首页 | 本学科首页   官方微博 | 高级检索  
     

我国典型化工行业VOCs排放特征及其对臭氧生成潜势
引用本文:武婷,崔焕文,肖咸德,翟增秀,韩萌. 我国典型化工行业VOCs排放特征及其对臭氧生成潜势[J]. 环境科学, 2024, 45(5): 2613-2621
作者姓名:武婷  崔焕文  肖咸德  翟增秀  韩萌
作者单位:天津市生态环境科学研究院, 国家环境保护恶臭污染控制重点实验室, 天津 300191;天津市生态环境科学研究院, 国家环境保护恶臭污染控制重点实验室, 天津 300191;天津迪兰奥特环保科技开发有限公司, 天津 300191
摘    要:选取了我国5种典型化工行业VOCs排放源进行了源排放特征分析,通过对70个VOCs源样品的分析,结果表明,烷烃是合成材料制造业、石化行业和涂料产品制造业的主导VOCs种类(占比分别为43%、63%和68%),烯烃是日用化学产品制造业的VOCs主要种类(46%),卤代烃在专用化学品制造业排放中占主导(43%);利用机器学习方法分析了上述行业的标志组分,发现癸烷和四氢呋喃是合成材料制造业源的特征标志组分,正丁醇和甲苯是日用化学产品制造业源的特征标志组分,1,2,3-三甲苯和1,3,5-三甲苯是石化行业源的特征标志组分,丙烯和3-甲基戊烷是涂料产品制造业的标志组分,对二甲苯和异丙苯是专用化学品制造业源的特征标志组分;并采用最大增量反应活性法(MIR)估算了各VOCs排放源的臭氧生成潜势(OFP),结果表明,在单位浓度总VOCs排放条件下,对臭氧生成潜势的贡献大小依次为日用化学产品制造业、专用化学品制造业、石化行业、合成材料制造业和涂料产品制造业.建议在今后的臭氧防控中,更应关注各行业所排放的关键活性物种,而不仅仅注重VOCs排放总量.

关 键 词:挥发性有机物(VOCs)  机器学习  臭氧(O3  臭氧生成潜势(OFP)  化工行业
收稿时间:2023-04-13
修稿时间:2023-07-25

Characteristics of VOCs Emissions and Ozone Formation Potential for Typical Chemicals Industry Sources in China
WU Ting,CUI Huan-wen,XIAO Xian-de,ZHAI Zeng-xiu,HAN Meng. Characteristics of VOCs Emissions and Ozone Formation Potential for Typical Chemicals Industry Sources in China[J]. Chinese Journal of Environmental Science, 2024, 45(5): 2613-2621
Authors:WU Ting  CUI Huan-wen  XIAO Xian-de  ZHAI Zeng-xiu  HAN Meng
Affiliation:State Key Laboratory on Odor Pollution Control, Tianjin Academy of Eco-Environmental Sciences, Tianjin 300191, China;State Key Laboratory on Odor Pollution Control, Tianjin Academy of Eco-Environmental Sciences, Tianjin 300191, China;Tianjin Sinodor Environmental Science and Technology Development Co., Ltd., Tianjin 300191, China
Abstract:This study selected five typical types of chemical industry volatile organic compounds (VOCs) emission characteristics in China for analysis. The results from 70 source samples showed that alkanes were the dominant VOCs category from synthetic material industry sources, petrochemical industry sources, and coating industry sources (accounting for 43%, 63%, and 68%, respectively); olefins were the main VOCs category from the daily supplies chemical industry (46%); and halogenated hydrocarbons were the dominate VOCs category from specialty chemicals industry account source emissions (43%). Additionally, the machine learning method was applied in this study to analyze the marker components of the above industries. The results showed that decane and tetrahydrofuran were the source markers of the synthetic material industry; n-butanol and toluene were the markers of the daily supplies industry source; 1,2,3-trimethylbenzene and 1,3,5-trimethylbenzene were the markers of the petrochemical industry source; propylene and3-methyl pentane were the source markers of the coating industry; and P-Xylene and cumene were the markers of the specialty chemicals industry source. The maximum incremental reactivity method (MIR) was used to estimate the ozone formation potential (OFP) of different VOCs-sources. The calculation results showed that when considering per unit TVOCs concentration emissions, the contribution to the ozone generation potential was in the order of the daily supplies chemical industry, specialty chemical industry, petrochemical industry, synthetic material industry, and coating industry. Therefore, we suggest that more attention should be paid to the key active species emitted by various industry sources rather than only the total amount of VOCs emissions in future ozone prevention and control efforts.
Keywords:volatile organic compounds(VOCs)  machine learning  ozone(O3  ozone formation potential (OFP)  chemical industry soureces
点击此处可从《环境科学》浏览原始摘要信息
点击此处可从《环境科学》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号