Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Drivers of PM2.5 air pollution deaths in China 2002–2017

Abstract

Between 2002 and 2017, China’s gross domestic product grew by 284%, but this surge was accompanied by a similarly prodigious growth in energy consumption, air pollution and air pollution-related deaths. Here we use a combination of index decomposition analysis and chemical transport modelling to quantify the relative influence of eight different factors on PM2.5-related deaths in China over the 15-year period from 2002 to 2017. We show that, over this period, PM2.5-related deaths increased by 0.39 million (23%) in China. Emission control technologies mandated by end-of-pipe control policies avoided 0.87 million deaths, which is nearly three-quarters (71%) of the deaths that would have otherwise occurred due to the country’s increased economic activity. In addition, energy-climate policies and changes in economic structure have also became evident recently and together avoided 0.39 million deaths from 2012 to 2017, leading to a decline in total deaths after 2012, despite the increasing vulnerability of China’s ageing population. As advanced end-of-pipe control measures have been widely implemented, such policies may face challenges in avoiding air pollution deaths in the future. Our findings thus suggest that further improvements in air quality must not only depend on stringent end-of-pipe control policies but also be reinforced by energy-climate policies and continuing changes in China’s economic structure.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Trends in factors affecting air pollution emissions, exposure and vulnerability in China 2002–2017.
Fig. 2: Economic and policy drivers of major air pollutant emissions in China 2002–2017.
Fig. 3: Drivers of changes in PM2.5 exposure and associated mortality 2002–2017.

Data availability

The MEIC emission inventory is available from www.meicmodel.org. The dataset generated during this study is available in the figshare repository https://doi.org/10.6084/m9.figshare.14493375. Source data are provided with this paper.

Code availability

The code of the WRF model is available at https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html. The code of the CMAQ model is available at https://github.com/USEPA/CMAQ/tree/5.0.1. The codes used for analysing data are available in the figshare repository https://doi.org/10.6084/m9.figshare.14493375.

References

  1. National data. National Bureau of Statistics of China http://data.stats.gov.cn (2018, 2019).

  2. Zhang, Q., He, K. & Huo, H. Cleaning China’s air. Nature 484, 161–162 (2012).

    Article  Google Scholar 

  3. 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 

  4. Circular of the State Council on Printing Out and Distribution of the National “11th Five-Year Plan” for Environmental Protection Report No. Guofa [2007] 37 (in Chinese) (State Council of the People’s Republic of China, 2007); http://www.gov.cn/zwgk/2007-11/26/content_815498.htm

  5. Guidelines of the Eleventh Five-Year Plan for National Economic and Social Development of the People’s Republic of China (in Chinese) (State Council of the People’s Republic of China, 2006); http://www.gov.cn/ztzl/2006-03/16/content_228841.htm

  6. Circular of the State Council on Printing Out and Distribution of the National “12th Five-Year Plan” for Environmental Protection Report No. Guofa [2011] 42 (in Chinese) (State Council of the People’s Republic of China, 2011); http://www.gov.cn/zwgk/2011-12/20/content_2024895.htm

  7. Circular of the State Council on Printing out and Distribution of the 12th Five-Year Plan for Energy Saving and Emission Reduction Report No. Guofa [2012] 40 (in Chinese) (State Council of the People’s Republic of China, 2012); http://www.gov.cn/zwgk/2012-08/21/content_2207867.htm

  8. Notice of the General Office of the State Council on Issuing the Air Pollution Prevention and Control Action Plan Report No. Guofa [2013] 37 (in Chinese) (State Council of the People’s Republic of China, 2013); http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm

  9. Zhang, Q. et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl Acad. Sci. USA 116, 24463–24469 (2019).

    Article  Google Scholar 

  10. Zheng, B. et al. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18, 14095–14111 (2018).

    Article  Google Scholar 

  11. Schreifels, J. J., Fu, Y. & Wilson, E. J. Sulfur dioxide control in China: policy evolution during the 10th and 11th Five-year Plans and lessons for the future. Energy Policy 48, 779–789 (2012).

