case study of delhi pollution

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What is polluting delhi’s air a review from 1990 to 2022.

1. Introduction

2. geography and meteorology, 3. ambient air quality, 3.1. ground measurements, 3.1.1. pre-2006 period, 3.1.2. 2006–2018 period, 3.1.3. post-2018 period, 3.2. satellite observations and reanalysis, 4. receptor, source, and other modelling studies, 4.1. source apportionment, 4.2. air quality forecasting and alert systems.

• The Copernicus Atmosphere Monitoring Service (CAMS) forecasting system is a service provided by the European Union ( https://atmosphere.copernicus.eu/data , accessed 22 February 2023) that uses a combination of mathematical models and satellite data to provide air quality forecasts at 40 km spatial resolution globally and at 10–12 km spatial resolution for select regions. It is designed to provide reliable forecasts of air quality across Europe with the use of the ESA’s geostationary satellite data. The global data can be visualised at https://www.windy.com (accessed 22 February 2023). The CAMS reanalysis archives from 1990 are also available for studying long-term trends;
• The Early Warning System (EWS) for Delhi by IITM is hosted at https://ews.tropmet.res.in (accessed 22 February 2023) and includes results from the WRF-Chem regional model and GEOS and WACCM global modelling systems as a combination of national, region, and cit- level hourly maps, time series, and comparison with data from the CPCB’s monitoring network. The system also includes the forecast of fog onset and visibility for Delhi and a summary of air quality forecasts for other cities;
• The NASA-GEOS system is operated by the Global Modelling and Assimilation Office (GMAO) to support a wide range of applications, including air, weather, and climate modelling ( https://gmao.gsfc.nasa.gov , accessed 22 February 2023). A 10-day air quality forecast for Delhi from the GEOS-5 model is included on the EWS portal. Like CAMS, the GEOS system also includes a data assimilation system (GEOS-DAS) with reanalysis archives from 1990 for studying long-term trends (like MERRA-2— https://giovanni.gsfc.nasa.gov/giovanni , accessed 22 February 2023);
• SAFAR ( https://safar.tropmet.res.in , accessed 22 February 2023) uses a combination of on-ground measurements, emission inventories, and mathematical models to predict air quality for the next three days. Since its inception for Delhi, the model has been replicated for the cities of Mumbai, Pune, and Ahmedabad;
• SILAM (System for Integrated modeLing of Atmospheric composition) is a global chemical transport model developed and maintained by the Finnish Meteorological Institute (FMI). As part of a memorandum of understanding, FMI shares air quality forecasts customised for the NCR Delhi region with the Indian Meteorological Department (IMD). These results are also included on the EWS portal;
• The Urban Emissions program (by the authors) uses the WRF-CAMx modelling system covering the Indian Subcontinent and Delhi’s airshed as a nest. The city results are shared at https://www.delhiairquality.info (accessed 22 February 2023) in the form of hourly and daily average maps, city-level hourly and daily average PM 2.5 source apportionment, district-level concentration and source apportionment time series, and real-time (updated every 6 h) comparison of results with data from CPCB’s monitoring network.

5. Sectoral History

5.1. transport sector, 5.1.1. vehicle and fuel standards, 5.1.2. pollution-under-check (puc) programme, 5.1.3. public transportation and cng introduction, 5.1.4. bus rapid transit (brt) system, 5.1.5. metrorail system, 5.1.6. para-transit system, 5.1.7. odd–even experiment, 5.1.8. electric vehicle (ev) promotion, 5.1.9. new expressways, 5.2. agricultural waste burning, 5.3. residential emissions, 5.4. waste management, 5.5. construction sector, 5.6. road dust, 5.7. electricity consumption and load sharing, 5.8. diwali firecrackers, 6. judicial and institutional engagement, 6.1. role of the judicial system, 6.1.1. environment pollution (prevention and control) authority (epca), 6.1.2. diesel to cng conversion, 6.1.3. diwali firecracker ban, 6.1.4. leapfrogging from bs4 to bs6 vehicle emission and fuel standards, 6.1.5. petcoke ban, 6.1.6. installation of smog towers, 6.1.7. national green tribunal (ngt).

• Vardhaman Kaushik vs. Union of India case [ 140 ]—the NGT ordered de-registration of all diesel vehicles older than 10 years and all petrol vehicles older than 15 years.
• Smt. Ganga Lalwani vs. Union of India and Ors. case [ 141 ]—the NGT took cognisance of crop burning as a significant cause of Delhi’s air pollution and ordered various steps to reduce crop burning in adjoining states. These include converting crop waste into organic manure, use of ISRO’s services to alter lice on crop burning incidents, etc.
• Almitra H. Patel and Ors. vs. Union of India [ 142 ]—the NGT prohibited open burning of waste and directed all states to implement the solid waste management rules. Using its authority, the NGT under the polluter pays principle, in October 2022, imposed an environment compensation fee of INR 9,000,000,000 on the Delhi government for undisposed waste in its landfills.
• Mayank Manohar and Paras Singh vs. Government of Delhi and Ors. [ 143 ]—the NGT directed the government to immediately shut down 4770 industrial units running illegally in the residential areas of Delhi and directed it to adopt coercive measures to recover compensation for illegal operation of such units in accordance with law apart from prosecution.

6.2. Role of Union Government

6.2.1. graded response action plan (grap).

• When AQI conditions land in the poor category, actions include ensuring strict enforcement of controls on garbage burning, brick kilns, power plants, ash ponds, construction sites, fireworks, and periodic wet sweeping of roads; vigilance on polluting vehicles, vehicles touting PUC norms and out of state trucks; deploying more traffic police; and posting information on social media.
• When AQI conditions land in the very poor category, actions include banning diesel generator sets, increasing parking fees, increasing bus services, stopping coal and wood burning at hotels, opening eateries and stationing guards at markets in residential areas, and increasing public awareness.
• When AQI conditions land in the severe category, actions include shutting down brick kilns, hot-mix plants, stone crushers, and power plants, intensifying public transport services, and wet-sweeping roads more frequently.
• Under emergency conditions, actions include closing entry of non-commodity trucks, closing all construction activities, introducing the odd–even formula, and additional measures as the authority sees fit (for example, in January 2023, all coal use was banned in the NCR region).

6.2.2. National Clean Air Programme (NCAP)

6.2.3. commission for air quality management (caqm), 6.2.4. fifteenth finance commission grant (xvfc), 7. final remarks, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Guttikunda, S.K.; Dammalapati, S.K.; Pradhan, G.; Krishna, B.; Jethva, H.T.; Jawahar, P. What Is Polluting Delhi’s Air? A Review from 1990 to 2022. Sustainability 2023 , 15 , 4209. https://doi.org/10.3390/su15054209

Guttikunda SK, Dammalapati SK, Pradhan G, Krishna B, Jethva HT, Jawahar P. What Is Polluting Delhi’s Air? A Review from 1990 to 2022. Sustainability . 2023; 15(5):4209. https://doi.org/10.3390/su15054209

Guttikunda, Sarath K., Sai Krishna Dammalapati, Gautam Pradhan, Bhargav Krishna, Hiren T. Jethva, and Puja Jawahar. 2023. "What Is Polluting Delhi’s Air? A Review from 1990 to 2022" Sustainability 15, no. 5: 4209. https://doi.org/10.3390/su15054209

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• Externally hosted supplementary file 1 Doi: 10.5281/zenodo.7595761 Link: https://doi.org/10.5281/zenodo.7595761 Description: Data: Ambient Air Quality, Reanalysis Fields, Satellite Retrievals, and Emissions Support Information for Delhi

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Delhi, the world’s most air polluted capital fights back

Subscribe to global connection, vinod thomas and vinod thomas distinguished fellow - asian institute of management, manila, former senior vice president - world bank chitranjali tiwari ct chitranjali tiwari associate fellow - jk lakshmipat university, jaipur.

November 25, 2020

After an unexpected respite as coronavirus lockdowns stalled economic activity, air  pollution  has returned to  pre-COVID-19 levels in Delhi, the world’s most air polluted capital city  (Figure 1).

Last month, ahead of the usual spike in winter, the Delhi administration launched an antipollution campaign. But to win, nothing short of sustained action on  multiple fronts  will suffice. Other Asian capitals too have faced pollution crises. But Delhi’s is extreme because of a combination of smoke from thermal plants and brick kilns in the capital region, effluents from a congested transportation network, stubble or biomass burning by farmers in neighboring states, and the lack of cleansing winds that causes air pollution to hang over the city. Even as technical solutions are within reach, the campaign must overcome the poor policy coordination among central, city, and local governments.

Delhi’s toxic haze is a deadly health risk to its residents, particularly children, the elderly, and the ill. Particulate matter—PM2.5 and PM10—far exceeds national and World Health Organization limits and is the  main culprit  for Delhi’s high incidence of cardiovascular damage. The city’s toxic air also contains high quantities of sulfur dioxide, nitrogen oxide, and carbon monoxide, putting people at  higher risk  of strokes, heart attacks, and high blood pressure, and worsening the respiratory complications from  COVID-19.

The main sources of Delhi’s particulate emissions are, in equal measure, particles from large power plants and refineries, vehicles, and stubble burning. The experiences of Bangkok, Beijing, and Singapore suggest that an ambitious but feasible goal is to cut air pollution by one-third by 2025, which, if sustained, could extend people’s  lives  by two to three years. The current effort is designed to confront all three sources, but strong implementation is needed.

Delhi is moving simultaneously on three fronts: energy, transport, and agriculture. In each case, East Asia offers valuable lessons.

• Coal-fired plants. Delhi’s environment minister has called for the closure of  11 coal-fired power plants operating within 300 kilometers of Delhi. But policy implementation must improve: All the plants have missed two deadlines to install flue-gas desulfurization units to reduce sulfur dioxide emissions. Last year,  10 coal-fired  power plants missed a December deadline to install pollution control devices.  Beijing provides valuable lessons in cutting concentrations of PM2.5 more than  40 percent  since 2013. Beijing substituted its four major coal-fired stations with natural gas plants. The city government ordered  1,200 factories  to shut with stricter controls and inspections of emitters.  Bangkok  had success with its inspection and maintenance program.
• Cleaner transport . Delhi has tried  pollution checking of vehicles by mobile enforcement teams, public awareness  campaigns , investment in mass rapid transport systems, and phasing out old commercial vehicles. The Delhi government’s recent  push  for electric vehicles shows promise, while the response of industry and the buy-in from customers will be key. Overall results in cutting pollution have been weak because of poor governance at every level. Better outcomes will be predicated on investment in public transportation, including integration of transport modes and last-mile connectivity. Unfortunately, Delhi Transport Corporation’s fleet  shrank  from 6,204 buses in 2013 to 3,796 buses in 2019, with most of the bus fleet aging. Delhi should look at  Singapore’s  regulation on car ownership and use; its improved transit systems; and promotion of pedestrian traffic and nonmotorized transport.
• Better farming practices . Burning of crop stubble in Delhi’s neighboring states has become a serious source of  pollution in the past decade. In 2019, India’s Supreme Court ordered a complete halt to the practice of stubble burning and reprimanded authorities in two of these states, Punjab and Haryana, for allowing this illegal practice to continue. Needed is the  political will to act , as poor farmers complain that they receive no financial support to dispose of post-harvest stubble properly. Delhi’s  “Green War Room”  signaling the fight against the smog, is analyzing satellite data on farm fires from Punjab and Haryana to identify and deal with the culprits. The  Indian Agricultural Research Institute  has proposed a low-cost way to deal with the problem of stubble burning by spraying a chemical solution to decompose the crop residue and turn it into manure. Better coordination is needed. In 2013, when  Singapore  faced a record-breaking haze due to agricultural waste burning in neighboring countries, the Environment Agency and ministries of education and manpower together issued guidelines based on a Pollution Standards Index to minimize the health impacts of haze.  Stubble burning  has been banned or discouraged in China, the United Kingdom, and Australia.

Delhi, projected to be the world’s most  populous  city by 2030, is motivated by a sense of urgency. Facing a growing environmental and health calamity, antipollution efforts are being strengthened. But to succeed, the different levels of government must harness the political will to invest more, coordinate across boundaries, and motivate businesses and residents to do their bit.

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“Air pollution in Delhi: Its Magnitude and Effects on Health”

Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India

Baridalyne Nongkynrih

Sanjeev kumar gupta.

Air pollution is responsible for many health problems in the urban areas. Of late, the air pollution status in Delhi has undergone many changes in terms of the levels of pollutants and the control measures taken to reduce them. This paper provides an evidence-based insight into the status of air pollution in Delhi and its effects on health and control measures instituted. The urban air database released by the World Health Organization in September 2011 reported that Delhi has exceeded the maximum PM10 limit by almost 10-times at 198 μ g/m3. Vehicular emissions and industrial activities were found to be associated with indoor as well as outdoor air pollution in Delhi. Studies on air pollution and mortality from Delhi found that all-natural-cause mortality and morbidity increased with increased air pollution. Delhi has taken several steps to reduce the level of air pollution in the city during the last 10 years. However, more still needs to be done to further reduce the levels of air pollution.

Pollution refers to the contamination of the earth's environment with materials that interfere with human health, quality of life or the natural functioning of the ecosystems. The major forms of pollution include water pollution, air pollution, noise pollution and soil contamination. Other less-recognised forms include thermal pollution and radioactive hazards. It is difficult to hold any one particular form responsible for maximum risk to health; however, air and water pollution appear to be responsible for a large proportion of pollution related health problems.

Of late, the air pollution status in Delhi has undergone many changes in terms of the levels of pollutants and the control measures taken to reduce them. This paper provides an evidence-based insight into the status of air pollution in Delhi and its effects on health and control measures instituted.

Status of Air Pollution in Delhi

Delhi (or the National Capital Territory of Delhi), is jointly administered by the central and state governments. It accommodates nearly 167.5 lakh people (2011 Census of India).( 1 )

Metros across the world bear the major brunt of environmental pollution; likewise, Delhi is at the receiving end in India.

