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.
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.
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COVID-19 爆发期间中国颗粒物空气污染（PM(2.5) 和 PM(10)）的分布模式如何变化：中国城市级别的时空调查。
DOI：
10.3390/ijerph17176274
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发表时间：
2020-08-28
影响因子：
--
通讯作者：
Zhan M
中科院分区：
文献类型：
--
作者：
Fan Z;Zhan Q;Yang C;Liu H;Zhan M
由于COVID-19期间交通及工业活动暂停，中国的颗粒物（PM）污染有所减少。然而，很少有研究探讨这种变化的时空格局和相关的影响因素，在全国城市规模。在这项研究中，使用空间自相关分析研究了2020年1月20日至4月8日期间PM2. 5和PM10与2019年同期相比下降率的聚类模式。四个气象因素和两个社会经济因素，即，回归分析采用了反映交通流动性影响的城市内部流动强度下降率（dIMI）和第二产业产值下降率（drSIOV）。然后，多尺度地理加权回归（MGWR），一个模型，允许特定的处理尺度为每个自变量，被应用于调查PM污染减少和影响因素之间的关系。为了比较，普通最小二乘（OLS）回归和经典的地理加权回归（GWR）也进行了。研究发现，中国各地PM2.5和PM10浓度分别降低了16%和20%，中部、东部和南部地区的PM污染明显减轻。在回归分析结果方面，MGWR优于其他两个模型，对PM2.5和PM10的R2分别为0.711和0.732。MGWR结果显示，这两个社会经济因素的影响比气象因素更显着。结果表明，交通流动性的减少导致中国东部地区PM2.5的相对下降幅度更大（例如，江苏省的城市），而它导致中国中部地区PM10的相对下降（例如，河南省）。工业运行的减少与中国东北地区PM10的下降有很大关系。该结果对于了解COVID-19爆发期间PM污染下降模式的空间变化至关重要，也为未来的空气污染控制提供了很好的参考。
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.
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