A Deep Forest Algorithm Based on TropOMI Satellite Data to Estimate Near-Ground Ozone Concentration

Abstract

The accurate estimation of near-ground ozone (O3) concentration is of great significance to human health and the ecological environment. In order to improve the accuracy of estimating ground-level O3 concentration, this study adopted a deep forest algorithm to construct a model for estimating near-ground O3 concentration. It is pointed out whether input data on particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations also affect the estimation accuracy. The model first uses the multi-granularity scanning technique to learn the features of the training set, and then it adopts the cascade forest structure to train the processed data, and at the same time, it adaptively adjusts the number of layers in order to achieve a better performance. Daily near-ground O3 concentrations in Shijiazhuang were estimated using satellite O3 column concentrations, ground-based PM2.5 and NO2 concentration data, meteorological element data, and elevation data. The deep forest model was compared with six models, namely, random forest, CatBoost, XGBoost, LightGBM, Decision Tree, and GBDT. The R-squared (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) of the proposed deep forest model were 0.9560, 13.2542, and 9.0250, respectively, which had significant advantages over other tree-based regression models. Meanwhile, the model performance was improved by adding NO2 and PM2.5 features to the model estimations, indicating the necessity of synergistic observations of NO2, PM2.5, and O3. Finally, the seasonal distribution of O3 concentrations in the Shijiazhuang area was plotted, with the highest O3 concentrations in the summer, the lowest in the winter, and the O3 concentration is in the middle of spring and autumn.