Estimation of ground-level NO₂ and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model

The major link between satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO₂) and ground-level concentrations is theoretically the NO₂ mixing height (NMH). Various meteorological parameters have been used as a proxy for NMH in existing studies. This study developed a nested XGBoost machine learning model to convert VCDs of NO₂ into ground-level NO₂ concentrations across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. This nested model was designed to directly incorporate NMH into the methodological framework to estimate satellite-derived ground-level NO₂ concentrations. The inner machine learning model predicted the NMH from meteorological parameters, which were then input into the main XGBoost machine learning model to predict the ground-level NO₂ concentrations from its VCDs. The inclusion of NMH significantly enhanced the accuracy of ground-level NO₂ concentration estimates; i.e., the R² values were improved from 0.73 to 0.93 in 10-fold cross-validation and from 0.88 to 0.99 in the fully trained model. Furthermore, NMH was identified as the second most important predictor variable, following the VCDs of NO₂. Subsequently, the satellite-derived ground-level NO₂ data were analyzed across subregions with varying geographic locations and urbanization levels. Highly populated areas typically experienced peak NO₂ concentrations during the early morning rush hour, whereas areas categorized as lightly populated observed a slight increase in NO₂ levels 1 or 2 h later, likely due to regional pollutant dispersion from urban sources. This study underscores the importance of incorporating NMH in estimating ground-level NO₂ from satellite column measurements and highlights the significant advantages of geostationary satellites in providing detailed air pollution information at an hourly resolution. © 2024 Naveed Ahmad et al.