) crystallises into 4 sub-questions that approach the topic of perception each from a different but complementary angle:

Schematic overview of the research questions and how they relate to the main research question.

• How does the public define air pollution? (cognition)
• Which associations does air pollution evoke in the public? (intuition)
• Which elements are perceived by the public as being air pollution and why? (mental schemes)
• Is perceived air pollution also seen as problematic by the public? (ethic)

We consider it relevant to investigate whether air pollution is problematized by the respondents as people can only be mobilised or stimulated to fight air pollution if they recognise it to be problematic. This research aims to enrich the limited body of qualitative research on health risk perception of air pollution. It is to our knowledge the first research investigating how air pollution is defined and identified by the public. The richness of the research lies in the different angles from which the study of ‘perception’ is approached and the detailed and complementary information that results thereout.

First, we present the results related to the definitions of air pollution given by our respondents. The definition question was designed to come to the essence of air pollution through a cognitive way of thinking stimulating the respondent to be concise, to the point and synthetic. To explore the sentimental dimension of respondent’s perceptions in a more intuitive way, an association exercise was done to invite the respondent to think in an open-minded way. The categorisations exercise intended to explore the symbolic dimension of air pollution through mental schemes handed by the respondent. Mental schemas are cognitive structures or mental representations that allows people to categorise knowledge about the world. These schemas help to simplify interactions with the world. In this paper, we focus more specifically on object schemas. 27 , 28 Expressing claims about the problematisation of air pollution encompasses a more ethical dimension through the values expressed towards the problematic character of air pollution. Both ‘feeling’ and ‘thinking’ are touched upon through these 4 questions by means of conscious and more unconscious processes. The insights gained from this research should contribute to the field of environmental epidemiology through a better understanding of how ‘the public’ perceives air pollution.

Methodology

This study adheres to a symbolic interactionist perspective, viewing social interaction in terms of the meaning that social actors attach to action and things. 29 In line with this perspective, we use a qualitative research methodology. We use individual face-to-face in-depth interviews with 51 respondents. The duration of the semi-structured interviews was 90 minutes on average.

Two themes were explored during the interview, perceptions about public green spaces within the Brussels-Capital Region (BCR) and perceptions about air pollution. During the recruitment, respondents were not informed that the subject of air pollution was going to be discussed to avoid that they would inform themselves about the topic as a preparation to the interview, thereby creating potential bias.

Recruitment and fieldwork

Respondents were recruited through the distribution of flyers, a call in a Brussels Facebook group, civil society organisations and schools and snowball-recruitment. The interviews were conducted between October 2019 and March 2020. Interviews were in Dutch or French. An incentive of 15 euros was granted to the respondents after participation to the interview.

Respondents were interviewed by the first and second author, at different places: the Vrije Universiteit Brussel, the respondent’s home, a place of preference of the respondent and in the civil society organisations or schools that helped with the recruitment.

The interviews were audio-recorded, transcribed, analysed and manually coded according to the pre-determined research questions.

The data used for this paper were generated through 3 questions in the interview guide.

The first one (to answer research question 2) consisted of a free association question at the start of the interview asking: ‘What does the word “air pollution” spontaneously make you think of? Which words, images, ideas, impressions, associations or feelings come to mind?’ In the second question (to answer research question 1) respondents were asked how they define air pollution: ‘Suppose I don’t know anything about air pollution and I ask you, what is air pollution, how would you define it?’ The third question (to answer research question 3) was a fairly comprehensive one that examined which specific elements 1 were categorised as air pollution and why: ‘I am going to list a few elements. For each item, I will ask whether you consider this to be air pollution or not. Then I am going to ask you why you think it is or is not air pollution. This question is not intended as a test. It does not matter whether your answer is right or wrong. All I want to understand is the reasoning behind your answer’ . With this question, it was our intention to get an idea of the mental schemes employed by the respondent to perceive a specific element as being air pollution or not.

We also investigated the extent to which identified air pollution is polemised. This issue did not belong to our initial research aim, but appeared to be important during discussions with our respondents resulting from the 3 aforementioned questions.

The analysis is supplemented with quotations of respondents characterised through an anonymous identification code that refers to some main characteristics such as age, gender, migration background and socioeconomic situation (see Appendix 1 ).

We recruited a diverse group of people in terms of age, gender, sociocultural background and socio-economic position (see details in Table 1 ). In what follows we report on the perceptions about air pollution through (1) definition, (2) association, (3) categorisation and (4) problematisation.

Overview of the respondents.

How does the public define air pollution? (Cognition)

To understand how air pollution is perceived, it is first important to know how it is defined. Analysis of the definitions of air pollution indicated that respondents most often referred to the sources of air pollution (n = 38) and also to its consequences (n = 26) while defining air pollution. In doing so, they exclusively denoted anthropogenic sources (no one mentioned natural sources).

‘Air pollution is actually what man makes, it’s what we produce more than the earth can handle. What the earth can convert into good air for us’. (R34)

Many respondents (n = 13) referred exclusively to cars as a source of air pollution in their definition.

‘They are the exhaust fumes from the cars and there is something in them that gets into the lungs and makes you cough and is not very healthy. So the less we drive, the better. Although public transport could be better too’. (R33)

Other sources mentioned by the respondents were boats, trucks, infrastructure works, fire, machinery, factories and aircraft. Less conventional sources such as cigarettes and barbecues were brought up as well.

