High concentrations of PM_2.5 are a primary cause of haze in the lower atmosphere. A better understanding of the spatial heterogeneity and driving factors of PM_2.5 concentrations is important for effective regional prevention and control. In this study, we carried out remote sensing inversion of PM_2.5 concentration data over a long time series and used spatial statistical analyses and a geographical detector model to reveal the spatial distribution and variation characteristics of PM_2.5 and the main influencing factors in the Yangtze River Delta from 2005 to 2015. Our results show that (1) The average annual PM_2.5 concentration in the Yangtze River Delta prior to 2007 displayed an increasing trend, followed by a decreasing trend after 2007 which eventually stabilized; and (2) climate regionalization and geomorphology were the dominant natural factors driving PM_2.5 concentration diffusion, while total carbon dioxide emissions and population density were the dominant socioeconomic factors affecting the formation of PM_2.5. Natural factors and socioeconomic factors together lead to PM_2.5 pollution. These findings provide an interpretation of PM_2.5 spatial distribution and the mechanisms influencing PM_2.5 pollution, which can help the Chinese government develop effective abatement strategies.

Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM_2.5 pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM_2.5 concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen’s slope estimator and the Mann–Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM_2.5 concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM_2.5 in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 μg/m³ from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM_2.5 concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 μg/m³) gradually decreased and gradient 3 (≥35 μg/m³) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM_2.5 concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM_2.5 emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM_2.5 concentrations and the mechanisms influencing anthropogenic PM_2.5 concentrations, which can help the Chinese government develop effective abatement strategies.