Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China

Atmospheric ozone (O-3) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological conditions, plateau regions contain high concentrations of atmospheric O-3. However, traditional experimental methods for determining O(3 )concentrations using automatic monitoring stations cannot predict O-3 trends. In this study, two machine learning models (a nonlinear auto-regressive model with external inputs (NARX) and a temporal convolution network (TCN)) were developed to predict O-3 concentrations in a plateau area in the Kunming region by considering the effects of meteorological parameters, air quality parameters, and volatile organic compounds (VOCs). The plateau O-3 prediction accuracy of the machine learning models was found to be much higher than those of numerical models that served as a comparison. The O-3 values predicted by the machine learning models closely matched the actual monitoring data. The temporal distribution of plateau O-3 displayed a high all-day peak from February to May. A correlation analysis between O-3 concentrations and feature parameters demonstrated that humidity is the feature with the highest absolute correlation (-0.72), and was negatively correlated with O3 3 concentrations during all test periods. VOCs and temperatures were also found to have high positive correlation coefficients with O-3 during periods of significant O-3 pollution. After negating the effects of meteorological parameters, the predicted O-3 concentrations decreased significantly, whereas they increased in the absence of NOx. Although individual VOCs were found to greatly affect the O-3 concentration, the total VOC (TVOC) concentration had a relatively small effect. The proposed machine learning model was demonstrated to predict plateau O-3 concentrations and distinguish how different features affect O-3 variations.