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

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.