Respondents rarely (n = 5) referred to specific pollutants (PM, NO 2 , soot. . .) but rather to vague terms such as particles, things, elements, small particles of carbon dioxide and small dust.

‘Air pollution is dirty particles in the air. I don’t know exactly what that is’. (R45)

In addition, they often (n = 4) referred to sources perceptible through the senses, such as the water vapour from nuclear power plants, which was perceived as polluting smoke, or to sensory manifestations of air pollution such as particulate matter on windows or white doors.

‘Air pollutants are particles that are in the air and go everywhere. On the plants, in the respiratory system. I also see it on the windows. The windows are dirty very fast. There are small dust particles on them’. (R46)

Next to sources, respondents also cited the negative health impact of air pollution (n = 24). No specific diseases or disorders were mentioned, instead reference was made to health problems in general, especially for vulnerable groups such as the elderly, people with respiratory problems and children. Some respondents also referred to the impact that air pollution has on the climate (n = 8).

‘They are tiny particles in the air that are created by man himself and caused by the way he lives. This results in the air becoming polluted causing atmospheric warming’. (R39)

In sum, our respondents saw air pollution solely as the consequence of human activity, perceived cars as the main source of air pollution without referring to specific pollutants. They mainly referred to sensory sources and manifestations of air pollution. The impact of air pollution on health and to a lesser extent on climate change seemed central in their definitions. Air pollution was perceived as ‘negative for health’ in general terms especially for ‘weaker’ persons in society.

What associations does air pollution evoke in the public? (Intuition)

To gain insight into the emotive and associative dimension of air pollution perception, we asked respondents which associations ‘air pollution’ evoked in them.

Air pollution seemed to be a vague concept for our respondents. They mainly associated it with sources such as cars and other motorised traffic (n = 28), to a lesser extent with general sources such as exhaust gases, chemical products, petrol or smog (=11) and to a limited extent (n = 4) with the specific pollutants PM and CO 2 . Besides the car and other motorised transport, other conventional sources of air pollution such as industry, agriculture and wood combustion were mentioned sporadically (n = 3). Air pollution was also associated with less conventional sources such as rubbish, barbecues, the smell of snacks, dustbins, smoking people, the smell of food, the smell of cigarettes, dog fouling and smelly places such as underground and train stations. The link with air pollution was made by the smell that these elements emit and, in the case of the barbeque, also the dust that thereout results.

Air pollution was also often associated with the word ‘disease’ (n = 16), mainly referring to health problems in general and to breathing, lung problems or asthma in particular. Respondents rarely referred to their own situation or concrete personal experience (n = 2). Connections with other health problems such as cancer, cardiovascular diseases, inflammation of the airways, worse mental health, brain damage, low birth weight or mortality were not made by our respondents, in contrast to expert knowledge. Air pollution was emotionally exclusively associated with negative feelings (n = 3) such as sadness, anger, concern and disappointment.

Respondents made less obvious or, from a scientific point of view, even incorrect associations. Certain visually perceptible elements were incorrectly labelled as air pollution, such as clouds for example.

Again, it appeared that respondents often associated air pollution with sources that can be sensed through smell, sight and sound. Consequently, air pollution is geographically associated with places with many perceived sources of air pollution, especially cars. The city as a place with high levels of air pollution was contrasted with places outside the city that were associated with cleaner air. Air pollution was thus mainly perceived as an urban phenomenon.

‘ . . . There is more air pollution in the city than in the countryside because there are many cars and many people. Houses are close together, people go around the city more by car than by public transport’. (R21) ‘On the countryside you don’t see smog. I feel much healthier when I am in the countryside’. (R24)

In addition, air pollution was associated with elements that are part of the local context in which it occurs and that contribute to it in a direct or indirect way, such as ‘Flemish people who come to Brussels by car’ and ‘a lack of bicycle lanes’. Thus, there is a tension between residents of the Brussels Region – about half of the inhabitants of Brussels do not own a car 30 – and the Flemish who commute to Brussels by car. This creates the image that it is the Flemish who come to pollute the Brussels’ air. The results also mirror the existing field of tension between those who travel mainly by bicycle and those who travel mainly by car within the BCR. Those who cycle complain that there is not enough (safe) space for cyclists and that it is ‘King Car’ that dominates the public space.

In sum, respondents associated air pollution with vague non-specific pollutants and were rather partial in their identification of air pollution sources. They associated air pollution with ‘negative health’ in general or with lung diseases that have a negative impact on breathing specifically. Furthermore, it appeared again that respondents associate air pollution with sensory perceptions, that they blur the distinction between climate and environmental problems, and make ‘erroneous’ associations from a scientific point of view. Air pollution is mainly perceived as an urban phenomenon for which the ‘other’ is blamed. Respondents made associations linked to the local context that reveal fields of tension between different actors.

What elements are perceived by the public as being air pollution and why? (Mental schemes)

We asked respondents if they would categorise specific elements as air pollution or not and why. The elements discussed in the interview were: particulate matter caused by forest fires, cigarette smoke (secondary smoke), pollen, particulate matter caused by wood burning (stove), ammonia from manure, methane caused by the intestinal system of livestock, water vapour, particulate matter caused by traffic and particulate matter caused by volcanic eruptions (see Appendix 2 for more information about this question).

