Spatial-temporal evolution characteristics of PM_2.5 and its driving mechanism: spatially explicit insights from...

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM_2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM_2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM_2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM_2.5 concentration within Shanxi Province, with PM_2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM_2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m³ overall. The Hurst exponent indicated a projected decrease in PM_2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM_2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM_2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM_2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM_2.5 and provided a reference for similar studies.