Identifying the determinants of natural, anthropogenic factors and precursors on PM pollution in urban agglomerations in China: Insights from optimal parameter-based geographic detector and robust geographic weighted regression models.

Revealing the spatial disparities and driving factors of PM pollution is crucial for controlling atmospheric pollution. However, the nexus between PM pollution and driving forces has rarely been examined, traditional geographic detector models ignore the shortcomings of data discretization methods determined by experience, and the spatial nonstationary effect of dominant factors on PM has not been considered. In this study, a comprehensive modelling framework was proposed that integrated optimal parameter-based geographic detector (OPGD) and robust geographic weighted regression (RGWR) models to explore the influence intensities, interactions and spatial heterogeneity effects of natural factors, anthropogenic factors and precursors on PM at the urban agglomeration scale in China. There w significant differences in the distribution of PM pollution across China. Socioeconomic and combustion emissions were the dominant anthropogenic factors causing PM pollution in northern areas, whereas vegetation and meteorological factors played critical roles as natural determinants in southern regions. However, precursors generated complex effects. The interactions of meteorology with natural and anthropogenic factors had bivariate and nonlinear enhancement effects. The associations between PM pollution and influencing factors demonstrated significant spatial heterogeneity. This key knowledge provided scientific guidance for understanding the mechanisms driving PM pollution, controlling particulate pollutants and achieving sustainable urban management.