A study funded by the World Bank Development Research Group was carried out in 1991-1994 to study the effects of air pollution.( 2 ) During the study period, the average total suspended particulate (TSP) level in Delhi was approximately five-times the World Health Organization's annual average standard. Furthermore, the total suspended particulate levels in Delhi during this time period exceeded the World Health Organization's 24-h standard on 97% of all days on which readings were taken. The study concluded that the impact of particulate matter on total non-trauma deaths in Delhi was smaller than the effects found in the United States of America, but found that a death associated with air pollution in Delhi caused more life-years to be lost because these deaths were occurring at a younger age.

A report by the Ministry of Environment and Forests, India, in 1997 reviewed the environmental situation in Delhi over concerns of deteriorating conditions.( 3 ) Air pollution was one of the areas of concern identified in this study. It was estimated that about 3000 metric tons of air pollutants were emitted every day in Delhi, with a major contribution from vehicular pollution (67%), followed by coal-based thermal power plants (12%). There was a rising trend from 1989 to 1997 as monitored by the Central Pollution Control Board (CPCB). The concentrations of carbon monoxide from vehicular emissions in 1996 showed an increase of 92% over the values observed in 1989, consequent upon the increase in vehicular population. The particulate lead concentrations appeared to be in control; this was attributable to the de-leading of petrol and restrictions on lead-handling industrial units. Delhi has the highest cluster of small-scale industries in India that contribute to 12% of air pollutants along with other industrial units.

Vehicular pollution is an important contributor to air pollution in Delhi. According to the Department of Transport, Government of National Capital Territory of Delhi, vehicular population is estimated at more than 3.4 million, reaching here at a growth rate of 7% per annum. Although this segment contributes to two-thirds of the air pollution, there has been a palpable decline compared to the 1995-1996 levels.

The PM 10 standard is generally used to measure air quality. The PM 10 standard includes particles with a diameter of 10 μm or less (0.0004 inches or one-seventh the width of a human hair). These small particles are likely to be responsible for adverse health effects because of their ability to reach the lower regions of the respiratory tract. According to the Air Quality Guideline by the World Health Organization, the annual mean concentration recommended for PM 10 was 20 μg/m 3 , beyond which the risk for cardiopulmonary health effects are seen to increase.( 4 ) Major concerns for human health from exposure to PM 10 include effects on breathing and respiratory systems, damage to lung tissue, cancer and premature death. Elderly persons, children and people with chronic lung disease, influenza or asthma are especially sensitive to the effects of particulate matter. The urban air database released by the World Health Organization in September 2011 reported that Delhi has exceeded the maximum PM 10 limit by almost 10-times at 198 μg/m 3 , trailing in the third position after Ludhiana and Kanpur.( 5 ) Vehicular emissions and industrial activities were found to be associated with indoor as well as outdoor air pollution in Delhi [ Table 1 ].( 6 – 9 )

Air pollutants in Delhi

Effects of Air Pollution on Health

A large number of studies in Delhi have examined the effect of air pollution on respiratory functions and the associated morbidity. The most comprehensive study among them was the one conducted by the Central Pollution Control Board in 2008, which identified significant associations with all relevant adverse health outcomes.( 10 ) The findings were compared with a rural control population in West Bengal. It was found that Delhi had 1.7-times higher prevalence of respiratory symptoms (in the past 3 months) compared with rural controls ( P < 0.001); the odds ratio of upper respiratory symptoms in the past 3 months in Delhi was 1.59 (95% CI 1.32-1.91) and for lower respiratory symptoms (dry cough,wheeze, breathlessness, chest discomfort) was 1.67 (95% CI 1.32-1.93). Prevalence of current asthma (in the last 12 months) and physician-diagnosed asthma among the participants of Delhi was significantly higher than in controls. Lung function was reduced in 40.3% individuals of Delhi compared with 20.1% in the control group. Delhi showed a statistically significant ( P < 0.05) increased prevalence of restrictive (22.5% vs. 11.4% in control), obstructive (10.7% vs. 6.6%) as well as combined (both obstructive and restrictive) type of lung functions deficits (7.1% vs. 2.0%). Metaplasia and dysplasia of airway epithelial cells were more frequent in Delhi, and Delhi had the greater prevalence of several cytological changes in sputum. Besides these, non-respiratory effects were also seen to be more in Delhi than in rural controls. The prevalence of hypertension was 36% in Delhi against 9.5% in the controls, which was found to be positively correlated with respirable suspended particulate matter (PM 10 ) level in ambient air. Delhi had significantly higher levels of chronic headache, eye irritation and skin irritation.

Several other community-based studies have found that air pollution is associated with respiratory morbidity.( 11 – 13 ) Numerous studies have reported an association between indoor air pollution and respiratory morbidity.( 14 – 19 )Some of these studies have concentrated on children's respiratory morbidity.( 15 , 17 , 19 ) Other studies in children have found similar correlations between particulate matter in ambient air and attention-deficit hyperactivity disorder( 20 ) between vehicular air pollution and increased blood levels of lead (a potential risk factor for abnormal mental development in children)( 21 ) and between decreased serum concentration of vitamin D metabolites and lower mean haze score (a proxy measure for ultraviolet-B radiation reaching the ground).( 22 )

Studies that have examined the compounding effect of meteorological conditions on air pollution found that winter worsened the air quality of both indoor air and outdoor air. They also found a positive correlation between the winter weather and rise in the number of patients with chronic obstructive airway disease in hospitals.( 12 , 16 )

There was a relative paucity of studies that measured outdoor air pollutant levels first hand and then tried to objectively correlate them to adverse health effects. However, some studies measured air pollutant levels and found a correlation with health-related events.( 17 , 19 )

A time-series study on air pollution and mortality from Delhi found that all-natural-cause mortality increased with increased air pollution.( 23 ) In another study, gaseous pollutants, in spite of being at a level lower than the permissible level, showed more consistent association with respiratory admissions.( 24 ) In a hospital-based study, an increase in emergency room visits for asthma, chronic obstructive airway disease and acute coronary events was reported with an increase in air pollutant levels.( 25 ) These studies are summarized in Table 2 .

Effects of air pollution in Delhi on health

Control Measures Instituted by the Government of Delhi

The nodal ministry for protecting the environment is the Ministry of Environment and Forests at the Centre and the Department of Environment of the Government of National Capital Territory of Delhi. The Central Pollution Control Board set up in 1974 under the Water Act is the principal watchdog for carrying out the functions stated in the environmental acts, implementation of National Air Quality Monitoring Programme and other activities. The Delhi Pollution Control Board is the body responsible at the state level.

From time to time, the judiciary has taken strong note of the deteriorating environmental conditions in Delhi in response to public litigations. One of the earliest such instances was the judgement passed by the Supreme Court of India to deal with the acute problem of vehicular pollution in Delhi in response to a writ petition filed in 1985. Subsequently, it ordered the shutdown of hazardous, noxious industries and hot-mix plants and brick kilns operating in Delhi.

Vehicular Policy

Control measures so far instituted include introduction of unleaded petrol (1998), catalytic converter in passenger cars (1995), reduction of sulfur content in diesel (2000) and reduction of benzene content in fuels (2000). Others include construction of flyovers and subways for smooth traffic flow, introduction of Metro rail and CNG for commercial transport vehicles (buses, taxis, auto rickshaws), phasing out of very old commercial vehicles, introduction of mandatory “Pollution Under Control” certificate with 3-month validity and stringent enforcement of emission norms complying with Bharat Stage II/Euro-II or higher emission norms. Introduction of The Air Ambience Fund levied from diesel sales and setting up of stringent emission norms for industries and thermal power stations are the other measures. Environmental awareness campaigns are also carried out at regular intervals. The Delhi Pollution Control Board conducts monthly Ambient Air Quality Monitoring at 40 locations in Delhi, and takes corrective action wherever necessary.

Industrial Policy

The first Industrial Policy for Delhi was introduced in 1982. Subsequently, a second Industrial policy (2010–2021) was issued by the Department of Industries, Government of Delhi. It is a comprehensive document envisioning higher industrial development in Delhi, with one of its mandates being to develop clean and non-polluting industries and details of steps to be undertaken in this direction have been described.

There are many other organizations that work synergistically with the government efforts to reduce air pollution. These include the Centre for Science and Environment and The Energy and Resources Institute, and the Indian Association for Air Pollution Control. Representatives of the industries include Confederation of Indian Industry and Society of Indian Automobile Manufacturers. Government agencies like Factories Inspectorate are also involved in the control of pollution. Research and academic institutions include National Environmental Engineering Research Institute, Indian Institute of Technology, Council of Scientific and Industrial Research institutions, Indian Agricultural Research Institute and various other academic institutions in and around Delhi. Professional organizations like the Indian National Science Academy, the Indian Institute of Chemical Engineers and the Indian Institute of Engineers are also involved in pollution control.

Benefits Accrued as a Result of Control Measures

Since the first act on pollution was instituted, huge progress has been made in terms of human resource, infrastructure development and research capability. Some studies tried to gather evidence for the effectiveness of control measures by comparing pre- and post-intervention health status. The study conducted by the Central Pollution Control Board demonstrated that spending 8-10 h in clean indoor environment can reduce health effects of exposure to chronic air pollution.( 10 ) A recent study found significant improvement in the respiratory health following large-scale government initiatives to control air pollution.( 26 ) It was reported that use of lower-emission motor vehicles resulted in a significant gain in disability-adjusted life-years in Delhi.( 27 ) Another study found significant evidence for reduction in respiratory illness following introduction of control measures.( 24 )

Most of the studies were ecological correlation studies, which are severely limited in their ability to draw causal inferences. But, considering the context that demanded the research, these were probably the best available designs to produce preliminary and,sometimes, policy-influencing evidences, as any other methodology would be unethical or operationally impossible.

The Government of National Capital Territory of Delhi has taken several steps to reduce the level of air pollution in the city during the last 10 years. The benefits of air pollution control measures are showing in the readings. However, more still needs to be done to further reduce the levels of air pollution. The already existing measures need to be strengthened and magnified to a larger scale. The governmental efforts alone are not enough. Participation of the community is crucial in order to make a palpable effect in the reduction of pollution. The use of public transport needs to be promoted. The use of Metro rail can be encouraged by provision of an adequate number of feeder buses at Metro stations that ply with the desired frequency. More frequent checking of Pollution Under Control Certificates needs to be undertaken by the civic authorities to ensure that vehicles are emitting gases within permissible norms. People need to be educated to switch-off their vehicles when waiting at traffic intersections. Moreover, the “upstream” factors responsible for pollution also need to be addressed. The ever-increasing influx of migrants can be reduced by developing and creating job opportunities in the peripheral and suburban areas, and thus prevent further congestion of the already-choked capital city of Delhi.

Health, as we all know, is an all-pervasive subject, lying not only within the domains of the health department but with all those involved in human development. Many great scholars from Charaka to Hippocrates have stressed the importance of environment in the health of the individual. Therefore, all those who play a role in modifying the environment in any way, for whatever reason, need to contribute to safeguard people's health by controlling all those factors which affect it.

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Conflict of Interest: None declared.

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India’s Air-pocalypse: Understanding the air pollution crisis in Delhi and beyond

A toxic spectre is haunting the nation. how shall we tackle it.

Published : Nov 06, 2023 11:55 IST - 2 MINS READ

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Students wear masks amid dense smog near the India Gate in New Dellhi on November 4, 2023. | Photo Credit: PTI

As of November 6, the air quality in Delhi remains critically poor, according to the System of Air Quality and Weather Forecasting and Research (SAFAR)-India. Reports suggest that this situation is expected to deteriorate further. Pollution levels in and around Delhi have consistently been in the “critical” category, with air quality index (AQI) readings in the range of 400 at various locations for the past few days. On the morning of November 6, SAFAR recorded an overall average AQI of 471. In response, the Delhi government has implemented a “work from home” policy for 50 per cent of government employees as part of their pollution control plan.

Data from the Central Pollution Control Board reveals that Delhi’s AQI has worsened by more than 200 points since October 27. The most severe air quality was reported on November 3, surpassing the previous high of 471 recorded on November 12, 2021. There has been some improvement due to better wind speed, but a dense and toxic haze still blankets the national capital for the sixth consecutive day.

Unfortunately, the issue of air pollution is not a new one and is not limited to Delhi or the National Capital Region (NCR). Several cities in India have been grappling with air pollution for decades, with little substantial action taken beyond political and policy discussions.

In the case of Delhi, there have been numerous studies, reports, papers, seminars, monographs, speeches, and explanations on the subject. A recent study has identified Delhi as the most polluted city in the world, with residents potentially losing a significant portion of their lifespan due to pollution. The study, called the Air Quality Life Index (AQLI), was published in August 2023 by the Energy Policy Institute at the University of Chicago. It also indicated that the entire Indian population of 1.3 billion people lives in areas with an annual average particulate pollution level exceeding the WHO’s limit of 5 μg/m3.

Still, nothing much has been done to check air pollution and the issue continues to persist like a haunting spectre in India. In this context, we have selected a collection of insightful stories from our archives to help you better understand the air pollution issue and make informed decisions regarding your use of fossil fuels. Please feel free to share your comments with us at [email protected].

Delhi's air pollution: Taming a killer

An odd (-even) formula

Season of smog: Not just Delhi, many north Indian cities are suffering

Breathing space for the capital

A scripted performance in Chandigarh puts stubble burning in focus

Mumbai in a haze: Pollution hits alarming levels

Maharashtra has the second-highest number of air pollution–related deaths in the country, air pollution can affect bone density.

Greenpeace study claims that Visakhapatnam and Hyderabad air most toxic in south India

India ranks at the bottom in a list of 180 countries in the 2022 Environmental Performance Index

India in search of clean air, global call to tackle air pollution.

India’s record heatwave vows to return: Can we survive the next?

Editor’s Note: We need a bigger, better heat action plan

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A vision to tackle Delhi’s dirty air

When thirty-eight-year-old Jasmine Shah moved to Delhi in 2013 and found himself engulfed in the city’s toxic winter smog, everything changed for him. 

“This scale of air pollution in Delhi, and the fact that it is among the top ten most polluted cities in the world, resulted in my urgent call to tackle the issue,” said Shah.

Targeting public transport seemed like the most logical way to start, with potential for bigger impact than tackling individuals, and Shah began by working with the government, creating momentum for policy change and more electric buses.

