Pollution Prevention Case Studies

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This chapter describes the reported pollution prevention case studies compiled from interviews, newsletters, magazines, reports, technical papers, books, and government documents.

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AMOCO and USEPA (1990) Pollution Prevention Project: Yorktown Refinery Project Work-plan, p. 35

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Bringer RP, Benforado DM (1993) Pollution Prevention and Total Quality Environmental Management. In Environmental Strategies Handbook, edited by VK Rao, McGraw-Hill Inc. pp. 165–188

CEQ (1990) The 21st Annual Report of the Council on Environmental Quality, Washington, DC, p. 90

Chemecology (1993) CHEMNOTES, June 1993. CMA (1993). Chemicology. A Chemical Manufacturing Association publication, Vol. 22, No. 6, pp. 14–15, October 1993

CMA (1993) Chemicology. A Chemical Manufacturing Association publication, Vol. 22, No. 6, pp. 14 —15, October 1993

Cortese A (1992) “Education for an Environmentally Sustainable Future,” Environmental Science and Technology, Vol. 26, No. 617

IBM (1993) A Progress Report on IBM and Environment, Corporate Environmental Programs, Somers, New York

Mullins ML (1994) “Industry Perspective: Environmental Health and Safety Challenges and Social Responsibilities” in Environmental Strategies Handbook, edited by RV Kolluru, McGraw-Hill, Inc, pp. 201–238

Nelson KE, Lindsly JA (1991) “Case Study: Winning Ideas Waste at Dow,” Pollution Prevention Review, pp. 167–174, spring 1991

NYSDEC (1990) Success Story Fact Sheet, compiled by New York State Department of Environmental Conservation, Albany, NY, April 1990

Podar MK (1990) “Integrated permits and multimedia pollution prevention,” Proceedings of the International Conference on Pollution Prevention: Clean Technology and Clean Products, June 10–13, 1990 in Washington, DC, p. 525

Post JE (1994) “Regulation, Markets, and Management Education,” in Environmental Strategies Handbook, edited by VK Rao, McGraw-Hill Inc, pp. 26–23

Susag RH (1989) “Pollution prevention pays: the 3M approach to waste management,” American Academy of Environmental Engineers, Proceedings of the Workshop on Environmental Issues Related to Industrial Competitiveness, April 9–12, 1989, pp. 110–111

USEPA (1990) Pollution Prevention News, USEPA Office of Pollution Prevention, Washington, DC, August 1990

USEPA (1991) Toxic in the Community — National and Local Perspectives. Chapter 6 The 33/50 Program Chemicals of The National Report on Pesticides and Toxic Substances (TS–779), EPA 560/4–91–014, September 1991

USEPA (1993) Pollution Prevention News, USEPA Office of Pollution Prevention and Toxics, Washington, DC, Nov—Dec, 1993

WEC (1994) Personal Communication with Mr. Romuald Michalek, Vice President of the World Environment Center, New York

WH (1991) The White House Office of the Press Secretary News Release, October 30, 1991

WH (1992) The White House Office of the Press Secretary News Release, December 12, 1992

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© 1995 Springer-Verlag Berlin Heidelberg

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Shen, T.T. (1995). Pollution Prevention Case Studies. In: Industrial Pollution Prevention. Environmental Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03110-0_14

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environmental pollution

Environmental Pollution

Jul 19, 2014

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Environmental Pollution. Pollution Control to save the Earth. Contents. What is Environmental Pollution?. The word pollution comes from the Latin word “ pollutionem ” meaning to defile or to make dirty.

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  • leaf abscission
  • indoor air pollution
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  • long chain phenolic chemicals
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Environmental Pollution Pollution Control to save the Earth

What is Environmental Pollution? The word pollution comes from the Latin word “pollutionem” meaning to defile or to make dirty. For normal and healthy living a conducive environment is required by all the living beings, including humans, livestock, plants, micro-organisms and the wildlife. The favourable unpolluted environment has specific composition. When this composition gets changed by addition of harmful substances, the environment is called polluted environment and the substances polluting it are called pollutants. Environmental pollution can, therefore, be defined as any undesirable change in the physical, chemical or biological characteristics of any component of the environment (air, water, soil), which can cause harmful effects on various forms of life or property.

