空气质量数值预报优化方法研究

中国环境科学 2018,38(6)：2047~2054 China Environmental Science 空气质量数值预报优化方法研究

赵俊日1,肖昕1*,吴涛2,李彦鹏3,贾红霞4(1.中国矿业大学环境与测绘学院,江苏徐州 221116；2.徐州环境监测中心站,江苏徐州 22118；3.江西应用技术职业学院,江西漳州 341000；4.中国环境新闻工作者协会北京 100095)

摘要：基于源清单“Nudging”修正方法和XGBoost算法对徐州市2016年12月13个监测站点的PM2．5、PM10、O3、SO2、NO2、CO等6种污染物浓度预报值进行修正,并分析了修正前后模式预报改善效果.在源清单“Nudging”修正部分,本文结合IDW空间插值算法对SO2、NO2 、CO等3种污染物浓度预报值进行修正,与修正前后模拟结果相比,采用同化源模拟的预报浓度值与观测值的相关系数提高了0.06~0.27不等,平均绝对误差和均方根误差减少的幅度较为明显,平均相对偏差(MFB)和平均相对误差(MFE)均在理想水平范围内,NO2修正效果最好,其次是SO2和CO.基于XGBoost算法的统计修正部分,本文结合WRF气象预报要素建立统计回归模型,对6种污染物进行统计修正,经滚动修正之后,预报偏低或偏高现象得到很大的改善,除了SO2之外,相关系数均提高到0.6~0.7左右,各项误差统计指标改进幅度非常明显.总体而言,本文采用的两种修正方法对中小尺度空气质量数值预报改进效果非常明显,反映了此优化方案的可行性和科学性.

关键词：Models-3/CMAQ；“nudging”修正；统计修正；数值预报优化

中图分类号：X51 文献标识码：A 文章编号：1000-6923(2018)06-2047-08

A revised approach to air quality forecast based on Models-3/CMAQ. ZHAO Jun-ri1, XIAO Xin1*, WU Tao2, LI Yan-peng3, JIA Hong-xia4 (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). China Environmental Science, 2018,38(6)：2047~2054

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.

Key words：Models-3/CMAQ；“nudging”scheme；statistical revision；optimization of numerical air quality prediction

作为当前大气环境科学研究的热点与难题,大气污染物预报预警可通过各类预报方法与手段相结合,对多种大气污染物在全球尺度下的不同类型污染过程进行模拟预测研究,成为城市.及区域大气复合污染控制研究的重要手段之一[1-3].大气污染物排放源清单是模型研究和相关控制策略制定的重要基础,但排放源估算取决于许多因素,包括社会经济、能源、土地利用、环境资料等[4].但由于统计数据的滞后性和排放因子及收稿日期：2017-11-21

基金项目：国家自然科学基金资助项目(41671524)

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