Comprehensive 24-hour ground-level ozone monitoring: Leveraging machine learning for full-coverage estimation in East...

Cloud cover often hinders satellite-derived ozone (O₃) concentration estimation, leading to incomplete spatial coverage. To address this limitation and obtain gap-free hourly ground-level O₃ estimates, this study developed a novel all-sky O₃ estimation model based on a light gradient boosting machine, combining clear- (cLearGBM) and cloudy-sky (cLoudGBM) models. Unlike earlier studies focusing mainly on daytime, this study provides comprehensive O₃ variations over a full 24-h cycle at an hourly 2 km resolution. The all-sky O₃ estimation model was developed using Himawari-8 brightness temperature (BT) as a key input, alongside other satellite-derived variables, meteorological variables, and auxiliary parameters, with ground-based O₃ observations serving as the dependent variable. The models were evaluated using three 10-fold cross-validation methods (random, spatial, and temporal), showing high estimation accuracy (cLearGBM: coefficient of determination (R²) = 0.90, root mean square error (RMSE) = 8.77 ppb; cLoudGBM: R² = 0.87, RMSE = 9.44 ppb). Notably, BT data improved the accuracy and spatial resolution of the O₃ estimates. The estimated ground-level O₃ distribution followed a typical diurnal pattern across the study area, with urban regions showing higher O₃ concentrations during the day and rural areas exhibiting higher concentrations at night. Compared to the Copernicus Atmosphere Monitoring Service reanalysis data, the proposed model offered a representation of East Asia that was 40 times better spatial resolution and 2.26 times more accurate when evaluated against in-situ observations. The 24-h ground-level O₃ data for East Asia provided by this study is expected to serve as a valuable foundation for applied research and to support effective O₃ pollution management.