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Article(id=1234106390378238655, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1234106384963400440, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731772800000, receivedDateStr=2024-11-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772163492053, onlineDateStr=2026-02-27, pubDate=1750348800000, pubDateStr=2025-06-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772163492053, onlineIssueDateStr=2026-02-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772163492053, creator=13701087609, updateTime=1772163492053, updator=13701087609, issue=Issue{id=1234106384963400440, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='6', pageStart='2961', pageEnd='3552', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1772163490763, creator=13701087609, updateTime=1772163969484, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1234108392948682946, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1234106384963400440, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1234108392948682947, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1234106384963400440, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2992, endPage=3000, ext={EN=ArticleExt(id=1234106391191933661, articleId=1234106390378238655, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=The emission characteristics of benzothiazoles and their derivatives from motor vehicles in tunnels, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

This study takes the Wujing Road Tunnel in Tianjin as an example to explore the emission characteristics of benzothiazoles (BTs) in particulate, gaseous pollutants, and road dust. The results show that the concentrations of particulate matter, gaseous pollutants, and BTs in road dust exhibit regular patterns, especially the concentration changes of 2-hydroxybenzothiazole (2-OH-BT) and benzothiazole (BT). Since BTs in the enclosed tunnel environment mainly originate from tire wear particles of motor vehicles, 2-OH-BT and BT can serve as important tracers for identifying non-exhaust emissions from motor vehicles. During the tunnel experiment, the daily traffic volume ranged from 11,972 to 16, 157 vehicles per day, the total carbon (TC) concentration was between 10.85and 15.75μg/m3, and the BTs concentration was between 3.33 and 8.41ng/m3. The gas-particle ratio values of 2-mercaptobenzothiazole (MBT), 2-OH-BT, and BT in the tunnel were generally higher than those in the receptor environment, and 2-OH-BT and BT were the dominant gaseous BTs components. This indicates that most MBT, 2-OH-BT, and BT generated from tire wear sources of motor vehicles are released in the gaseous phase, so the gaseous BTs should not be overlooked. For the calculation of motor vehicle emission factors, the average emission factors of organic carbon (OC), elemental carbon (EC), and PM2.5 in the Wujing Road Tunnel were 2.80, 1.60, and 13.77mg/(km⋅vehicle), respectively. In the health risk assessment model, the daily exposure to BTs through ingestion was the highest. The daily intake for children and adults was 12.03 and 1.29ng/(kg⋅d), respectively. The total daily exposure for children was more than nine times that of adults, indicating that children may face a greater health threat from traffic pollution than adults.

, correspAuthors=Ting WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Wen-di WEI, Ting WANG, Jun-yu CHANG, Hong-jun MAO), CN=ArticleExt(id=1234106398490022050, articleId=1234106390378238655, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=隧道机动车源中苯并噻唑及其衍生物排放特征, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

以天津市五经路隧道为例，探讨了隧道环境中颗粒态，气态污染物及道路尘中苯并噻唑及其衍生物(BTs)的排放特征.结果表明，隧道环境中，颗粒物，气态污染物及道路尘中的BTs浓度变化呈现出规律性，尤其是二羟基苯并噻唑(2-OH-BT)和苯并噻唑(BT)的浓度变化显著.由于在封闭的隧道环境中BTs主要来源于机动车轮胎磨损颗粒物，因此2-OH-BT和BT可作为识别机动车非尾气排放的重要标识物.隧道实验期间日车流量为11972~16157辆/d，总碳(TC)浓度为10.85~15.75 µg/m3，BTs浓度为3.33~8.41ng/m3，隧道中2-巯基苯并噻唑(MBT)，2-OH-BT和BT气粒比值普遍高于受体环境的气粒比值，而2-OH-BT和BT为主要气态BTs的优势组分，表明机动车轮胎磨损源产生的MBT，2-OH-BT和BT大部分以气态挥发，因此气相中BTs不可被忽视.对于机动车排放因子计算，五经路隧道机动车OC，EC，PM2.5平均排放因子分别为2.80，1.60，13.77mg/(km·辆).健康风险评价模型中通过摄食途径摄入BTs的日暴露量最高，儿童和成人的日摄入量为12.03和1.29ng/(kg·d)，儿童的总日暴露量是成人的9倍以上，因此儿童可能会比成人遭受更大的交通污染健康威胁.

, correspAuthors=王婷, authorNote=null, correspAuthorsNote=
* 责任作者，副研究员，
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魏文迪(2001-)，女，山东滨州人，南开大学硕士研究生，主要从事机动车排放与暴露评估研究.发表论文1篇..

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魏文迪(2001-)，女，山东滨州人，南开大学硕士研究生，主要从事机动车排放与暴露评估研究.发表论文1篇..

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魏文迪(2001-)，女，山东滨州人，南开大学硕士研究生，主要从事机动车排放与暴露评估研究.发表论文1篇..

