Spatiotemporal Big Data for PM 2.5 Exposure and Health Risk Assessment during COVID-19

Hongbin He, Yonglin Shen, Changmin Jiang, Tianqi Li, Mingqiang Guo and Ling Yao
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Hongbin He: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Yonglin Shen: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Changmin Jiang: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Tianqi Li: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Mingqiang Guo: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Ling Yao: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

IJERPH, 2020, vol. 17, issue 20, 1-19

Abstract: The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM 2.5 exposure and health risk during COVID-19, and its driving factors have been analyzed using spatiotemporal big data, including Tencent location-based services (LBS) data, place of interest (POI), and PM 2.5 site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM 2.5 concentration firstly. Then, population exposure and health risks of PM 2.5 during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM 2.5 pollution, the relationship between PM 2.5 concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM 2.5 concentrations distributed with a U-shape, i.e., with a decrease followed by an increase from January to December. In terms of spatial distribution, the PM 2.5 concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM 2.5 in the west is significantly lower than that in the Middle East. Urban gross domestic product (GDP) and urban green area are negatively correlated with PM 2.5 ; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM 2.5 pollution. In terms of reducing the health risks and PM 2.5 pollution, several pointed suggestions to improve the status has been proposed.

Keywords: spatiotemporal big data; empirical orthogonal function (EOF); geographic weighted regression (GWR); population distribution; COVID-19 (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 (3)

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