Spatiotemporal mapping and multiple driving forces identifying of PM2.5 variation and its joint management strategies across China

Facing the challengeable PM2.5 pollution management across China, it is of significance to identify key pollution driving factors with a nation-region-city perspective and establish targeted joint management strategies on the trans-provincial scale. To identity hot study regions, the spatiotemporal pollution variation of PM2.5 in 2016 was explored using multi-scale spatial autocorrelation analysis based on the ground monitoring data for Chinese 366 cities. In the identified study regions, the relationship of natural factors and PM2.5 was analyzed on annual and daily scales via the correlation analyses, and the multiple socioeconomic driving forces of PM2.5 were identified utilizing geographic weighted regression (GWR) and principal component analysis. Consequently, 111 cities were identified as the hot regions, where their annual means of PM2.5 (AM) were 60.40 ± 14.23 μg·m−3 exceeding the Chinese level II standard and WHO Air Quality Guideline IT-1 (35 μg·m−3). The elevation and annual accumulative rainfall were found to be negatively correlated with the AM (p