Measuring the urban-rural and spatiotemporal heterogeneity of the drivers of PM2.5-attributed health burdens in China from 2008 to 2021 using high-resolution dataset

Yang Guan; Bing Rong; Lei Kang; Nannan Zhang; Changbo Qin

doi:10.1016/j.jenvman.2023.118940

Measuring the urban-rural and spatiotemporal heterogeneity of the drivers of PM_2.5-attributed health burdens in China from 2008 to 2021 using high-resolution dataset

J Environ Manage. 2023 Nov 15:346:118940. doi: 10.1016/j.jenvman.2023.118940. Epub 2023 Sep 22.

Authors

Yang Guan¹, Bing Rong², Lei Kang³, Nannan Zhang⁴, Changbo Qin⁵

Affiliations

¹ Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin, 300191, China; Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100041, China.
² Center of Environmental Status and Plan Assessment, Chinese Academy of Environmental Planning, Beijing, 100041, China.
³ Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA.
⁴ Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100041, China. Electronic address: zhangnn@caep.org.cn.
⁵ Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100041, China; The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing, 100041, China. Electronic address: qincb@caep.org.cn.

PMID: 37741197
DOI: 10.1016/j.jenvman.2023.118940

Abstract

Urbanization has been considered a driver of PM_2.5 pollution and the attributed health burden. This study systematically measured the spatiotemporal and urban-rural heterogeneity of PM_2.5-attributed health burden drivers, including income, population, baseline mortality rate, and PM_2.5 level. The results reveal the significantly positive contribution of disposable income and the periodical and urban-rural differentiation of population contribution to PM_2.5-attributed health burden. The difference in driver performance due to socioeconomic development and urbanization stages might be an important determinant for different or even opposite results of previous studies. Policymaking for mitigating PM_2.5-attributed health risk could incorporate the re-assessment and driver determination for PM_2.5-attributed health burden into the construction and development plan from the overall urbanization perspective. The urbanization-perspective driver decomposition could be synergized with the flow analysis, equality evaluation, and policy benefit estimation to achieve further direction-determining and quantitative assessment of the urban-rural PM_2.5 health risk management strategies.

Keywords: Driver decomposition; Multiscale dataset; PM(2.5); Premature death; Urban-rural heterogeneity.