PM_2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS

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.