[Establishment and Effective Evaluation of Haikou Ozone Concentration Statistical Prediction Model].

This study selected 15 key predictors of the maximum of 8-hour averaged ozone (O) concentration (O-8h), using the O concentration of Haikou and ERA5 reanalysis data from 2015 to 2020, and constructed a multiple linear regression (MLR) model, support vector machine (SVM) model, and BP neural network (BPNN) model, to predict and test the O-8h concentration of Haikou in 2021. The results showed that the absolute value of correlation coefficients between the O-8h and related key prediction factors was mainly among 0.2 and 0.507. The 1 000 hPa relative humidity (RH), wind direction (WD), and 875 hPa meridional wind () showed a good indicative effect on the O-8h, with the absolute correlation value exceeding 0.4. The three prediction models could predict the seasonal variation in the O-8h in Haikou, which was larger in the winter half year and smaller in the summer half year. The root mean square error(RMSE) was the smallest (22.29 μg·m) in the BPNN model. The correlation coefficients between the predicted values of three statistical models and observations were ranked as 0.733 (BPNN) > 0.724 (SVM) > 0.591 (MLR), all passing the 99.9% significance test. For the prediction of the O-8h level, we found that TS scores of these three prediction models decreased with the increase in O-8h concentration level. Relatively, the point over rate and not hit rate increased with the rise in O-8h concentration level. TS scores of the SVM and BPNN model were relatively larger than those of MLR, especially in the light pollution level with TS scores remaining above 70%, indicating a better prediction capability.