资源环境科技发展态势分析平台

Based on prevailing numerical forecasting models (Community Multiscale Air Quality [CMAQ] model , Comprehensive Air Quality Model with Extensions, and Nested Air Quality Prediction Modeling System) and observations from monitoring stations in Hong Kong, we employ a set of autoregressive integrated moving average (ARIMA) models with numerical forecasts (ARIMAX) to improve the forecast of air pollutants including PM2.5, NO2, and O-3. The results show significant improvements in multiple evaluation metrics for daily (1-3days) and hourly (1-72hr) forecast. Forecasts on daily 1-hr and 8-hr maximum O-3 are also improved. For instance, compared with CMAQ, applying CMAQ-ARIMA reduces average root-mean-square errors (RMSEs) at all stations for daily average PM2.5, NO2, and O-3 in the next 3days by 14.3-21.0%, 41.2-46.3%, and 47.8-49.7%, respectively. For hourly forecasts in the next 72hr, reductions in RMSEs brought by ARIMAX using CMAQ are 18.2% for PM2.5, 32.1% for NO2, and 36.7% for O-3. Large improvements in RMSEs are achieved for nonrural PM2.5 and rural NO2 using ARIMAX with three numerical models. Dynamic hourly forecast shows that ARIMAX can be applied for forecast of 7- to 72-hr PM2.5, 4- to 72-hr NO2, and 4- to 6-hr O-3. Besides applying ARIMAX for NO2, we recommend a mixed forecast strategy to ARIMAX for normal values of PM2.5 and O-3 and employ numerical models for outputs above 75th percentile of historical observations. Our hybrid ARIMAX method can combine the advantage of ARIMA and numerical modeling to assist real-time air quality forecasting efficiently and consistently.