Mengwei Jia1,3, Xinghong Cheng 2, Tianliang Zhao3, Chongzhi Yin3, Xiangzhi Zhang4, Xianghua Wu5, Liming Wang5, Renjian Zhang6


Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
State Key Laboratory of Severe Weather, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jiangsu Provincial Environmental Monitoring Center, Nanjing 210029, China
School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China



Received: May 29, 2019
Revised: June 5, 2019
Accepted: June 26, 2019
Download Citation: ||https://doi.org/10.4209/aaqr.2019.05.0275  

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Cite this article:
Jia, M., Cheng, X., Zhao, T., Yin, C., Zhang, X., Wu, X., Wang, L. and Zhang, R. (2019). Regional Air Quality Forecast Using a Machine Learning Method and the WRF Model over the Yangtze River Delta, East China. Aerosol Air Qual. Res. 19: 1602-1613. https://doi.org/10.4209/aaqr.2019.05.0275


HIGHLIGHTS

  • A forecast model of PM2.5 based on WRF and ANN was established over the YRD in China.
  • The average index of agreement ranges from 74% to 77% in the whole year.
  • Even in the haze pollution, this model still presents an excellent forecast ability.

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.


Keywords: Regional air quality forecast; BP Network; WRF model; heavy haze; Yangtze River Delta

 



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