Types of Environmental Pollution Air Pollution Water Pollution Soil Pollution Marine Pollution Noise Pollution Thermal Pollution Nuclear Hazards

Types of Pollutants 1) On the basis of decomposition - A) Non degradable Pollutants • e.g., Aluminium cans, Hg, Pb, Cd , lonf chain phenolic chemicals, DDT. • They pass on from one biological system to another. • B) Bio-degradable Pollutants. • e.g., Domestic sewage 2) On the basis of nature - A) Physical pollutants: Heat, noise, radioactive pollutants B) Chemical Pollutants: Oxides of carbon, sulphur, nitrogen, different halogen gases. C) Biological pollutants: Bacteria, virus, fungi etc.

Types of Pollutants 3) On the basis of state - A) Solid particulate pollutant B) Liquid particulate pollutant: Leaked oil slicks oil sea water, dissolved solids, oils and grease C) Gaseous particulate pollutant: CFCs, SOx, NOx etc. 4) On the basis of their existence in nature - A) Quantitative pollutant: These are the substances which occur in the environment but becomes pollutant when their concentration reaches beyond a threshold value in the environment. Example: CO2, NO2 etc B) Qualitative pollutant: These are the substances which do not occur in the environment but are passed into it through human activity. Example: fungicides, herbicides, DDT etc

Types of Pollutants 5) On the basis of their of source of origin - A) Point source pollutant: When the origin source is point e.g., emissions from stack. B) Line source pollutant: transportation of material by conveyor belts, road rollers, run off seepage etc. C) Area source pollutant: When the source is an area e.g., blasting in mines

Air Pollution It is an atmospheric condition in which certain substances (including the normal constituents in excess) are present in concentrations which can cause undesirable effects on man and his environment. These substances include gases, particulatematter, radioactive substances etc. Gaseous pollutants include oxides of sulphur (mostly SO2,SO3) oxides of nitrogen mostly (NO and NO2 or NOx) carbon monoxide (CO), volatile organic compounds (mostly hydrocarbons) etc. “Particulatepollutantsincludesmoke, dust, soot, fumes, aerosols, liquiddroplets, pollen grains etc. Radioactive pollutants include Radon-222, iodine-131,strontium-90, plutonium-239 etc.

Classification of Air Pollutants 1) On the basis of decomposition - A) Non degradable Pollutants e.g. Aluminium cans, Hg, Pb, Cd , long chain phenolic chemicals, DDT. They pass on from one biological system to another. B) Bio-degradable Pollutants e.g. Domestic sewage. 2) On the basis of chemical composition - Organic pollutants e.g. Hydrocarbons, aldehydes, ketones, amines etc. B) Inorganic Pollutants e.g. Carbon compounds (CO and carbonates), sulphur compounds (H2S, SO2, H2SO4), nitrogen compounds (NOx, NH3), different halogen compounds (HF, HCl and metallic fluorides)

Classification of Air Pollutants Inorganic Particles (e.g. Fly ash, silica, asbestos and dusts from transport, mining, metallurgical and other industrial activities 3) On the basis of state of matter - A) Gaseous Pollutants Which get mixed with the air and do not normally settle out, e.g., CO, NOx and SO2 B) Particulate Pollutants Which comprise of finely divided soilds or liquids and often exist in colloidal state as aerosols, e.g., smoke, fumes, dust, mist, fog, smog and sprays

Sources of Air pollution 1) Natural sources • Natural contaminants usually present in the air are pollen, fungal spores, bacteria and marsh gas. • CO from the breakdown of methane • Volcanic eruptions • Forest fire release smoke and harmful trace gases • Salt spray from oceans • Dust storms. 2) Man-made sources • thermal power plants, industrial units, • vehicular emissions, • fossil fuel burning, • agricultural activities

Sources of Air pollution Thermal power plants have become the major sources of generating electricity in India as the nuclear power plants couldn’t be installed as planned. The main pollutants emitted are fly ash and SO2. Metallurgical plants also consume coal and produce similar pollutants. Fertilizer plants, smelters, textile mills, tanneries, refineries, chemical industries, paper and pulp mills are other sources of air pollution. Automobile exhaust is another major source of air pollution. Automobiles release gases such as carbon monoxide (about 77%), oxides of nitrogen (about 8%) and hydrocarbons (about 14%). Heavy duty diesel vehicles spew more NOx and suspended particulate matter (SPM) than petrol vehicle which produce more carbon monoxide and hydrocarbons.

