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Socio-demographic factors shaping the future global health burden from air pollution

Abstract

Exposure to ambient particulate matter (PM2.5) currently contributes to millions of global premature deaths every year. Here, we assess the pollution and health futures in five 2015–2100 scenarios using an integrated modelling framework. On the basis of a global Earth System Model (GFDL-ESM4.1), we find lower ambient PM2.5 concentrations, both globally and regionally, in future scenarios that are less fossil fuel-dependent and with more stringent pollution controls. Across the five scenarios, the global cumulative PM2.5-related deaths vary by a factor of two. However, the projected deaths are not necessarily lower in scenarios with less warming or cleaner air. This is because while reducing PM2.5 pollution lowers the exposure level, increasing the size of vulnerable populations can significantly increase PM2.5-related deaths. For most countries, we find that changes in socio-demographic factors (for example, ageing and declining baseline mortality rates) play a more important role than the exposure level in shaping future health burden.

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Fig. 1: An integrated modelling framework to assess future warming levels and PM2.5-related health burden.
Fig. 2: Cumulative CO2 emissions and PM2.5-related premature deaths from 2015 to 2100.
Fig. 3: Current and future distributions of annual mean PM2.5 concentrations and annual total premature deaths.
Fig. 4: Relative contributions of four individual factors to the 2015–2100 changes in PM2.5-related premature deaths.
Fig. 5: Projected ageing pattern and baseline mortality rates under all five scenarios.
Fig. 6: Projected energy mix and PM2.5 concentrations under three selected scenarios.

Data availability

All the data used in this study are publicly available and can be downloaded from the following links. (1) Ambient PM2.5 data: https://esgf-node.llnl.gov/search/cmip6/ (select Activity = ‘ScenarioMIP’, Institution ID = ‘NOAA-GFDL’, Variable = ‘mmrpm2p5’). (2) Future population data (two links): https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10 and https://beta.sedac.ciesin.columbia.edu/data/set/popdynamics-pop-projection-ssp-2010-2100. (3) GCAM energy mix data: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10. (4) Future baseline mortality rate projection: https://drupalwebsitepardee.s3-us-west-2.amazonaws.com/pardee/public/IFs+with+Pardee+7_45+Aug+22+2019.zip. (5) Future CO2 emissions data: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10. The data we processed and used to make the plots in this study are available at https://doi.org/10.5281/zenodo.7143285.

Code availability

Projections on future baseline mortality rates were retrieved using the International Futures (v7.45) software. No other software was used to collect the data. Python, MATLAB and R were used for data analysis, as well as ArcGIS Pro (v2.5) and Microsoft Excel (v2022). All computer codes are available at https://doi.org/10.5281/zenodo.7143285.

References

  1. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Burden by Risk 1990–2019 (Institute for Health Metrics and Evaluation, 2020); http://ghdx.healthdata.org/record/ihme-data/gbd-2019-burden-by-risk-1990-2019

  2. Burnett, R. et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc. Natl Acad. Sci. USA 115, 9592–9597 (2018).

    Article  CAS  Google Scholar 

  3. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Burden by Risk 1990–2017 (Institute for Health Metrics and Evaluation, 2018); http://ghdx.healthdata.org/record/ihme-data/gbd-2017-burden-risk-1990-2017

  4. Health Impacts of PM2.5 (State of Global Air, 2022); https://www.stateofglobalair.org/health/pm

  5. Vohra, K. et al. Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: results from GEOS-Chem. Environ. Res. 195, 110754 (2021).

    Article  CAS  Google Scholar 

  6. Lelieveld, J. et al. Effects of fossil fuel and total anthropogenic emission removal on public health and climate. Proc. Natl Acad. Sci. USA 116, 7192–7197 (2019).

    Article  CAS  Google Scholar 

  7. Scovronick, N. et al. The impact of human health co-benefits on evaluations of global climate policy. Nat. Commun. 10, 2095 (2019).

    Article  Google Scholar 

  8. Vandyck, T., Keramidas, K., Tchung-Ming, S., Weitzel, M. & Van Dingenen, R. Quantifying air quality co-benefits of climate policy across sectors and regions. Clim. Change 163, 1501–1517 (2020).

    Article  CAS  Google Scholar 

  9. Markandya, A. et al. Health co-benefits from air pollution and mitigation costs of the Paris Agreement: a modelling study. Lancet Planet. Health 2, e126–e133 (2018).

    Article  Google Scholar 

  10. Liang, X. et al. Air quality and health benefits from fleet electrification in China. Nat. Sustain. 2, 962–971 (2019).

