The article presents a novel approach to estimating daily surface NO₂ concentrations at 1 km resolution across Europe. Using the S-MESH framework, the study employs an XGBoost model that integrates TROPOMI satellite observations with auxiliary data like night light radiance and meteorological factors. The model achieves robust performance, highlighting the value of satellite data and machine learning in air quality monitoring. It also explores feature importance using explainable AI techniques like SHAP.
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