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基于WRF-Chem模拟验证的天水市主城区大气污染源排放清单
引用本文:刘扬,王颖,刘灏,秦闯,王思潼,李博,郭春晔.基于WRF-Chem模拟验证的天水市主城区大气污染源排放清单[J].中国环境科学,2022,42(1):32-42.
作者姓名:刘扬  王颖  刘灏  秦闯  王思潼  李博  郭春晔
作者单位:1. 兰州大学大气科学学院, 甘肃 兰州 730000;2. 兰州大学, 半干旱气候变化教育部重点实验室, 甘肃 兰州 730000;3. 青海省海南藏族自治州气象局, 青海 海南 813000
基金项目:甘肃省科技计划项目(18JR2RA005);
摘    要:通过部门调研、现场调查和遥感解译等方法获取天水市主城区大气污染源活动水平数据, 采用排放因子法估算了天水市主城区10类污染源的9种污染物排放量, 构建了2019年天水市主城区高分辨率排放清单, 并采用横向比较法和模式验证法评估了排放清单的合理性.结果表明: (1)2019年天水市主城区SO2、NOx、CO、VOCs、NH3、PM10、PM2.5、BC和OC的排放量分别为2702, 8829, 82670, 10460, 7551, 14221, 8252, 1682和2814t.化石燃料固定燃烧源为SO2、CO和颗粒物的主要贡献源, 移动源是NOx和VOCs的主要贡献源, NH3排放主要来源于农业源.(2)天水市主城区SO2、NOx、颗粒物、CO和VOCS的排放高值区主要集中在人口和工业密集的河谷地形内, NH3排放高值区分布在周边耕地区域.(3)民用散烧源的空间分配方案对模拟结果有重要影响, 在河谷地形集中供热范围外, 根据城中村的分布对民用散烧源进行空间分配较为合理.(4)WRF-Chem模式模拟的4, 7, 10和12月的SO2、NO2、O3、CO、PM10和PM2.5日均浓度与同期监测的污染物浓度相关系数分别为0.767, 0.502, 0.618, 0.462, 0.647和0.654.

关 键 词:大气污染物  排放清单  时空分布  WRF-Chem模式  
收稿时间:2021-05-14

Air pollutants emission inventory for the main urban area of Tianshui City based on verification by WRF-Chem simulation
LIU Yang,WANG Ying,LIU Hao,QIN Chuang,WANG Si-tong,LI Bo,GUO Chun-ye.Air pollutants emission inventory for the main urban area of Tianshui City based on verification by WRF-Chem simulation[J].China Environmental Science,2022,42(1):32-42.
Authors:LIU Yang  WANG Ying  LIU Hao  QIN Chuang  WANG Si-tong  LI Bo  GUO Chun-ye
Institution:1. College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China;2. Key Laboratory for Semi-Arid Climate Change attached to Ministry of Education, Lanzhou University, Lanzhou 730000, China;3. Hainan Tibetan Autonomous Prefecture Meteorological Bureau, Qinghai Province, Hainan 813000, China
Abstract:An emission inventory, including ten emission categories and nine air pollutants, was developed for the main urban area of Tianshui city (EITS) using emission factor approach. Sector survey, on-site investigation and remote sensing interpretation method have been used to derive the activity data of air pollution sources used in EITS. The rationality of EITS was further validated by comparing with other emission inventories and model verification. The results suggested that: (1)The total emissions in the study area were 2702t SO2, 8829t NOx, 82670t CO, 10460t VOCs, 7551t NH3, 14221t PM10, 8252t PM2.5, 1682t BC and 2814t OC in 2019. Fossil fuel burning was the major contributor to SO2、CO and PM emissions, traffic was the main source of NOx and VOCs emissions, while agriculture was the dominate contributor to NH3 emission; (2) High-emissions of SO2、NOx、PM、CO and VOCs appears in river valley, while NH3 emission maximizes outside of the river valley where cultivated lands were dominate; (3) Spatial allocation of residential fuel consumption sources had an important effect on model simulations, with more reasonable results when the location of village outside of the river valley region was used for spatial allocation; (4) Correlation coefficients between WRF-Chem modeled and observed daily averages of SO2、NO2、O3、CO、PM10 and PM2.5 concentrations were 0.767、0.502、0.618、0.462、0.647 and 0.654, respectively, indicating the reasonability of the EITS.
Keywords:air pollutants  emission inventory  time and space distribution  WRF-Chem model  
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