    Article  Google Scholar 

  12. Xue, T. et al. Rapid improvement of PM2.5 pollution and associated health benefits in China during 2013–2017. Sci. China Earth. Sci. 62, 1847–1856 (2019).

    Article  Google Scholar 

  13. Ma, Z., Liu, R., Liu, Y. & Bi, J. Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective. Atmos. Chem. Phys. 19, 6861–6877 (2019).

    Article  Google Scholar 

  14. Li, M. et al. Anthropogenic emission inventories in China: a review. Natl Sci. Rev. 4, 834–866 (2017).

    Article  Google Scholar 

  15. Ang, B. W. The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33, 867–871 (2005).

    Article  Google Scholar 

  16. Community multiscale air quality modeling system. US Environmental Protection Agency https://www.epa.gov/cmaq (2019).

  17. 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  Google Scholar 

  18. Zhou, M. et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 394, 1145–1158 (2019).

    Article  Google Scholar 

  19. Lei, Y., Zhang, Q., He, K. B. & Streets, D. G. Primary anthropogenic aerosol emission trends for China, 1990–2005. Atmos. Chem. Phys. 11, 931–954 (2011).

    Article  Google Scholar 

  20. Liu, F. et al. High-resolution inventory of technologies, activities, and emissions of coal-fired power plants in China from 1990 to 2010. Atmos. Chem. Phys. 15, 13299–13317 (2015).

    Article  Google Scholar 

  21. Wu, Y. et al. On-road vehicle emissions and their control in China: a review and outlook. Sci. Total Environ. 574, 332–349 (2017).

    Article  Google Scholar 

  22. Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D. & Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371 (2015).

    Article  Google Scholar 

  23. Yu, H. et al. Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003–2017. Atmos. Chem. Phys. 20, 139–161 (2020).

    Article  Google Scholar 

  24. Gu, Y. et al. Impacts of sectoral emissions in China and the implications: air quality, public health, crop production, and economic costs. Environ. Res. Lett. 13, 084008 (2018).

    Article  Google Scholar 

  25. Chen, W. H. et al. Regional to global biogenic isoprene emission responses to changes in vegetation from 2000 to 2015. J. Geophys. Res. 123, 3757–3771 (2018).

    Article  Google Scholar 

  26. Maji, K. J. Substantial changes in PM2.5 pollution and corresponding premature deaths across China during 2015–2019: a model prospective. Sci. Total Environ. 729, 138838 (2020).

    Article  Google Scholar 

  27. Wei, Y., Wang, Z., Wang, H., Li, Y. & Jiang, Z. Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data. PLoS ONE 14, e0212772 (2019).

    Article  Google Scholar 

  28. World Economic Outlook: Growth Slowdown, Precarious Recovery (International Monetary Fund, 2019).

  29. Li, K. et al. Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proc. Natl Acad. Sci. USA 116, 422–427 (2019).

    Article  Google Scholar 

  30. Li, K. et al. A two-pollutant strategy for improving ozone and particulate air quality in China. Nat. Geosci. 12, 906–910 (2019).

    Article  Google Scholar 

  31. Gallagher, K. S., Zhang, F., Orvis, R., Rissman, J. & Liu, Q. Assessing the policy gaps for achieving China’s climate targets in the Paris Agreement. Nat. Commun. 10, 1256 (2019).

    Article  Google Scholar 

  32. Huo, H. et al. Examining air pollution in China using production- and consumption-based emissions accounting approaches. Environ. Sci. Technol. 48, 14139–14147 (2014).

    Article  Google Scholar 

  33. Watts, N. et al. Health and climate change: policy responses to protect public health. Lancet 386, 1861–1914 (2015).

    Article  Google Scholar 

  34. 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  Google Scholar 

  35. Vandyck, T. et al. Air quality co-benefits for human health and agriculture counterbalance costs to meet Paris Agreement pledges. Nat. Commun. 9, 4939 (2018).

    Article  Google Scholar 

  36. Rauner, S., Hilaire, J., Klein, D., Strefler, J. & Luderer, G. Air quality co-benefits of ratcheting up the NDCs. Climatic Change 163, 1481–1500 (2020).

