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北京市机动车尾气排放因子研究
摘要点击 2607  全文点击 1466  投稿时间:2014-12-15  修订日期:2015-03-02
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中文关键词  机动车尾气  排放因子  COPERT模型  车载测试系统  北京
英文关键词  vehicle exhaust  emission factor  COPERT model  on-board measurement system  Beijing
作者单位E-mail
樊守彬 北京市环境保护科学研究院, 北京 100037
国家城市环境污染控制工程技术研究中心, 北京 100037 
fanshoubin@163.com 
田灵娣 北京市环境保护科学研究院, 北京 100037
首都师范大学资源环境与旅游学院, 北京 100048 
tianlingdi0@sina.com 
张东旭 北京市环境保护科学研究院, 北京 100037
国家城市环境污染控制工程技术研究中心, 北京 100037 
 
曲松 北京市环境保护科学研究院, 北京 100037
国家城市环境污染控制工程技术研究中心, 北京 100037 
 
中文摘要
      通过调研北京市机动车车型构成、车辆行驶工况、环境温度、油品品质等基础数据,利用COPERT Ⅳ 模型计算了机动车尾气中CO、NOx、HC和PM的排放因子. 应用车载测试系统对典型轻型汽油客车和柴油货车的实际道路排放因子进行测量,并将测量结果与模型计算结果对比,结果发现国Ⅳ标准下,轻型汽油客车的CO排放因子的实测数据是模型数据的0.96倍,NOx的实测数据是模型数据的0.64倍,HC的实测数据是模型数据的4.89倍. 对于国Ⅲ排放标准的柴油货车,轻型、中型和重型货车的CO排放因子,实测数据分别是模型数据的1.61、1.07和1.76倍,NOx排放因子的实测数据是模型数据的1.04、1.21和1.18倍,HC排放因子的实测数据是模型数据的3.75、1.84和1.47倍,PM排放因子则为模型数据是实测数据的1.31、3.42和6.42倍.
英文摘要
      Based on the investigation of basic data such as vehicle type composition, driving conditions, ambient temperature and oil quality, etc., emission factors of vehicle exhaust pollutants including carbon monoxide(CO), nitrogen oxides(NOx), hydrocarbons(HC) and particulate matter(PM) were calculated using COPERT Ⅳ model. Emission factors of typical gasoline passenger cars and diesel trucks were measured using on-board measurement system on actual road. The measured and modeled emission factors were compared and the results showed that: the measured emission factors of CO, NOx and HC were 0.96, 0.64 and 4.89 times of the modeled data for passenger cars conforming to the national Ⅳ emission standard. For the light, medium and heavy diesel trucks conforming to the national Ⅲ emission standard, the measured data of CO emission factors were 1.61, 1.07 and 1.76 times of the modeled data, respectively, the measured data of NOx emission factors were 1.04, 1.21 and 1.18 times of the modeled data, and the measured data of HC emission factors were 3.75, 1.84 and 1.47 times of the modeled data, while the model data of PM emission factors were 1.31, 3.42 and 6.42 times of the measured data, respectively.

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