From UN Environment, Valentin Foltescu, Senior Programme and Science Officer of the Climate & Clean Air Coalition, added: 

“If current policies aimed at reducing air pollution are effectively enforced, air quality will be no worse in 2030 than now, despite population growth, rapid urbanization and an ever-increasing demand for goods and services, but neither will air quality be better.

“What do we need then to make the air breathable amid rapid economic development? We must move quickly to low and zero emissions solutions and comprehensive transformations. We should aim for full electrification of sectors in combination with clean energy.”

Today, as Vice Chairperson of the Dialogue and Development Commission of Delhi, an internal think tank of the Delhi government, we asked Shah what advice he has for young people interested in working alongside government towards effective policy change.

What inspired you to tackle air pollution?

A couple of winters in Delhi brought me face to face with the toxic smog that engulfs Delhi during this period. I realized that we needed more long-term measures to tackle air pollution since what we have is a health emergency in India. Particulate matter—hazardous pollution particles suspended in the air—is highly concentrated in most Indian cities and nearly 10–15 times the World Health Organization’s safety limits. And unfortunately, the problem disproportionately affects children and the elderly. Something had to be done.

What inspired you to get involved in the transport industry specifically?

The city of Delhi is well devoted to tackling air pollution and the government had already prioritized this issue on their agenda. In 2016, Delhi became the first city in India to launch a very successful Odd-Even scheme as an emergency measure to restrict the movement of private vehicles on polluted days. But since the transport sector is a top contributor to particulate emissions, I was motivated to look for long-term solutions. In February 2018, I advised the Delhi government in preparation of a comprehensive Green Budget—a 26-point long-term action plan to tackle air pollution in Delhi, with a major focus on the transport sector. Two of the most important initiatives in this were the introduction of 1,000 electric buses in Delhi and a comprehensive electric vehicle policy.

What challenges have you faced in introducing electric buses to Delhi?

With the political will already in place, the challenges we faced in Delhi were largely infrastructural and operational. We had to undertake a detailed study of the city and its transport networks before planning the electric bus routes. We undertook site visits to China to learn from best practices, since no other city outside of China has committed to bringing such a large number of electric buses. I am happy to say that we have already initiated procurement of 385 e-buses for Delhi, which should arrive by November. By 2020, we aim to bring in 1,000 e-buses.

What is your advice to youth interested to inspire change through policy action?

I encourage all young people to be persistent, creative and innovative. We have the know-how to beat air pollution. Build networks and connect with relevant people whose vision aligns with yours, and together we can combat air pollution. I believe this is achievable but requires patience and hard work.

What is your hope for the future in terms of tackling air pollution?

Air pollution is a complex problem. There are various sources of pollutants—dust, transport, industrial emissions. A comprehensive set of strategies is required to tackle the problem, and our vision for the future in Delhi is to work on all of these. In the transport sector, we are taking many steps to work towards a zero-carbon city and to ensure 25 per cent of all new vehicles in Delhi are electric by 2024. We are developing more pedestrian walkways and cycle lanes in the city to encourage walking and cycling. My vision for the future of India is to see the political leadership at national level fully acknowledge air pollution as a health emergency and act upon it. There are many solutions to tackle air pollution but we need political commitment and specific budgets and action plans to address this rising health issue.

Air pollution is the theme for  World Environment Day  on 5 June 2019 hosted by China. The quality of the air we breathe depends on the lifestyle choices we make every day. Learn more about how air pollution affects you, and what is being done to clean the air. What are you doing to reduce your emissions footprint and #BeatAirPollution? 

The 2019 World Environment Day is hosted by China.

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Battling the Winter Smog: Delhi's Pollution Predicament

• 28 Oct 2023
• 11 min read
• GS Paper - 2
• Government Policies & Interventions
• Environmental Pollution & Degradation

This editorial is based on “Delhi's battle against pollution” which was published in The Hindustan Times on 24/10/2023. It talks about the deteriorating air quality and the factors that contribute to the pollution crisis.

For Prelims: Air quality index , SAFAR , stubble burning, Smog , Temperature inversion,

For Mains: Delhi Air pollution and its causes, Government initiatives and Way Forward

Recently, Delhi got a trailer of the inevitable environmental misery that awaits it in the coming months: Air pollution. On Some day, the air quality index surpassed 300 on a scale topping out at 500, as a blanket of haze shrouded the skies and a distinct smell of dust and smoke pervaded the outdoors. Luckily, winds picked up the following day and the skies cleared up.

For the 20 million residents of the city (and millions more in neighboring states), it was a relief for the air to improve — from “very poor” to just “poor”.

Delhi’s pollution is a serious health hazard that affects millions of people every year. According to a study by the Indian Council of Medical Research , air pollution was responsible for 1.67 million deaths in India in 2019 , and Delhi had the highest per capita mortality rate due to air pollution among all states.

What are the Reasons behind Rising Pollution levels in Delhi during Winters?

• According to SAFAR , in 2021, stubble burning's contribution to Delhi pollution was 25%.
• Stubble burning emits toxic pollutants in the atmosphere containing harmful gasses like Carbon Monoxide (CO), methane (CH4), carcinogenic polycyclic aromatic hydrocarbons, volatile organic compounds (VOC).
• According to a study conducted by National Physical Laboratory, 72% of Delhi’s wind in winters comes from the northwest.
• For example, on October 25, 2023, the air quality improved marginally when the wind changed direction from north to northeast.
• Temperature inversion affects Delhi’s pollution in winter, when the weather is cold and calm. The pollutants from stubble burning, vehicle emissions, industrial emissions, and other sources accumulate in the lower atmosphere and form a thick layer of smog .
• Dry and Still Air: In winters, there is less rainfall and wind speed, which means that the pollutants do not get washed away or diluted by fresh air. The pollutants remain suspended in the air for longer periods of time.
• A study by IIT Delhi noted that vehicular emissions contribute around 25% to Delhi’s PM2.5 levels.
• A 2015 study conducted by IIT-Kanpur states that 17-26% of all particulate matter in Delhi in winters is because of biomass burning.

Government Initiatives to Control Delhi's Pollution

• Green War Room: A nine-member team that monitors the actions taken by 20 government agencies against pollution on a real-time and daily basis.
• Anti-Pollution Campaign: Delhi Government has recently launched a major anti-pollution campaign, Yuddh Pradushan Ke Viruddh, which includes a tree transplantation and other such initiatives.
• Green Delhi App: A mobile app that allows citizens to report any instances of pollution such as garbage burning, industrial emissions, or traffic congestion.
• Bio-Decomposer : A solution developed by PUSA institute that helps farmers decompose the crop residue in their fields without burning it. The government provides free spraying of bio-decomposer in Delhi’s farmlands.
• Water Sprinklers: The use of water sprinklers, mechanized road sweeping machines, anti-smog guns, and sprinkling facilities on high-rise buildings to reduce dust and particulate matter in the air.
• Industry Pollution: The monitoring of industrial sites and ensuring that they use clean and authorized fuel. The government has also extended piped natural gas (PNG) to industries and set up the country’s first e-waste eco-park in Delhi.
• PUC Certificates: The enforcement of pollution under control (PUC) certificates for vehicles and banning trucks that carry non-essential goods from entering the city. The government has also hired 1,000 private CNG vehicles to augment the public transport system.
• Smog Towers: The installation of smog towers that use large fans and filters to purify the air. The first smog tower has been set up at Connaught Place and has shown positive effects.
• Pollution Hotspots: The identification of 21 pollution hotspots in Delhi and deploying special teams to monitor and mitigate the sources of pollution in these areas.

What Measures should be taken to Control Delhi’s Pollution?

• A congestion charge is a fee that drivers have to pay to enter or use certain areas or roads that are prone to traffic congestion.
• Cap-and-Trade for Industrial Emissions: A cap-and-trade system sets a limit on industrial emissions and promotes a market-driven approach to reducing pollution. This system creates financial incentives for industries to reduce their emissions and invest in cleaner technologies, ultimately leading to a decrease in overall pollution.
• For instance, the New Engineering Education Transformation (NEET) cohort’s drone system is designed to provide real-time air quality data with a 15-meter resolution that is publicly accessible through a user-friendly interface.
• Vertical Gardens: Vertical gardens are an aesthetically pleasing and environmentally beneficial addition to urban areas. They not only enhance the visual appeal of the city but also help purify the air by absorbing carbon dioxide and releasing oxygen. Additionally, they can provide habitats for birds and insects, contributing to urban biodiversity.
• Rewards for Low-Carbon Lifestyles: Encouraging citizens to adopt low-carbon lifestyles through a rewards system is an innovative approach. By providing incentives like points or vouchers or tax benefits for eco-friendly behaviors such as using public transport or carpooling, people are more likely to make environmentally conscious choices, reducing their carbon footprint.

UPSC Civil Services Examination Previous Year Question (PYQ)

Q. In the cities of our country, which among the following atmospheric gases are normally considered in calculating the value of Air Quality Index? (2016)

• Carbon dioxide
• Carbon monoxide
• Nitrogen dioxide
• Sulfur dioxide

Select the correct answer using the code given below:

(a) 1, 2 and 3 only (b) 2, 3 and 4 only (c) 1, 4 and 5 only (d) 1, 2, 3, 4 and 5

Q. Describe the key points of the revised Global Air Quality Guidelines (AQGs) recently released by the World Health Organisation (WHO). How are these different from its last update in 2005? What changes in India’s National Clean Air Programme are required to achieve revised standards? (2021)

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New Delhi’s Air Turns Toxic, and the Finger-Pointing Begins

Schools and factories close. India’s Supreme Court blasts the government’s do-nothing response. But Delhi residents continue to suffer from the bad air.

By Hari Kumar and Emily Schmall

NEW DELHI — A thick blanket of noxious haze has settled over the Indian capital of New Delhi, burning eyes and lungs, forcing schools to close and prompting ardent calls from residents for action.

India’s leaders have responded with what has become an annual tradition: by pointing fingers at one another.

The central government, run by Prime Minister Narendra Modi, is accusing city officials of inaction, and vice versa. The country’s Supreme Court has stepped in to shut down factories and order farmers to stop burning fields. But the court’s other efforts, which last year included ordering the installation of a pair of air-scrubbing filter towers, have been derided as ineffectual .

The airborne murk and the towers stand as symbols of India’s deep political dysfunction. The choking pollution has become an annual phenomenon , and the country’s scientists can accurately predict the worst days. But deep partisanship and official intransigence have hindered steps that could help clear the air.

New Delhi’s residents don’t agree who is at fault, but they agree that more must be done.

“These last three weeks I became a refugee. I was so sick that I couldn’t take it anymore,” said Jai Dhar Gupta, the owner of a business that sells air pollution mitigation tools, such as home air purifier machines and face masks.

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Suggested Citation: Jalan, Ishita and Hem H. Dholakia. 2019.  What is Polluting Delhi’s Air? Understanding Uncertainties in Emissions Inventory.  New Delhi: Council on Energy, Environment and Water.

Fixing Delhi's air quality  requires a deep understanding of the sources that contribute to air pollution. Despite multiple source apportionment studies specific to Delhi NCR, policymakers can’t design an effective action plan due to varying estimates.  This study  brings clarity on the existent discrepancies and attempts to understand the gaps and opportunities to  develop emissions inventory  for source apportionment studies.

Our study focuses on five emission inventory studies (CPCB 2010; IIT Kanpur 2015; TERI 2018; SAFAR 2018; Guttikunda 2018) on Delhi NCR. It draws comparisons between emissions inventories for ten sources of air pollution for PM 10 and PM 2.5 .

Key Observations

• Transport, in terms of overall trend, is the largest contributor of PM 2.5 and its contribution ranges from 17.9 per cent (Guttikanda 2018) to 39.2 per cent (SAFAR 2018). However, according to IIT Kanpur (2016), transport is the second largest contributor after road dust while Guttikanda (2018) found industry as the major contributor of PM 2.5 , followed by road dust and transport.
• Road dust is the largest contributor of PM 10 and its contribution ranges from 35.6 per cent (TERI 2018) to 65.9 per cent (Guttikanda 2018). This observation is common across all the five studies, owing to the common methodology they used. Yet, with the wide range of estimation for the sector, it is clear that different studies have included other factors in the methodology of developing an emissions inventory.

Sector-wise contribution to PM 2.5 (%)

Source: CEEW analysis, 2019

Sector-wise contribution to PM 10 (%)

Why Do Emissions Inventories Currently Vary?

• Study regions chosen These five emission inventory studies have considered different geographical boundaries and regions for their analysis. This has led to varying estimates and discrepancies depending on the inclusion/exclusion of peripheral or across the border emissions.
• Source of air pollution The components of the sources of air pollution are determined after mapping the sources of air pollution in the study region. Variation occurs based on the selection by researchers as well as resource constraint causing unreliable estimates.
• Differences in data collection/sampling In the process of data collection, differences could occur due to varying data sources, sampling methods, sampling points, quality of surveys, and seasonal changes during sampling.
• Understanding of sectoral activity Experts often differ in their understanding of the magnitude of activity in a particular sector. This leads to variations in calculating direct emissions, emissions from fuel consumption, and reduction in pollution as a result of pollution abatement technologies.
• Calculating emissions factor Different emission factors have been employed by different studies leading to variation in final estimates for the emissions inventory. Emissions factors are used to estimate the quantity of pollutants released based on data collected.

HAVE A QUERY?

Executive Summary

In India, every year, air pollution causes 1.24 million deaths. In Indian cities, most of the year, the average concentration of particulate matter (PM) exceeds Central Pollution Control Board (CPCB) standards. Making decisions on mitigation and control requires an understanding of air pollution sources. Source apportionment estimates the contribution of each source.

The process uses two methodologies - top-down and bottom-up. The two methodologies complement each other in cross-checking and validating the source apportionment analysis; therefore, it is advised to use both for a region. Delhi is popular in the narrative of air pollution, and it has been covered extensively by source apportionment studies (CPCB 2010; IIT Kanpur 2016; TERI 2018; SAFAR 2018; Guttikunda 2018).

These studies have played an instrumental role in describing the variety of sources that contribute to air pollution in Delhi and the National Capital Region (NCR), but their estimates differ significantly. Given that source apportionment information guides pollution mitigation policy and actions, differences can make the determination of exact sources uncertain and air quality improvement measures ineffective.