Firstly, data showed that there was no unanimity in the categorisation of elements as air pollution or not (see Appendix 3 ). The only exception concerned ‘particulates caused by traffic’, which was unanimously categorised as air pollution.

Secondly, the categorisation of an element as air pollution was only to a limited extent based on the element itself. For example, relatively few respondents categorised ‘particulate matter’ independently of its context. There was unanimity on the categorisation of ‘particulate matter caused by traffic’ as air pollution, but no unanimity regarding ‘particulate matter caused by forest fires’, ‘particulate matter caused by wood burning’ and ‘particulate matter caused by volcanic eruptions’, notwithstanding the fact that these all refer to the same element: of ‘particulate matter’.

In this case, when respondents have no explicit knowledge of a certain element being air pollution or not through education or media, people fall back on different mental schemes while categorising elements as air pollution. Based on the data, we were able to identify 5 different mental schemes: the origin of the element, the health impact of the element, the climate impact of the element, sensory perceptions of the element and functionality of the element. An overview of the mental schemes used per element can be found in Appendix 4 .

Mental scheme 1: The origin of the element

The most common mental scheme related to the origin of the element. It mainly evaluates whether the element has an anthropogenic or a natural origin. Elements of anthropogenic origin were usually perceived as air pollution, whereas elements of natural origin were generally not perceived as air pollution. The origin of an element was however not unambiguously determined by different respondents and involved different dimensions: the element itself, the source from which the element raised and the origin of the source.

An element itself could be associated by its name or by its origin with positive, natural things or, on the contrary, with negative, artificial things. For example, according to this mental scheme, ‘pollen’ was not perceived as air pollution because it is a ‘natural’ element.

‘Pollen? No. That’s natural, isn’t it?’ (R4)

Ammonia and methane, on the other hand, were perceived by many respondents as chemical non-natural elements and therefore as air pollution.

‘Ammonia from manure is, I think, definitely air pollution because chemicals are then released into the air and that causes bad air quality . . . Methane, again, is chemical so bad’. (R5)

The same logic was applied to the element ‘particulate matter caused by traffic’.

‘Yes, it is air pollution, because it is not natural’. (R27)

The second dimension is the source from which the element originates . For example, some respondents did not categorise particulate matter from untreated, natural wood as air pollution, whereas particulate matter resulting from the combustion of treated wood or from petrol was categorised as air pollution because the source was perceived as non-natural or no longer natural.

‘I don’t think “particulate matter caused by wood burning” is air pollution because to me, anything natural has no negative impact on health. Unless the wood was processed of course’. (R9)

Similarly, the element ‘water vapour’ was generally not seen as air pollution since the raw material from which water vapour is formed – water – is perceived as natural. If, on the other hand, the water is not or no longer natural, it was categorised as air pollution.

‘It depends on what kind of water one evaporates. If you evaporate water from a clean river, it is not air pollution at all, but if you evaporate water from the toilets, that is pollution. Water from factories is polluted’. (R27)

The origin of the source from which the element originates is the third dimension of the origin and can be natural or in contrast human initiated. Several respondents did not perceive particulate matter from a volcano as air pollution, whereas particulate matter from wood combustion was perceived as air pollution. Although both cases involve the same element (particulate matter), the ‘ignition mechanism’ behind them is different. After all, a volcanic eruption is a natural phenomenon where burning wood is initiated by human activity.

‘I don’t think particulate matter from a volcano is air pollution because it is something natural’. (R9)

Another example relates to the element ‘ammonia from manure’. Those who perceived the source of the element ammonia – livestock, animal husbandry or manure – as natural, did not categorise it as air pollution. Accordingly, one respondent perceived ‘farts’ as the source of ‘methane caused by the intestinal system of livestock’. Since farts were perceived as natural, the resulting methane was also perceived as such and therefore not categorised as air pollution.

‘No, methane caused by the intestinal system of cattle is not air pollution because everyone farts, that’s human, that’s natural’. (R3)

For other respondents, the categorisation of this element as air pollution depended on the scale at which animals are farmed. Methane caused by the intestinal system of cattle’ farmed on a large scale was perceived as unnatural and was therefore categorised as air pollution.

‘It is natural, but in nature you never find such concentrations of cows together, producing so much. So actually it is not natural but human’. (R33)

Mental scheme 2: The health impact of the element

The second most common mental schema concerned the perceived health impact of the element, evaluating the extent to which the element has a negative health impact. Elements that were perceived as harmful, were categorised as air pollution.

‘Actually, from the moment there is a harmfulness, I think there is pollution. So, I think we have to define air pollution from the point of view of harmfulness. Because otherwise you shouldn’t call it pollution, then it’s just an aspect of the air like pollen’. (R32)

The health impact of an element was not unambiguously determined by different respondents and includes different dimensions: a time dimension, a spatial dimension and the experience dimension.

The time dimension refers to both the duration of exposure and the duration of the health impact. For example, ‘pollen’ was often not considered to be air pollution because there is no continuous exposure. Pollen exposure and any resulting health problems were considered as of temporary, seasonal nature.

