Shanxi Province, a major hub of coal production and industrial activity in China, faces severe PM2.5 pollution and complex co-emission sources that challenge air quality monitoring. This study developed multiple machine learning models to estimate daily PM2.5 concentrations in 2022 by integrating ground-based PM2.5 observations, Himawari-8 measurements, CO, NO2 and ultraviolet aerosol index (UVAI) derived from the TROPOspheric Monitoring Instrument (TROPOMI), and ERA5. Ten-fold cross-validation showed that incorporating TROPOMI-derived CO, NO2 and UVAI produced the highest predictive performance, reducing root mean square error (RMSE) and mean absolute error (MAE) by 14% and 16% respectively. Meanwhile, SHapley Additive exPlanations (SHAP) analysis and partial dependence analysis identified CO as the most important species driving PM2.5 prediction, with all three variables showing positive contributions to PM2.5 that increased with higher values. This emphasis on the importance of CO and NO2 is physically realistic given that CO is co-emitted with primary aerosols, and NO2 is a secondary aerosol precursor. However, such work has not been previously considered by the community focusing on surface PM2.5 prediction using AI models and satellite observations. The model revealed pronounced spatiotemporal gradients in PM2.5 across Shanxi, peaking in the final 5 weeks before Chinese New Year (62.0 μg m−3), and minimizing in summer (21.0 μg m−3), while remaining persistently high in southern and eastern industrial regions. The steady PM2.5 rise in the final 5 weeks before Chinese New Year, suggesting that pre-holiday production activities may contribute to short-term emission intensification, while demonstrating that traditional season-based analysis approaches may miss these extremes. These findings demonstrate the effectiveness of integrating multi-sensor satellite observations with interpretable machine learning for characterizing pollution dynamics in complex industrial regions.

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