Stronger policy required to substantially reduce deaths from PM2.5 pollution in China

Abstract

Air pollution kills nearly 1 million people per year in China. In response, the Chinese government implemented the Air Pollution Prevention and Control Action Plan (APPCAP) from 2013 to 2017 which had a significant impact on reducing PM_2.5 concentration. However, the health benefits of the APPCAP are not well understood. Here we examine the spatiotemporal dynamics of annual deaths attributable to PM_2.5 pollution (DAPP) in China and the contribution from the APPCAP using decomposition analysis. Despite a 36.1% increase in DAPP from 2000 to 2017, The APPCAP-induced improvement in air quality achieved substantial health benefits, with the DAPP in 2017 reduced by 64 thousand (6.8%) compared to 2013. However, the policy is unlikely to result in further major reductions in DAPP and more ambitious policies are required to reduce the health impacts of air pollution by 2030 and meet the United Nation's Sustainable Development Goal 3.

Fig. 1. Changes in the driving factors of deaths attributable to PM_2.5 pollution.

a population‐weighted PM_2.5 concentration^,. b total population^,. c age structure. d age‐standardized death rate of diseases. The PM_2.5 concentration was weighted by population to indicate the overall exposure at the whole of China. IHD, COPD, LC, LRI, and DM2 refer to ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, lower respiratory infection, and diabetes mellitus type 2, respectively. Source data are provided as a Source Data file.

Fig. 2. Changes in the deaths attributable to PM_2.5 pollution (DAPP) and corresponding age‐standardized death rate in China.

a China’s PM_2.5 concentration and population density (averages from 2000 to 2017). b spatial change in the DAPP between 2000 and 2017. c the dynamics of the DAPP and corresponding age‐standardized death rate for China. d regional changes in the DAPP and corresponding age‐standardized death rate between 2000 and 2017. Shade and error bars refer to the 90% confidence intervals. Source data are provided as a Source Data file.

Fig. 3. Contributions of different factors to changes in deaths attributable to PM_2.5 pollution (DAPP) between 2000–2013 and 2013–2017.

a The total effects in different periods, the sum of the effect caused by different driving factors in each period equals the net change in DAPP. b the average effects per year, which equals the total effect divided by the length of each period. Error bars represent 90% confidence intervals. Yellow, red and blue bars represent different periods (2000–2017, 2000–2013, 2013–2017). Source data are provided as a Source Data file.

Fig. 4. Changes in deaths attributable to PM_2.5 pollution from 2017 to 2030 under different scenarios.

a Following the current trend (population‐weighted PM_2.5 will be 35 μg m⁻³ in 2030). b Taking a more ambitious target (population‐weighted PM_2.5 will be 10 μg m⁻³ in 2030). The effects of changes in population, age structure and death rates are different between the two scenarios because they are jointly affected by the changes in PM_2.5 concentration (see Methods for details). Error bars represent 90% confidence intervals. Source data are provided as a Source Data file.