CTM-Assisted Generative AI Framework for Satellite-to-Surface Estimation of Ground-Level Air Pollutants

 Accurate monitoring of ground-level air pollutants is essential for exposure assessment and air quality management, but conventional modeling approaches exhibit significant limitations. Chemical Transport Models (CTMs) are computationally intensive and prone to systematic bias, while data-driven models often lack physical consistency and poorly represent long-range transport. To address these limitations, we present a novel hybrid modeling framework with three key innovations. First, satellite retrievals are employed as primary predictors rather than CTM outputs, thereby reducing computational demands. Second, a dual-target learning strategy prioritizes satellite-to-surface relationships, while CTM outputs are incorporated as soft physical constraints in data-sparse regions. Third, a generative diffusion model is integrated to improve the representation of long-range pollutant transport. Focusing on nitrogen dioxide (NO₂), the completed framework achieves superior daily predictive accuracy (R² = 0.72, RMSE = 3.70 ppb), outperforming precursor models. Its successful extension to sulfur dioxide (SO₂) and fine particulate matter (PM_2.5) demonstrates broad applicability. This study provides a physically informed and computationally efficient solution for scalable generation of high-fidelity, spatially continuous ground-level air quality fields.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2024-00404042 and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00343921).