By comparing the existing emissions inventories for Delhi or NCR, this study aims to explain the differences in these estimates. To detail these differences, we focus on PM 10 and PM 2.5 in transport, industries, power plants, road dust, and construction - the five major contributing sectors. An emissions inventory uses the bottom-up method and forms the basis for a source apportionment study. A dispersion model is used to calculate the distribution of pollution using the emissions inventory and meteorological data as input parameters.

The biggest contributor of PM 2.5 is the transport sector; its contribution ranges from 17.9 per cent to 39.2 per cent. Road dust is the second largest source of PM 2.5 ; it contributes between 18.1 per cent and 37.8 per cent. It also contributes 35.6 per cent to 65.9 per cent of PM 10 as the largest source. Similar trends exist for other sectors. The differences are due to each study’s domain area; year; sampling season chosen; and methodologies of sampling, estimation, and emissions factors. These factors explain the discrepancies only partially, however; emissions inventories are different for other, unexplained reasons.

To improve the understanding of air pollution and formulation of policy, several changes are necessary. Information on sampling frame and sample details needs to be transparent. Uncertainty should be quantified to explain the spread of observations for a sector. Multipleyear inventories would capture the dynamic nature of air pollution and enable accurate, realtime information. Common regulatory guidelines would help in building robust inventories. Source apportionment based on emissions inventories and dispersion modelling should be reconciled with receptor modelling to enable convergence between the modelling and measurement approaches.

Discussion and Recommendations

In comparing various emissions inventories of air pollutants for Delhi and the NCR, this study finds significant differences in their estimates of total pollutant load and, especially, sectoral emissions.

To improve air quality, we need to design effective emissions inventories and, in turn, harmonise the inventories. To create better emissions inventories, we need to improve data transparency, quantify uncertainties, develop multiple-year inventories, common guidelines, and reconcile the top-down and bottom-up methods.

Improve data transparency

We can infer that differences in studies result from activity data or emissions factors, but we need transparent data to understand the reasons for discrepancies. In the transport sector, inventory depends on the number of on-road vehicles, their age distribution, fuel type, and VKT. Because there is no common database, studies rely on primary data collection efforts.

The study surveys were carried out across several locations - 72 locations for the TERI (2018) study and 87 locations for the SAFAR (2018) study - but it is unclear whether these constitute a representative sample. If the studies used a purposive sampling approach, it may introduce a bias; and it may not be appropriate to generalise their findings to the NCR. This lack of transparency and information in sampling frame and sample details is common to all sectors and studies.

Quantify uncertainties

There are two sets of uncertainties. The first arises from activity data such as fuel consumption and efficiency of pollution control equipment. The second set of uncertainties can be attributed to emissions factors, even though most of these are determined based on controlled experiments. When considered together, the uncertainties can compound.

Uncertainties for PM 2.5 may be as high as 86 per cent for the power sector, 201 per cent for industry, 94 per cent for road transport, and 259 per cent for the domestic sector; for the entire inventory, overall uncertainty may be as high as 145 per cent (Kurokawa et al. 2013).

However, no study except Guttikunda (2018) provides standard deviations for inventory estimates. This makes it difficult to gauge the spread and confidence levels for each parameter. Therefore, authors need to quantify the uncertainty in their studies.

Develop multiple-year inventories

Air pollution is dynamic in nature. As policies to control different sources are put in place, the total pollution level changes, as does the relative contribution of different sources. Developing multi-year inventories helps pollution control agencies identify pollution sources and design control responses accurately and on time. Further, continual emissions monitoring systems that measure pollutant loads in the industrial and power sectors are more accurate than a bottom-up calculation based on fuel use.

This comparison posits, and we argue here, that an emissions inventory needs to be continually updated. However, each of the studies considered here developed an emissions inventory for a single year. Therefore, we recommend that ministries and academic/research groups collaborate to build an ongoing, long-term emissions inventory that is updated every 1-3 years.

Evolve common guidelines

The USEPA lays down guidelines for state and local agencies to collect comprehensive and detailed estimates of pollutants and develop a single, common National Emissions Inventory. In India, however, the CPCB does not offer similar guidelines or directives. Therefore, several uncertainties arise in inventory development, and studies become difficult to compare. There is a need to develop inventories and carry out source apportionment studies across India.

Reconcile top-down and bottom-up methods

There is a need to better reconcile source apportionment based on emissions inventories and dispersion modelling with receptor modelling (Pant et al., 2012) and, thereby, bring about convergence between the modelling and measurement approaches.

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Delhi dwellers losing 7.8 years to PM2.5 pollution: Report

The report, on air quality life index (aqli) published by the energy policy institute at the university of chicago (epic) on wednesday, is based on analysis of 2022 pollution data.

Delhi residents are losing 7.8 years of their life expectancy to increased PM2.5 concentration, according to a report published by a US-based research institute, which said the loss can be mitigated by reducing the level of the pollutant to the World Health Organization (WHO) standard of 5 micrograms per cubic metre (µg/m3)

The report, on Air Quality Life Index (AQLI) published by the Energy Policy Institute at the University of Chicago (EPIC) on Wednesday is based on analysis of 2022 pollution data. It said that even containing the level of PM2.5 — particulate matter having diameter less than 2.5 microns, a major pollutant in Delhi — to the national standard ormore lenient level of 40µg/m3 can increase life expectancy of 18.7 millionDelhi residents by 4.3 years.

The AQLI is a pollution index that quantifies the causal relationship between long-term human exposure to air pollution and life expectancy. The life expectancy calculations made by AQLI are based on a pair of peer-reviewed studies. By comparing two subgroups of the population that experienced prolonged exposure to different levels of particulate air pollution, the studies were able to plausibly isolate the effect of particulate air pollution from other factors that affect health.

To be sure, Delhi’s average PM2.5 concentration in 2022 was 84.3µg/m3. This made Delhi the most-polluted state or Union territory in the country, the report said.

According to WHO, life expectancy at birth in India was 67.3 years in 2021. This is up by nearly five years from the average life expectancy of 62.1 years in 2000.

“If all of India were to reduce particulate pollution to meet the WHO guideline, residents in Delhi—India’s capital and most populous city—would see the maximum benefits, with its residents gaining 7.8 years of life expectancy. In North 24 Parganas – a district in West Bengal and the country’s second most populous district, residents would gain 3.6 years of life expectancy,” said report on Tuesday.

Delhi consistently ranks among the world’s most polluted cities each year, impacted by a cocktail of pollutants that are at their peak in the winter months from November till January. Pollution in Delhi spikes following the harvesting of crop in the northern plains, which often clashes with Diwali, leading to a mixture of smoke from residue burning and firecrackers hanging over the capital’s air for weeks. Other than these, Delhi is also impacted by localised sources such as industries, vehicles and burning of waste, keeping levels of pollutants well above permissible standards.

The report found that after Delhi, Uttar Pradesh was the most polluted, with an annual average PM2.5 level of 65.5µg/m3, with residents of the state expected to gain 5.9 years if the WHO standard is met and 2.5 years if the national standard is met. For Haryana, the corresponding figures were 5.2 years and 1.8 years, respectively.

The report, despite showing Delhi residents losing considerable years in terms of life expectancy, nevertheless posits an improvement from a previous study done on 2021 PM2.5 level.

Last year’s EPIC report, based on 2021 data, revealed Delhi’s PM 2.5 level was 126.51µg/m3 — up from the level of 111.6µg/m3 recorded in 2020. The previous report posited an increase of 11.9 years in life expectancy if WHO standards were met and 8.5 years if national standard of 40µg/m3was met.

The report found that not just Delhi, but other states and UTs also showed an improvement in 2022. The average PM2.5 concentration for India reduced from 49µg/m3 in 2021 to 41.4µg/m3 in 2022, which is still over eight times the WHO standard, but close to the national standard.

“If these reductions are sustained, an average Indian is likely to live nine months longer compared to what they would have if they were exposed to levels similar to the last decade. Further, if pollution in India met the WHO guideline, Indian citizens could gain an additional 3.6 years onto their life expectancy,” the report said.

Tanushree Ganguly, director of AQLI, said that over the years, Delhi’s PM2.5 concentration has largely remained over 100 µg/m3, barring 2020, when Covid-19 curbs kept levels low.

“In 2022, PM2.5 levels in Delhi were 17% lower than 2021 and the average of 2016 to 2021. While it is difficult to conclusively separate the effects of weather from policy implementation in explaining this decline — especially since the entire South Asian region, including Bangladesh, Nepal, and Pakistan, reported reduced particulate concentrations — sustaining these reductions in Delhi could increase average life expectancy in the city by 1.6 years,” she said.

Environmental activist Bhavreen Kandhari said even if there is an improvement in relation to 2021, 7.8 years was a significant sum of years being lost to bad air. “It is important the national standards are also brought down, as the WHO standards are closer to clean air. We still need to work hard to bring down the years being lost and that can only be done if both state and the Centre acknowledge the health risk associated with air pollution,” she said.

Anumita Roychowdhury, executive director, research and advocacy at the Centre for Science and Environment (CSE), said Delhi still has a long way to go to bring down its PM2.5 level, but an aggressive and stringent approach towards electrification, integrated public transport and low-emission zones with cycling or walking facilities could help in this regard.

“We also need to improve our waste management, which means 100% waste collection, segregation and processing. This eliminates burning of waste and landfills. We also need to replace dirty fuels for cooking with cleaner fuels and implement clean energy across all sectors,” she said.

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Delhi Minister directs DPCC to ensure functioning of air pollution control mechanisms ahead of winter

NEW DELHI: Delhi Environment Minister Gopal Rai on Tuesday directed the principal secretary (environment) to ensure that the Real Time Source Apportionment infrastructure, an exercise to determine the air quality status based on real-time pollutant measurements and meteorological conditions, is fully operational before winter begins.

“... it is directed that the existing infrastructure set up for conducting the study on Real Time Source Apportionment at Deen Dayal Upadhaya Marg, be made fully operational by Delhi Pollution Control Committee (DPCC), well before the onset of Winter Season so that precise data about the sources of pollution and their quantum can be collected and mitigation measures could be taken accordingly,” Rai said.

During winters, the capital’s air quality reaches a hazardous level due to low wind speed, stubble burning, vehicular pollution, and its peculiar geographical location.

A study on ‘Real Time Apportionment’ was undertaken to formulate policies to tackle the menace of air pollution.

The government decided to engage IIT Kanpur to conduct the study. In July 2021, a cabinet decision awarded the project to IIT Kanpur, which, in consultation with TERI and IIT Delhi, submitted its report on September 25 last year.

Rai said that in the last review meeting of DPCC, it was learnt that there is a delay in making the super site fully functional.

DPCC in limbo

Counsel for the Delhi govt submitted that out of the 344 sanctioned posts in the Delhi Pollution Control Committee (DPCC), as many as 233 are vacant. Anguished over it, the apex court asked the Delhi government to fill these vacancies by April 30, 2025.

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Premium buses to help in reducing air pollution: Delhi minister Gahlot

Delhi transport minister kailash gahlot on wednesday inspected the first lot of buses launched under the delhi motor vehicles licensing of aggregator (premium buses) scheme 2023.

Gahlot even drove a bus around the Rajghat depot during his inspection. | Photo: PTI

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Shekhar Kapur Expresses Concern Over Growing Pollution Levels In Delhi

Shekhar Kapur penned a long note in the image caption talking about how times have changed. (File)

Legendary filmmaker Shekhar Kapur, who is known for films like 'Bandit Queen', 'Elizabeth', 'Masoom', is concerned about the growing pollution levels in the national capital.

On Wednesday, the filmmaker took to his Instagram and shared a hazy picture of Delhi's cityscape blanketed in the smog.

He penned a long note in the caption talking about how much times have changed.

He wrote, "Yes, it's polluted. Yes,it is not what Delhi was 50 years ago when I lay on the terrace of our house at night and stared at the night sky and could often see the Milky Way. The wondering about the night sky when I asked my mother 'How far does space go?' 'Forever .. my son .. forever', Those Words. Yes, the terrace of our house in unpolluted Delhi that created the desire, no, not desire but need to tell stories".

He further mentioned, "For there is no definition, nothing in physics, nothing in our imagination .. that can define 'forever' except by telling a story. And so as a kid, overwhelmed by the 'forevereverness' of space I said those magic words to myself: 'Once upon a time', and have been repeating those words to myself ever since again and again for story telling is our survival kit. It all started lying on my Charpai on the terrace in Delhi. How could I ever forget Delhi? And so this picture of Delhi from the window of my hotel room .. #stories #forever #dehi #space #milkyway #storytelling #space @kaverikapur @sohaila.kapur".

Earlier, the director took to his Instagram, and pondered over solitude and loneliness, and what separates the two. He shared a picture of the famous sculpture 'The Thinker' by Auguste Rodin which depicts a nude male figure sitting on a rock in deep thought.

The filmmaker shared that he actually never knows when he gets lonely except when he suddenly feels intense pangs of loneliness.

