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

Author

Listed:
  • Hongbin He

    (School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
    Institute of International Rivers and Eco-security, Yunnan University, Kunming 650500, China)

  • Yonglin Shen

    (School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, 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)

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.

Suggested Citation

  • Hongbin He & Yonglin Shen & Changmin Jiang & Tianqi Li & Mingqiang Guo & Ling Yao, 2020. "Spatiotemporal Big Data for PM 2.5 Exposure and Health Risk Assessment during COVID-19," IJERPH, MDPI, vol. 17(20), pages 1-19, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7664-:d:432163
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    References listed on IDEAS

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    1. Yonglin Shen & Ling Yao, 2017. "PM 2.5 , Population Exposure and Economic Effects in Urban Agglomerations of China Using Ground-Based Monitoring Data," IJERPH, MDPI, vol. 14(7), pages 1-15, July.
    2. Gang Lin & Jingying Fu & Dong Jiang & Wensheng Hu & Donglin Dong & Yaohuan Huang & Mingdong Zhao, 2013. "Spatio-Temporal Variation of PM 2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China," IJERPH, MDPI, vol. 11(1), pages 1-14, December.
    3. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    4. J. Lelieveld & J. S. Evans & M. Fnais & D. Giannadaki & A. Pozzer, 2015. "The contribution of outdoor air pollution sources to premature mortality on a global scale," Nature, Nature, vol. 525(7569), pages 367-371, September.
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