‘I think that pollen is a natural phenomenon that is not harmful to health but that can trigger allergic reactions but that it is not harmful to health in the long term in the way that air pollution is. I think pollen can just cause an annual allergic reaction that also stops and that also has no long-term effects on health or on nature. I would not call it pollution. Pollution is really something that is harmful. Pollen is more of an element that is in the air and that can be inhaled and that can cause temporary irritation that is not harmful to overall health, but only irritation. Just like the sound of small children causes irritation, just like other natural things can cause irritation but are not harmful’. (R32)

Similarly, particulate matter caused by burning wood is limited in duration, as it is only in the cold evenings that stoves are lit. The exposure to and impact of ‘particulate matter caused by traffic’, on the other hand, was not perceived as being seasonal but continuous.

‘Yes cars have a very big impact on air pollution. We can say that cars circulate in the streets 24/7’. (R41)

Similarly, ‘particulate matter caused by volcanic eruptions’ was not categorised as air pollution because of its perceived short duration.

‘A volcanic eruption does not last very long. The particulate matter goes out of the air. Because it is short, it is not air pollution’. (R6)

The spatial dimension also determines how respondents assess the health impact of elements. This spatial dimension consists of several aspects: the scale of the health impact, the concentration of the source and the distance to the source.

The scale at which people experience a health impact determined if an element is categorised as air pollution or not. For example, the proportion of the population that suffers from the effects of ‘pollen’ is perceived to be limited compared to the proportion of the population that suffers from the effects of exposure to ‘particulate matter caused by traffic’.

‘There are people who are allergic to pollen but I don’t think that is air pollution. It is the reproduction of the plants. It can also cause health problems for some people but those are more exceptions. It is not dangerous for everyone. Smoking is dangerous for everyone’. (R46)

Some respondents stated that ‘pollen’ is air pollution for people who are sensitive to it, but not for people who are not affected by it. They ‘individualise’ the phenomenon of air pollution.

‘Yes, pollen is air pollution for people who suffer from it’. (R2)

Similarly, the concentration of the source influences the extent to which resulting elements were perceived as negative for health. The health impact of particulate matter caused by wood combustion for instance was perceived as relatively limited due to its perceived low concentration. Particulate matter caused by forest fires, on the other hand, was perceived as having a negative impact on health since the resulting concentration of particulate matter is perceived as being much higher.

‘Forest fires are pollution because it is so very massive but wood burning, yes, if everyone is doing that now, then yes. Then I think it can be very polluting. But it remains fairly minimal compared to your air. But it will have an impact on air quality for a while. You’re going to smell that strongly for a while’. (R5)

The distance to the source also influences the perception of the health impact. For example, the distance to traffic and the resulting particulate matter was perceived as relatively small. Particulate matter from wood combustion was perceived as remaining far away because it is emitted at a height via the chimney, as a result of which its impact on health was estimated to be more limited.

‘That’s not good for your health. I do think that’s something because it’s above the roofs, that that dissipates faster so that’s less harmful to you as an individual anyway if you’re downstairs’. (R13)

In addition, personal experiences played a role in evaluating the negative health impact of a certain element. A respondent who experienced a direct, severe and perceived as with air pollution related physical reaction to ‘particulate matter from wood burning’ said:

‘Yes, that is air pollution. Although I only became aware of it later in life, namely when there was an awareness campaign by the Flemish government and I worked for the Flemish government. And I couldn’t believe it at first, but I’ve experienced it first-hand because I was at my father’s house the other day. He has a wood-burning stove and the heating was broken. I lit the stove. I know it’s not good for the environment but I felt like burning wood for once and the next day I had a severe asthma attack. I’m not used to doing that so maybe I didn’t do it right, maybe I didn’t let it soak in and I really felt it’. (R32)

Although many respondents fell back on either the single mental schema of origin or the mental schema of health impact, there were also respondents who combined both and perceived elements of natural origin as harmless in terms of health.

‘I think everything that is natural, that is not man-made, is good, that it does not have a negative impact on us’. (R4)

Mental scheme 3: The impact of the element on the climate

The third frequently used mental scheme related to the perceived impact of the element on the climate. A perceived negative impact on the climate usually resulted in the element being categorised as air pollution. For example, methane was associated with global warming and was therefore categorised as air pollution by some respondents.

‘Methane is a gas that is certainly one of the causes of global warming, so yes. It is also in the air. So that pollutes the climate, but on the other hand, it doesn’t affect us as much, but it’s still bad for the climate. So, yes’. (R14)

The element ‘cigarette smoke’ was also categorised as air pollution due to its perceived negative impact on the climate.

‘A cigarette is something that is on fire and also puts CO 2 into the air I think’. (R11)

With regard to ‘particulate matter caused by traffic’, one respondent commented:

‘Yes, that is simply the biggest contributor to greenhouse gases in the atmosphere’. (R15)

Mental scheme 4: Sensory perceptions

The fourth less frequently used mental scheme related to the sensory. When elements were associated with negative sensory perceptions, they were categorised as air pollution on that basis. For example, particulate matter caused by traffic was associated with a perceptible odour.

‘I think you notice that when you come outside. Then you smell it and feel it and it doesn’t feel good’. (R30)

Besides odour, sight proved to be also important in the perception of air pollution. For example, one respondent remarked in relation to particulate matter caused by traffic:

‘Yes, you notice it when it rains. Then the sky is grey and if you look at the raindrops, you can see that they are not entirely clear. That there are particles in them’. (R37)

Mental scheme 5: Functionality of the element

A final least frequently used mental scheme, related to the perceived functionality of the element. Certain elements were not categorised as air pollution because of their function within a particular ecosystem. For example, water vapour was not perceived as air pollution as it is part of a natural cycle.

