Fusion of ground-based monitoring, satellite observations, and chemical transport modeling for estimating highly dynamic and spatially seamless PM 2.5 concentrations

Fine particulate matter (PM2.5) poses significant risks to both public health and ecological systems. Multi-source data, including ground monitoring (accurate but spatially limited), satellite remote sensing (large-scale but with gaps), and chemical transport models (CTM; continuous but with parameterization errors), offer complementary perspectives. However, existing fusion methods often rely on the simple stacking of these datasets, failing to fully exploit their underlying potential. This study introduces a novel framework for hourly 1-km seamless PM2.5 estimation by deeply extracting the mechanism and spatial information from these multi-source data. Initially, we generate seamless aerosol optical depth (AOD) coverage by combining continuous WRF-Chem AOD with satellite AOD retrievals. Temporal components of PM2.5 and vertical structure of AOD, extracted as physics-informed features from the WRF-Chem model, are then incorporated. Subsequently, a geospatial intelligent light gradient boosting machine model is introduced to derive the final PM2.5 estimates, taking advantage of the spatial distribution properties of monitoring sites. The model demonstrates high accuracy in site-based cross-validation, with an R2 of 0.87, an RMSE of 13.42 μg/m3, and an MAE of 8.68 μg/m3. Additionally, a thorough analysis was conducted of a PM2.5 pollution episode, incorporating meteorological conditions to examine its evolution. The results provided an accurate representation of the seamless 1-km/h PM2.5 concentration distribution (spanning both day and night) and offered a comprehensive understanding of pollutant movement and transformation. By shifting from simple data integration to deep mechanisms and spatial information mining, this approach provides a robust solution for high-dynamic, seamless air quality mapping.