Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020. (20th June 2022)

• Record Type: Journal Article
• Authors: Conibear, Luke; Reddington, Carly L.; Silver, Ben J.; Chen, Ying; Arnold, Stephen R.; Spracklen, Dominick V.
• Is Part Of: GeoHealth. Volume 6:Number 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-20
• Subjects: emulators -- air quality -- China -- machine learning -- health impact assessment -- emissions; Environmental health -- Periodicals; Electronic journals; Periodicals; 616.98
• DOI: 10.1029/2021GH000567
• Languages: English
• ISSNs: 2471-1403

Abstract

Abstract: Anthropogenic emissions and ambient fine particulate matter (PM2.5 ) concentrations have declined in recent years across China. However, PM2.5 exposure remains high, ozone (O3 ) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010–2020. We show that PM2.5 exposure peaked in 2012 at 52.8 μg m −3, with contributions of 31% from industry and 22% from residential emissions. In 2020, PM2.5 exposure declined by 36% to 33.5 μg m −3, where the contributions from industry and residential sources reduced to 15% and 17%, respectively. The PM2.5 disease burden decreased by only 9% over 2012 where the contributions from industry and residential sources reduced to 15% and 17%, respectively 2020, partly due to an aging population with greater susceptibility to air pollution. Most of the reduction in PM2.5 exposure and associated public health benefits occurred due to reductions in industrial (58%) and residential (29%) emissions. Reducing national PM2.5 exposure below the World Health Organization Interim Target 2 (25 μg m −3 ) would require a further 80% reduction in residential and industrial emissions, highlighting the challenges that remain to improve air quality in China. Plain Language Summary: Atmospheric models are useful to simulate air quality, weather, and climate, and to explore processes and mechanisms in detail. However, they are computationally expensive due to their complexity. This limits the experiments that are feasible. One solution to this problem is to use emulators. Emulators are machine learning models that are trained with atmospheric model simulations. They are cheaper to run, enabling much more experimentation. Here, we used emulators to predict air quality and health in China from emission changes (averaged per sector over all species). The emulators accurately predicted the spatial variation and magnitude of fine particulate matter (PM2.5 ) concentrations across China. We found that PM2.5 exposure peaked in 2012 at 52.8 μg m −3 . For comparison, this is ten times larger than the WHO Air Quality Guideline. The main contributors to these air pollution levels in 2012 were industrial (31%) and residential (22%) emissions. Over 2012–2020, PM2.5 exposure reduced by 36%, attaining the National Air Quality Target of 35 μg m −3 . This air quality improvement avoided 187, 800 premature deaths each year by 2020. Most of these health benefits were due to lower industrial (58%) and residential (29%) emissions. This study highlights the value of emulators in air quality research. Key Points: We used emulators of long‐term air quality in China to estimate the emission sector impacts on air pollution and health over 2010–2020 PM2.5 exposure peaked in 2012 at 52.8 μg m −3, primarily from industrial (31%) and residential (22%) emissions PM2.5 exposure reduced by 36% in 2020, reducing the disease burden by 9% mostly from lower industrial (58%) and residential (29%) emissions