‘Water vapour, no, that’s just rain. That falls down and that is part of the cycle’. (R21)

Methane caused by the intestinal system of cattle’ was not perceived as air pollution by some respondents as it was associated with manure and manure was perceived as good for the soil.

‘No, methane is not an air pollutant. It is an energy that we can use. It is a raw material’. (R46)

The element ‘pollen’ was not categorised as air pollution because of its link with green elements in the neighbourhood.

‘For me it is, because I have hay fever. But is that air pollution? Not ultimately, except for people. That’s not actually air pollution but I would still prefer, no I don’t want less pollen because that means even less green, so yes. I don’t see that as air pollution because I think that if there is pollen in the neighbourhood, there is also greenery in the neighbourhood. And because I don’t think that’s really bad for your health because ultimately that’s just the, the fact that there are flowers growing or grass living. That seems to me to be rather a positive thing’. (R23)

We conclude that different mental schemes with different dimensions were used to categorise or not elements as air pollution. We did not observe unanimity in the use of these mental schemes: different mental schemes were used by different respondents, but also within one and the same respondent. Frequently, different mental pictures were weighed against each other about an element, whereby respondents nuanced or questioned the categorisations they had made.

‘Yes, in the strictest sense of the word, I think that is air pollution. It causes the air quality in the immediate vicinity of the volcano to drop drastically and become very unhealthy. On the other hand, I am now contradicting myself because I just said that it is man-made and a volcanic eruption is not man-made but I would still classify it as air pollution’. (R29) ‘Ok, health is important to me, but to me climate is still ahead of health in the sense that if our climate is all fucked up, which we are doing well, then we have nothing to worry about in terms of our health because we are not going to be here anyway. So for me, climate is ahead of health’. (R14)

Is perceived air pollution perceived as problematic by the public? (Ethic)

If people are to be mobilised to reduce air pollution, it is important that elements categorised as air pollution are problematized. Our data showed that categorised elements were not always problematized to the same extent by our respondents. We therefore examined why specific forms of air pollution are problematized, while others are not. We identified 2 mechanisms that influence the extent to which air pollution is relativized or problematized: comparative problematisation and perceived avoidability of emissions.

Comparative problematisation

Comparative problematisation resulted in the problematisation or conversely in the relativisation of perceived air pollution. Comparative problematisation contained several dimensions: a spatial dimension, a time dimension and a source dimension.

In a spatial perspective, respondents relativized air pollution by claiming that air quality was worse elsewhere than in their own environment. Other respondents, on the other hand, polemised the existing air quality by comparing it with places where they felt air quality was better.

Some respondents put air pollution into perspective by comparing pollution levels with a past in which there was much more air pollution or by stating that it has always existed.

‘Yes, particulate matter is pollution. I don’t think we should attach the importance to it that we do now. That is something completely different. Because we can’t say that the world has just become civilised from five years ago. Before five years ago there was no talk about that. There are many things that are now suddenly very important, but that is the way the media works. The way that influence works from all sides. Particulate matter has always existed. There is relatively less particulate matter than there used to be, despite the honking, because there used to be a lot of people who burned coal, or burned wood. A few hundred years ago there was a lot of particulate pollution. But now all of a sudden that’s a hot topic. . . .And pollution from burning wood has always been there. It has been going on for six million years, when they were roasting mammoth legs on a wood fire’. (R2) ‘And then there are the combustion residues of all kinds. Like CO emissions, particulates and so on. These have always been present. The stove in the Middle Ages, the open fire in the castles that was’. (R8)

Other respondents, on the other hand, problematized the current air quality by comparing it with a past in which, according to them, air pollution was much more limited.

Several respondents balanced different forms or sources of air pollution against each other. Certain forms or sources were seen as worse and therefore more problematic. For example, particulate matter caused by forest fires was generally perceived to be less problematic than air pollution from cars. Factories and cars were perceived as not natural while forest fires were generally considered as natural.

‘I would say yes and no. Yes, that (particulate matter caused dose fires) makes the air dirty but that is not as bad as factories or cars because it comes from nature. The others are manufactured substances and they are worse than fire from nature’. (R50)

Perceived avoidability

Another mechanism influencing the problematisation of air pollution was its perceived avoidability. Air pollution was often problematized when it was perceived as avoidable. When it was perceived as inevitable, it was often relativised.

‘So what can be mitigated as a negative effect from human action, I do think is pollutant and so I also think that due attention should be paid to mitigating it’. (R8)

For example, ‘particulate matter caused by traffic’ was perceived as problematic air pollution when it was considered avoidable, whereas ‘particulate matter caused by forest fires’ was not considered as problematic when forest fires were seen as a natural phenomenon making them unavoidable. As a result, particulate matter from an ignited forest fire was problematized where particulate matter from an ignited forest fire was relativised.

‘It is the smoke from the fire that remains in the air but it is not done intentionally. It’s just a natural disaster and no one can really do anything about it’. (R20)

Similarly, categorised air pollution consisting of ‘particulate matter caused by volcanic eruptions’ was never perceived as problematic as respondents considered it as unavoidable.