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• Published: 03 September 2024

Unveiling spatial variations in atmospheric CO 2 sources: a case study of metropolitan area of Naples, Italy

• Roberto M. R. Di Martino   ORCID: orcid.org/0000-0001-6435-2759 1 ,
• Sergio Gurrieri   ORCID: orcid.org/0000-0003-4085-0440 1 ,
• Antonio Paonita   ORCID: orcid.org/0000-0001-9124-5027 1 ,
• Stefano Caliro   ORCID: orcid.org/0000-0002-8522-6695 2 &
• Alessandro Santi   ORCID: orcid.org/0000-0002-1549-9786 2  

Scientific Reports volume  14 , Article number:  20483 ( 2024 ) Cite this article

Metrics details

• Atmospheric chemistry
• Atmospheric science
• Climate sciences
• Environmental sciences
• Geochemistry
• Natural hazards
• Solid Earth sciences
• Volcanology

In the lower atmosphere, CO 2 emissions impact human health and ecosystems, making data at this level essential for addressing carbon-cycle and public-health questions. The atmospheric concentration of CO 2 is crucial in urban areas due to its connection with air quality, pollution, and climate change, becoming a pivotal parameter for environmental management and public safety. In volcanic zones, geogenic CO 2 profoundly affects the environment, although hydrocarbon combustion is the primary driver of increased atmospheric CO 2 and global warming. Distinguishing geogenic from anthropogenic emissions is challenging, especially through air CO 2 concentration measurements alone. This study presents survey results on the stable isotope composition of carbon and oxygen in CO 2 and airborne CO 2 concentration in Naples’ urban area, including the Campi Flegrei caldera, a widespread hydrothermal/volcanic zone in the metropolitan area. Over the past 50 years, two major volcanic unrests (1969–72 and 1982–84) were monitored using seismic, deformation, and geochemical data. Since 2005, this area has experienced ongoing unrest, involving the pressurization of the underlying hydrothermal system as a causal factor of the current uplift in the Pozzuoli area and the increased CO 2 emissions in the atmosphere. To better understand CO 2 emission dynamics and to quantify its volcanic origin a mobile laboratory was used. Results show that CO 2 levels in Naples’ urban area exceed background atmospheric levels, indicating an anthropogenic origin from fossil fuel combustion. Conversely, in Pozzuoli's urban area, the stable isotope composition reveals a volcanic origin of the airborne CO 2 . This study emphasizes the importance of monitoring stable isotopes of atmospheric CO 2 , especially in volcanic areas, contributing valuable insights for environmental and public health management.

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Introduction.

The equilibrium among natural CO 2 emissions, biotic uptake on land, and ocean absorption regulates long-term fluctuations in airborne CO 2 , establishing the greenhouse effect essential for the biosphere's existence on Earth. Human activities, particularly fossil fuel combustion, vehicle mobility, house heating, and waste management, disrupt the carbon cycle, leading to an increase in airborne CO 2 levels 1 , 2 , 3 , 4 , 5 . Disruption of this equilibrium worsens the effects of global warming and climate changes.

Global temperature data from Copernicus ( https://climate.copernicus.eu/ accessed on 2024, January 10), shows that the mean near-surface temperature in 2023 was ~ 1.4 ± 0.12 °C above the 1850–1900 average. This marked the warmest year in the 174-year observational record, surpassing the joint warmest years of 2016 and 2020. Notably, the last decade (2014–2023) encompasses the nine warmest years on record. Real-time data from specific locations reveals a continued increase in CO 2 levels in 2023, while consolidated concentration datasets of CO 2 , methane, and nitrous oxide reached their highest records in 2022.

Several causes contribute to global warming and climate change 6 . Since the eighteenth century the industrialization has led to the gradual abandonment of rural areas and the concentration of people in urbanized zones. Industries, mainly relying on electrical power generated by hydrocarbon combustion, settled in suburban areas contribute significantly to CO 2 emissions 5 , 7 . Urban growth, characterized by skyscrapers and increased vehicle mobility, results in continuous large-scale carbon dioxide release, predominantly concentrated in urban areas, significantly impacting the global atmospheric composition.

Earth degassing, driven by natural sources like soil respiration, volcanic degassing, and photosynthesis, contributes to atmospheric CO 2 concentrations 8 . Regions of active volcanism, responsible for a significant portion of natural gas emissions, release CO 2 of magmatic origin, particularly during eruptions, accounting for ~ 1% of global CO 2 emissions annually 9 , 10 , 11 . Although this percentage is modest on a global scale, locally, natural emissions may have a more substantial environmental impact, raising hazards for local populations 12 , 13 , 14 , 15 . For example, during the recent outgassing crisis at Vulcano, Italy 16 , 17 , gas hazards increased due to either diffuse degassing or crater plume emissions, though human health risk threshold value was not exceeded 18 , 19 , 20 .

Naples, with around 1 million residents, ranks third in population among Italian cities and is the most densely populated city in Europe. Its strategic location in Mediterranean shipping routes and heavy ship traffic in the harbour make it a potential major source of anthropogenic CO 2 . The city is located in a volcanic area with active volcanic and hydrothermal zones, making it an ideal study area to investigate the coexistence of human-related and natural CO 2 emissions.

This study presents the results of a spatial survey on airborne CO 2 in the metropolitan area of Naples. The survey aimed to collect measurements of airborne CO 2 concentration and stable isotopes of CO 2 to differentiate between volcanic and anthropogenic sources, identifying sources that elevate airborne CO 2 concentrations above the background. The study area includes Naples’ downtown and a broad urbanized zone extending from the western edge of Vesuvius volcano to Bacoli and Cuma in the east, and Agnano crater in the north, encompassing the active volcanic/hydrothermal zone of Campi Flegrei (Fig.  1 a). The Campi Flegrei area has experienced significant volcanic activity, including supereruptions, the oldest one dating back 40,000 years 21 , 22 . This area exhibits continuous degassing and seismic activity (i.e., Solfatara and Pisciarelli in the municipality of Pozzuoli). Anomalies in CO 2 emissions occur from soils via diffuse degassing and from fumaroles 23 , 24 , 25 , 26 , 27 , particularly in the Solfatara area (Zone A in Fig.  1 a). The most recent eruption originated from Monte Nuovo in 1538 A.D. Since then, this system has been in a state of persistent degassing and fluctuating seismic activity, leading to ground motion known as bradiseism. The study area has also increased the degassing since 2005 and is currently in unrest 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 . Human-related and geologic CO 2 emissions have distinct stable isotopic signatures, allowing differentiation in the air at the district scale through a combination of concentrations and isotopic measurements 12 , 13 , 18 , 19 , 20 , 37 , 38 , 39 . The results of the spatial survey enable a comparison between volcanic CO 2 emissions and those of anthropogenic origin in the urbanized area of Naples.

Study area, the route used during survey and dataset distribution. The survey was conducted in May 2023. ( a ) The blue line represents the route used during the survey. The selected subsets for Solfatara area (orange zone A), downtown Naples (green zone B), and airport (ice blue zone C) are shown. ( b ) Probability plot for concentration dataset. Global average value of airborne CO 2 concentration is reported as reference. (blue line indicates 423 ppm vol) for a comparison with the average CO 2 concentration over the target area (50% cumulative probability). ( c ) Histograms for both the oxygen isotope (δ 18 O–CO 2 ) and carbon isotope (δ 13 C–CO 2 ) compositions. ( d ) Three-dimensional view of the study area showing the atmospheric CO 2 concentration measurements at their respective locations. The height of the vertical bars is proportional to the concentration levels. The colour scale and bar height indicate that the highest CO 2 concentration was detected near the port. ( e ) Placement of the measurements within the study area. The colour scale is identical to that in subplot ( d ) and indicates the CO 2 concentration measured in the air. The maps ( a ), ( d ), and ( e ) were generated in Qgis 3.34 environment ( https://qgis.org/download/ ).

We developed a measurement program to detect and quantify the spatial variability of CO 2 concentration and its stable isotopes in the near-surface air of the Naples metropolitan area (Fig.  1 a). The dataset enables a better determination of the influence of meteorological factors and multiple greenhouse gas sources on the nature of the urban CO 2 dome 40 , 41 , 42 , 43 , which is considerably more challenging to identify than its mere presence. For this study, the wind direction was selected as the meteorological factor influencing CO 2 dispersal, while other meteorological factors (e.g., temperature, atmospheric pressure, relative humidity) can be averaged over the survey's completion time (11.4 h of acquisition during daytime over 24 h), as variations at the meteorological station from National Research Counsil (i.e., C.N.R. Long: 432,409; Lat: 4,520,399 UTM) are likely suitable for the entire Naples metropolitan area. Throughout the survey period, the weather remained consistently sunny. Table 1 presents the statistics of both environmental variables and atmospheric measurements.

Figure  1 b–c shows statistical distributions of measurements collected during the survey. The dataset collected in the Naples metropolitan area shows airborne CO 2 concentrations higher than 423 ppm vol (Fig.  1 b), which is the global reference for airborne CO 2 concentration for May 20, 2023 ( https://www.climate.gov/climatedashboard accessed on July 2, 2024). The probability plot 44 reveals three independent subsets of CO 2 concentrations. The 50% cumulative distribution indicates that the average value for the background CO 2 concentration in the urban area of Naples is 448.1 ± 1.0 ppm vol. The background population comprises more than 98.9% of the cumulative dataset, while the anomalous subset constitutes less than 0.1% of the cumulative dataset, with CO 2 concentrations exceeding 1300 ppm vol (Fig.  1 b).

Regarding stable isotopes, the carbon isotope composition of airborne CO 2 (reported in delta notation δ 13 C–CO 2 against the Vienna Pee Dee isotopic ratio-VPDB) shows values more 13 C-depleted than the theoretical background air (δ 13 C–CO 2  =  − 8‰ vs VPDB). This result indicates that a source of CO 2 forces airborne CO 2 concentration above background values. This gas source has a 13 C-depleted isotopic signature and establishes an urban CO 2 dome in the Naples metropolitan area. Furthermore, the statistical parameters of the data distribution (skewness =  − 2.27, kurtosis = 15.74) indicate that the dataset has a peak at δ 13 C–CO 2  =  − 10.40‰, which is more 13 C-depleted than the theoretical atmospheric CO 2 value 45 .

The range of values for δ 18 O-CO 2 is wide compared to the spatial and temporal scales of the collected measurements. The oxygen isotope composition of airborne CO 2 depends on both the hydrology of the region and oxygen isotope fractionation in plant leaves during photosynthesis 46 , 47 , 48 . These factors change over spatial and temporal scales different from those of the measurement acquisition (i.e., ~ 10 4 –10 5  m and approximately 24 h, respectively). The oxygen isotope values are almost normally distributed (skewness =  − 1.50, kurtosis = 7.3) throughout the study area (Fig.  2 b). Gaussian fitting of the oxygen values has a peak at δ 18 O–CO 2  =  − 3.16‰ versus VPDB, which is more 18 O-depleted than the expected value for a coastal area of the Mediterranean region 49 , 50 , 51 , 52 .

Spatial variations of the CO 2 measurements collected during survey throughout the target area (cell size 10 m). The maps were generated in Qgis 3.34 environment ( https://qgis.org/download/ ) using Measurement interpolation generated in SAGA GIS environment ( https://saga-gis.sourceforge.io/en/index.html ). ( a ) Spatial variation of the airborne CO 2 concentration. ( b ) Spatial variation of the oxygen isotope composition of the airborne CO 2 . ( c ) Spatial variation of the carbon isotope composition of the airborne CO 2 . Traces of the concentration profiles are reported (black lines). See text for description.

The collected dataset was utilized to investigate the spatial variation of airborne CO 2 (Fig.  2 ). These data allow investigation of whether urban CO 2 sources affect atmospheric chemistry at a district scale or over the urban area (i.e., at the local scale, ~ 10 4 –10 5  m). The results illustrate a heterogeneous distribution of airborne CO 2 concentration over the Naples metropolitan area, with a concentration gradient from the coast to the inland, likely influenced by local atmospheric circulation. Granieri et al. 14 , who conducted detailed micrometeorological studies on atmospheric circulation in the Naples area for gas dispersal simulations, noted a diurnal sea breeze blowing from SW to NE, pushing clean air inland from the seaside during morning hours. A supply of clean air from the sea would dilute CO 2 concentration at relatively low levels. However, measured CO 2 concentrations in the urban area of Naples suggest that atmospheric circulation is insufficient to reduce atmospheric CO 2 concentrations to background levels, at least on days with similar weather conditions to the ones of the day of measurements. Further research should address the issue concerning the critical atmospheric circulation conditions that help to reduce the concentration level of CO 2 . The implementation of atmospheric CO 2 monitoring programs in urban areas, particularly when integrated with stable isotope composition analyses, is posited as an effective method for detecting anthropogenic or natural forcings influencing atmospheric CO 2 levels. Elevated atmospheric CO 2 concentrations are frequently correlated with increased levels of other pollutants, suggesting that these monitoring programs can significantly enhance public health management strategies. Additionally, in urbanized regions located within volcanic zones, atmospheric CO 2 monitoring is crucial for mitigating volcanic risks associated with gas emissions (i.e., the gas hazard) 19 . Examination of the dataset reveals areas with high airborne CO 2 concentrations, notably near Naples' harbour, where the highest CO 2 concentration was measured (Fig.  1 d,e), and the district of Museo square, among others (Zone B in Fig.  1 a).

Figure  2 illustrates additional zones with high concentrations of airborne CO 2 . The airborne CO 2 concentrations achieve 572 ppm vol in a zone situated in the northeastern sector of the investigated area. While this level does not surpass any established risk threshold for human health 53 , it exceeds the reference value recorded at NOAA Global Monitoring Laboratory for the investigated time frame (424 ppm by volume) by > 33%. A land use survey in the metropolitan area of Naples reveals the presence of the airport, particularly the runways and aircraft parking areas adjacent to the route used for data collection. Another zone exhibiting elevated concentrations of airborne CO 2 is identified on the western side of downtown Naples, within the municipality of Pozzuoli (i.e., transect B–B′ in Fig.  3 b). This area is renowned for its evidence of the underlying volcanic hydrothermal system of Campi Flegrei 26 , 27 , 28 , 29 , with airborne CO 2 concentrations reaching 567 ppm vol. The spatial distribution of airborne CO 2 concentrations in this zone appears more heterogeneous compared to other areas, attributable to the presence of several high concentration nuclei near Bagnoli and Baia (Figs.  2 a and 3 a), eastward and westward of Solfatara, respectively.

Transects through selected zones of the study area to inspect lateral variations of airborne CO 2 concentration (blue line), δ 18 O–CO 2 (red line), and δ 13 C-CO 2 (blue line). ( a ) A–A′ transect (Bacoli). ( b ) B–B′ transect (Solfatara). ( c ) C–C′ transect (Downtown). ( d ) D–D′ transect (Portici).

The δ 18 O–CO 2 has been recognized as a tracer of photosynthesis and the hydrologic cycle's effects on airborne CO 2 . These processes play a pivotal role in the fractionation of oxygen in airborne CO 2 at vastly different spatiotemporal scales. While the hydrologic cycle exhibits seasonal effects at the regional scale, notable changes in vegetation (e.g., transition from C3 to C4 or CAM plant dominant types) account for variations in the oxygen isotope composition due to differences in photosynthesis. Since the survey was completed in a few hours, the spatial variations in the oxygen isotope composition resulting from these processes are expected to have negligible effects on the spatial variations of δ 18 O–CO 2 , which constitutes an ancillary factor for identifying variations in the source of CO 2 at the district scale 12 , 13 , 18 , 20 , 51 , 54 .

