Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong

摘要

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 PM_(2.5), NO_2, and O_3. The results show significant improvements in multiple evaluation metrics for daily (1–3 days) and hourly (1–72 hr) 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 PM_(2.5), NO_2, and O_3 in the next 3 days by 14.3–21.0%, 41.2–46.3%, and 47.8–49.7%, respectively. For hourly forecasts in the next 72 hr, reductions in RMSEs brought by ARIMAX using CMAQ are 18.2% for PM_(2.5), 32.1% for NO_2, and 36.7% for O_3. Large improvements in RMSEs are achieved for nonrural PM_(2.5) and rural NO_2 using ARIMAX with three numerical models. Dynamic hourly forecast shows that ARIMAX can be applied for forecast of 7- to 72-hr PM_(2.5), 4- to 72-hr NO_2, and 4- to 6-hr O_3. Besides applying ARIMAX for NO_2, we recommend a mixed forecast strategy to ARIMAX for normal values of PM_(2.5) 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.