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Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19

文献类型：期刊论文

作者He, Hongbin1,3; Shen, Yonglin2,3; Jiang, Changmin3; Li, Tianqi3; Guo, Mingqiang3; Yao, Ling2
刊名INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
出版日期2020-10-01
卷号17期号:20页码:19
关键词spatiotemporal big data empirical orthogonal function (EOF) geographic weighted regression (GWR) population distribution COVID-19
DOI10.3390/ijerph17207664
通讯作者Shen, Yonglin(shenyl@cug.edu.cn) ; Guo, Mingqiang(guomingqiang@cug.edu.cn)
英文摘要The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM2.5 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 PM2.5 site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM2.5 concentration firstly. Then, population exposure and health risks of PM2.5 during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM2.5 pollution, the relationship between PM2.5 concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM2.5 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 PM2.5 concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM2.5 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 PM2.5; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM2.5 pollution. In terms of reducing the health risks and PM2.5 pollution, several pointed suggestions to improve the status has been proposed.
WOS关键词PARTICULATE MATTER ; URBAN AREAS ; POLLUTION ; ENVIRONMENT ; REGRESSION ; MORTALITY ; CHINA
资助项目State Key Laboratory of Resources and Environmental Information System ; National Natural Science Foundation of China[41771380] ; National Natural Science Foundation of China[41701446] ; National Natural Science Foundation of China[41971356]
WOS研究方向Environmental Sciences & Ecology ; Public, Environmental & Occupational Health
语种英语
WOS记录号WOS:000585584100001
出版者MDPI
资助机构State Key Laboratory of Resources and Environmental Information System ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/156623]  
专题中国科学院地理科学与资源研究所
通讯作者Shen, Yonglin; Guo, Mingqiang
作者单位1.Yunnan Univ, Inst Int Rivers & Ecosecur, Kunming 650500, Yunnan, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China
推荐引用方式
GB/T 7714		 He, Hongbin,Shen, Yonglin,Jiang, Changmin,et al. Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19[J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,2020,17(20):19.
APA He, Hongbin,Shen, Yonglin,Jiang, Changmin,Li, Tianqi,Guo, Mingqiang,&Yao, Ling.(2020).Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19.INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,17(20),19.
MLA He, Hongbin,et al."Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19".INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 17.20(2020):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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