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[中国机动车挥发性有机物排放及其对大气环境的影响]

[VOCs Emission from Motor Vehicles in China and Its Impact on the Atmospheric Environment].

作者信息

Chen Tian-Zeng, Ge Yan-Li, Liu Yong-Chun, He Hong

机构信息

State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.

College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Huan Jing Ke Xue. 2018 Feb 8;39(2):478-492. doi: 10.13227/j.hjkx.201705129.

DOI:10.13227/j.hjkx.201705129
PMID:29964807
Abstract

Volatile organic compounds (VOCs) are important precursors of O and secondary organic aerosols (SOAs). In order to fully understand the impacts of VOCs from motor vehicles on urban air quality, the current state-of-the-art research results on source apportionment are systematically introduced. The contribution of VOCs from motor vehicles to secondary pollutants in China is further discussed. It was found that motor vehicle exhaust is the largest source of urban atmospheric VOCs, and the average contribution ratio is up to 36.8%, wherein motorcycles and light gasoline vehicles are the main emission sources. The results showed that VOCs from vehicles play an important role in O and SOA formation in urban areas, whereas with the improvement of emission standards and operating conditions, the motor vehicle emission factors and ozone formation potentials (OFPs) are obviously reduced. Moreover, the composition profile of the exhaust is mainly composed of aromatic hydrocarbons and alkenes, which contribute more to secondary pollution. This work can aid in providing data and theoretical support for the control of VOCs from motor vehicles in the future.

摘要

挥发性有机化合物(VOCs)是臭氧和二次有机气溶胶(SOAs)的重要前体物。为全面了解机动车排放的VOCs对城市空气质量的影响,系统介绍了当前源解析方面的最新研究成果。进一步探讨了中国机动车排放的VOCs对二次污染物的贡献。研究发现,机动车尾气是城市大气VOCs的最大来源,平均贡献率高达36.8%,其中摩托车和轻型汽油车是主要排放源。结果表明,机动车排放的VOCs在城市地区臭氧和SOA形成中起重要作用,而随着排放标准和运行条件的改善,机动车排放因子和臭氧生成潜势(OFPs)明显降低。此外,尾气的成分谱主要由芳烃和烯烃组成,对二次污染的贡献更大。这项工作有助于为未来机动车VOCs的控制提供数据和理论支持。

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