Machine learning-based estimation of ground-level NO concentrations over China.

Most current scientific research on NO remote sensing focuses on tropospheric NO column concentrations rather than ground-level NO concentrations; however, ground-level NO concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO concentrations throughout China based on the tropospheric NO column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO and its influencing factors. The R values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO concentrations indicates that pollution decreased continuously from 2018 to 2020.