Spatial Patterns, Drivers and Heterogeneous Effects of PM2.5: Experience from China
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School of Business Administration, Zhongnan University of Economics and Law,182 Nanhu Ave., Wuhan 430073, China
Wenlan School of Business, Zhongnan University of Economics and Law, 182 Nanhu Ave., Wuhan 430073, China
School of Economics, Shandong University of Finance and Economics, NO.40 Shungeng Rd, Jinan, Shandong, 250014, China
Submission date: 2022-04-21
Final revision date: 2022-07-15
Acceptance date: 2022-07-16
Online publication date: 2022-09-26
Publication date: 2022-12-08
Corresponding author
Chengye Jia   

Economics Department, Shandong University of Finance and Economics, China
Pol. J. Environ. Stud. 2022;31(6):5633-5647
PM2.5 not only affects air visibility, but also can enter the lungs and blood through the respiratory tract, causing important damage to the human respiratory system, cardiovascular and cerebrovascular system. Infants, children, the elderly, patients with cardiovascular disease and chronic lung disease have become more sensitive to it, and has become an important factor endangering public health. Identifying PM2.5 spatial and temporal characteristics and influencing factors can provide key information for urban atmospheric environmental governance and public health improvement. Previous studies have only explored the influencing factors of PM2.5, while ignoring which is the more important factors. Firstly, this study explores the spatial-temporal evolution characteristics and spatial correlation characteristics of PM2.5 distribution in 285 cities in China. Then, a selection model--- Bayesian model average method is applied to identify which variables are more likely to affect PM2.5 in China. We find that (1) From 2000 to 2018, the high-value concentration areas of PM2.5 distribution in China were mainly distributed in the central and eastern regions, and showed the trend of “moving eastward”. (2) Among all variables in this study, population density, utilization rate of common industrial solid wastes, per capita Gross Domestic Product (GDP), proportion of secondary industry to GDP, centralized treatment rate of sewage treatment plant and industrial emission of sulfur dioxide are the most important drivers to predict PM2.5 in China. In addition, we found that the relationship between the selected variables and PM2.5 tends to change over time. In addition, we also show that the influence of selected variables on PM2.5 depends on the distribution of PM2.5, that is, there is a heterogeneous effect.
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