Fusion-DeepRSM for Satellite-Derived PM2.5 Estimation and Precursor Sensitivity Prediction

Abstract Accurate estimation of fine particulate matter (PM2.5) and characterization of precursor-response relationships are crucial for human health protection and air quality management. However, current approaches remain limited by the high computational cost and uncertainties of chemical transport models (CTMs), as well as the sample imbalance and limited interpretability of observation-based machine learning methods. Here, we developed Fusion-DeepRSM, an observation-constrained extension of the Deep Learning Response Surface Model (DeepRSM) framework that integrates CTM simulations, multisource satellite observations, and ground-based measurements to derive spatially continuous PM2.5 concentrations and characterize precursor-PM2.5 response relationships. Results from ground-based observations and CTM-based testbed experiments show that Fusion-DeepRSM effectively alleviates the sample imbalance in conventional models and mitigates PM2.5 overestimation in rural regions, with an average reduction of 4.13 μg/m3 while maintaining high predictive accuracy (R2 = 0.872 and RMSE = 11.82 μg/m3). In addition, Fusion-DeepRSM successfully quantifies precursor-PM2.5 response relationships, revealing pronounced seasonal and regional heterogeneity consistent with established atmospheric chemistry understanding. With high computational efficiency, Fusion-DeepRSM further quantifies the effects of meteorological conditions and pollutant concentrations on PM2.5 variability. During the nationwide PM2.5 decline from 2020 to 2023, unfavorable meteorology partially offset pollutant-related improvements (+1.2 versus −1.8 μg/m3), while changes in precursor pollutant conditions played an important role in the February 2023 PM2.5 rebound in the Beijing–Tianjin–Hebei region. These findings demonstrate the strong potential of Fusion-DeepRSM for air quality modeling and region-specific pollution mitigation.