Regional Air Quality Forecast Using a Machine Learning Method and the WRF Model over the Yangtze River Delta, East China

Abstract

ABSTRACT A statistical forecasting method of air quality based on meteorological elements with high spatiotemporal resolution simulated by the Weather Research and Forecasting (WRF) model and a back-propagation (BP) neural network was established to predict 72 h PM2.5 mass concentrations over the Yangtze River Delta (YRD) region of eastern China. Short-term statistical forecasting of air quality in 25 major cities in the YRD region was conducted and the PM2.5 forecast was validated using the corresponding surface PM2.5 observational data in this study. Results indicate that the short-term air quality forecasting system has a ability to accurately forecast PM2.5 concentration in the major cities in the YRD region. The average index of agreement (IA) between PM2.5 forecasts and observations in the four seasons ranges from 74% to 77%, and the root mean square error (RMSE) fall between 15.2 µg m–3 and 33.0 µg m–3. The data with PM2.5 concentration greater than 115 µg m–3 are selected to establish the EXP-Polluted model and then used to predict PM2.5 concentration during heavy haze periods in 2017. The RMSEs of PM2.5 forecasts during severe haze periods are improved by 44.1%, which compared to predictions using the EXP-All Time model constructed by the full-year data.