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


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.

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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.

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Data availability

The MEIC emission inventory is available from The dataset generated during this study is available in the figshare repository Source data are provided with this paper.

Code availability

The code of the WRF model is available at The code of the CMAQ model is available at The codes used for analysing data are available in the figshare repository


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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



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.

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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

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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).

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