Explainable GeoAI reveals spatially varying and nonlinear associations of PM _2.5 pollution with driving factors in Chinese cities from 2015 to 2022

PM2.5 pollution remains a critical environmental and public health challenge in China despite post-2015 improvements. However, our understanding of the spatially heterogeneous and nonlinear associations of its driving factors remains limited. To fill this gap, we employ an explainable geospatial artificial intelligence (GeoAI) framework that integrates the geographical random forest (GRF) model and the Shapley additive explanations (SHAP) approach to examine the associations between 16 determinants and PM2.5 concentrations. Based on a nationwide and multi-year analysis across 288 cities selected from all 336 Chinese cities between 2015 and 2022, the results show that GRF achieves at least a 0.04 higher R² than baseline models. Our analysis reveals three findings. First, population density is the most influential factor in 52.39% of cities; combined with temperature, road density, and gas supply, these four dominate over 95% of cities. Second, drivers exhibit significant spatially varying and nonlinear associations. For instance, population density correlates positively with PM2.5 in the North China Plain but negatively in sparsely populated areas; and the association of temperature follows an inverted U-shaped pattern. Third, these spatial and nonlinear associations undergo temporal changes. These findings offer insights for future environmental management strategies to mitigate the negative impacts of various drivers.