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乌海市高分辨率大气污染源排放清单构建及其在臭氧污染成因探究中的应用
摘要点击 1045  全文点击 287  投稿时间:2022-01-09  修订日期:2022-02-22
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中文关键词  源排放清单  WRF-Chem模式  臭氧(O3)  乌海  污染成因
英文关键词  emissions inventory  WRF-Chem model  ozone(O3)  Wuhai  cause of pollution formation
作者单位E-mail
张瑞欣 兰州大学大气科学学院, 半干旱气候变化教育部重点实验室, 兰州 730000 zhangrx18@lzu.edu.cn 
楚波 乌海市生态环境局, 乌海 016000  
尚春林 内蒙古自治区环境监测总站乌海分站, 乌海 016000  
曹喜萍 内蒙古自治区环境监测总站乌海分站, 乌海 016000  
李光耀 兰州大学大气科学学院, 半干旱气候变化教育部重点实验室, 兰州 730000  
朱玉凡 兰州大学大气科学学院, 半干旱气候变化教育部重点实验室, 兰州 730000  
刘晓 兰州大学大气科学学院, 半干旱气候变化教育部重点实验室, 兰州 730000  
夏佳琦 兰州大学大气科学学院, 半干旱气候变化教育部重点实验室, 兰州 730000  
陈强 兰州大学大气科学学院, 半干旱气候变化教育部重点实验室, 兰州 730000 chenqqh@lzu.edu.cn 
中文摘要
      乌海市是我国典型的煤焦化工业基地,大气污染物排放总量较大且近年来夏季O3污染问题逐渐突出,明确大气污染物排放特征,探究O3污染形成机制是客观认识其O3污染现状,科学制定污染控制措施的基础.基于"系数法"采用自下而上的方式构建了2018年乌海市高分辨率大气污染源排放清单(HEI-WH18),利用WRF-Chem对HEI-WH18的适用性和准确性进行评估,并结合模式诊断模块探究了乌海市夏季O3污染形成的原因.排放清单结果表明,2018年乌海市SO2、NOx、CO、PM10、PM2.5、VOCs、NH3、BC和OC的排放总量分别为65943、40934、172867、159771、47469、69191、1407、1491和1648 t ·a-1.与MEIC清单相比,利用HEI-WH18能更好地捕捉到O3及其前体物的排放变化规律和量级,适用于乌海市夏季O3的模拟及其来源分析研究.从O3及前体物的空间分布来看,乌海市海勃湾城区白天为O3高值区,3个工业园区无论白天和夜间均为O3低值区和NO2高值区,CO的空间分布特征与煤层及矸石堆自燃源一致.根据对O3污染过程的诊断分析,边界层中高层O3浓度的升高主要是平流输送和化学过程共同作用的结果,低层O3浓度的升高是垂直混合和平流输送导致的,化学过程在低层的贡献较为复杂,其正贡献起到了维持高O3浓度的作用,负贡献结合平流输送造成了O3污染的最终消散.
英文摘要
      Wuhai is a typical coking industrial base including three industrial parks within its jurisdiction. The emission amount of air pollutants is considerable here, and O3 pollution has become serious in recent years. Clarifying the air pollutant emission characteristics and exploring the formation mechanism of O3 are the basis for objectively understanding the O3 pollution and formulating scientific prevention and control measures. This study established the high-resolution emission inventory of Wuhai in 2018 (HEI-WH18) based on the "coefficient method," evaluated the applicability and accuracy of HEI-WH18 using the WRF-Chem model, and explored the causes of O3 pollution in summer using WRF-Chem diagnosis module output. The HEI-WH18 showed that the total emissions amount of SO2, NOx, CO, PM10, PM2.5, VOCs, NH3, BC, and OC were 65943, 40934, 172867, 159771, 47469, 69191, 1407, 1491, and 1648 t·a-1, respectively. HEI-WH18 could capture the variation and magnitude of O3 and its precursors better than the MEIC, which was suitable for the O3 simulation and source analysis in summer. From the perspective of spatial distribution, Haibowan was a high-value area of O3 during the daytime, and the three industrial parks were low-value areas of O3 and high-value areas of NO2 during the daytime and nighttime. The spatial distribution characteristics of CO were consistent with the spontaneous combustion of coal and coal gangue sources. According to the diagnostic analysis of two O3 pollution processes, the O3 increase in the upper boundary layer was mainly related to the advection transport and chemical process, and it was caused by vertical mixing and the advection transport process in the lower boundary layer. The contribution of the chemical process in the lower boundary layer was complicated, and its positive contribution played a role in maintaining a high O3 concentration, whereas its negative contribution combined with advection transport resulted in the final dissipation of O3 pollution.

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