A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R-2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants.

Automated summary

A novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations in eastern China. The model demonstrated high accuracy and robustness compared to in situ measurements and other machine learning techniques. This study provides an efficient and exact method for estimating ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants.