- 期刊
- OpenAccess
Regional Air Quality Forecast Using a Machine Learning Method and the WRF Model over the Yangtze River Delta, East China
摘要
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 PM_(2.5) 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 PM_(2.5) forecast was validated using the corresponding surface PM_(2.5) observational data in this study. Results indicate that the short-term air quality forecasting system has a ability to accurately forecast PM_(2.5) concentration in the major cities in the YRD region. The average index of agreement (IA) between PM_(2.5) 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 PM_(2.5) concentration greater than 115 μg m^(-3) are selected to establish the EXP-Polluted model and then used to predict PM_(2.5) concentration during heavy haze periods in 2017. The RMSEs of PM_(2.5) 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.
延伸閱讀
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