Enhancing Seasonal PM2.5 Estimations in China through Terrain–Wind–Rained Index (TWRI): A Geographically Weighted Regression Approach

Abstract

PM2.5 concentrations, closely linked to human health, are significantly influenced by meteorological and topographical factors. This study introduces the Terrain–Wind–Rain Index (TWRI), a novel index that integrates the Terrain–Wind Closed Index (TWCI) with relative humidity to quantitatively examine the coupling effect of natural elements on PM2.5 concentration and its application to PM2.5 inversion. By employing Geographically Weighted Regression (GWR) models, this study evaluates the inversion results of PM2.5 concentrations using TWRI as a factor. Results reveal that the annual average correlation between TWRI and site-measured PM2.5 concentrations increased from 0.65 to 0.71 compared to TWCI. Correlations improved across all seasons, with the most significant enhancement occurring in summer, from 0.51 to 0.66. On the inversion results of PM2.5, integrating TWRI into traditional models boosted accuracy by 1.3%, 5.4%, 4%, and 7.9% across four seasons, primarily due to the varying correlation between TWRI and PM2.5. Furthermore, the inversion results of coupled TWRI more effectively highlight the high value areas in closed areas and the low value areas in humid areas.