Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China.

Abstract High ozone concentrations have adverse effects on human health and ecosystems. In recent years, the ambient ozone concentration in China has shown an upward trend, and high-quality prediction of ozone concentrations has become critical to support effective policymaking. In this study, a novel hybrid model combining wavelet decomposition (WD), a gated recurrent unit (GRU) neural network and a support vector regression (SVR) model was developed to predict the daily maximum 8 h ozone. We used the ground ozone observation data in six representative megacities across China from Jan. 1, 2015 to Jun. 15, 2020 for model training, and we used data from Jun. 15 to Dec. 31, 2020 for model testing. The results show that the developed model performs very well for megacities; against observations, the model obtains an average cross-validated R2 (coefficient of determination) ranging from 0.90 for Shanghai to 0.97 for Chengdu in the one-step predictions, thereby indicating that the model outperformed any single algorithm or other hybrid algorithms reported. The developed model can also capture high ozone pollution episodes with an average accuracy of 92% for the next five days in inland cities. This study will be useful for the environmental health community to prevent high ozone exposure more efficiently in megacities in China and shows great potential for accurate ozone prediction using machine learning approaches.