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The contribution of outdoor air pollution sources to premature mortality on a global scale

Nature volume 525, pages 367–371 (2015)Cite this article

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Abstract

Assessment of the global burden of disease is based on epidemiological cohort studies that connect premature mortality to a wide range of causes1,2,3,4,5, including the long-term health impacts of ozone and fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5)3,4,5,6,7,8,9. It has proved difficult to quantify premature mortality related to air pollution, notably in regions where air quality is not monitored, and also because the toxicity of particles from various sources may vary10. Here we use a global atmospheric chemistry model to investigate the link between premature mortality and seven emission source categories in urban and rural environments. In accord with the global burden of disease for 2010 (ref. 5), we calculate that outdoor air pollution, mostly by PM2.5, leads to 3.3 (95 per cent confidence interval 1.61–4.81) million premature deaths per year worldwide, predominantly in Asia. We primarily assume that all particles are equally toxic5, but also include a sensitivity study that accounts for differential toxicity. We find that emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest impact on premature mortality globally, being even more dominant if carbonaceous particles are assumed to be most toxic. Whereas in much of the USA and in a few other countries emissions from traffic and power generation are important, in eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to PM2.5, with the estimate of overall health impact depending on assumptions regarding particle toxicity. Model projections based on a business-as-usual emission scenario indicate that the contribution of outdoor air pollution to premature mortality could double by 2050.

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Figure 1: Mortality linked to outdoor air pollution in 2010.
Figure 2: Source categories responsible for the largest impact on mortality linked to outdoor air pollution in 2010.

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Acknowledgements

We are grateful to the EDGAR team of the Joint Research Centre in Ispra, Italy, for the emission data. We acknowledge support from the Distinguished Scientist Fellowship Program at the King Saud University, Riyadh. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 226144.

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Authors and Affiliations

  1. Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, 55128, Germany

    J. Lelieveld & A. Pozzer

  2. The Cyprus Institute, Energy, Environment and Water Research Center, Nicosia, 1645, Cyprus

    J. Lelieveld & D. Giannadaki

  3. Harvard School of Public Health, Boston, 02215, Massachusetts, USA

    J. S. Evans

  4. Cyprus International Institute for Environment and Public Health, Cyprus University of Technology, Limassol, 3041, Cyprus

    J. S. Evans

  5. King Saud University, College of Science, Riyadh, 11451, Saudi Arabia

    M. Fnais

Authors
  1. J. Lelieveld
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  2. J. S. Evans
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  3. M. Fnais
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  4. D. Giannadaki
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Contributions

J.L., A.P. and M.F. planned the research, A.P. performed the model calculations, J.L., A.P., D.G. and J.S.E. analysed the results, and J.L. and J.S.E. wrote the paper. All authors contributed to the manuscript.

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Correspondence to J. Lelieveld.

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Extended data figures and tables

Extended Data Figure 1 Source categories responsible for the largest impact on mortality linked to outdoor air pollution in 2010 from a sensitivity calculation with carbonaceous aerosol having a five times larger impact than inorganic and crustal compounds.

IND, industry; TRA, land traffic; RCO, residential energy use (for example, heating, cooking); BB, biomass burning; PG, power generation; AGR, agriculture; and NAT, natural.

Extended Data Figure 2 Increase in mortality linked to outdoor air pollution from 2010 to 2050 (business-as-usual scenario).

Units (colour coded), deaths per area of 100 km × 100 km. In the white areas, no additional mortality is projected.

Extended Data Figure 3 Comparison of EMAC model calculated aerosol optical depth (AOD) with AERONET observations, using all available measurements worldwide in the year 2010.

Although the comparison with individual data points shows a large scatter (left panel), the bias is small (MBE), and time averaging improves the agreement. The middle panel shows a comparison of the monthly means, and the right panel the annual means (that is, showing individual stations) for which the mean error (root mean square error, RMSE) is smallest, the correlation highest and the bias absent. The long-dashed line indicates absolute agreement, the bold short-dashed lines agreement within a factor of two and the short-dashed lines agreement within a factor of ten.

Extended Data Table 1 WHO regions, mortality strata, child and adult mortality characteristics, and the countries and territories included
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Extended Data Table 2 Premature mortality related to PM2.5 and O3 in 2010
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Extended Data Table 3 Premature mortality by PM2.5 and O3 related diseases in 2010 in countries where it exceeds 9,000 individuals per year (<5 and ≥30 years old)
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Extended Data Table 4 Premature mortality related to PM2.5 and O3 in 2025
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Extended Data Table 5 Premature mortality related to PM2.5 and O3 in 2050
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Extended Data Table 6 Population and premature mortality (deaths per year) related to PM2.5 and O3 in the most polluted megacities and conurbations in 2010, 2025 and 2050
Full size table
Extended Data Table 7 Premature mortality related to PM2.5 and O3 for the population aged <5 years and ≥30 years
Full size table

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Lelieveld, J., Evans, J., Fnais, M. et al. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371 (2015). https://doi.org/10.1038/nature15371

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