A revised approach to air quality forecast based on Models-3/CMAQ
ZHAO Jun-ri, XIAO Xin, WU Tao, LI Yan-peng, JIA Hong-xia
1. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China; 2. Xuzhou Environmental Monitoring Central Station, Xuzhou 221018, China; 3. Jiangxi College of Applied Technology, Ganzhou 341000, China; 4. China Forum of Environmental Journalists, Beijing 100095, China
Abstract：In this study, the forecast values of hourly PM2.5、PM10、O3、SO2、NO2、CO concentrations at 13 environmental monitoring stations in Xuzhou city during December 2016 were corrected using nudging scheme and XGBoost algorithm, and improvement model prediction before and after correction were analyzed. A method combining nudging scheme and IDW interpolation algorithm was adopted by modifying the forecast values of SO2、NO2、CO concentrations, results showed that the correlation coefficient between the predicted concentration and the observation simulated by the assimilation source increased by 0.06~0.27, and the mean absolute error and the root mean square error decreased obviously, the average relative deviation (MFB) and average relative error (MFE) were within the ideal range, had best effect on NO2 followed by SO2 and CO. The part of statistical revision which based on XGBoost algorithm, by introducing WRF meteorological forecast elements established a statistical regression model, which could be used for modifying the forecast values of PM2.5、PM10、O3、SO2、NO2、CO concentrations. Results showed that lower or higher than normal conditions were greatly improved,with the exception of SO2, the correlation coefficient increased to about 0.6~0.7, the reduction of the error of statistical indicators was very obvious.
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