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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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.
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.
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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.
The authors declare no competing interests.
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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