典型汽车涂装行业VOCs排放及其异味特征  

Volatile organic compound emissions and odor characteristics of typical automotive coating industry

作　　者：纪宇婧 陈妤晴 谢龙飞 王诗怡 孙晓晶 卢莉雯 黄皓旻 叶代启 JI Yu-jing;CHEN Yu-qing;XIE Long-fei;WANG Shi-yi;SUN Xiao-jing;LU Li-wen;HUANG Hao-min;YE Dai-qi(School of Environment and Energy,South China University of Technology,Guangzhou 510006,China;National Engineering Laboratory for VOCs Pollution Control Technology and Equipment,Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control(SCUT),Guangdong Provincial Engineering and Technology Research Centre for Environmental Risk Prevention and Emergency Disposal,South China University of Technology,Guangzhou 510006,China)

机构地区：[1]华南理工大学环境与能源学院,广东广州510006 [2]华南理工大学挥发性有机物污染治理技术与装备国家工程实验室,广东省大气环境与污染控制重点实验室,广东省环境风险防控与应急处置工程技术研究中心,广东广州510006

出　　处：《中国环境科学》2025年第4期1799-1809,共11页China Environmental Science

基　　金：国家自然科学基金资助项目(52370112)。

摘　　要：针对位于中国华北和华南地区典型汽车制造厂涂装车间不同工序所产生废气进行采样与检测,详细分析挥发性有机化合物(VOCs)的排放特征以及异味特征,从多个维度深入探究异味特征预测方法.结果表明,OVOCs、烷烃和芳香烃是废气中的主要组分,OVOCs的气味活性值(OAV)贡献为73.80%~99.03%,是最关键的气味贡献物质类别.乙醛、乙酸正丁酯、乙酸仲丁酯、正丁醛在废气处理设备进口和出口处均有显著气味贡献.通过电子鼻对废气样本进行异味分类,在进口和出口分类准确率分别为100%和98.1%.在异味定量分析上,通过回归分析发现OVOCs物质浓度与OAV_(max)和OAV_(sum)呈现良好线性相关关系.结合BP神经网络的电子鼻技术可有效预测OAV_(max)和OAV_(sum),且OVOCs物质浓度、OAV_(max)、OAV_(sum)与臭气浓度之间存在对数关系.This study conducted sampling and testing on the waste gases generated from different processes in typical automobile manufacturing plant coating workshops located in northern and southern China.It meticulously analyzed the emission characteristics of volatile organic compounds(VOCs)and odor characteristics,and extensively explored odor characteristic prediction methods from multiple dimensions.The results revealed that OVOCs,alkanes,and aromatic hydrocarbons are the predominant components in the exhaust gases,with OVOCs constituting 73.80%to 99.03%of the odor activity value(OAV),thereby classifying them the most significant odor-contributing substance category.Acetaldehyde,n-butyl acetate,isobutyl acetate,and n-butyraldehyde all significantly contributed to the odor at both the inlet and outlet of the treatment equipment.Furthermore,electronic noses were used to classify waste gas samples,achieving 100%and 98.1%accuracy rates for inlet and outlet,respectively.Quantification of odour was achieved through regression analysis,which revealed a strong linear correlation between OVOCs substance concentration and OAV_(max)and OAV_(sum).The electronic nose technology combined with BP neural networks was found to be an effective predictor of OAV_(max)and OAV_(sum).Additionally,a logarithmic relationship was observed between OVOCs substance concentration,OAV_(max),OAV_(sum),and odor concentration.

关 键 词：汽车涂装 挥发性有机物(VOCs) 排放特征 异味特征 

分 类 号：X51[环境科学与工程—环境工程]