tetano
Editor, Senior Moderator
Int J Environ Res Public Health
. 2020 Oct 21;17(20):E7664.
doi: 10.3390/ijerph17207664.
Spatiotemporal Big Data for PM [SUB]2.5[/SUB] Exposure and Health Risk Assessment during COVID-19
Hongbin He[SUP] 1 2 [/SUP], Yonglin Shen[SUP] 1 3 [/SUP], Changmin Jiang[SUP] 1 [/SUP], Tianqi Li[SUP] 1 [/SUP], Mingqiang Guo[SUP] 1 [/SUP], Ling Yao[SUP] 3 [/SUP]
Affiliations
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[SUB]2.5[/SUB] 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[SUB]2.5[/SUB] site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM[SUB]2.5[/SUB] concentration firstly. Then, population exposure and health risks of PM[SUB]2.5[/SUB] during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM[SUB]2.5[/SUB] pollution, the relationship between PM[SUB]2.5[/SUB] concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM[SUB]2.5[/SUB] 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[SUB]2.5[/SUB] concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM[SUB]2.5[/SUB] 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[SUB]2.5[/SUB]; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM[SUB]2.5[/SUB] pollution. In terms of reducing the health risks and PM[SUB]2.5[/SUB] pollution, several pointed suggestions to improve the status has been proposed.
Keywords: COVID-19; empirical orthogonal function (EOF); geographic weighted regression (GWR); population distribution; spatiotemporal big data.
. 2020 Oct 21;17(20):E7664.
doi: 10.3390/ijerph17207664.
Spatiotemporal Big Data for PM [SUB]2.5[/SUB] Exposure and Health Risk Assessment during COVID-19
Hongbin He[SUP] 1 2 [/SUP], Yonglin Shen[SUP] 1 3 [/SUP], Changmin Jiang[SUP] 1 [/SUP], Tianqi Li[SUP] 1 [/SUP], Mingqiang Guo[SUP] 1 [/SUP], Ling Yao[SUP] 3 [/SUP]
Affiliations
- PMID: 33096649
- DOI: 10.3390/ijerph17207664
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[SUB]2.5[/SUB] 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[SUB]2.5[/SUB] site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM[SUB]2.5[/SUB] concentration firstly. Then, population exposure and health risks of PM[SUB]2.5[/SUB] during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM[SUB]2.5[/SUB] pollution, the relationship between PM[SUB]2.5[/SUB] concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM[SUB]2.5[/SUB] 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[SUB]2.5[/SUB] concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM[SUB]2.5[/SUB] 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[SUB]2.5[/SUB]; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM[SUB]2.5[/SUB] pollution. In terms of reducing the health risks and PM[SUB]2.5[/SUB] pollution, several pointed suggestions to improve the status has been proposed.
Keywords: COVID-19; empirical orthogonal function (EOF); geographic weighted regression (GWR); population distribution; spatiotemporal big data.