Estimation of PM2.5 concentration in Yangquan city based on MODIS image and meteorological data and analysis of spatial and temporal variation

Abstract

Abstract This study employed Moderate-Resolution Imaging Spectroradiometer (MODIS)aerosol optical depth data meteoro logical data, Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and ground monitoring data for particulate matter (PM2.5) to construct a model for estimating the PM2.5 concentration in Yangquan City, Shanxi Province, from 2018 to 2022. The spatial and temporal changes in the PM2.5 concentration were analyzed. The results revealed the following: (1) The random forest model was more accurate than the multiple linear regression model. The spring model R² increased by 59.7%, and The Root Mean Square Error(RMSE) decreased by 96.2%. The summer model R² increased by 110%, and the RMSE decreased by 96.3%. The autumn model R² increased by 12.4%, and the RMSE decreased by 95.3%. The winter model R² increased by 25%, and the RMSE decreased by 97.9%. (2) The concentration of PM2.5 decreased by 16.6 µg/m³ from 2018 to 2022. The highest concentration of PM2.5 was 55–85 µg/m³ in winter, and the lowest concentration of PM2.5 was 25–40 µg/m³ in summer. The spatial distribution ofPM2.5from the southwest to the northeast, first slightly decreased, then continued to rise, and then stabilized. From the northwest to the southeast, there was a low-middle and high-middle distribution pattern. The PM2.5 pollution was concentrated in the southern urban center and the main industrial areas.