The kriging interpolation of the δ 18 O-CO 2 dataset reveals a zone with slightly 18 O-depleted airborne CO 2 westward of downtown Naples, where the δ 18 O–CO 2  =  ~  − 2‰. Near Baia, where high concentrations of CO 2 were measured (Fig.  2 a), the airborne CO 2 exhibits more 18 O-depleted values, reaching δ 18 O–CO 2  =  − 5.38‰ through a steep isotopic gradient (e.g., transect A–A′ in Fig.  3 a). The δ 18 O–CO 2 abruptly increases to approximately − 2‰ northwestwardly along the transect A–A′ (Fig.  3 a). Airborne CO 2 shows less 18 O-depleted values near Solfatara. A concentration profile across the Pozzuoli area (Fig.  3 b) depicts the least 18 O-depleted CO 2 in the air, having δ 18 O–CO 2  =  − 0.06‰ in the vicinity of Solfatara and toward the northeast (Fig.  3 b). The δ 18 O-CO 2 values decrease to an average of − 2.5‰ northeast of Astroni. Downtown Naples has been identified as an area where airborne CO 2 exhibits more 18 O-depleted values, although zones with δ 18 O–CO 2  <  − 6.5‰ are heterogeneously distributed between Pianura and Capodimonte, where CO 2 exhibits more 18 O-depleted CO 2 (i.e., transect C–C′ in Fig.  3 c). In this zone, heavily 18 O-depleted CO 2 (δ 18 O–CO 2  <  − 16.0‰) was measured in the harbour district.

Additionally, a wide zone elongated NW–SE exhibits δ 18 O–CO 2  <  − 5.3‰, extending from the eastern edge of downtown Naples to the west of Torre del Greco, coinciding with a densely urbanized area and a widespread industrialized area (i.e., Area Est-Centro direzionale). Figure  3 d illustrates a step gradient of δ 18 O–CO 2 that separates the coastal zone where δ 18 O–CO 2  =  ~  − 5.06‰ from the inland area where δ 18 O–CO 2  =  ~  − 2.85‰. The ∆ 18 O–CO 2  = 2.21 represents an order of magnitude greater than the accuracy of the oxygen isotope determination (± 0.25‰). In summary, the spatial variations of the measurements show strong fluctuations of δ 18 O–CO 2 in different zones. Kriging interpolation of the δ 18 O–CO 2 dataset reveals areas with slightly 18 O-depleted airborne CO 2 westward of Naples' downtown, and more 18 O-depleted values eastwards of downtown Naples. Similarly, wide variations in δ 13 C–CO 2 values correspond to spatial variations in the carbon isotopic signature of airborne CO 2 (Fig. six). Dataset statistics indicate that airborne CO 2 is 13 C-depleted compared to standard air. Cross-sections show trends indicating potential CO 2 sources with 13 C-depleted or enriched signatures in different areas, with notable variations near Baia and downtown Naples. These results suggest considerable variability in emission sources at the scale of the urbanized zone, and a dominant source of CO 2 with a 13 C-depleted signature. This expectation arises because the carbon isotope signature of airborne CO 2 can track the source of the gas 55 , 56 .

The cross-section through the urbanized areas of Bacoli (Figs.  2 a and 3 a) shows an average value of δ 13 C–CO 2  =  − 10.5‰, indicating airborne CO 2 to be more 13 C-depleted than theoretical air and global reference values recorded by NOAA ( https://www.climate.gov/climatedashboard accessed on July 2, 2024). A significant change in the carbon isotope composition of airborne CO 2 is evident at Baia, where a decrease to a value of δ 13 C–CO 2  <  − 14‰ was measured, coinciding with concentration values higher than those measured at Bacoli (Fig.  2 c). Low values of δ 13 C-CO 2 indicates that a heavily 13 C-depleted source of CO 2 is responsible for forcing airborne CO 2 above background levels and is the main contributor to increased CO 2 concentration. The carbon isotope composition increases to less 13 C-depleted values north of Cuma and achieves δ 13 C–CO 2  =  ~  − 9‰ in the northern zone of the target area. The B–B′ cross-section shows a different trend compared to the A–A′ profile (Fig.  3 a,b respectively). Specifically, δ 13 C–CO 2 decreases from approximately − 9 to − 11‰. Continuing along the Solfatara profile (Fig.  3 b), a sudden increase in δ 13 C–CO 2 value is observed, reaching values of approximately − 8‰ at the highest concentration values observed along the same profile.

This trend appears to be clearly opposite to that observed in the A–A′ profile, suggesting the presence of potential CO 2 sources with a less 13 C-depleted signature compared to those forcing airborne CO 2 concentration in adjacent areas. The alternative hypothesis, suggesting that clean air with δ 13 C–CO 2  =  − 8‰ produces the observed values, can be dismissed based on the evidence that 13 C-enrichment correlates with an increase in CO₂ concentration. This trend contradicts the expectation of a decrease in CO₂ concentration, which would be consistent with the clean air hypothesis. Furthermore, the A–A′, C–C′, and D–D′ profiles demonstrate that CO₂ concentrations exhibit opposite trends in comparison with δ 13 C–CO 2 . Specifically, these transects reveal that increases in CO₂ concentration coincide spatially with decreases in δ 13 C–CO 2 , indicating that the effective source of CO₂ in these zones is more 13 C-depleted. In the surrounding area, δ 13 C–CO 2 values average around − 10‰ regardless of CO 2 concentration in the air. These 13 C-depleted values reduce evidences of spatial 13 C-enrichment in airborne CO 2 . Therefore, the gas source which causes rise in CO 2 concentration above background levels in the area of Solfatara has a carbon isotope composition only slightly 13 C-depleted compared to the VPDB standard. Accordingly, to the northeast of the Astroni crater, δ 13 C–CO 2 decrease sharply to values ranging between − 10 and − 11‰. Moreover, zones with high airborne CO 2 concentrations near both Bagnoli and Posillipo also show heavily 13 C-depleted isotopic composition (i.e., δ 13 C–CO 2  =  − 14.69‰ and δ 13 C–CO 2  =  − 13.85‰, respectively).

The C–C′ profile (Fig.  3 c) crosses Naples’ downtown (Fig.  2 ), which is busiest by vehicle during morning hours. The airborne CO 2 has δ 13 C–CO 2 values from − 17.65 to − 8.54‰ with an average δ 13 C–CO 2  =  ~  − 11‰. High CO 2 concentrations along this profile occur at the harbour district (Fig.  3 c and Fig.  2 a), which coincides with the zone having the most 13 C-depleted values of airborne CO 2 (Fig.  2 c). A comparison with other profiles reveals that a 13 C-depleted source of CO 2 forces the airborne CO 2 concentration in downtown Naples more efficiently than in peripheral zones to the west (i.e., Bacoli, Baia, and Posillipo). This source is less effective in forcing CO 2 concentration in the zone near Pozzuoli (B–B′ profile), where the source of CO 2 has a less 13 C-depleted carbon isotope composition. This northwest-oriented profile shows a zone with less 13 C-depleted values of airborne CO 2 to northwest (Fig.  2 c), consistent with a decrease in airborne CO 2 concentrations (Fig.  2 a).

Remarkable variations in the stable isotope composition of airborne CO 2 can be identified east of the urban area of Naples (Fig.  2 c). In concordance with δ 18 O–CO 2 , δ 13 C–CO 2 shows remarkable variations along the seaside compared to the inland along the D–D′ profile (Fig.  3 d). Airborne CO 2 concentrations fluctuate, superimposed on a decrease from the seaside to the inland. According to this trend, the carbon isotope composition shows an opposite trend from the most 13 C-depleted values in the coastal zone to the less 13 C-depleted CO 2 inland, revealing that potential sources of CO 2 with heavily 13 C-depleted signatures force airborne CO 2 concentration in the coastal zone near Portici and Torre del Greco. These sources are less effective in forcing CO 2 concentration inland, near San Giorgio a Cremano.

Measurements of CO 2 concentration, combined with stable isotope compositions of airborne CO 2 , provide relevant data for distinguishing between natural and anthropogenic CO 2 emissions in the atmosphere, and potentially tracking the gas dispersal from various sources of greenhouse gases at the urban spatial scale (i.e., 10 4 –10 5  m). This method overcomes the inherent difficulty of studying CO 2 dispersion caused by its high background level and subtle spatial variations of airborne CO 2 concentration. Indeed, various sources of CO 2 have different isotopic signatures for both carbon and oxygen.

There are several methods for tracking the dispersion of gases emitted from a source into the atmosphere. The methods commonly used to track gas dispersion are based on models that require a priori knowledge of the source, the amount of gas emitted, and the geometry of the dispersion area. Isotopic studies combined with atmospheric chemistry follow a different paradigm. The data collected from field measurements underwent analysis utilizing the Keeling plot method mass balance models for oxygen and carbon isotopes 49 , 50 , 57 . The Keeling plot method facilitates the determination of the primary CO 2 source at the local level using observational data.

At the same time, the mass balance model for oxygen and carbon isotopes allows an assessment of the influences of the individual CO 2 sources on the local air composition. The mathematical expressions governing this model were developed within the framework of previous studies 20 and are expounded concisely upon in the method section dedicated to assessing additional CO 2 in the atmosphere. This method allows for detecting the forcing effects introduced by the gas sources on the composition of the atmosphere. The measurements utilized in the theoretical model results (see Eq. ( 11 ) in this study) furnish point-by-point estimates of additional CO 2 concentration (i.e., the C fs ) along the trajectory.

Subsequently, the interpolation of C fs values employing the Kriging algorithm model facilitates the simulation of CO 2 dispersion. This algorithm generates a predictive layer for δ 13 C–CO 2 , δ 18 O–CO 2 , CO 2 concentration, and C fs. This method has been successfully applied to detect chemical and isotopic effects on the air in the La Fossa caldera on the island of Vulcano, both during periods of quiescent outgassing and during the recent period of increased volcanic outgassing in 2021 20 .

The Keeling plot illustrates a correlation between the carbon isotope composition of CO 2 and the inverse of airborne CO 2 concentration. Figure  4 shows the concentration dataset normalized by the global reference for airborne CO 2 concentration (i.e., 423 ppm vol). Each straight line on this plot represents binary mixing between the atmospheric background and an additional CO 2 source. The intercept on the isotopic axis provides the carbon isotopic signatures, facilitating the identification of the CO 2 emission source.

The correlation between δ 13 C–CO 2 and the inverse of airborne CO 2 concentration (i.e., Keeling plot). Data were normalized against the Global reference values recorded by NOAA (a https://www.climate.gov/climatedashboard accessed on July 2, 2024. ( a ) Dataset collected over the target area. ( b ) Urbanized areas of Naples. Green circles distinguish the subset of measurement collected near the airport (zone C in Fig.  1 a) from those collected in downtown Naples (zone B in Fig.  1 a). ( c ) Pozzuoli–Solfatara–Agnano area (zone A in Fig.  1 a) Yellow circles distinguish the subset of magmatic origin from that of anthropogenic origin in the area (blue circles).

Figure  4 a displays several mixing lines between background air and various potential sources of CO 2 , including natural (e.g., soil and plant respiration or volcanic degassing) and anthropogenic origins (e.g., combustion of fossil fuels or natural gas and landfill CO 2 emissions), whose isotopic signatures were retrieved from previous studies 37 . A geometric mean regression is recommended for the analysis of a scattered dataset (i.e., R 2  < 0.980) in the Keeling plot due to the inherent bias associated with determining the carbon isotopic signature through the utilization of a linear regression model 58 . The line representing the isotopic signature of the forcing source can be derived by applying a standard regression and subsequently dividing by the r-coefficient. This corrective approach aims to approximate the geometric mean regression through the utilization of a standard estimate obtained from a linear regression model.

The dataset collected over the target area reveals a variety of mixing lines, highlighting the inherent complexity of identifying a single CO 2 source. The alignments of δ 13 C–CO 2 in the Keeling plot suggests that fossil fuel combustion is a significant source of greenhouse gases, resulting in airborne CO 2 concentrations ranging from > 600 to ~ 1410 ppm vol (i.e., normalized values are from 0.7 to 0.3, respectively). However, multiple CO 2 sources can influence airborne CO 2 concentrations in the target area, especially at low to intermediate values (i.e., from 423 to 600 ppm vol, corresponding to normalized values ranging 0.7–1). These results support findings that human-related activities, such as urban mobility by vehicles and household heating, predominantly based on the combustion of fossil fuels, contribute significantly to rise the airborne CO 2 concentration. Nonetheless, natural CO 2 emissions, such as those from volcanic outgassing, which is estimated on the synoptic scale to account for approximately 1% of total annual emissions, can locally play a pivotal role in the amount of CO 2 injected into the atmosphere.

A sector in Naples’ downtown (i.e., Zone B in Fig.  1 a), distinct from Zone A, which includes the Campi Flegrei volcanic/hydrothermal zone and the western suburbs of Naples (i.e., Bagnoli and Posillipo), can serve as a test site to quantify the specific contribution to increasing airborne CO 2 concentrations caused by human-related emissions. Figure  4 b illustrates δ 13 C–CO 2 against CO 2 concentrations, showing good agreement with the mixing line between background air and CO 2 produced by fossil fuel combustion, characterized by a heavily 13 C-depleted signature (i.e., δ 13 C–CO 2  =  − 29.94‰). Furthermore, data collected in the airport zone (i.e., Zone C in Fig.  1 a), where high levels of airborne CO 2 concentrations have been measured, indicate that the CO 2 source affecting both concentration and isotope composition of airborne CO 2 is of anthropogenic origin (i.e., δ 13 C–CO 2  =  − 29.31 ‰).