‘From a purely material point of view, yes. If that is a natural phenomenon. Radioactive radiation is also there as a natural phenomenon. Is that positive or negative, no, it is there. That is a fact. . .The forest fires in Brazil are an example of a forest fire that is not a natural phenomenon. It is malicious. But if pollution arises from a natural phenomenon, there is no way around it. Then you have to accept that natural phenomenon and its consequences’. (R8) ‘That’s like those forest fires. That’s not intentional but it’s bad. It’s still bad but just the fact that it’s not intentional, it’s ok’. (R20)

Another example relates to ‘particulate matter caused by burning wood’. When wood was burnt for fun and therefore perceived as avoidable, it was problematized; when wood was burnt for survival to heat or cook on, the resulting air pollution was put into perspective as it was considered unavoidable.

‘I keep playing with the tension you can find between the causing or the natural process. A wood-burning stove is effectively polluting. Can it be replaced? If that wood-burning stove is to serve only to see a bit of atmospheric pleasant flames, then I would say, maybe not so much. If it is necessary to heat you, then yes’. (R8)

The same reasoning was applied to the categorisation of ‘cigarette smoke’ as air pollution. For the following respondent, the ‘avoidability’ of cigarette smoke determined the extent to which it was problematized.

‘Smoking is disturbing to the environment, it is polluting. It is air pollution because it is a negative consequence of an action that can be avoided’. (R8)

Discussion, Limitations, Further Research and Conclusion

The aim of this research was to understand the perception of air pollution by the public in the BCR. We investigated this perception through 4 sub-questions that approached the topic of perception each from a different but complementary angle: definition, association, categorisation and problematisation.

The first dimension investigated how the public defines air pollution. Data showed that respondents depict air pollution solely as a consequence of human activity, thereby portraying the car as the main source of air pollution without referring to specific pollutants such as NO 2 , O 3 or PM. This observation aligns with the findings of Dimitriou and Christidou. 19

A recurrent theme in definitions is the negative health impact of air pollution. Respondents refer to health in general terms and tend to link this negative impact to ‘vulnerable’ groups in society rather than to their own health. Furthermore, in line with earlier research, respondents refer in their definitions to sensory sources and manifestations of air pollution. The most frequent mentioned pollutant was PM, the most tangible of all. Intangible pollutants such as NO 2 , or SO 2 were not noticed at all.

The second dimension that we studied concerned the associations that air pollution evokes in the public. The gathered data partially overlapped with the data gathered on definitions but were complementary and gave more detail. Respondents seemed partial in identifying sources of air pollution and in identifying negative health outcomes derived from these sources. They also made associations that deviate from scientific knowledge and that demonstrated ambiguities and misunderstandings about air pollution. Finally, respondents seemed to perceive air pollution as an exclusively urban phenomenon caused by ‘the other’.

Related to the perception of the public about the health impact of air pollution, 3 insights are relevant. First, nevertheless the negative impact of air pollution was central in the definitions of, the associations with and the categorisation of air pollution, it was not mentioned by all respondents. In line with former research, a relatively big share of people does not consequently link air pollution to health problems. 19 , 26

Second, when reference is made to health impacts of air pollution this is done in a general and often partial way. Former research stated that people’s perceptions tend to be influenced less by scientifically derived information and more by local and personal experiences. 31 These experiences are more acute and relate more easily to respiratory complaints than other long-term impacts that are less obvious from the perspective of the public (eg, cognition and depressive symptoms). Third, respondents tend to link the negative health impact of air pollution to vulnerable groups rather than to their own health.

These insights contrast with the established scientific body of knowledge showing a diverse set of serious, often long lasting negative impacts of air pollution on health for all. These scientific insights obviously do not reach the public, which results in an under-estimation of the health impact associated with air pollution. Respondents clearly underestimate the probability and the severity of the harm resulting from air pollution. This has implications for their health risk perception. A study on the relationship between perceived likelihood of a threat, perceived severity of a threat and the motivation to act, established an interaction between likelihood and severity. The motivation to take precautions essentially vanished when either probability or severity was perceived as zero. 32 Also Bickerstaff and Walker 33 found that their respondents related air pollution to poor health at a general level and that only few identified health problems directly affecting themselves. People might not deny the health risk of air pollution but its personal effect as a psychological reaction to avoid psychic anxiety.

Our results also emphasise that perceptions of air pollution are context dependent. Respondents refer to pollution sources that are part of their daily lives and the society they are living in. The emphasis that Brussels respondents lay on the car as the most important source of air pollution and the agreement among them that PM resulting from traffic is air pollution is an illustration of this. Indeed, at the time of the interviews, there was a lot of debate about the polluting impact of cars and civil society (Filter Café Filtré) was protesting against car traffic near schools in different neighbourhoods across the city. It is illustrated that local actions, media and social networks can impact public perception about air pollution. 34 This partial focus on cars as the main source of air pollution might however result in an underestimation of the actual exposure to air pollution from other sources. For NO 2 , 44% of the concentrations in the BCR originate from traffic 35 but for PM 10 , 59% of the emissions is caused by the heating of buildings and (only) 38% by the transport sector. 36

The third dimension related to the categorisation of elements as being air pollution and the reasons behind this categorisation. Our research led to the identification of 5 mental schemes present during the categorisation and identification processes related to air pollutants. These schemes allow for a better understanding of the hidden, partly unconscious rationales behind such processes. However, there was no unanimity about the categorisation of elements as being air pollutants or not, except for PM caused by traffic.