    Article  Google Scholar 

  11. Buonocore, J. J. et al. Health and climate benefits of different energy-efficiency and renewable energy choices. Nat. Clim. Change 6, 100–105 (2016).

    Article  Google Scholar 

  12. Wu, R. et al. Air quality and health benefits of China’s emission control policies on coal-fired power plants during 2005–2020. Environ. Res. Lett. 14, 094016 (2019).

    Article  Google Scholar 

  13. Gallagher, C. L. & Holloway, T. Integrating air quality and public health benefits in U.S. decarbonization strategies. Front. Public Health 8, 563358 (2020).

    Article  Google Scholar 

  14. Thompson, T. M., Rausch, S., Saari, R. K. & Selin, N. E. A systems approach to evaluating the air quality co-benefits of US carbon policies. Nat. Clim. Change 4, 917–923 (2014).

    Article  Google Scholar 

  15. Peng, W., Yang, J., Lu, X. & Mauzerall, D. L. Potential co-benefits of electrification for air quality, health, and CO2 mitigation in 2030 China. Appl. Energy 218, 511–519 (2018).

    Article  CAS  Google Scholar 

  16. West, J. J. et al. Co-benefits of mitigating global greenhouse gas emissions for future air quality and human health. Nat. Clim. Change 3, 885–889 (2013).

    Article  CAS  Google Scholar 

  17. Choma, E. F. et al. Health benefits of decreases in on-road transportation emissions in the United States from 2008 to 2017. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2107402118 (2021).

  18. Liu, Y. et al. Population aging might have delayed the alleviation of China’s PM2.5 health burden. Atmos. Environ. 270, 118895 (2021).

    Article  Google Scholar 

  19. Kruk, M. E. et al. High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob. Health 6, e1196–e1252 (2018).

    Article  Google Scholar 

  20. Chowdhury, S., Dey, S. & Smith, K. R. Ambient PM2.5 exposure and expected premature mortality to 2100 in India under climate change scenarios. Nat. Commun. 9, 318 (2018).

    Article  Google Scholar 

  21. Yin, H. et al. Population ageing and deaths attributable to ambient PM2·5 pollution: a global analysis of economic cost. Lancet Planet. Health 5, e356–e367 (2021).

    Article  Google Scholar 

  22. IPCC Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V., et al.) (Cambridge Univ. Press,Cambridge, UK and New York, NY, USA, 2021).

  23. O’Neill, B. C. et al. Achievements and needs for the climate change scenario framework. Nat. Clim. Change 10, 1074–1084 (2020).

    Article  Google Scholar 

  24. O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).

    Article  Google Scholar 

  25. Horowitz, L. W. et al. The GFDL global atmospheric chemistry-climate model AM4.1: model description and simulation characteristics. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2019MS002032 (2020).

  26. Dunne, J. P. et al. The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): overall coupled model description and simulation characteristics. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2019MS002015 (2020).

  27. Krasting, J. P. et al. NOAA-GFDL GFDL-ESM4 Model Output Prepared for CMIP6 ScenarioMIP Version 20180701. (Earth System Grid Federation, 2018); https://doi.org/10.22033/ESGF/CMIP6.1414

  28. International Futures (IFs) Modeling System V. 7. 45 (Frederick S. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, 2020); https://pardee.du.edu/access-ifs

  29. Murray, C. L. et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1223–1249 (2020).

    Article  Google Scholar 

  30. Stanaway, J. D. et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study. Lancet 392, 1923–1994 (2018).

    Article  Google Scholar 

  31. Hausfather, Z. & Peters, G. P. Emissions – the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).

    Article  CAS  Google Scholar 

  32. Ou, Y. et al. Can updated climate pledges limit warming well below 2 °C? Science 374, 693–695 (2021).

    Article  CAS  Google Scholar 

  33. Global Health Impacts of Air Pollution (State of Global Air, 2020).https://www.stateofglobalair.org/sites/default/files/documents/2020-10/soga-2020-report-10-26_0.pdf

  34. Coates, M. M. et al. Burden of disease among the world’s poorest billion people: an expert-informed secondary analysis of Global Burden of Disease estimates. PLoS ONE 16, e0253073 (2021).

    Article  CAS  Google Scholar 

  35. Rao, S. et al. Future air pollution in the Shared Socio-economic Pathways. Glob. Environ. Change 42, 346–358 (2017).

    Article  Google Scholar 

  36. Tibrewal, K. & Venkataraman, C. Climate co-benefits of air quality and clean energy policy in India. Nat. Sustain. 4, 305–313 (2021).