Figure  4 c illustrates the complex distribution of concentration and carbon isotope composition values detected in the study area, predominantly located in the urban area of Pozzuoli, in the western suburbs of Naples. Results of cluster analysis applied to a subset of measurements collected in the Zone A (Fig.  1 a) reveal that multiple CO 2 sources play an almost equivalent role in elevating the concentration of airborne CO 2 above background levels. One subset of measurements, with CO 2 concentrations in the range 423–700 ppm vol, exhibits an isotopic signature in good agreement with the mixing line between background air and CO 2 produced by the combustion of fossil fuels (i.e., δ 13 C–CO 2  =  − 32.93‰). Another subset of measurements indicates that δ 13 C–CO 2 of the air increases as CO 2 concentrations rise due to the influence of a less 13 C-depleted CO 2 source, with δ 13 C–CO 2 ≈ − 1.97‰. Although slightly lower, this value aligns with the carbon isotopic signature of CO 2 emitted from Pisciarelli and Bocca Grande fumaroles.

Those data retrieved from application of laboratory techniques to condensed fumarolic fluids have accuracy ± 0.1‰ 26 . Differences in the range Δ 13 C < 0.4‰ can be neglected because of the accuracy of the measurements with Deltaray (i.e., ± 0.25‰ according to 12 , 13 , 18 , 37 , 58 ).

Equation ( 11 ), included in the method section, facilitates the calculation of additional CO 2 in the air owing to either natural (i.e., volcanic/hydrothermal CO 2 ) or anthropogenic (i.e., produced by the combustion of fossil fuels) emissions. This calculation is based on input parameters in a theoretical model and measurements of airborne CO 2 concentration, δ 13 C–CO 2 , and δ 18 O–CO 2 in the field. C fs provides the concentration of the forcing source of CO 2 , exceeding local background levels in the atmosphere. A combination of the positioning of the endogenous sources of CO 2 and results of the Keeling plot helps distinguish the application of the mass balance model to the dispersal of volcanic CO 2 in the zone Solfatara (i.e., Zone A in Fig.  1 a) and the dispersal of CO 2 produced by the combustion of fossil fuels downtown Naples (i.e., Zone B in Fig.  1 a).

Figure  5 shows dispersions of CO 2 from anthropogenic origin in Naples’ downtown. In particular, the excess CO 2 concentrations in air produced by hydrocarbon combustion, which has a 13 C-depleted isotope composition compared to standard air (Fig.  4 b). For the calculation of the additional amount of CO 2 in the air, an anthropogenic source of CO 2 with the isotopic signature δ 13 C =  − 31.00‰ and δ 18 O =  − 16.00‰ has been adopted as the model parameters. Figure  5 a shows the fossil fuel-derived CO 2 has a heterogeneous distribution across the target area. A CO 2 dome 40 , 41 , 42 , 43 appears irregular and has numerous lobulations. The dome encloses islands where hydrocarbon combustion forces the CO 2 above the atmospheric background and generates concentration peaks even greater than + 300 ppm above the airborne CO 2 background (Fig.  5 b). One such island of high CO 2 concentration is well delineated in the harbour area, which is renowned for being among the Mediterranean's major harbours. In fact, the burning of hydrocarbons sustains the majority of the ship traffic in these areas. Another area with high CO 2 concentrations is located in the western downtown, near one of the most densely populated areas of Naples.

Dispersal of anthropogenic CO 2 in downtown Naples (cell size 10 m). The maps were generated in Qgis 3.34 environment ( https://qgis.org/download/ ) using Measurement interpolation generated in SAGA GIS environment ( https://saga-gis.sourceforge.io/en/index.html ). ( a ) CO 2 concentration map that shows the CO 2 concentration excess above the reference background. The concentration excess value of 83 ppm vol has been set as the threshold for transparency. ( b ) Vertical profile (black line in subplot a) of the excess CO 2 concentration across downtown Naples. ( c ) Wind vectors and speed recorded at C.N.R. station. ( d ) Wind direction frequency during survey.

The results of isotopic investigations prove the anthropogenic origin of atmospheric CO 2 . It is reasonable to assume that most of the anthropogenic CO 2 found in downtown Naples is the result of hydrocarbon combustion produced by urban mobility, given that the average air temperature during measurement collection was 22 °C (with an air temperature range of 19–23 °C). Within the one-day measurement acquisition timescale, variations in wind intensity and direction affecting the dispersion of CO 2 cannot be ruled out. This is particularly expected in Zone B, where the wind can influence the dispersion of emitted plumes near Naples' harbour. However, the data on wind direction (Fig.  5 c) and speed indicate that during the acquisition time window, the atmospheric circulation brought in SSW air, which is generally less enriched in anthropogenic CO 2 . Given the morphology of the study area and the local effects of densely built environments (Fig.  5 d), it is reasonable to assume a dilution effect of anthropogenic CO 2 due to the influence of less CO 2 —rich air from the sea. Accordingly, the anthropogenic CO 2 concentration along the C–C′ profile (Fig.  5 c) shows a notable increase in airborne CO 2 near the harbour and above a pedestrian area, suggesting that proximal sources of greenhouse gas emissions in the nearby areas are responsible for the increase in CO 2 above background levels.

Measurements collected at Pozzuoli (Zone A in Fig.  1 a) reveal multiple origins for CO 2 present in the air, namely volcanic and anthropogenic. Although human-related activities cause high concentrations of airborne CO 2 , a comparison with downtown can be made concerning the dispersal of geogenic CO 2 in the Pozzuoli area because the Campi Flegrei volcanic/hydrothermal system was in a state of unrest at the time of measurement collection 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 . Results of the cluster analysis provide a subset for calculating the amount of geogenic CO 2 that the main degassing zones at Campi Flegrei discharge into the atmosphere. The isotope composition and the airborne CO 2 concentration values of this subset were used in Eq. ( 11 ), with the values for the isotopic signatures of both carbon and oxygen for CO 2 emitted at Solfatara and Pisciarelli in May 2023 serving as model parameters (i.e., δ 13 C–CO 2  =  − 1.67‰ vs VPDB and δ 18 O–CO 2  =  − 7.85‰ vs VPDB) to obtain point-to-point calculations of volcanic CO 2 dispersal. These values provide insight into the dispersal of volcanic CO 2 from the main degassing vents at Campi Flegrei based on direct measurements and model parameters (Fig.  6 a). A comparison with the downtown map (Fig.  5 a) shows a more homogeneous dispersal of volcanic CO 2 along an N–S oriented dispersal zone. Furthermore, the volcanic CO 2 concentration is higher than 124 ppm vol above background levels in the area lying between Pozzuoli and Pisciarelli alone. Considering a background air CO 2 concentration of 423 ppm vol and the volcanic input calculated in the present study, this result is in good agreement with dispersal simulations averaged over a whole diurnal cycle obtained using the DISGAS software 14 . Measurements of concentration, corroborated by isotopic determinations, reveal volcanic CO 2 dispersion in the area of Bagnoli and eastward towards the urbanized area of Naples. In this area, measurements of airborne CO 2 concentrations alone are not able to track the dispersal of volcanic CO 2 because comparable absolute concentration values are found throughout the urban areas, where the additional CO 2 has anthropogenic origins. According to Granieri et al. 14 , inland air circulation prevails during nighttime in the Gulf of Naples, when volcanic CO 2 dispersal occurs towards the sea.

Dispersal of volcanic CO 2 in Pozzuoli-Solfatara zone (cell size 10 m). The maps were generated in Qgis 3.34 environment ( https://qgis.org/download/ ) using Measurement interpolation generated in SAGA GIS environment ( https://saga-gis.sourceforge.io/en/index.html ). ( a ) CO 2 concentration map that shows the CO 2 concentration excess above the reference background. The concentration excess value of 75 ppm vol has been set as the threshold for transparency. ( b ) Wind vectors and speed recorded at C.N.R. station. ( c ) Wind direction frequency during survey. ( d ) Vertical profile (black line in subplot a) of the excess CO 2 concentration across Pozzuoli-Solfatara area.

At the time of the survey, weather datasets reveal that NW winds blew from Solfatara towards the sea, even during the early morning (i.e., by ~ 8:00 UTC), after which sea breeze dominated air circulation from the SE throughout the daytime hours (Figura 6b,c). Arguably, the dispersal of volcanic CO 2 results from a combination of volcanic CO 2 dispersal and the residual layer that develops during nighttime and has not yet been disrupted by diurnal atmospheric turbulence. These results show that spatial surveys for studying airborne CO 2 helps in identifying multiple sources of greenhouse gases at the district scale of urban areas. Furthermore, stable isotope measurements allow an assessment of the impact of either volcanic degassing or anthropogenic emissions on airborne CO 2 concentrations.

The results of this study illustrate that integrating measurements of carbon and oxygen isotopic composition with those of CO 2 concentration aids in elucidating the genesis and development of CO 2 dome in urbanized areas. This represents a step forward in evaluating the impact of specific carbon dioxide sources, whether anthropogenic or natural, on the progression of climate change, as it facilitates the discernment of the underlying causes of urban domes through direct investigations.

The findings of this study also suggest that surveys conducted in urban areas such as Naples can be utilized to identify the primary regions for continuous monitoring of both natural and anthropogenic CO 2 emissions against global warming. Climate change has reached a global scale and threatens the stability of various vital sectors, including infrastructure, the economy, electricity production, international relations, biodiversity, and freshwater and food resources. Climate change affects all regions of the world, and its macroscopic effects manifest through extreme weather events, producing vast damage in cities and rural areas.

The international community is implementing a series of measures to combat ongoing climate change, which significantly impacts economic and social systems globally. For instance, several ambitious plans aim to reduce greenhouse gas emissions by 2050, mainly CO 2 . To achieve such ambitious goals, it is crucial to estimate and monitor CO 2 emissions, especially in urban areas where most CO 2 is produced through hydrocarbon combustion. Currently, no monitoring tools are available to detect near-real-time CO 2 emissions for individual countries. Therefore, efforts to monitor CO 2 in the air on a regional scale (synoptic ~ 10 6  m) with low latency (through the publication of hourly, daily, weekly, and annual data) via networks of stations installed in densely urbanized areas are becoming increasingly relevant. However, monitoring CO₂ in the atmosphere is not straightforward due to the high background concentration (approximately 400 ppm vol), which limits the potential for spatial variability. Consequently, monitoring the concentration alone may not always provide sufficient data for real-time estimation. Various studies demonstrate that integrating isotopic and concentration data provides information on the origin of CO 2 emissions 12 , 13 , 16 , 18 , 20 , 37 , 38 , 39 , 58 , 59 , 60 .

The δ 18 O–CO 2 largely depends on the CO 2 partitioning among the atmosphere, hydrosphere, lithosphere, and biosphere and can be deciphered through isotopic fractionation processes. Recent studies 12 , 18 , 19 , 20 show that it is possible to quantify atmospheric CO 2 emissions from natural and anthropogenic sources, isotopically characterized by δ 13 C–CO 2 and δ 18 O–CO 2 values, through integrated monitoring of atmospheric CO 2 concentration, isotopic composition, and meteorological data (direct investigations).

Therefore, the implementation of an active monitoring system is urgent and represents a paradigm shift in quantifying atmospheric CO 2 emissions at the scale of individual urbanized areas, compared to the currently applied methods based on statistical data at the national level for countries that are signatories to the United Nations Framework Convention on Climate Change 61 .

Instrument setup

The instrument employed for data acquisition in this study is a Delta Ray–Thermo Fisher Scientific. It measures the concentration of the isotopologues 13 COO, 12 COO, and CO 18 O based on the adsorption strength of light in the mid-infrared region (~ 4.3 μm) following the Lambert–Beer law. The 13 C/ 12 C and 18 O/ 16 O ratios are calculated using different concentration ratios of the isotopologues, while the total CO 2 concentration is determined by summing the concentrations of the three CO 2 isotopologues. Stable isotope ratios are expressed in agreement with the VPDB scale using the δ-notation (i.e., δ 13 C–CO 2 and δ 18 O–CO 2 , respectively) within the CO 2 concentration range of 200–3500 ppm vol.

The Delta Ray instrument is equipped with the QTegra software. A specially designed template includes protocols for recording δ 13 C–CO 2 , δ 18 O–CO 2 , and CO 2 concentration values, along with information on the sample list, acquisition parameters, referencing, evaluation settings, and sample definition. Instrument calibration and referencing against two working standards ensure an accuracy of ± 0.25‰ for isotope determinations and ± 1 ppm vol for CO 2 concentration measurements.

The instrument records each measurement of δ 13 C–CO 2 and δ 18 O–CO 2 at a frequency of 1 Hz. Before data acquisition, the instrument conducts isotope ratio referencing on the working standards at a fixed CO 2 concentration (i.e., CO 2  = 400 ppm vol) approximating background airborne CO 2 . After purging the unknown air sample for 60 s, the instrument skips the purge and measures the concentration of CO 2 isotopologues in the air. Once the air has purged the gas inlet, the instrument calculates δ 13 C–CO 2 and δ 18 O–CO 2 , as well as CO 2 concentration.

Measurement strategies

An off-road vehicle housed the instrument, and the equipment for measuring δ 13 C–CO 2 , δ 18 O–CO 2 , and airborne CO 2 concentrations during the studies across the urbanized zone of Naples. The positioning of the vehicle was recorded by a global positioning system device (GARMIN GPSMAP® 64 s), time-synchronized with the Delta Ray's internal clock. In specific urban environments 12 , 37 , 38 , 39 , 55 , 56 , 62 and, more recently, in volcanic regions 18 , 20 , investigations have been conducted utilizing mobile laboratories to analyze the spatial variability of CO 2 .

An inverter (12 V input–output, pure sine wave) was connected to the car's electrical system, supplying power to the instrument (~ 300 W). A stainless-steel capillary (1/16 in.; Swagelok-typeTM, 3 m long) was connected to the instrument's inlet, with the other end attached to the front of the car roof (~ 2.3 m above the ground) to avoid potential contamination from the gasoline engine exhaust. The air passed through a filter (2 μm, 1/16 in, capillary aperture) to prevent contamination from dust on the roads. Considering the volume of the sampling capillary, the instrument's flow rate (approximately 100–110 ml min −1 ), and the average speed of the mobile laboratory (approximately 3.5 m s −1 ), the delay between measurements and their corresponding positions is approximately 25 m. This delay is comparable to the GPS positioning.

