Spatiotemporal Big Data for PM_2.5 Exposure and Health Risk Assessment during COVID-19

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• COVID-19 significantly impacts regional environments and human health.
• Assessing fine particulate matter (PM2.5) exposure and associated health risks during the pandemic is crucial.

Purpose of the Study:

• To assess PM2.5 exposure and health risks during COVID-19.
• To analyze the spatiotemporal variations and driving factors of PM2.5 pollution.

Main Methods:

• Utilized empirical orthogonal function (EOF) for spatiotemporal PM2.5 variation analysis.
• Assessed population exposure and health risks using location-based services (LBS) data.
• Employed geographically weighted regression (GWR) to analyze PM2.5 concentration and points of interest (POI) data.

Main Results:

• Monthly PM2.5 concentrations exhibited a U-shaped trend.
• PM2.5 concentrations notably declined in Central and North China.
• Health risks were lower in western regions compared to the Middle East.
• Urban factories were the largest contributors to PM2.5 pollution.

Conclusions:

• Urban GDP and green areas negatively correlate with PM2.5.
• Roads, taxis, buses, and factories positively correlate with PM2.5.
• Reducing health risks and PM2.5 pollution requires targeted strategies, with urban factories being a key focus.