    Article  Google Scholar 

  37. Li, N. et al. Air quality improvement co-benefits of low-carbon pathways toward well below the 2°C climate target in China. Environ. Sci. Technol. 53, 5576–5584 (2019).

    Article  Google Scholar 

  38. National Bureau of Statistics of China Regional Input–Output Table of China 2002, 2007, 2012 (China Statistics Press, 2008, 2011, 2016).

  39. National Bureau of Statistics of China China Industry Statistical Yearbook 2013, 2017 (China Statistics Press, 2013, 2017).

  40. National Bureau of Statistics of China China Yearbook of Agricultural Price Survey 2018 (China Statistics Press, 2018).

  41. National Bureau of Statistics of China China Price Statistical Yearbook 2016–2018 (China Statistics Press, 2016–2018).

  42. National Bureau of Statistics of China China Statistical Yearbook 2018 (China Statistics Press, 2018).

  43. National Bureau of Statistics of China China Energy Statistical Yearbook 2005, 2016, 2018 (China Statistics Press, 2006, 2017, 2019).

  44. Skamarock, W. et al. A Description of the Advanced Research WRF Version 3, NCAR Technical Note, Mesoscale and Microscale Meteorology Division (National Center for Atmospheric Research, 2008).

  45. Zheng, B. et al. Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China. Atmos. Chem. Phys. 15, 2031–2049 (2015).

    Article  Google Scholar 

  46. Zheng, Y. et al. Air quality improvements and health benefits from China’s clean air action since 2013. Environ. Res. Lett. 12, 114020 (2017).

    Article  Google Scholar 

  47. Bey, I. et al. Global modeling of tropospheric chemistry with assimilated meteorology: model description and evaluation. J. Geophys. Res. 106, 23073–23095 (2001).

    Article  Google Scholar 

  48. Li, M. et al. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 17, 935–963 (2017).

    Article  Google Scholar 

  49. Guenther, A. B. et al. The model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions. Geosci. Model Dev. 5, 1471–1492 (2012).

    Article  Google Scholar 

  50. Gong, S. A parameterization of sea-salt aerosol source function for sub-and super-micron particles. Glob. Biogeochem. Cycles 17, 1097 (2003).

    Article  Google Scholar 

  51. Foroutan, H. et al. Development and evaluation of a physics‐based windblown dust emission scheme implemented in the CMAQ modeling system. J. Adv. Model. Earth Syst. 9, 585–608 (2017).

    Article  Google Scholar 

  52. Emery, C., Tai, E. & Yarwood, G. Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Ozone Episodes (ENVIRON International Corporation, 2001).

  53. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze Report No. EPA -454/B-07-002 (US Environmental Protection Agency, 2007).

  54. Xue, T. et al. Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: a machine learning method with inputs from satellites, chemical transport model, and ground observations. Environ. Int. 123, 345–357 (2019).

    Article  Google Scholar 

  55. Zhai, S. et al. Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology. Atmos. Chem. Phys. 19, 11031–11041 (2019).

    Article  Google Scholar 

  56. Zhang, X. et al. The impact of meteorological changes from 2013 to 2017 on PM2.5 mass reduction in key regions in China. Sci. China Earth Sci. 62, 1885–1902 (2019).

    Article  Google Scholar 

  57. Ding, D., Xing, J., Wang, S., Liu, K. & Hao, J. Estimated contributions of emissions controls, meteorological factors, population growth, and changes in baseline mortality to reductions in ambient PM2.5 and PM2.5-related mortality in China, 2013–2017. Environ. Health Perspect. 127, 067009 (2019).

    Article  Google Scholar 

  58. Zhao, Y., Nielsen, C. P., Lei, Y., McElroy, M. B. & Hao, J. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos. Chem. Phys. 11, 2295–2308 (2011).

    Article  Google Scholar 

  59. Zhang, Q. et al. Asian emissions in 2006 for the NASA INTEX-B mission. Atmos. Chem. Phys. 9, 5131–5153 (2009).

    Article  Google Scholar 

  60. Hu, J., Chen, J., Ying, Q. & Zhang, H. One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system. Atmos. Chem. Phys. 16, 10333–10350 (2016).

