High-Spatiotemporal-Resolution Estimation of Ground-Level Ozone in China Based on Machine Learning

Abstract

High concentrations of ground-level ozone (O3) pose a significant threat to human health. Obtaining high-spatiotemporal-resolution information about ground-level O3 is of paramount importance for O3 pollution control. However, the current monitoring methods have a lot of limitations. Ground-based monitoring falls short in providing extensive coverage, and remote sensing based on satellites is constrained by specific spectral bands, lacking sensitivity to ground-level O3. To address this issue, we combined brightness temperature data from the Himawari-8 satellite with meteorological data and ground-based station data to train four machine learning models to obtain high-spatiotemporal-resolution information about ground-level O3, including Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Among these, the CatBoost model exhibited superior performance, achieving a ten-fold cross-validation R2 of 0.8534, an RMSE of 17.735 μg/m3, and an MAE of 12.6594 μg/m3. Furthermore, all the selected feature variables in our study positively influenced the model. Subsequently, we employed the CatBoost model to estimate averaged hourly ground-level O3 concentrations at a 2 km resolution. The estimation results indicate a close relationship between ground-level O3 concentrations and human activities and solar radiation.