Sources of Air pollution • Indoor Air Pollution • use of fuels like coal, dung-cakes, wood, kerosene in their kitchens (incomplete • combustion produces carbon monoxide. Coal contains varying amounts of sulphur which on burning produces sulphur dioxide. • Fossil fuel burning produces black soot. These pollutants i.e. CO, SO2, soot and many others like formaldehyde, benzene α pyrene (BAP) are toxic and harmful for health) • Smoking (pyrene is also found in cigarette smoke) • using wood as fuel for cooking inhales BAP (equivalent to 20 packets of cigarette a day) • radioactive Radon (Ra222) causes indoor air pollution in concrete buildings.

Effects of Air pollution 1) Effects on human health - • A) Irritation on the respiratory tract. • B) Irritation of eye, nose and throat. • C) Pb particulate cause convulsions, coma and even death • D) HF causes fluorosis and mottling of teeth • E) A variety of pollens can initiate asthmatic attack • F) Hg results in kidney, nerve and brain damage • G) Ni causes respiration damage. • H)Cd particulates through cigarrette smoking cause cardiovascular diseases , kidney and liver damage and even death.

Effects of Air pollution 2) Effects on plants - • SO2 bleaches the leaf surface and causes chlorosis • NO2 causes premature leaf fall and necrosis. • O3 damage leaves • PAN is responsible for suppressed growth, silvering of lower leaf surface • Ethylene induces leaf abscission, leaf epinasty • CO causes curling and premature falling of leaves.

Effects of Air pollution 3) Effect on climate - • Increase of CO2 • Global warming • Melting of ice, glaciers • Thinning of ozone layer • Penetration of UV rays

Effects of Air pollution 4) Effects on aquatic life – Air pollutants mixing up with rain can cause high acidity (lower pH) in fresh water lakes. This affects aquatic life especially fish. Some of the freshwater lakes have experienced total fish death. 5) Effect on materials – Because of their corrosiveness, particulates can cause damage to exposed surfaces. Presence of SO2 and moisture can accelerate corrosion of metallic surfaces. SO2 can affect fabric, leather, paint, paper, marble and limestone. Ozone in the atmosphere can cause cracking of rubber. Oxides of nitrogen can also cause fading of acetate, cotton and rayon fibres.

Control Measures of Air pollution • Air pollution can be minimized by the following methods: • · Siting of industries after proper Environmental Impact Assessment studies • · Using low sulphur coal in industries • · Removing sulphur from coal (by washing or with the help of bacteria) • · Removing NOx during the combustion process • · Removing particulate from stack exhaust gases by employing electrostatic • precipitators, bag-house filters, cyclone separators, scrubbers etc. • · Vehicular pollution can be checked by regular tune-up of engines, • replacement of old, more polluting vehicles, installing catalytic converters, • by engine modification to have fuel efficient (lean mixtures) to reduce CO • and hydrocarbon emissions and burning fuels slow and cooler to reduce • NOx emission (Honda Technology) • · Using mass transport system, bicycles etc. • · Shifting to less polluting fuels (hydrogen gas) • · Using non-conventional sources of energy • · By using biological filters and bio-scrubbers • · By planting more trees

