Hourly Seamless Surface O(3) Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region

Surface ozone (O(3)) is an important atmospheric trace gas, posing an enormous threat to ecological security and human health. Currently, the core objective of air pollution control in China is to realize the joint treatment of fine particulate matter (PM(2.5)) and O(3). However, high-accuracy near-surface O(3) maps remain lacking. Therefore, we established a new model to determine the full-coverage hourly O(3) concentration with the WRF-Chem and random forest (RF) models combined with anthropogenic emission data and meteorological datasets. Based on this method, choosing the Beijing-Tianjin-Hebei (BTH) region in 2018 as an example, full-coverage hourly O(3) maps were generated at a horizontal resolution of 9 km. The performance evaluation results indicated that the new model is reliable with a sample (station)-based 10-fold cross-validation (10-CV) R(2) value of 0.94 (0.90) and root mean square error (RMSE) of 14.58 (19.18) µg m(−3). In addition, the estimated O(3) concentration is accurately determined at varying temporal scales with sample-based 10-CV R(2) values of 0.96, 0.98 and 0.98 at the daily, monthly, and seasonal scales, respectively, which is highly superior to traditional derivation algorithms and other techniques in previous studies. An initial increase and subsequent decrease, which constitute the diurnal variation in the O(3) concentration associated with temperature and solar radiation variations, were captured. The highest concentration reached approximately 112.73 ± 9.65 μg m(−3) at 15:00 local time (1500 LT) in the BTH region. Summertime O(3) posed a high pollution risk across the whole BTH region, especially in southern cities, and the pollution duration accounted for more than 50% of the summer season. Additionally, 43 and two days exhibited light and moderate O(3) pollution, respectively, across the BTH region in 2018. Overall, the new method can be beneficial for near-surface O(3) estimation with a high spatiotemporal resolution, which can be valuable for research in related fields.