This categorisation of elements as being air pollution did not happen in a vacuum but in a specific context that influenced this process through 5 mental schemes: the origin of the element, its health impact, its impact on the climate, sensory perceptions and functionality of the element.

Our respondents – especially the younger ones – seemed very concerned about the climate. At the time of the interviews, weekly manifestations were organised and frequented by many students to stress the importance of climate action. However, respondents blur the distinction between climate problems and environmental problems. This distinction made by the scientific community seems absent among the public. And indeed, nevertheless different problems, air pollution and climate change are intertwined. 37 Air quality is closely linked to the earth’s climate and ecosystems globally. Many of the drivers of air pollution (ie, combustion of fossil fuels) are also sources of greenhouse gas emissions. Policies to reduce air pollution, therefore, can for many pollutants offer a ‘win-win’ strategy for both climate and health, lowering the burden of diseases attributable to air pollution and contributing to the near- and long-term mitigation of climate change. 38 - 40 Linking the topic of air pollution to climate change in sensitising communications might thus increase the motivation of the public to support specific measures aimed at limiting air pollution.

Saksena 14 already stated that if air pollution is not recognised as such, one will not act upon it. We agree with this statement but argue that an extra step is required for action to be undertaken once air pollution has been ‘identified’ or recognised: problematisation. Therefore, a fourth dimension of the perception about air pollution that we studied was its problematisation.

Respondents tended to problematize or on the contrary to relativize the identified air pollution through comparative problematizing or through the perceived avoidability of the identified air pollution. We identified 3 dimensions of comparative problematisation, a spatial dimension, a time dimension and a source dimension. Related to the avoidability of the identified air pollution, it is often problematized when it is perceived as avoidable. Whereas when it is perceived as inevitable, it is often relativised.

The observed relativisation of the problematic character of (identified) air pollution through comparative problematizing aligns with a disassociation strategy that has been labelled by others as ‘othering’. 41

The observation that the avoidability of the identified air pollution is linked to its problematisation, aligns partly with earlier research. Xu et al. 21 found in this respect that when people feel powerless about an issue which they have to bear with, that they tend to allocate little concern to it.

Our research contributed to a better understanding of how the public in Brussels perceive air pollution.

This research illustrates that the notion of air pollution is difficult for the public to conceptualise. The public’s perceptions are diverse, subjective and often deviate from the way in which air pollution is conceptualised by the scientific community.

It should increase the awareness among experts and policy makers that perceptions about air pollution are far from universal and consensual but on the contrary individual and contested. These insights are highly relevant: to fight air pollution, it is key that all actors communicate at the same conceptual level. Important is that health promoters are/become aware that there might be a communication bias because of different perceptions about air pollution. There is indeed room and need for communication/information/sensitisation about the negative impacts of air pollution on health taking the severity and the probability of its impact into account, the different sources of air pollution, and the different ways to combat it.

To develop successful health campaigns and sensitisation strategies and to find carrying capacity for the implementation of policy measures to fight air pollution, an understanding of the perceptions of the ‘target group’ is key.

There is no room to elaborate on how these health campaigns and sensitisation strategies should look like concretely, but we think that it is worthwhile to give some relevant suggestions that were touched upon by our respondents during the interview. It is important to take into account the trustworthiness of the information sources, 31 , 41 the scale of the information, 33 , 42 , 43 the comprehensiveness of information 31 , 33 , 44 and the degree of affect in information. 45 , 46

Limitations and further research

This study has several limitations.

Firstly, a bias might have occurred resulting from the recruitment of the respondents. Those willing to do an interview – knowing that it was about public green spaces (they didn’t know in advance that another important part of the interview was on air pollution) – might have been more ‘nature-minded’ resulting in the recruitment of profiles that were more against the perceived main contributor of air pollution ‘the car’. However, since we were aware of this potential bias during the recruitment phase, we decided to provide an incentive of 15 euros in cash in order to also attract a diverse mix of people, some ecology-minded, some not. Some of the respondents motivated by this incentive to participate in the interview might have not been intrinsically motivated to participate but this was seen as an advantage to increase the diversity of profiles and perceptions in the research.

Secondly, concerning the problematisation of identified air pollution, our results were only partial since this topic was initially not the focus of our research and questions did not explicitly focus on this topic. However, since it appeared relevant, we dedicated attention to and reported about it. Other research explicitly focussing on the topic, identifies more factors that influence the concern related to air pollution such as personal health experiences, uncontrollability or powerlessness, crowding-out effects, perceived benefits, perceived fairness, delays of health effects and habituation. 21

The identification of the different mental schemes to categorise elements as being air pollution or not, is novel. Further research could further finetune and compare these results. First, it would be interesting to investigate through quantitative research methods, whether different social groups – in terms of age, sex, socioeconomic situation or socio-cultural background – tend to rely on specific mental schemes to further finetune understandings about how perceptions develop and their implications for targeted health campaigns and sensitisation strategies. Another interesting research project could investigate whether different social groups have different associations with air pollution. Secondly, if perceptions are context-dependent, it would be interesting to repeat this research in a totally different social, cultural or political context. In this respect Douglas 47 developed a ‘cultural theory’ of risk in which she considers dirt – and pollution – as a ‘matter out of place’ in terms of the range of powers and dangers symbolically constructed in a cultural universe. She states that dirt is not a unique and isolated phenomenon. Where there is dirt, there is a symbolic system. Dirt is the by-product of an organisation and of a classification of matters that causes the rejection of non-appropriate elements. These elements are not on the right place according to a dominating or ruling symbolic system. Repeating this research in other places, in another cultural universes with other symbolic systems might be interesting to pinpoint differences and analogies between them.