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  • Published: 08 May 2024

A meta-analysis on global change drivers and the risk of infectious disease

  • Michael B. Mahon   ORCID: orcid.org/0000-0002-9436-2998 1 , 2   na1 ,
  • Alexandra Sack 1 , 3   na1 ,
  • O. Alejandro Aleuy 1 ,
  • Carly Barbera 1 ,
  • Ethan Brown   ORCID: orcid.org/0000-0003-0827-4906 1 ,
  • Heather Buelow   ORCID: orcid.org/0000-0003-3535-4151 1 ,
  • David J. Civitello 4 ,
  • Jeremy M. Cohen   ORCID: orcid.org/0000-0001-9611-9150 5 ,
  • Luz A. de Wit   ORCID: orcid.org/0000-0002-3045-4017 1 ,
  • Meghan Forstchen 1 , 3 ,
  • Fletcher W. Halliday 6 ,
  • Patrick Heffernan 1 ,
  • Sarah A. Knutie 7 ,
  • Alexis Korotasz 1 ,
  • Joanna G. Larson   ORCID: orcid.org/0000-0002-1401-7837 1 ,
  • Samantha L. Rumschlag   ORCID: orcid.org/0000-0003-3125-8402 1 , 2 ,
  • Emily Selland   ORCID: orcid.org/0000-0002-4527-297X 1 , 3 ,
  • Alexander Shepack 1 ,
  • Nitin Vincent   ORCID: orcid.org/0000-0002-8593-1116 1 &
  • Jason R. Rohr   ORCID: orcid.org/0000-0001-8285-4912 1 , 2 , 3   na1  

Nature ( 2024 ) Cite this article

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  • Infectious diseases

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors 1 . Studies have shown that infectious disease risk is modified by changes to biodiversity 2 , 3 , 4 , 5 , 6 , climate change 7 , 8 , 9 , 10 , 11 , chemical pollution 12 , 13 , 14 , landscape transformations 15 , 16 , 17 , 18 , 19 , 20 and species introductions 21 . However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host–parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

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Data availability.

All the data for this Article have been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 .

Code availability

All the code for this Article has been deposited at Zenodo ( https://doi.org/10.5281/zenodo.8169979 ) 52 and GitHub ( https://github.com/mahonmb/GCDofDisease ) 53 . R markdown is provided in Supplementary Data 1 .

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Acknowledgements

We thank C. Mitchell for contributing data on enemy release; L. Albert and B. Shayhorn for assisting with data collection; J. Gurevitch, M. Lajeunesse and G. Stewart for providing comments on an earlier version of this manuscript; and C. Carlson and two anonymous reviewers for improving this paper. This research was supported by grants from the National Science Foundation (DEB-2109293, DEB-2017785, DEB-1518681, IOS-1754868), National Institutes of Health (R01TW010286) and US Department of Agriculture (2021-38420-34065) to J.R.R.; a US Geological Survey Powell grant to J.R.R. and S.L.R.; University of Connecticut Start-up funds to S.A.K.; grants from the National Science Foundation (IOS-1755002) and National Institutes of Health (R01 AI150774) to D.J.C.; and an Ambizione grant (PZ00P3_202027) from the Swiss National Science Foundation to F.W.H. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

These authors contributed equally: Michael B. Mahon, Alexandra Sack, Jason R. Rohr

Authors and Affiliations

Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Alexandra Sack, O. Alejandro Aleuy, Carly Barbera, Ethan Brown, Heather Buelow, Luz A. de Wit, Meghan Forstchen, Patrick Heffernan, Alexis Korotasz, Joanna G. Larson, Samantha L. Rumschlag, Emily Selland, Alexander Shepack, Nitin Vincent & Jason R. Rohr

Environmental Change Initiative, University of Notre Dame, Notre Dame, IN, USA

Michael B. Mahon, Samantha L. Rumschlag & Jason R. Rohr

Eck Institute of Global Health, University of Notre Dame, Notre Dame, IN, USA

Alexandra Sack, Meghan Forstchen, Emily Selland & Jason R. Rohr

Department of Biology, Emory University, Atlanta, GA, USA

David J. Civitello

Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

Jeremy M. Cohen

Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA

Fletcher W. Halliday

Department of Ecology and Evolutionary Biology, Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA

