Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model

Abstract

Recently, the near-ground ozone pollution has become an important factor restricting economic development and ecological environment protection. Due to the aging equipment of satellite sensors and the limitations of spatial resolution, the current approach utilizing satellite remote sensing observation faces challenges in effectively monitoring small-scale areas with sufficient data. Taking the near-ground ozone concentration as the research object, this article combined multiple classical machine learning (ML) methods based on tree models and developed a temporally ensemble model to achieve the estimation of near-surface ozone in the 1 km2 area of the Yangtze River Delta region in China. In the ensemble model, the coefficient of determination (R2) of the 10-fold cross-validation was 0.91, and the root-mean-square error was 9.21 μg/m3. All evaluation indicators confirm that our approach was more accurate than some conventional ML models. The predicted spatial errors were evenly distributed, which indicated the superior spatial stationarity of the ensemble model. On the temporal scale, the ozone distribution predicted by the model agreed well with the results of ground-based meteorological station monitoring, both showing distinct seasonal trends. On the spatial scale, the model output reflected well the refined spatial variation of near-ground ozone at a small scale and captured the “medium-high-low” trend of near-ground ozone concentration in Shanghai and the trend of “low-medium” in Hangzhou, China. In contrast, the satellite observation data cannot well reflect the differences in details. In the future, this model will have good application potential in the refined monitoring of polluting gases across the country.

Keywords

• Air pollution
• high spatio-temporal resolution
• machine learning (ML)
• near-ground ozone
• temporally ensemble model