High-resolution full-coverage ozone (O) estimates using a data-driven spatial random forest model in Beijing-Tianjin-Hebei region, China.

The Beijing-Tianjin-Hebei (BTH) region is severely polluted by ozone (O). Accurate O estimates are essential for identifying high-polluted zones and developing targeted interventions to relieve the burden of diseases. Although many studies have estimated high-resolution O concentrations in BTH, the estimation accuracies are still insufficient. In this study, we incorporated data-driven spatial weight matrices (DDWs) into a random forest (RF) model to fully utilize both the spatial homogeneity and heterogeneity of maximum daily 8-h ozone concentration (MDA8O), and obtained full-coverage MDA8O concentrations at 1 km×1 km in BTH from 2014 to 2022. DDW-RF exhibited satisfactory accuracy (10-fold cross-validation R =0.937, RMSE=13.919 μg/m). Overall O level presented a spatial pattern of lower in the north and higher in the southeast and showed a distinct temporal trend, i.e., first increasing and then decreasing during 2014-2021 and increasing slightly in 2022. The accurate MDA8O estimates indicates that more attention and resources should be poured into the areas adjacent to Bohai Rim, Shandong and Henan. Regulated operation of factories under specific meteorological conditions and upgrading industrial structure and production modes are recommended to mitigate the formation of O precursors and reduce O generation. Our findings provide evidence and reference for environmental cleaning policies and targeted interventions.