Due to inherent errors in the chemical transport models, inaccuracies in the input data, and simplified chemical mechanisms, ozone (O3) predictions are often biased from observations. Accurate O3 predictions can better help assess its impacts on public health and facilitate the development of effective prevention and control measures. In this study, we used a random forest (RF) model to construct a bias-correction model to correct the bias in the predictions of hourly O3 (O3-1h), daily maximum 8-h O3 (O3-Max8h), and daily maximum 1-h O3 (O3-Max1h) concentrations from the Community Multi-Scale Air Quality (CMAQ) model in the Yangtze River Delta region. The results show that the RF model successfully captures the nonlinear response relationship between O3 and its influence factors, and has an outstanding performance in correcting the bias of O3 predictions. The normalized mean biases (NMBs) of O3-1h, O3-Max8h, and O3-Max1h decrease from 15.8%, 20.0%, and 17.0.% to 0.5%, -0.8%, and 0.1%, respectively; correlation coefficients increase from 0.78, 0.90, and 0.89 to 0.94, 0.95, and 0.94, respectively. For O3-1h and O3-Max8h, the original CMAQ model shows an obvious bias in the central and southern Zhejiang region, while the RF model decreases the NMB values from 54% to -1% and 34% to -4%, respectively. The O3-1h bias is mainly caused by the bias of nitrogen dioxide (NO2). Relative humidity and temperature are also important factors that lead to the bias of O3. For high O3 concentrations, the temperature bias and O3 observations are the major reasons for the discrepancy between the model and the observations.
Keywords: CMAQ; O(3) bias; Random forest; Yangtze River Delta.
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