违反了 PRIMARY KEY 约束 'PK_t_counter'。不能在对象 'dbo.t_counter' 中插入重复键。 语句已终止。 基于观测、模拟和同化数据的PM2.5污染回顾分析-Evaluating the PM2.5 Pollution over Beijing-Hebei-Tianjin Region Based on Observations, Simulations, and Data Assimilation Results
doi:  10.3878/j.issn.1006-9585.2016.14284
基于观测、模拟和同化数据的PM2.5污染回顾分析

Evaluating the PM2.5 Pollution over Beijing-Hebei-Tianjin Region Based on Observations, Simulations, and Data Assimilation Results
摘要点击 592  全文点击 516  投稿时间:2014-12-30  
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基金:  中国科学院战略性先导科技专项XDB05030200,国家自然科学基金项目41575128、41305111
中文关键词:  最优插值  资料同化  PM2.5
英文关键词:  Spatial interpolation  Data assimilation  PM2.5
                 
作者中文名作者英文名单位
黄思HUANG Si中国科学院大气物理研究所大气边界层物理与大气化学国家重点试验室, 北京 100191;中国科学院大学, 北京 100049
唐晓TANG Xiao中国科学院大气物理研究所大气边界层物理与大气化学国家重点试验室, 北京 100191
王自发WANG Zifa中国科学院大气物理研究所大气边界层物理与大气化学国家重点试验室, 北京 100191
陈焕盛CHEN Huansheng中国科学院大气物理研究所大气边界层物理与大气化学国家重点试验室, 北京 100191
刘冰LIU Bing中国环境监测总站, 北京 100012
朱江ZHU Jiang中国科学院大气物理研究所大气边界层物理与大气化学国家重点试验室, 北京 100191
引用:黄思,唐晓,王自发,陈焕盛,刘冰,朱江.2016.基于观测、模拟和同化数据的PM2.5污染回顾分析[J].气候与环境研究,21(6):700-710,doi:10.3878/j.issn.1006-9585.2016.14284.
Citation:HUANG Si,TANG Xiao,WANG Zifa,CHEN Huansheng,LIU Bing,ZHU Jiang.2016.Evaluating the PM2.5 Pollution over Beijing-Hebei-Tianjin Region Based on Observations, Simulations, and Data Assimilation Results[J].Climatic and Environmental Research(in Chinese),21(6):700-710,doi:10.3878/j.issn.1006-9585.2016.14284.
中文摘要:
      基于观测数据空间插值、数值模拟以及最优插值同化方法构建了京津冀地区PM2.5(空气动力学当量直径小于等于2.5 μm的颗粒物,即细颗粒物)空间插值数据、模拟数据和同化数据,并首次比较分析了三种数据在PM2.5污染回顾分析上的应用潜力和优缺点。针对2014年2月19~28日京津冀地区PM2.5污染过程的分析发现:(1)观测空间插值数据难以完整表征PM2.5污染的时空演变特征,在没有观测覆盖区域误差较大,容易出现虚假的高低值中心;(2)模拟数据具有较高时空分辨率,对PM2.5污染时空演变特征描述更加细致,但在这次污染过程中仍存在较大不确定性,其均方根误差大于100 μg/m3;(3)同化数据不仅能对PM2.5空间分布特征进行细致描述,其数据精度在独立验证站点也显著高于模拟数据,其均方根误差比模拟数据低约50%,与站点观测数据的相关系数也比模拟数据高0.2以上。基于PM2.5同化数据,对这次京津冀PM2.5污染过程的时空演变特征进行了详细回顾分析,发现这次污染过程存在自京津冀南部PM2.5污染累积并向北输送发展的生成特点,消亡过程为风向转换下自北向南清除,造成京津冀南部城市先污染后清除,北部城市后污染先清除,并且有慢累积、快清除的特征。从发展演变过程中污染所占空间面积来看,25日PM2.5污染范围最大,覆盖模式第三区域60.5%面积。
Abstract:
      This study investigated the advantages and limitations of three different methods for the evaluation of a PM2.5 pollution episode over Beijing-Heibei-Tianjin (BHT) region during 19-28 February 2014. The three methods are the Cressman spatial interpolation using observations, numerical simulation with a 3-dimensional chemical transport model, and data assimilation combining both observations and simulations. The main results are as follows. First, the Cressman spatial interpolation using only the sparse surface observations could not well reproduce the spatial-temporal variation of PM2.5 concentration during this episode. It tended to produce artificial high and low PM2.5 concentration centers over some areas where observations were missing. Second, the numerical simulation using a Nested Air Quality Prediction Modeling System (NAQPMS) could provide high spatial-temporal resolution details of PM2.5 concentration over the BHT region. However, this method was limited by large uncertainties in the simulation results and the root mean square error (RMSE) of the simulated surface PM2.5 concentration was higher than 100 μg/m3. Third, the data assimilation using optimal interpolation algorithm performed better than the above two methods in reproducing the evolution of the episode. It could reproduce the spatial pattern of surface PM2.5 concentration during this episode over the BHT region with high accuracy and high spatial and high temporal resolution, and the RMSE of the PM2.5 concentration was lower than the simulated data by about 50%. Finally, the assimilation results of surface PM2.5 were used to evaluate the evolution of the PM2.5 pollution episode. It was found that the PM2.5 concentration over southern Hebei province increased to a high level at the beginning of this episode. The polluted areas then extended to northern Hebei, Beijing, and Tianjin. On 27 February, surface winds over this region systematically turned to be northerlies with enhanced wind speed. Thereby the high concentration of PM2.5 decreased rapidly from north to south. Based on this result with high accuracy, it could be found that the PM2.5 pollution covered a largest area on February 25th, which accounts for about 60.5% of the third model domain.
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