Sarah A. Knutie

You can also search for this author in PubMed   Google Scholar

Contributions

J.R.R. conceptualized the study. All of the authors contributed to the methodology. All of the authors contributed to investigation. Visualization was performed by M.B.M. The initial study list and related information were compiled by D.J.C., J.M.C., F.W.H., S.A.K., S.L.R. and J.R.R. Data extraction was performed by M.B.M., A.S., O.A.A., C.B., E.B., H.B., L.A.d.W., M.F., P.H., A.K., J.G.L., E.S., A.S. and N.V. Data were checked for accuracy by M.B.M. and A.S. Analyses were performed by M.B.M. and J.R.R. Funding was acquired by D.J.C., J.R.R., S.A.K. and S.L.R. Project administration was done by J.R.R. J.R.R. supervised the study. J.R.R. and M.B.M. wrote the original draft. All of the authors reviewed and edited the manuscript. J.R.R. and M.B.M. responded to reviewers.

Corresponding author

Correspondence to Jason R. Rohr .

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Competing interests.

The authors declare no competing interests.

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Peer review information.

Nature thanks Colin Carlson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 prisma flowchart..

The PRISMA flow diagram of the search and selection of studies included in this meta-analysis. Note that 77 studies came from the Halliday et al. 3 database on biodiversity change.

Extended Data Fig. 2 Summary of the number of studies (A-F) and parasite taxa (G-L) in the infectious disease database across ecological contexts.

The contexts are global change driver ( A , G ), parasite taxa ( B , H ), host taxa ( C , I ), experimental venue ( D , J ), study habitat ( E , K ), and human parasite status ( F , L ).

Extended Data Fig. 3 Summary of the number of effect sizes (A-I), studies (J-R), and parasite taxa (S-a) in the infectious disease database for various parasite and host contexts.

Shown are parasite type ( A , J , S ), host thermy ( B , K , T ), vector status ( C , L , U ), vector-borne status ( D , M , V ), parasite transmission ( E , N , W ), free living stages ( F , O , X ), host (e.g. disease, host growth, host survival) or parasite (e.g. parasite abundance, prevalence, fecundity) endpoint ( G , P , Y ), micro- vs macroparasite ( H , Q , Z ), and zoonotic status ( I , R , a ).

Extended Data Fig. 4 The effects of global change drivers and subsequent subcategories on disease responses with Log Response Ratio instead of Hedge’s g.

Here, Log Response Ratio shows similar trends to that of Hedge’s g presented in the main text. The displayed points represent the mean predicted values (with 95% confidence intervals) from a meta-analytical model with separate random intercepts for study. Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. See Table S 3 for pairwise comparison results. Effects of the five common global change drivers ( A ) have the same directionality, similar magnitude, and significance as those presented in Fig. 2 . Global change driver effects are significant when confidence intervals do not overlap with zero and explicitly tested with two-tailed t-test (indicated by asterisks; t 80.62  = 2.16, p = 0.034 for CP; t 71.42  = 2.10, p = 0.039 for CC; t 131.79  = −3.52, p < 0.001 for HLC; t 61.9  = 2.10, p = 0.040 for IS). The subcategories ( B ) also show similar patterns as those presented in Fig. 3 . Subcategories are significant when confidence intervals do not overlap with zero and were explicitly tested with two-tailed one sample t-test (t 30.52  = 2.17, p = 0.038 for CO 2 ; t 40.03  = 4.64, p < 0.001 for Enemy Release; t 47.45  = 2.18, p = 0.034 for Mean Temperature; t 110.81  = −4.05, p < 0.001 for Urbanization); all other subcategories have p > 0.20. Note that effect size and study numbers are lower here than in Figs. 3 and 4 , because log response ratios cannot be calculated for studies that provide coefficients (e.g., odds ratio) rather than raw data; as such, all observations within BC did not have associated RR values. Despite strong differences in sample size, patterns are consistent across effect sizes, and therefore, we can be confident that the results presented in the main text are not biased because of effect size selection.

Extended Data Fig. 5 Average standard errors of the effect sizes (A) and sample sizes per effect size (B) for each of the five global change drivers.