    Article  Google Scholar 

  37. Fourth National Climate Assessment Vol. II (U.S. Global Change Research Program, 2018); https://doi.org/10.7930/NCA4.2018

  38. IPCC Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, Cambridge, UK and New York, NY, USA, 2022).

  39. Feng, L. et al. The generation of gridded emissions data for CMIP6. Geosci. Model Dev. 13, 461–482 (2020).

    Article  CAS  Google Scholar 

  40. Spiller, E., Proville, J., Roy, A. & Muller, N. Z. Mortality risk from PM2:5: a comparison of modeling approaches to identify disparities across racial/ethnic groups in policy outcomes. Environ. Health Perspect. 129, 127004 (2021).

    Article  Google Scholar 

  41. O’Neill, M. S. et al. Health, wealth, and air pollution: advancing theory and methods. Environ. Health Perspect. 111, 1861–1870 (2003).

    Article  Google Scholar 

  42. A conversation on the impacts and mitigation of air pollution. Nat. Commun. 12, 5823 (2021).

  43. Liu, J. Y. et al. The importance of socioeconomic conditions in mitigating climate change impacts and achieving Sustainable Development Goals. Environ. Res. Lett. 16, 014010 (2020).

    Article  Google Scholar 

  44. O’Neill, B. C. et al. The effect of education on determinants of climate change risks. Nat. Sustain. 3, 520–528 (2020).

    Article  Google Scholar 

  45. Peng, W. et al. Climate policy models need to get real about people - here’s how. Nature 594, 174–176 (2021).

    Article  CAS  Google Scholar 

  46. O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  47. Lamontagne, J. R. et al. Large ensemble analytic framework for consequence-driven discovery of climate change scenarios. Earths Future 6, 488–504 (2018).

    Article  Google Scholar 

  48. van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article  Google Scholar 

  49. Kriegler, E. et al. A new scenario framework for climate change research: the concept of shared climate policy assumptions. Clim. Change 122, 401–414 (2014).

    Article  Google Scholar 

  50. Tebaldi, C. et al. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn. 12, 253–293 (2021).

    Article  Google Scholar 

  51. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Article  Google Scholar 

  52. Bauer, N. et al. Shared socio-economic pathways of the energy sector – quantifying the narratives. Glob. Environ. Change 42, 316–330 (2017).

    Article  Google Scholar 

  53. Turnock, S. T. et al. Historical and future changes in air pollutants from CMIP6 models. Atmos. Chem. Phys. 20, 14547–14579 (2020).

    Article  CAS  Google Scholar 

  54. KC, S. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).

    Article  Google Scholar 

  55. Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 084003 (2016).

    Article  Google Scholar 

  56. Jones, B. & O’Neill, B. C. Global Population Projection Grids Based on Shared Socioeconomic Pathways (SSPs), 2010–2100 (NASA Socioeconomic Data and Applications Center, 2017); https://doi.org/10.7927/H4RF5S0P

  57. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results (Institute for Health Metrics and Evaluation, 2018); http://ghdx.healthdata.org/gbd-results-tool

  58. Hughes, B. B. et al. Projections of global health outcomes from 2005 to 2060 using the International Futures integrated forecasting model. Bull. World Health Organ. 89, 478–486 (2011).

    Article  Google Scholar 

  59. Cohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389, 1907–1918 (2017).

    Article  Google Scholar 

  60. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  61. Calvin, K. et al. The SSP4: a world of deepening inequality. Glob. Environ. Change 42, 284–296 (2017).

    Article  Google Scholar 

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Acknowledgements

H.Y. and W.P. thank the funding support from the Monash-Penn State Collaboration Developmental Funds. W.P. acknowledges support from the National Science Foundation under Grant No. 2108984. D.M.W. was supported by the National Science Foundation Office of International Science and Engineering Grant No. 2020677. We thank Noah Scovronick, Mark Budolfson, and W.P.’s research group for feedback on this work.

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H.Y. and W.P. conceived and designed the study. H.Y., W.P. and X.H. performed the analysis with data input from D.M.W. and L.H. H.Y. and W.P. wrote the manuscript with important input from all authors.

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Correspondence to Wei Peng.

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Yang, H., Huang, X., Westervelt, D.M. et al. Socio-demographic factors shaping the future global health burden from air pollution. Nat Sustain (2022). https://doi.org/10.1038/s41893-022-00976-8

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