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Based on annual average PM 2.5  gridded dataset, this study first analyzed the spatiotemporal pattern of PM 2.5  across Mainland China during 1998–2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM 2.5  were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM 2.5  concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM 2.5  concentrations greater than 35 μg/m 3  significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM 2.5 . Additionally, the Moran’s I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM 2.5  in Mainland China. The effects of each latent factor on PM 2.5  in various regions were different. Therefore, regional measures and strategies for controlling PM 2.5  should be formulated in terms of the local impacts of specific factors.

Spatiotemporal Pattern of PM2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression