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PM 2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS

Author

Listed:
  • Dan Xu

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Wenpeng Lin

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
    Yangtze River Delta Urban Wetland Ecosystem National Field Observation and Research Station, Shanghai 200234, China)

  • Jun Gao

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
    Yangtze River Delta Urban Wetland Ecosystem National Field Observation and Research Station, Shanghai 200234, China)

  • Yue Jiang

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Lubing Li

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Fei Gao

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

Abstract

Assessing personal exposure risk from PM 2.5 air pollution poses challenges due to the limited availability of high spatial resolution data for PM 2.5 and population density. This study introduced a seasonal spatial-temporal method of modeling PM 2.5 distribution characteristics at a 1-km grid level based on remote sensing data and Geographic Information Systems (GIS). The high-accuracy population density data and the relative exposure risk model were used to assess the relationship between exposure to PM 2.5 air pollution and public health. The results indicated that the spatial-temporal PM 2.5 concentration could be simulated by MODIS images and GIS method and could provide high spatial resolution data sources for exposure risk assessment. PM 2.5 air pollution risks were most serious in spring and winter, and high risks of environmental health hazards were mostly concentrated in densely populated areas in Shanghai-Hangzhou Bay, China. Policies to control the total population and pollution discharge need follow the principle of adaptation to local conditions in high-risk areas. Air quality maintenance and ecological maintenance should be carried out in low-risk areas to reduce exposure risk and improve environmental health.

Suggested Citation

  • Dan Xu & Wenpeng Lin & Jun Gao & Yue Jiang & Lubing Li & Fei Gao, 2022. "PM 2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS," IJERPH, MDPI, vol. 19(10), pages 1-24, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:6154-:d:818656
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    References listed on IDEAS

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    1. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    2. 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.
    3. Amit K. Gorai & Francis Tuluri & Paul B. Tchounwou, 2014. "A GIS Based Approach for Assessing the Association between Air Pollution and Asthma in New York State, USA," IJERPH, MDPI, vol. 11(5), pages 1-25, May.
    4. Chenchen Wang & Yifan Tu & Zongliang Yu & Rongzhu Lu, 2015. "PM 2.5 and Cardiovascular Diseases in the Elderly: An Overview," IJERPH, MDPI, vol. 12(7), pages 1-11, July.
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