First satellite-based regional hourly NO₂ estimations using a space-time ensemble learning model: A case study for...

Surface Nitrogen dioxide (NO₂) concentrations have been generated with satellite retrievals using multiple statistical algorithms. However, they are often given at coarse frequencies ("snapshot", daily or even longer), limiting their applications in epidemiological studies and assessing the evolution of NO₂ pollution. This study investigated the potential applicability of Himawari-8 derived hourly fine particulate matter concentrations in producing hourly NO₂ concentrations by constructing a space-time ensemble model. The Beijing-Tianjin-Hebei (BTH) region, one of the serious pollution regions in China, is the study region chosen. The proposed model performs well in estimating hourly NO₂ concentration with a high cross-validation (CV) coefficient of determination (R² = 0.81) and low CV root-mean-square (RMSE = 9.71 μg/m³), mean prediction errors (MPE = 6.33 μg/m³), and relative prediction errors (RPE = 22.5%). On daily, monthly, seasonal, and annual time scales, CV R² increases to 0.89, 0.93, 0.97, and 0.99, respectively. The annual mean model estimated NO₂ concentration over BTH region is 28.2 ± 6.5 μg/m³, with relatively higher NO₂ concentrations are seen in southern and southeastern BTH. Winter experiences the most severe NO₂ concentrations, followed by autumn, spring, and summer. Surface NO₂ concentrations are higher (lower) in the morning (afternoon) and tend to decrease gradually with time. The model generally captures the hourly evolution of NO₂ concentrations for the severe pollution episode but shows some underestimations. The annual mean NO₂ concentrations were 2.8% lower on the weekend than on weekdays. In addition, the weekend effects of NO₂ concentrations are larger at rush hour and lower in the noon. The hourly NO₂ products derived from proposed approach are potentially useful for improving our understanding of the source, evolution, and transportation behavior of NO₂ pollution episodes and for exposure- and health-related research. The proposed approach also enriches the potential applications of geostationary satellites (e.g., Himawari-8).