The displayed points represent the mean predicted values (with 95% confidence intervals) from the generalized linear mixed effects models with separate random intercepts for study (Gaussian distribution for standard error model, A ; Poisson distribution for sample size model, B ). Points that do not share letters are significantly different from one another (p < 0.05) based on a two-sided Tukey’s posthoc multiple comparison test with adjustment for multiple comparisons. Sample sizes (number of studies, n, and effect sizes, k) for each driver are as follows: n = 77, k = 392 for BC; n = 124, k = 364 for CP; n = 202, k = 380 for CC; n = 517, k = 1449 for HLC; n = 96, k = 355 for IS.

Extended Data Fig. 6 Forest plots of effect sizes, associated variances, and relative weights (A), Funnel plots (B), and Egger’s Test plots (C) for each of the five global change drivers and leave-one-out publication bias analyses (D).

In panel A , points are the individual effect sizes (Hedge’s G), error bars are standard errors of the effect size, and size of the points is the relative weight of the observation in the model, with larger points representing observations with higher weight in the model. Sample sizes are provided for each effect size in the meta-analytic database. Effect sizes were plotted in a random order. Egger’s tests indicated significant asymmetries (p < 0.05) in Biodiversity Change (worst asymmetry – likely not bias, just real effect of positive relationship between diversity and disease), Climate Change – (weak asymmetry, again likely not bias, climate change generally increases disease), and Introduced Species (relatively weak asymmetry – unclear whether this is a bias, may be driven by some outliers). No significant asymmetries (p > 0.05) were found in Chemical Pollution and Habitat Loss/Change, suggesting negligible publication bias in reported disease responses across these global change drivers ( B , C ). Egger’s test included publication year as moderator but found no significant relationship between Hedge’s g and publication year (p > 0.05) implying no temporal bias in effect size magnitude or direction. In panel D , the horizontal red lines denote the grand mean and SE of Hedge’s g and (g = 0.1009, SE = 0.0338). Grey points and error bars indicate the Hedge’s g and SEs, respectively, using the leave-one-out method (grand mean is recalculated after a given study is removed from dataset). While the removal of certain studies resulted in values that differed from the grand mean, all estimated Hedge’s g values fell well within the standard error of the grand mean. This sensitivity analysis indicates that our results were robust to the iterative exclusion of individual studies.

Extended Data Fig. 7 The effects of habitat loss/change on disease depend on parasite taxa and land use conversion contexts.

A) Enemy type influences the magnitude of the effect of urbanization on disease: helminths, protists, and arthropods were all negatively associated with urbanization, whereas viruses were non-significantly positively associated with urbanization. B) Reference (control) land use type influences the magnitude of the effect of urbanization on disease: disease was reduced in urban settings compared to rural and peri-urban settings, whereas there were no differences in disease along urbanization gradients or between urban and natural settings. C) The effect of forest fragmentation depends on whether a large/continuous habitat patch is compared to a small patch or whether disease it is measured along an increasing fragmentation gradient (Z = −2.828, p = 0.005). Conversely, the effect of deforestation on disease does not depend on whether the habitat has been destroyed and allowed to regrow (e.g., clearcutting, second growth forests, etc.) or whether it has been replaced with agriculture (e.g., row crop, agroforestry, livestock grazing; Z = 1.809, p = 0.0705). The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included enemy type (A), reference land use type (B), or land use conversion type (C). Data for (A) and (B) were only those studies that were within the “urbanization” subcategory; data for (C) were only those studies that were within the “deforestation” and “forest fragmentation” subcategories. Sample sizes (number of studies, n, and effect sizes, k) in (A) for each enemy are n = 48, k = 98 for Virus; n = 193, k = 343 for Protist; n = 159, k = 490 for Helminth; n = 10, k = 24 for Fungi; n = 103, k = 223 for Bacteria; and n = 30, k = 73 for Arthropod. Sample sizes in (B) for each reference land use type are n = 391, k = 1073 for Rural; n = 29, k = 74 for Peri-urban; n = 33, k = 83 for Natural; and n = 24, k = 58 for Urban Gradient. Sample sizes in (C) for each land use conversion type are n = 7, k = 47 for Continuous Gradient; n = 16, k = 44 for High/Low Fragmentation; n = 11, k = 27 for Clearcut/Regrowth; and n = 21, k = 43 for Agriculture.

