How Did Distribution Patterns of Particulate Matter Air Pollution (PM 2.5 and PM 10 ) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Zhiyu Fan, Qingming Zhan, Chen Yang, Huimin Liu and Meng Zhan
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Zhiyu Fan: School of Urban Design, Wuhan University, 8 Donghu South Road, Wuhan 430072, China
Qingming Zhan: School of Urban Design, Wuhan University, 8 Donghu South Road, Wuhan 430072, China
Chen Yang: College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Huimin Liu: Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China
Meng Zhan: School of Urban Design, Wuhan University, 8 Donghu South Road, Wuhan 430072, China

IJERPH, 2020, vol. 17, issue 17, 1-19

Abstract: Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM 2.5 and PM 10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM 2.5 and PM 10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R 2 of 0.711 and 0.732 for PM 2.5 and PM 10 , respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM 2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM 10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM 10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

Keywords: COVID-19; PM pollution; spatiotemporal patterns; spatial correlation analysis; influencing factors; multi-scale geographically weighted regression (MGWR) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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