Transboundary health impacts of transported global air pollution and international trade (original) (raw)

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (41625020, 41629501, 41422502, 41222036 and 41541039) and China’s National Basic Research Program (2014CB441301 and 2014CB441303). Q.Z. and K.H. are supported by the Collaborative Innovation Center for Regional Environmental Quality and the Cyrus Tang Foundation. The work at Argonne National Laboratory acknowledges the Modeling, Analysis and Predictability (MAP) programme of the National Aeronautics and Space Administration (NASA) under Proposal No. 08-MAP-0143, for which we thank D. Considine (NASA) and M. Chin (NASA Goddard Space Flight Center). H.H. acknowledges the support of the National Natural Science Foundation of China (71322304). Z.L. acknowledges the support from the National Natural Science Foundation of China (41501605). D.G. acknowledges the support from the National Key R&D Program of China (2016YFA0602604), the UK Economic and Social Research Council (ES/L016028/1), the UK Natural Environment Research Council (NE/N00714X/1), and the British Academy (AF150310). We thank T. Xue for discussions on statistics.

Author information

Author notes

  1. Qiang Zhang, Xujia Jiang and Dan Tong: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, 100084, China
    Qiang Zhang, Xujia Jiang, Dan Tong, Steven J. Davis, Hongyan Zhao, Guannan Geng, Tong Feng, Kebin He & Dabo Guan
  2. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
    Xujia Jiang, Bo Zheng & Kebin He
  3. Department of Earth System Science, University of California, Irvine, 92697, California, USA
    Steven J. Davis
  4. Energy Systems Division, Argonne National Laboratory, Argonne, 60439, Illinois, USA
    Zifeng Lu & David G. Streets
  5. Department of Atmospheric and Oceanic Sciences, Laboratory for Climate and Ocean-Atmosphere Studies, School of Physics, Peking University, Beijing, 100871, China
    Ruijing Ni, Yingying Yan & Jintai Lin
  6. School of Population and Public Health, University of British Columbia, Vancouver, V6T 1Z3, British Columbia, Canada
    Michael Brauer
  7. Department of Physics and Atmospheric Science, Dalhousie University, Halifax, B3H 4R2, Nova Scotia, Canada
    Aaron van Donkelaar & Randall V. Martin
  8. Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, 02138, Massachusetts, USA
    Randall V. Martin
  9. Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
    Hong Huo
  10. Resnick Sustainability Institute, California Institute of Technology, Pasadena, 91125, California, USA
    Zhu Liu
  11. Department of Civil and Environmental Engineering, Princeton University, Princeton, 08544, New Jersey, USA
    Da Pan
  12. School of Public Health, Fudan University, Shanghai, China
    Haidong Kan
  13. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
    Kebin He
  14. School of International Development, University of East Anglia, Norwich, NR4 7TJ, UK
    Dabo Guan

Authors

  1. Qiang Zhang
  2. Xujia Jiang
  3. Dan Tong
  4. Steven J. Davis
  5. Hongyan Zhao
  6. Guannan Geng
  7. Tong Feng
  8. Bo Zheng
  9. Zifeng Lu
  10. David G. Streets
  11. Ruijing Ni
  12. Michael Brauer
  13. Aaron van Donkelaar
  14. Randall V. Martin
  15. Hong Huo
  16. Zhu Liu
  17. Da Pan
  18. Haidong Kan
  19. Yingying Yan
  20. Jintai Lin
  21. Kebin He
  22. Dabo Guan

Contributions

Q.Z., J.L. and K.H. conceived the study. Q.Z. led the study. Z.Lu and D.G.S. provided emissions data. M.B., A.v.D. and R.V.M. provided PM2.5 exposure data. D.T., H.Z., T.F. and D.G. calculated emissions. G.G. conducted GEOS-Chem simulations. X.J. conducted estimates of health impacts. Q.Z., X.J., S.J.D., G.G. and J.L. interpreted the data. Q.Z., X.J., D.T., S.J.D., H.Z. and G.G. wrote the paper with input from all co-authors.

Corresponding authors

Correspondence toQiang Zhang, Steven J. Davis, Jintai Lin or Kebin He.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks G. Janssens-Maenhout, P. Jha and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Definition of the 13 world regions used here.

a–i, Maps show the number of deaths related to air pollution produced (that is, emitted) in the rest of east Asia (a), the rest of Asia (b), Russia (c), eastern Europe (d), Canada (e), the Middle East and north Africa (f), Latin America (g), sub-Saharan Africa (h) and the rest of the world (i).

a–i, Maps show the number of deaths related to goods and services consumed in the rest of east Asia (a), the rest of Asia (b), Russia (c), eastern Europe (d), Canada (e), the Middle East and north Africa (f), Latin America (g), sub-Saharan Africa (h) and the rest of the world (i).

Extended Data Figure 4 Differences in worldwide premature mortality in 2007 between production- and consumption-related PM2.5 air pollution.

a–d, Maps show the number of deaths worldwide related to consumption in the given region minus the number of deaths worldwide related to production in that region, for China (a), western Europe (b), the USA (c) and India (d).

Extended Data Figure 5 Uncertainty ranges.

a, b, Uncertainties relating to Fig. 2. The ranges at the top of each panel represent the 95% CI for the number of attributable deaths in the region indicated by the column. The ranges at the right of each panel represent the 95% CI for the total number of worldwide deaths caused by pollution produced in the region indicated by the row (a) or related to the consumption of products in that region that are produced there or elsewhere (b). Each cell in the grid shows the standard deviation of the fraction of deaths (%); darker shading in the off-diagonal cells highlights larger standard deviations.

Source data

Extended Data Figure 6 Summary of global premature mortality per capita due to transported PM2.5 pollution and traded products.

a, e, Worldwide mortality due to pollution produced (that is, emitted) in each region (a) or related to products consumed in each region (e). b, f, Mortality in each region due to pollution produced in that region (b) or related to products consumed in that region (f). c, g, Mortality in all other regions due to pollution produced in each region (c) or related to products consumed in each region (g). d, h, Mortality in each region due to pollution produced elsewhere (d) or related to products consumed elsewhere (h). All data are normalized according to regional populations (reported as deaths per one million people). Error bars denote 95% CIs, determined by uncertainties in the GEOS-Chem-simulated fractional contribution of PM2.5 exposure and in the total PM2.5-related mortality.

Source data

Extended Data Figure 7 Methodology framework to access PM2.5 mortality from production and consumption for each region.

Extended Data Table 1 Premature mortality related to PM2.5 air pollution in 2007

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Zhang, Q., Jiang, X., Tong, D. et al. Transboundary health impacts of transported global air pollution and international trade.Nature 543, 705–709 (2017). https://doi.org/10.1038/nature21712

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