Extended Data Fig. 8 The effects of common global change drivers on mean infectious disease responses in the literature depends on whether the endpoint is the host or parasite; whether the parasite is a vector, is vector-borne, has a complex or direct life cycle, or is a macroparasite; whether the host is an ectotherm or endotherm; or the venue and habitat in which the study was conducted.

A ) Parasite endpoints. B ) Vector-borne status. C ) Parasite transmission route. D ) Parasite size. E ) Venue. F ) Habitat. G ) Host thermy. H ) Parasite type (ecto- or endoparasite). See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and the focal independent variable (whether the endpoint measured was a host or parasite, whether the parasite is vector-borne, has a complex or direct life cycle, is a macroparasite, whether the study was conducted in the field or lab, habitat, the host is ectothermic, or the parasite is an ectoparasite).

Extended Data Fig. 9 The effects of five common global change drivers on mean infectious disease responses in the literature only occasionally depend on location, host taxon, and parasite taxon.

A ) Continent in which the field study occurred. Lack of replication in chemical pollution precluded us from including South America, Australia, and Africa in this analysis. B ) Host taxa. C ) Enemy taxa. See Table S 2 for number of studies and effect sizes across ecological contexts and global change drivers. See Table S 3 for pairwise comparison results. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variables included the main effects and an interaction between global change driver and continent, host taxon, and enemy taxon.

Extended Data Fig. 10 The effects of human vs. non-human endpoints for the zoonotic disease subset of database and wild vs. domesticated animal endpoints for the non-human animal subset of database are consistent across global change drivers.

(A) Zoonotic disease responses measured on human hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on non-human (animal) hosts (Z = 2.306, p = 0.021). Note, IS studies were removed because of missing cells. (B) Disease responses measured on domestic animal hosts responded less positively (closer to zero when positive, further from zero when negative) than those measured on wild animal hosts (Z = 2.636, p = 0.008). These results were consistent across global change drivers (i.e., no significant interaction between endpoint and global change driver). As many of the global change drivers increase zoonotic parasites in non-human animals and all parasites in wild animals, this may suggest that anthropogenic change might increase the occurrence of parasite spillover from animals to humans and thus also pandemic risk. The displayed points represent the mean predicted values (with 95% confidence intervals) from a metafor model where the response variable was a Hedge’s g (representing the effect on an infectious disease endpoint relative to control), study was treated as a random effect, and the independent variable of global change driver and human/non-human hosts. Data for (A) were only those diseases that are considered “zoonotic”; data for (B) were only those endpoints that were measured on non-human animals. Sample sizes in (A) for zoonotic disease measured on human endpoints across global change drivers are n = 3, k = 17 for BC; n = 2, k = 6 for CP; n = 25, k = 39 for CC; and n = 175, k = 331 for HLC. Sample sizes in (A) for zoonotic disease measured on non-human endpoints across global change drivers are n = 25, k = 52 for BC; n = 2, k = 3 for CP; n = 18, k = 29 for CC; n = 126, k = 289 for HLC. Sample sizes in (B) for wild animal endpoints across global change drivers are n = 28, k = 69 for BC; n = 21, k = 44 for CP; n = 50, k = 89 for CC; n = 121, k = 360 for HLC; and n = 29, k = 45 for IS. Sample sizes in (B) for domesticated animal endpoints across global change drivers are n = 2, k = 4 for BC; n = 4, k = 11 for CP; n = 7, k = 20 for CC; n = 78, k = 197 for HLC; and n = 1, k = 2 for IS.

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Peer review file, supplementary data 1.

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Mahon, M.B., Sack, A., Aleuy, O.A. et al. A meta-analysis on global change drivers and the risk of infectious disease. Nature (2024). https://doi.org/10.1038/s41586-024-07380-6

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Gas Stove Pollution Risk Is Greatest in Smaller Homes, Study Finds

Gas-burning ranges, a significant contributor to indoor pollution, can produce and spread particularly high levels of some pollutants in smaller spaces.

Yannai Kashtan leans over a small, white stove, adjusting a knob.

