Spatial and temporal distribution and influencing factors of PM2.5 in 3 km Beijing-Tianjin-Hebei region based on GTWR model
PM2.5 is closely related to air quality and public health, and many studies use remote sensing combined with other auxiliary data models to invert PM2.5 concentration to capture the spatial and temporal distribution of PM2.5 in various regions. Aiming at the problem of low data resolution in the Beijing-Tianjin-Hebei region, this study adopts 3 km resolution aerosol optical depth( AOD) data and 12 auxiliary variables to establish a geographically and temporally weighted regression model ( GTWR) to estimate the PM2.5 concentration distribution in the 3 km Beijing-Tianjin-Hebei region during 2020—2022. The results show that:①the R2 of GTWR model data ( 0.86 ) is better than that of OLS model data ( 0.66 ) and GWR model data ( 0.78 ) . ②The spatial and temporal distribution of PM2.5 concentration in the Beijing-Tianjin-Hebei region during 2020—2022 is negatively correlated with the terrain. The low-value area is mainly distributed in the high-lying mountainous area, and the high-value area is mainly distributed in the low-lying plain.③The seasonal mean concentration of PM2.5 in Beijing-Tianjin-Hebei from 2020 to 2022 was significantly different as follows:winter (60.88μg/m3), autumn (37.78 μg/m3), spring (31.75 μg/m3), summer (22.16 μg/m3). ④The correlation between PM2.5 concentration and AOD is the strongest. It is concluds that the combination of 3 km resolution AOD data and GTWR model has good applicability in retrieving PM2.5 concentration.
PM2.5aerosol optical depthGTWRspatial and temporal distribution
万方数据
