High-Resolution Satellite-Based PM2.5 Concentration Data Acquired During the COVID-19 Outbreak Throughout China: Model, Variations, and Reasons

Abstract

High spatial resolution and broad spatial coverage data on fine particulate matter (PM2.5) are of great significance to estimating the exposure to PM2.5. However, this type of data is currently very limited worldwide. In addition, the COVID-19 pandemic in China, starting in January 2020, may have led to significant variations in the PM2.5 concentrations. To identify the variations and causes of PM2.5 concentrations before and after the COVID-19 pandemic from January 23 to March 24 during 2018–2020, in this article, a geographically weighted regression model with a 1 km spatial resolution covering all of mainland China was developed. The overall R and RMSE values of the model cross validation were 0.91 and 17.19 μg/m3, respectively, indicating that the model performed satisfactorily in estimating the PM2.5 values. Then, based on the satellite-based PM2.5 values, the results show that the PM2.5 values fluctuated significantly across mainland China before and after the COVID-19 outbreak. Additionally, the mean PM2.5 values decreased by 5.41 μg/m3 in 2020 compared to 2019. In Hubei Province, the mean PM2.5 values increased by 1.85 μg/m3 in 2019 compared to 2018, whereas they dramatically decreased by 23.18 μg/m3 in 2020 compared to 2019. Finally, the results show that anthropogenic factors were primarily responsible for the variations in the PM2.5 concentrations in Heilongjiang, Jilin, and Liaoning provinces; whereas, both meteorological and anthropogenic factors were responsible for the variations in Hubei, Henan, Anhui, Shandong, and Jiangsu provinces during the study period. These results provide an important reference for the future development of air pollution control policies in China.

Keywords

• China
• COVID-19
• high-resolution
• particulate matter (PM2.5) concentrations
• satellite remote sensing