A route of approximately 170 km (Table 1 ) was designed in the laboratory to obtain a continuous, non-overlapping path, covering various environments in the wide urbanized area of Naples (Fig.  1 a). The route includes Miseno, Bacoli, Agnano, Campi Flegrei caldera, Pozzuoli, Capodimonte, Bagnoli, Posillipo to the east of Naples' downtown, and Portici, Ercolano, Torre del Greco, and San Giorgio a Cremano to the west, respectively. The route was planned to ensure that segments did not overlap, preventing an increase in the statistical weight of some route segments over others. The route was meticulously followed using a routing application (e.g., Google Maps). The survey was completed in thirteen hours at an average speed of 13 km h −1 , with the spatial density of measurements corresponding to the metric order (~ 4 m average distance between measurements). The dataset encompasses ~ 41,000 georeferenced measurements for δ 13 C–CO 2 , δ 18 O–CO 2 , and CO 2 concentration, respectively 63 . This method was already employed for a simultaneous airborne CO 2 spatial survey at Vulcano and revealed the dispersion of volcanic CO 2 through direct measurements 18 , 20 .

Data processing approach

The data acquired from onsite measurements underwent processing utilizing the Keeling plot approach and mass balance models for oxygen and carbon isotopes. The Keeling plot enables the identification of the predominant CO 2 source at the local scale through observational data. The mass balance model for oxygen and carbon isotopes aims to quantify the impact of the CO 2 source on the local air. The algebraic equations for the model were developed as part of a previous study 18 and are detailed in the following paragraph of this paper, addressing the assessment of either volcanic or anthropogenic CO 2 in the air at Naples’ urban area. This methodology integrates measurements of stable CO 2 isotopes in the air with isotopic signatures of both the local CO 2 source, determined through the Keeling plot method 49 , 50 , and CO 2 in the background air. The theoretical outcomes of the model facilitate the partitioning of CO 2 in the air between the local background air and the CO 2 source.

The Keeling plot 49 , 50 , is the method broadly used to identify the isotopic signature of the gas source that increases CO 2 concentrations at the atmospheric background. The Keeling plot method facilitates the examination of the primary origin of atmospheric CO 2 by analyzing the δ 13 C–CO 2 against the reciprocal of CO 2 concentration. This method relies on mass balance principles, wherein a local CO 2 source alters the concentration from the atmospheric baseline. Mathematically, this is expressed by equations:

where C and δ 13 C denote CO 2 concentration and δ 13 C–CO 2 , respectively. Subscripts denote measured values (m), atmospheric background (a), and local source (fs). The linear combination of these equations generates a straight line in the δ 13 C versus 1/C plot 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 as delineated by equation

Equation ( 3 ) provides insight into the carbon isotope composition of the local CO 2 source under constant background and CO 2 source conditions.

To identify the main CO 2 source downtown Naples, a subset of measurements was selected. This subset encompasses measurements collected in an area of 24.60 km 2 centred in the Plebiscito square district of Naples (436,676.0 E and 4,520,817.0 N). Another subset, with its centre in the Pozzuoli area (Lat: 428,058.0 E; Long: 4,520,131.0 N, UTM), was selected for comparative purposes with data collected in Naples’ downtown. Specifically focusing on Pozzuoli (Zone A), the assessment focused on volcanic CO 2 as the primary source of CO 2 in the air. The measurements used in the theoretical model results (Eq. ( 11 ) reported below in this study) provide the concentration of the isotopically marked CO 2 source (e.g., volcanic or anthropogenic), causing the airborne CO 2 concentration to exceed the background concentration, point by point within the area. In the case of the Solfatara-Pisciarelli degassing area (Zone A in Fig.  1 ), the circular area is 47.28 km 2 , and the theoretical model provides the concentration of volcanic CO 2 (C V ). Following this calculation, the interpolation of C V values using the Kriging algorithm generates simulations of CO 2 from the forcing source (volcanic or anthropogenic for Solfatara-Pisciarelli and Naples' downtown, respectively). This algorithm produces the prediction layer for δ 13 C–CO 2 , δ 18 O–CO 2 , CO 2 concentration, and concentration (C V or C F , respectively) based on the assumption that each interpolating variable changes linearly with the distance between adjacent measurements. This assumption aligns with the expected homogeneity of spatial variations in atmospheric variables at the local scale 7 . Kriging interpolation is a geostatistical method used to estimate unknown values of each spatial variable based on known measurements of δ 13 C–CO 2 , δ 18 O–CO 2 , and CO 2 concentration at specific measurement points. The spatial correlation of the data is modeled using a Gaussian variogram, a standard variogram model defined by the equation:

where γ(h) is the semivariance at lag distance h, C 0 is the nugget, C is the partial sill, and a is the range. The kriging system of equations is set up using the Gaussian variogram model to determine the weights assigned to each known data point. These weights are calculated to minimize the estimation variance for the unknown points. The Gaussian model ensures smooth interpolation with continuous and differentiable transitions between estimated values, reflecting the assumed autocorrelation structure of the data.

Based on variogram analysis, CO 2 concentration measurements (Supplementary Fig. S1 online) are spatially dependent up to 700 m (i.e., the range), beyond which they become substantially independent. The range for δ 13 C–CO 2 , indicating the distance at which spatial correlation between carbon isotope measurements becomes negligible, has also been set to 700 m for kriging interpolation. For δ 18 O–CO 2 measurements, the range was determined to be 800 m. The partial sill was calculated as 1780 for CO₂ concentration, 1.62 for δ 13 C–CO 2 , and 1.65 for δ 18 O–CO 2 , indicating the variance attributable to the spatial structure for each variable. Simulations of stable isotope variables, airborne CO 2 concentration, and volcanic CO 2 dispersion were executed using the SAGA GIS software package ( https://saga-gis.sourceforge.io/en/index.html ).

Quantification of the CO 2 input in the atmosphere

An appropriate mass balance model for airborne CO 2 incorporates both isotopic parameters and concentration. Utilizing literature values for δ 13 C–CO 2 and δ 18 O–CO 2 of standard air (e.g., δ 13 C–CO 2  =  − 8‰ and δ 18 O–CO 2  =  − 0.1‰ 50 ) alongside values specific to CO 2 of external sources (e.g., either volcanic/hydrothermal or fossil fuel derived CO 2 ), an isotopic mass balance model incorporates four unknowns: background air CO 2 concentration, CO 2 concentration in the forcing source of gas, air CO 2 mixing fraction, and volcanic CO 2 mixing fraction.

The model is expressed by Eq. ( 5 ), which represents the CO 2 concentration in the air:

where C represents the CO 2 concentration and X denotes the mixing fraction between forcing source and atmospheric CO 2 , with subscripts m, a, and fs referring to measured, background, and local forcing source of CO 2 , respectively. This model operates under the assumptions that external source (i.e., volcanic or fossil fuel derived CO 2 ) significantly elevates CO 2 concentration relative to background levels.

The binary mixing equation to determine the relative weights of CO 2 from volcanic and atmospheric sources is given by Eq. ( 6 ):

Similarly, Eqs. ( 7 )

describe the isotopic mass balance models for carbon and oxygen isotopes of CO 2 , respectively. The combination of Eq. ( 6 ) and ( 7 ) provides Eq. ( 9 ), which allows for the calculation of X a

Using Eq. ( 9 ) in Eq. ( 8 ) yields Eq. ( 10 ), enabling the determination of X FS

By employing both Eqs. ( 9 ) and ( 10 ) and rearranging Eq. ( 5 ), we derive Eq. ( 11 )

which provides the concentration of CO 2 produced by the local effective gas source in the air C fs .

Airborne CO 2 partitioning of between volcanic and human related components

Cluster analysis was conducted to explore the relationships between airborne CO 2 concentrations and carbon isotope composition. Cluster analysis facilitates the classification of observational datasets into distinct classes based on specified similarity criteria. The objective of this analysis is to discern several groups of data that exhibit internal homogeneity (i.e., similarity criteria) while displaying heterogeneity among themselves concerning both CO 2 concentration and stable isotope compositions (i.e., δ 13 C–CO 2 and δ 18 O–CO 2 values). Various clustering methods are available for partitioning datasets (e.g., k-means, hierarchical, and two-way clustering), each differing in the requirement of preselecting the number of clusters, statistical properties of the dataset, or computational complexity.

Hierarchical clustering enables the grouping of objects such that those within a group are similar to each other and distinct from objects in other groups. Hierarchical clustering holds an advantage over alternative methods as it obviates the necessity of specifying the number of clusters a priori. The hierarchical structure of clusters can be formed using partitioning algorithms, initially considering all objects as individual clusters. Subsequently, through an iterative process, objects are assigned to different clusters based on principles maximizing the inter-cluster distances. One variant of hierarchical clustering is agglomerative clustering, where each object begins as its own cluster, and pairs of smaller clusters are successively merged until all data is encompassed within a single cluster. Essentially, hierarchical clustering assesses object similarity (i.e., distance) to form new clusters. Cluster merging is predicated on the Euclidean distance metric, reflecting the sum of squares of object coordinates in Euclidean space. Calculation of Euclidean distances leads to the updating of the distance matrix, with the iterative process culminating in the merging of the last two clusters into a final cluster encompassing the entire dataset.

Multiple approaches exist for computing inter-cluster distances and updating the proximity matrix, with some (e.g., single linkage or complete linkage) assessing minimum or maximum distances between objects from different clusters. In the cluster analysis of our dataset, we employed the Ward approach, which evaluates cluster variance rather than directly measuring distances, aiming to minimize variance among clusters. In Ward's method, the distance between two clusters is contingent upon the increase in the sum of squares when the clusters are combined. Ward's method implementation seeks to minimize the sum of squares distances of points from cluster centroids. In contrast to other distance-based methods, Ward's method exhibits less susceptibility to noise and outliers. Hence, in this paper, the Ward method is preferred over alternative methods for clustering.

Conclusions

This study presents findings from a spatial survey conducted in the metropolitan area of Naples, Italy, aimed at examining potential variations in atmospheric CO 2 sources. The urban zone of Naples was chosen due to its diverse CO 2 sources, including those from both geological (e.g., volcanic/hydrothermal emissions) and anthropogenic (e.g., combustion-related) origins. Situated within the extensive volcanic zone hosting Vesuvius, Campi Flegrei, and active volcano on Ischia, Naples provides a compelling location for such investigation owing to its dense urban population compared to other urban areas in the European continent.

Identification of CO 2 sources was facilitated through a combination of stable isotopic analysis and concentration measurements. Stable isotopic composition (i.e., carbon and oxygen isotopic ratios) and airborne CO 2 concentration were measured using a high-precision laser-based analyzer installed in an SUV vehicle. Measurements, recorded at 1 Hz, were synchronized with GPS data to ascertain spatial positioning, achieving a spatial resolution on a metric scale.

Spatial variations in both isotopic composition and concentration were derived from the dataset using the kriging algorithm with Gaussian autocorrelation. Resulting maps delineated three zones characterized by elevated CO 2 concentrations exhibiting distinct stable isotopic signatures. The zone with the highest CO 2 concentration encompassed Naples’ downtown and harbour district, while intermediate concentrations were observed inland across the urban area. Spatial simulations indicated lower CO 2 concentrations along the seaside to the west of downtown, consistent with local morning atmospheric circulation patterns oriented from SW to NE. Additional zones of heightened CO 2 concentrations were identified near the airport, situated northeast of downtown, and in proximity to inhabited areas such as Pozzuoli and Pisciarelli, near Solfatara to the west. These last areas (Pozzuoli and Pisciarelli) exhibit manifestations of a broad hydrothermal/magmatic system beneath the Campi Flegrei, constituting a geological source of airborne CO 2 . Anthropogenic CO 2 emissions, primarily from vehicular engine combustion, were found to elevate CO 2 concentrations above background levels in downtown Naples, near the airport, and in the vicinity of Solfatara.

A mixing model incorporating stable isotope composition and airborne CO 2 concentration allowed quantification of CO 2 contributions from different sources. Geochemical modeling based on this approach revealed spatial dispersal patterns of additional CO 2 near Solfatara and downtown Naples, with volcanic CO 2 dispersing northeastward under prevailing morning winds northeast oriented. This volcanic CO 2 extends beyond the hydrothermal zone, supplementing anthropogenic CO 2 emissions from vehicular traffic.

This study underscores the utility of combining isotopic and CO 2 concentration data for discerning the dispersion of both endogenous greenhouse CO 2 and emissions from anthropogenic activities. Particularly relevant in densely populated volcanic/hydrothermal regions, this methodology effectively distinguishes between natural and anthropogenic gas emissions in the atmosphere, overcoming challenges associated with high background levels and subtle spatial variations of the airborne CO 2 . Measurements in Naples were collected within a single day, during the diurnal phase of the planetary boundary layer (PBL) evolution, under turbulent conditions and mixing of the atmospheric layer closest to the ground. Consequently, the CO 2 dispersal maps represent average conditions for the urban area of Naples. Establishing monitoring programs for the concentration and isotopic composition of airborne CO 2 in Naples and other cities is crucial for studying the impact of the daily evolution of the PBL on potential variations in airborne CO 2 . This is particularly important in areas where geogenic sources (i.e., volcanic or hydrothermal) coexist with anthropogenic CO 2 emissions (e.g., from fossil fuel combustion) resulting from high population density.

Data availability

The datasets generated during and/or analyzed during the current study are available in the ZENODO repository, https://zenodo.org/records/11300873 .

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Contributions

R.M.R.D.M.: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing Original Draft, Writing—Review & Editing, Visualization, Supervision; S.G.: Conceptualization, Investigation, Writing—Review & Editing; A.P.: Writing—Review & Editing; S.C.: Writing—Review & Editing; A.S.: Investigation, Writing—Review & Editing.

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Di Martino, R.M.R., Gurrieri, S., Paonita, A. et al. Unveiling spatial variations in atmospheric CO 2 sources: a case study of metropolitan area of Naples, Italy. Sci Rep 14 , 20483 (2024). https://doi.org/10.1038/s41598-024-71348-9

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Published : 03 September 2024

DOI : https://doi.org/10.1038/s41598-024-71348-9

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