    Article  Google Scholar 

  61. Zhang, X. et al. Enhancement of PM2.5 concentrations by aerosol–meteorology interactions over China. J. Geophys. Res. 123, 1179–1194 (2018).

    Article  Google Scholar 

  62. He, J. et al. Multi-year application of WRF-CAM5 over East Asia–part I: comprehensive evaluation and formation regimes of O3 and PM2.5. Atmos. Environ. 165, 122–142 (2017).

    Article  Google Scholar 

  63. Ang, B. W. LMDI decomposition approach: a guide for implementation. Energy Policy 86, 233–238 (2015).

    Article  Google Scholar 

  64. Sahu, S. K., Chen, L., Liu, S., Ding, D. & Xing, J. The impact of aerosol direct radiative effects on PM2.5-related health risk in Northern Hemisphere during 2013–2017. Chemosphere 254, 126832 (2020).

    Article  Google Scholar 

  65. Fu, Y., Tai, A. P. K. & Liao, H. Impacts of historical climate and land cover changes on fine particulate matter (PM2.5) air quality in East Asia between 1980 and 2010. Atmos. Chem. Phys. 16, 10369–10383 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (41921005 to Q.Z., 41625020 to Q.Z., 91744310 to Q.Z. and 42005135 to G.G.).

Author information

Authors and Affiliations

Authors

Contributions

Q.Z. designed the research. Y.Z., G.G. and T.X. performed the research. H.Z., D.T., B.Z., M.L., F.L. and C.H. processed emission data. G.G., Y.Z., Q.Z., S.J.D. and K.H. interpreted data. G.G., Y.Z., Q.Z. and S.J.D. wrote the paper with input from all co-authors.

Corresponding author

Correspondence to Qiang Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Rafael Borge and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Clare Davis, Rebecca Neely.

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

Extended data

Extended Data Fig. 1 Methodology framework to estimate drivers of China’s PM2.5-related deaths.

The MEIC, LMDI, WRF, CMAQ, and GEMM represent the Multi-resolution Emission Inventory for China, the Logarithmic Mean Divisia Index decomposition analysis, the Weather Research and Forecasting Model, the Community Multiscale Air Quality Model, and the Global Exposure Mortality Model, respectively.

Extended Data Fig. 2 Sectoral contributions of major air pollutant emissions in 2002–2017.

Sectoral contributions of SO2, NOx, and primary PM2.5 emissions for 11 sectors in 2002, 2007, 2012, and 2017.

Source data

Extended Data Fig. 3 Changes in PM2.5 concentrations associated with changes in economic structure in China from 2002 to 2007.

Changes in economic structure majorly increased PM2.5 concentrations over populated northern provinces such as Hebei, Shandong, and Henan, whose economy highly relies on heavy industries.

Source data

Extended Data Fig. 4 Effects of interannual meteorological variations on the national population-weighted monthly mean PM2.5 concentrations.

Results for the sub-periods (a) 2002–2007, (b) 2007–2012, and (c) 2012–2017, respectively. These results are derived based on simulations of ‘BASE’ and ‘Fix emission’ scenarios.

Source data

Extended Data Fig. 5 Trends in air pollutant emissions and emission removal rates in China over 2002–2017 for (a) SO2, (b) NOx, and (c) PM2.5.

The blue and orange lines represent actual and estimated unabated emissions, respectively. The red line represents average removal rates.

Source data

Extended Data Table 1 Emission reduction measures implemented in China from 2002 to 2017
Extended Data Table 2 Variations in NOx emissions in major sectors and the changes induced by end-of-pipe control policies during 2002–2017 (unit: Tg)
Extended Data Table 3 Sensitivity tests of national population-weighted annual mean PM2.5 concentrations

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–6 and Tables 1–7.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Geng, G., Zheng, Y., Zhang, Q. et al. Drivers of PM2.5 air pollution deaths in China 2002–2017. Nat. Geosci. 14, 645–650 (2021). https://doi.org/10.1038/s41561-021-00792-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-021-00792-3

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing