Estimation of the Near-Surface Ozone Concentration with Full Spatiotemporal Coverage across the Beijing-Tianjin-Hebei Region Based on Extreme Gradient Boosting Combined with a WRF-Chem Model

With the intensification of global warming and economic development in China, the near-surface ozone (O₃) concentration has been increasing recently, especially in the Beijing-Tianjin-Hebei (BTH) region, which is the political and economic center of China. However, O₃ has been measured in real time only over the past few years, and the observational records are discontinuous. Therefore, we propose a new method (WRFC-XGB) to establish a near-surface O₃ concentration dataset in the BTH region by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) algorithm. Based on this method, the 8-h maximum daily average (MDA8) O₃ concentrations are obtained with full spatiotemporal coverage at a spatial resolution of 0.1° × 0.1° across the BTH region in 2018. Two evaluation methods, sample- and station-based 10-fold cross-validation (10-CV), are used to assess our method. The sample-based (station-based) 10-CV evaluation results indicate that WRFC-XGB can achieve excellent accuracy with a high coefficient of determination (R²) of 0.95 (0.91), low root mean square error (RMSE) of 13.50 (17.70) µg m⁻³, and mean absolute error (MAE) of 9.60 (12.89) µg m⁻³. In addition, superb spatiotemporal consistencies are confirmed for this model, including the estimation of high O₃ concentrations, and our WRFC-XGB model outperforms traditional models and previous studies in data mining. In addition, the proposed model can be applied to estimate the O₃ concentration when it has not been measured. Furthermore, the spatial distribution analysis of the MDA8 O₃ in 2018 reveals that O₃ pollution in the BTH region exhibits significant seasonality. Heavy O₃ pollution episodes mainly occur in summer, and the high O₃ loading is distributed mainly in the southern BTH areas, which will pose challenges to atmospheric environmental governance for local governments.