This qualitative research investigated how the public in Brussels perceives air pollution and is an attempt to enrich the limited body of qualitative research in the field. We studied this perception from 4 different, complementary angles: definition, association, categorisation and problematisation.

This research illustrates that the notion of air pollution is difficult for the public to conceptualise and that their perceptions are diverse, subjective, context dependent and often deviate from conceptualisations and definitions by the scientific community.

Respondents underestimate the probability and severity of the harm involved and its problematisation depends on comparative strategies and its perceived avoidability. We identified 5 mental schemes by means of which elements are categorised, or not categorised by respondents as being air pollution: (1) the source of the element, (2) the health impact of the element, (3) the impact of the element on the climate, (4) sensory perceptions and (5) the functionality of the element.

We hope to have contributed to a better understanding of how the public in Brussels perceives air pollution and to an increased awareness among experts and policy makers that perceptions about air pollution are far from universal and consensual but on the contrary individual and contested. After all, these understandings and awareness are key in order to fight air pollution in a successful way through the development of effective and targeted health campaigns, sensitisation strategies in order to create common ground for the implementation of measures to fight air pollution successfully.

Appendix 1.

Characteristics respondents.

Additional information on which elements were perceived as air pollution and why?

In order to answer research questions 3 and 4, the answers to the following question were analysed: ‘I am going to list some elements. I am going to ask you each time, is this element according to you air pollution yes or no? Then I am going to ask you why you think this element is or is not air pollution. This question is not intended as a test. It does not matter whether your answer is right or wrong. All I want to understand is the reasoning behind your answer’.

This question was not meant to gauge the respondent’s knowledge, but to get a picture on the basis of which mental schemes the respondent does or does not perceive a specific element as being air pollution. The following elements were discussed (always in the same order):

• Particulate matter caused by forest fires
• Cigarette smoke (secondary smoke)
• Particulate matter caused by wood burning (stove)
• Ammonia from manure
• Methane caused by the intestinal system of livestock
• Water vapour
• Particulate matter caused by traffic
• Particulate matter caused by volcanic eruptions

The different elements were chosen in such a way that there was variation in different pollutants. We also created a variation of sources within the same element ‘particulate matter’ and a variation of sources within the particulate matter pollutant (forest fires, wood burning, traffic, volcanic eruptions). We also provided variation in elements from natural sources and from anthropogenic sources. We also included elements that are by definition not air pollution (pollen, water vapour) but are potentially perceived as such.

In order to avoid guessing and meaningless reflections, the respondent was clearly told that if he or she really had no idea to what extent the element was or was not air pollution, or even did not know the element, this was no problem and would not be further asked. For example, there were several respondents who could not say anything about ammonia from manure and methane caused by the intestinal system of cattle. Also, not everyone was familiar with the element particulate matter.

Furthermore, it should be specified that respondents did not find this an easy exercise and their answers were often formulated in the form of ‘I think’ rather than ‘I am sure’.

Within the framework of this exercise, respondents were also confronted with inconsistencies of their own answers in order to obtain a deeper reflection and more refined answers.

Appendix 3. Frequency table for categorising elements as air pollution or not

Appendix 4. An overview of the mental schemes used per element (an ‘X’ indicates that for the elements in the left column, the classification of an element as air pollution happened or through ‘knowledge’ that an element is air pollution [first column] or through the application of different mental schemes)

1. Elements here refer to particulate matter from different sources, smoke, pollen, ammonia, methane and water vapour. These elements form the basis for our analysis. Elements should thus not be understood as defined from a chemistry or physics point of view as substances that cannot be broken down into simpler components by any non-nuclear chemical reactions.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: from Innoviris, the regional institute for research and innovation of the Brussels-Capital Region for the research reported in this article.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

New research shows improving air quality isn’t a simple fix in Fairbanks

ANCHORAGE, Alaska (KTUU) - Winter air quality has been an issue in the Fairbanks area for decades. Toward the end of 2023, the Environmental Protection Agency (EPA) said the air quality has improved but there is still work to be done to meet federal standards for particulate matter.

One action taken to improve air quality is the use of low-sulfur heating fuel, but new research from the University of Alaska Fairbanks shows it’s not that simple.

While low sulfur fuel reduces the amount of sulfates in the air, the researchers found the PH of the air is increased, which encourages the formation of other fine particles from a different chemical.

“It makes the picture of air quality in Fairbanks more complicated,” James Campbell, a doctoral student at UAF, said. “It makes it more difficult than just reducing one thing, because there are potentially other things are going to be formed when you do that.”

Campbell says the goal is to improve air quality in Fairbanks. He’s currently trying to determine the source of formaldehyde in the air which is a component of the chemicals being formed.

Listen to the full conversation on In Depth Alaska.

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