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

Ambient PM2.5 pollution, particulate matter smaller than 2.5 μm in diameter, is a leading environmental risk factor for global health, causing millions of deaths annually. Deaths attributable to PM2.5 pollution (DAPP) are influenced by PM2.5 concentration, demographic factors, and disease death rates. China, experiencing rapid industrialization and urbanization, suffers significantly from PM2.5 pollution. To address this, the Chinese government launched the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, aiming to reduce PM2.5 concentrations in cities by 10–25% by 2017. This massive undertaking, costing approximately $270 billion and encompassing various sectors, aimed to significantly improve air quality. While the Ministry of Ecology and Environment declared the PM2.5 target achieved, the health benefits remain unclear. Previous studies analyzing the APPCAP's impact on health have yielded inconsistent results due to variations in data, methodologies, and the consideration of confounding factors. This study aims to provide a comprehensive and reliable assessment of the spatiotemporal dynamics of DAPP in China and the contribution of the APPCAP to its reduction, clarifying the policy's health impact and guiding future air pollution control strategies to meet the United Nations' Sustainable Development Goal (SDG) Target 3.9.

Existing literature on the health impacts of the APPCAP in China presents varied and sometimes conflicting conclusions. Some studies directly link the decline in DAPP to the APPCAP's success, while others attempt to isolate the effect of PM2.5 concentration changes while holding other factors constant. Inconsistent trends in DAPP have been reported, with some showing a decline after 2013 and others showing a continued increase. These discrepancies stem from differences in input data (such as death rates and age structures), disease selection, and the exposure-response functions used to model the relationship between PM2.5 and disease risk. The lack of a robust and comprehensive understanding of the spatiotemporal dynamics of DAPP and the relative contributions of driving factors necessitates a more thorough investigation to accurately assess the health benefits of the APPCAP and inform future policy.

This study analyzed the effects of PM2.5 concentration changes on DAPP before (2000–2013) and after (2013–2017) the APPCAP's implementation. DAPP was estimated from 2000 to 2017 using the GBD 2017 epidemiological model and long-term PM2.5 data at a 0.01-degree spatial resolution. The study used data from the Atmospheric Composition Analysis Group and China’s National Urban Air Quality Real Time Publishing Platform. Data for 2017 PM2.5 concentrations were extrapolated based on 2016 data and the ratio of monitoring data between 2016 and 2017. The spatiotemporal dynamics of DAPP were analyzed, and a decomposition method from GBD was employed to assess the relative contributions of changes in population, PM2.5 concentration, age structure, and disease death rates on DAPP. The decomposition method involved sequentially introducing each factor into the DAPP equation; the difference between consecutive steps estimates each factor's contribution. The study calculated the effects under all 24 possible sequences of the four factors and averaged the results for each factor. A sequential Mann–Kendall test was used to detect abrupt changes in trends. Finally, future DAPP trends to 2030 were projected under two scenarios: a 'Trend' scenario, assuming continuation of existing policies, and an 'Ambitious' scenario, assuming a more stringent policy resulting in a substantial reduction in PM2.5 concentrations. Both scenarios used UN population projections and assumed a 30% reduction in disease death rates based on the Healthy China 2030 Plan. The study considered six diseases linked to PM2.5 pollution and fifteen age groups in the analysis.

The study revealed a 36.1% increase in DAPP in China from 2000 to 2017, with the North region experiencing the most significant increase. Despite this overall increase, the APPCAP's implementation in 2013 resulted in a slowing of the DAPP growth rate. From 2013–2017, the average annual increase in DAPP dropped from 2.1% to 1.0%. The APPCAP is estimated to have reduced DAPP in 2017 by 64,000 (57–64 thousand) compared to 2013, a 6.8% (5.3–9.1%) reduction. Decomposition analysis showed that while changes in PM2.5 concentration led to an increase in DAPP from 2000–2013 (127,000), they caused a decrease of 64,000 from 2013–2017. Other factors, particularly population aging, contributed significantly to the overall increase in DAPP. The projection of future DAPP under different scenarios revealed that the ‘Trend’ scenario, which assumes the continuation of existing policies, results in a minor reduction of 18,000 deaths in 2030 compared to 2017 levels. In contrast, the ‘Ambitious’ scenario, which assumes a more stringent policy leading to a population-weighted PM2.5 concentration of 10 μg m−3 by 2030, projects a substantial reduction of 421,000 deaths compared to 2017.

The findings highlight that while the APPCAP achieved some success in reducing PM2.5 and slowing the growth of DAPP, its impact is far from sufficient to meet SDG Target 3.9's goal of substantially reducing deaths from air pollution. The study's decomposition analysis clarifies why DAPP continued to rise despite air quality improvements; demographic changes and other factors significantly outweigh the gains from air quality improvements. The contrasting outcomes from the 'Trend' and 'Ambitious' scenarios underscore the necessity of stronger policies. The results emphasize that a more ambitious approach focused on significant PM2.5 reduction is crucial to substantially reduce DAPP by 2030. This requires a concerted effort to address PM2.5 emissions, particularly from China's energy sector. The study's findings offer crucial insights for policymakers in China and other developing countries facing similar challenges.

This study provides a comprehensive and reliable assessment of the spatiotemporal trends in DAPP in China and the impact of the APPCAP. Although the APPCAP led to a reduction in DAPP, the overall increase in DAPP from 2000 to 2017 demonstrates the need for far more ambitious air pollution control policies. To achieve a substantial reduction in DAPP and meet SDG Target 3.9, China must adopt stronger policies, including stricter emission standards, emission taxes, public awareness campaigns, and integrated approaches that consider the co-benefits of air pollution abatement and climate action. Future research should focus on refining DAPP estimations by incorporating higher-resolution data, accounting for individual behavior, and developing more sophisticated models that incorporate diverse socioeconomic and environmental factors.

This study acknowledges several limitations. First, while 90% confidence intervals were provided, uncertainties remain in DAPP estimation due to data limitations, exposure assessment, and exposure-response function choices. National-level data on age structure and death rates were used, which may overestimate DAPP in some regions. The annual average PM2.5 concentration was used as a proxy for outdoor exposure, potentially overestimating actual exposure. Second, meteorological factors influencing PM2.5 concentrations were not explicitly considered. Third, the counterfactual assumption that without the APPCAP PM2.5 would have remained at 2013 levels might be an oversimplification. Fourth, the future scenarios were simplified and did not fully account for the complex interplay between emission abatement, climate change, and air quality. Future research should improve the estimation of DAPP by incorporating higher-resolution datasets and models that account for a wider range of factors.