基于CMAQ模式和自适应偏最小二乘回归法的中国地区PM_(2.5)浓度动力-统计预报方法研究  

Dynamical-statistical forecasting of PM_(2.5) concentration based on CMAQ model and adapting partial least square regression method in China

作　　者：程兴宏[1,2] 刁志刚[3] 胡江凯 徐祥德[1] 张建春 李德平[5] 

机构地区：[1]中国气象科学研究院灾害天气国家重点实验室,北京100081 [2]中国气象局大气化学重点开放实验室,北京100081 [3]南京信息工程大学大气物理学院,南京210044 [4]国家气象中心,北京100081 [5]北京市气象局,北京100089

出　　处：《环境科学学报》2016年第8期2771-2782,共12页Acta Scientiae Circumstantiae

基　　金：环保公益性行业科研专项(No.201409027;201509001);国家高技术研究发展计划项目(No.2011AA05A302)~~

摘　　要：采用CMAQ模式和自适应偏最小二乘回归法相结合的动力-统计预报方法,对2014年1—12月全国252个环境监测站的PM_(2.5)浓度逐时预报值进行了滚动订正,分析了订正前后PM_(2.5)浓度的时空变化特征,重点研究该方法在中国不同地区不同季节的适用性.结果表明:CMAQ模式预报的PM_(2.5)浓度年平均和秋冬季季节平均偏差表现为非均匀空间分布特征,即辽宁、山东部分地区、川渝地区及华中、华东、华南大部分地区预报偏高,京津冀和西部大部分地区预报偏低;订正后PM_(2.5)浓度与实测值的空间分布较一致,上述偏高和偏低地区的PM_(2.5)浓度预报误差显著减小;秋冬季PM_(2.5)浓度预报和订正偏差均大于年平均值.全国区域平均PM_(2.5)浓度实测值存在明显的季节变化特征,1—3月和11—12月较大,其他月份较小;PM_(2.5)浓度预报误差较大,多数时刻预报偏低,尤其是1—3月和11—12月偏低较明显;订正后PM_(2.5)浓度与实测值较接近,而且时间变化趋势较一致,秋冬季PM_(2.5)浓度预报和订正偏差亦明显大于春夏季.秋冬季4个重点污染区域中,京津冀地区PM_(2.5)实测浓度的区域平均值较大,川渝地区次之,长三角和珠三角地区较小;珠三角地区PM_(2.5)浓度预报和订正效果较好,川渝和长三角地区次之,京津冀地区相对较差;经滚动订正后,全年和秋冬季时段PM_(2.5)浓度订正值与实测值的相关系数均显著增加,误差显著减小,尤其是秋冬季订正效果较好.川渝地区的订正改进幅度最大,长三角和京津冀地区次之,珠三角地区较小.本文方法均适用于非污染日和污染日全国范围的PM_(2.5)预报浓度订正,两种天气过程PM_(2.5)浓度的订正效果均较好;该方法对于改进京津冀地区污染日的PM_(2.5)浓度预报更有效,其他3个地区非污染日的订正改进效果优于污染日.本文研究结果可为改进空气质量预报、重霾污染天气预警和防�In this study, hourly PM2.5 cnneentrations at 252 envirnnmental monitoring stations in China during January -Deeenber 2014 forecasted by thereal-time running Fifth-Generation Penn State/NCAR Mesoscale Model （MM5）-Community Muhiseale Air Quality （CMAQ） model system are corrected using the dynamical-statistical method based on CMAQ model and adapting partial least square regression techniques. Temporal and spatial variations of PM2.5 concentrations betbre and after cmrrection are analyzed with a fncus on the applicability of the dyoantieal-statistit al method in different areas and seasons in China. It is shown that the spatial distributions of both annual and seasonal （for autumn amt winter） averages of PM2.5 eoncentrations forecasted by the MM5-CMAQ model system are inhomogeneous. Forecast PM2.5 concentrations are larger than observatious in parts of Liaoning and Shuudoug provinces, Sichuan and Chongqing prnvinees and most areas of Central China, East China and South China. Forecast values ar＇e smaller in Beijing-Tiaujin- Hebei region and in most areas of West China. Alter eorrection, the spatial distributions of forecast PM2.s eom：enttntions are in good cousislence with observations, and forecast errors in the above areas decrease significantly. Forecast and corrected deviations of seasonally averaged PM2.5 concentrations in autumn and winter in most areas of China are larger than the annual averaged values. There is an nbviousJy seasonal variation of observed PM2.5 eoncentrations, with higher values in Jan., Feb., Mar., Nov. and Dec. Forecast errors are larger, with predietion values less than observaliom, in most of the time. Corrected PM2.5 eoneentrations and its temporal variation are close to observations. Forecast attd corrected ,teviatinns iu autunm and winter tne larger than those in spring and summer. During autumn and winter, among the four seriously polluted regions in （ihiua, Beijing-Tiaujin-ttebei region shows the highest nbserved PM2.5 eonceutrations, followed by Siehuan and Chong

关 键 词：PM2.5浓度 动力-统计预报方法 CMAQ模式 自适应偏最小二乘回归法 

分 类 号：X513[环境科学与工程—环境工程]