How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level.

Zhiyu Fan,Meng Zhan,Chen Yang,Huimin Liu,Qingming Zhan

doi:10.3390/ijerph17176274

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 PM2.5 and PM10 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 PM2.5 and PM10 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 R2 of 0.711 and 0.732 for PM2.5 and PM10, 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 PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 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.

Highlights

China, as the largest developing country in the world [1], has experienced a rapid process of urbanization and suffered from the accompanying air pollution problems
The spatial patterns of both PM2.5 and PM10 were similar for these 3 study periods with high
2018–2019, obvious policies of central government on air pollution, many industrial enterprises were transferred from clustering patterns were found in north, central and east regions of China, whereas, in 2019–2020, it showed strong clustering patterns of high decline rates of PM2.5 and PM10 in these regions

Summary

Objectives

Our goal was to derive the pattern of PM2.5 and PM10 change by comparing the concentrations in two study periods of 2019 and 2020, respectively, for prefecture cities of China and to further analyze the influencing factors

Methods

Results

Conclusion