By Hiroko Tabuchi

For decades, scientists have worked to clean up air pollution from factories, cars and power plants. But researchers are increasingly turning their attention to the air that people breathe indoors. And one appliance has come to the fore as a source of pollutants harmful to human health: the humble gas stove.

A new study from researchers at Stanford University sheds light on how much Americans may be exposed, indoors, to nitrogen dioxide, which comes from burning coal and gas and has been linked to asthma and other respiratory conditions.

The researchers found that, across the country, short-term nitrogen dioxide exposure from typical gas stove use frequently exceeded benchmarks set by both the World Health Organization and the United States Environmental Protection Agency. In the longer term, using gas or propane stoves meant that the typical American could breathe in three-quarters of the nitrogen dioxide levels deemed safe by the W.H.O. within their own homes.

As with outdoor pollution , disadvantaged households may be more exposed, the researchers found. Because gas more easily spreads throughout smaller spaces, people in homes smaller than 800 square feet were exposed to four times more nitrogen dioxide in the long term than people in homes larger than 3,000 square feet, the study found. Black and Latino households were exposed to 20 percent more nitrogen dioxide compared with the national average.

“We’ve done a really good job in this country of reducing outdoor pollution,” said Rob Jackson, professor of earth system science at the Stanford Doerr School of Sustainability and a principal investigator on the study, which was published on Friday in Science Advances. “But we’ve ignored the risks that people face indoors. And that’s the air that we’re breathing most of the time.”

And though home cooks who use a gas stove are particularly exposed to nitrogen dioxide, he said, “we’re getting a better handle on the migration of pollution down the hall, to the living room and the bedroom.”

The focus on gas stoves isn’t without critics. When a Biden administration official spoke about the health hazards of gas stoves last year, Republican politicians and their allies accused the administration of overreach and of planning to ban gas stoves outright.

Next week, House Republicans are set to meet on a bill called the Hands Off Our Home Appliances Act, which would make it harder for the Department of Energy to set more stringent energy-efficiency standards on household appliances, including gas stoves.

Health experts say that the health risks posed by gas stoves are significant. “There really is no safe amount of exposure to these toxicants produced by gas or propane, or any fossil fuel, outside or inside,” said Kari Nadeau, chairwoman of the Department of Environmental Health at the Harvard T.H. Chan School of Public Health.

The Stanford study estimated that long-term exposure to nitrogen dioxide from stoves was likely causing up to 50,000 cases of asthma in children.

Some cities and counties have tried to move away from gas altogether, as part of a transition to cleaner forms of energy. Over the past few years, more than 140 cities and local governments have sought to restrict gas hookups in new buildings or have taken other measures to end the use of natural gas in new buildings, though those measures have been challenged in court .

“It isn’t ideal to tell people, they have to rip a perfectly good gas stove out of their home,” Dr. Jackson said. But requiring new homes to install electric stoves, which the study found had virtually no harmful emissions, made sense, he said. “Otherwise, we’re putting dirty polluting infrastructure into the next set of homes, and it will be there and 50 years. No one benefits from that.”

The Stanford team took direct measurements of nitrogen dioxide emissions and concentrations at about 100 homes in San Francisco, Los Angeles, New York City and other major U.S. cities, and used indoor air-quality monitoring and epidemiological risk calculations to estimate exposure and health consequences.

They found that home cooks were exposed to three times more nitrogen dioxide pollution compared to the average, said Yannai Kashtan, a Ph.D. candidate at Stanford and the study’s lead researcher. Mr. Kashtan was the subject of a recent article on the debate at Stanford about fossil fuel funding for climate research.

For this study, the researchers also found that the pollution traveled quickly out of the kitchen, down hallways, and into living rooms and bedrooms.

Good ventilation, for example turning on the range hood or opening a window, helped to reduce exposure. But more than that, the study found that “the kind of stove you cook on matters the most,” Mr. Kashtan said. “Ultimately, the best way is to reduce pollution at the source.”

Hiroko Tabuchi covers the intersection of business and climate for The Times. She has been a journalist for more than 20 years in Tokyo and New York. More about Hiroko Tabuchi

Learn More About Climate Change

Have questions about climate change? Our F.A.Q. will tackle your climate questions, big and small .

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