Establishment of a Combined Model for Ozone Concentration Simulation with Stepwise Regression Analysis and Artificial Neural Network

With the development of industrialization and the increase in the number of motor vehicles in megacities in China, ozone pollution has become a prominent problem. Although different models have been used on ozone concentration simulation, the accuracy of different models still varies. In this study, the performance of two models including a linear stepwise regression (SR) model and a non-linear artificial neural network (ANN) model on the simulation of ozone concentration were analyzed in the Jing-Jin-Ji region, which is one of the most polluted areas in China. Results showed that the performance of the ANN model (adjusted R-2 = 0.8299, RMSE = 22.87, MAE = 16.92) was better than the SR model (adjusted R-2 = 0.7324, RMSE = 28.61, MAE = 22.30). The performance of the ANN on simulating an ozone pollution event was better than the SR model since a higher probability of detection (POD) and threat score (TS) values were obtained by the ANN model. The model performance for spring, autumn and winter was generally higher than that for summer, which may because the weights of factors on simulating high and low ozone concentrations were different. The method proposed by this study can be used in ozone concentration estimation.