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

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 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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

Keywords: COVID-19; PM pollution; influencing factors; multi-scale geographically weighted regression (MGWR); spatial correlation analysis; spatiotemporal patterns.

Figure 1

Particulate matter (PM) ground observation sites distribution in China.

Figure 2

PM concentrations in Chinese prefectural cities of the study period in 2018 ((a) for PM_2.5 and (d) for PM₁₀); 2019 ((b) for PM_2.5 and (e) for PM₁₀); and 2020 ((c) for PM_2.5 and (f) for PM₁₀).

Figure 3

Temporal statistics. (a,b) The days proportion distribution of daily average PM_2.5 and PM₁₀ concentrations, respectively; (c,d) The hourly PM_2.5 and PM₁₀ concentrations change, respectively.

Figure 4

Decline rates of PM_2.5 and PM₁₀. (a,b) The drPM_2.5 in 2018–2019 and 2019–2020, respectively. (c,d) The drPM₁₀ in 2018–2019 and 2019–2020, respectively.

Figure 5

Spatial clusters of drPM_2.5 and drPM₁₀. (a,b) The results of drPM_2.5 in 2018–2019 and 2019–2020, respectively; (c,d) The results of drPM₁₀ in 2018–2019 and 2019–2020, respectively.

Figure 6

Spatial coefficients of decline of intra-city mobility intensity (dIMI) ((a) for the PM_2.5 model and (b) for the PM₁₀ model), decline rates of the secondary industrial output values (drSIOV); ((c) for the PM_2.5 model and (d) for the PM₁₀ model) and dHUM; ((e) for the PM_2.5 model and (f) for the PM₁₀ model).