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Atmospheric Pollution Research，2023，14(7):379-387., articleTitle=Estimating vehicular emission factors and vehicle-induced turbulence: Application of an air quality sensor array for continuous multipoint monitoring in a tunnel, refAbstract=null)], funds=[Fund(id=1234106407654576984, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, awardId=63241318; 63241322; 63243126, language=CN, fundingSource=中央高校基本科研业务费资助项目(63241318; 63241322; 63243126), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1234106398846537937, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, xref=null, ext=[AuthorCompanyExt(id=1234106398854926548, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, companyId=1234106398846537937, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Tianjin Key Laboratory of Traffic Pollution Control, State Key Laboratory of Particulate Air Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China), AuthorCompanyExt(id=1234106398863315156, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, companyId=1234106398846537937, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=南开大学环境科学与工程学院，天津市城市交通污染防治研究重点实验室，国家环境保护城市空气颗粒物污染防治重点实验室，天津 300071)])], figs=[ArticleFig(id=1234106403862925937, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Fig.1, caption=Comparison of daytime and nighttime traffic flow in tunnel experiment in 2019, figureFileSmall=JOwC9EtlV4gjtTK4l2mJhg==, figureFileBig=QF6SigQ9j9seqq6EzZhsMQ==, tableContent=null), ArticleFig(id=1234106403971977847, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=图1, caption=2019年隧道实验白天与夜间车流量对比, figureFileSmall=JOwC9EtlV4gjtTK4l2mJhg==, figureFileBig=QF6SigQ9j9seqq6EzZhsMQ==, tableContent=null), ArticleFig(id=1234106404328493725, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Fig.2, caption=Spearman correlation of OC and EC at the entrance and exit of Wujing Road tunnel, figureFileSmall=wNq39M4Fr9ImqLbCC1v7sA==, figureFileBig=Asdyk3NJ2Ife+MpleCEEuw==, tableContent=null), ArticleFig(id=1234106404471100079, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=图2, caption=五经路隧道入口和出口的OC，EC的Spearman相关性, figureFileSmall=wNq39M4Fr9ImqLbCC1v7sA==, figureFileBig=Asdyk3NJ2Ife+MpleCEEuw==, tableContent=null), ArticleFig(id=1234106405901357760, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Fig.3, caption=BTs concentration in particulate, gaseous pollutants and road dust in tunnel environment, figureFileSmall=/PJclsX/bwJxZKvR+MUi5Q==, figureFileBig=cb8EhhoJovIcTEsSRYD9yQ==, tableContent=null), ArticleFig(id=1234106406039769800, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=图3, caption=隧道环境中颗粒态，气态污染物及道路尘中BTs浓度, figureFileSmall=/PJclsX/bwJxZKvR+MUi5Q==, figureFileBig=cb8EhhoJovIcTEsSRYD9yQ==, tableContent=null), ArticleFig(id=1234106406194959061, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Fig.4, caption=Daily exposure of human body to BTs in road dust and airborne particulate matter, figureFileSmall=hk3MpOEaeTadgINZ1ue1Ww==, figureFileBig=Uuq2dAucnUKHPfeLKQ0How==, tableContent=null), ArticleFig(id=1234106406333371107, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=图4, caption=人体对道路尘和空气颗粒物中BTs的日暴露量, figureFileSmall=hk3MpOEaeTadgINZ1ue1Ww==, figureFileBig=Uuq2dAucnUKHPfeLKQ0How==, tableContent=null), ArticleFig(id=1234106406471783157, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Table 1, caption=

Meanings and units of each parameter

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参数含义儿童取值成人取值单位
IngR摄食尘量200100mg/d
inhR呼吸摄入尘量7.620m3/d
PEF颗粒物排放因子1.36×1091.36×109m3/kg
SA暴露皮肤面积28005700cm2
SL皮肤附着因子0.20.7mg/(cm2·d)
ABS吸入因子0.0010.001/
ED暴露年限624a
EF暴露频率180180d/a
BW平均体重1570kg
AT(非致癌)平均作用时间ED×365ED×365d
AT(致癌)平均作用时间2555025550d
), ArticleFig(id=1234106406652138241, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=表1, caption=

各参数含义及单位

, figureFileSmall=null, figureFileBig=null, tableContent=
参数含义儿童取值成人取值单位
IngR摄食尘量200100mg/d
inhR呼吸摄入尘量7.620m3/d
PEF颗粒物排放因子1.36×1091.36×109m3/kg
SA暴露皮肤面积28005700cm2
SL皮肤附着因子0.20.7mg/(cm2·d)
ABS吸入因子0.0010.001/
ED暴露年限624a
EF暴露频率180180d/a
BW平均体重1570kg
AT(非致癌)平均作用时间ED×365ED×365d
AT(致癌)平均作用时间2555025550d
), ArticleFig(id=1234106406777967374, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Table 2, caption=

Distribution characteristics of carbon components in Wujing Road tunnel (µg/m3)

, figureFileSmall=null, figureFileBig=null, tableContent=
组分2017年2019年
入口出口入口出口
OC6.89±1.998.58±1.8810.24±2.7711.47±5.55
EC3.96±1.113.61±0.912.36±0.633.12±1.61
OC10.35±0.261.05±0.290.36±0.080.47±0.38
OC22.63±0.613.27±0.812.19±0.412.87±1.25
OC31.72±0.552.02±0.863.55±0.203.38±1.25
OC41.43±0.471.41±0.431.62±0.362.25±1.87
EC13.36±1.283.33±0.932.89±0.813.68±1.58
EC21.25±0.611.06±0.490.65±0.220.55±0.40
EC30.21±0.120.26±0.010.21±0.180.36±0.42
OPC0.75±0.530.82±0.201.36±0.421.30±0.66
), ArticleFig(id=1234106406903796512, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=表2, caption=

五经路隧道碳组分分布特征(µg/m3)

, figureFileSmall=null, figureFileBig=null, tableContent=
组分2017年2019年
入口出口入口出口
OC6.89±1.998.58±1.8810.24±2.7711.47±5.55
EC3.96±1.113.61±0.912.36±0.633.12±1.61
OC10.35±0.261.05±0.290.36±0.080.47±0.38
OC22.63±0.613.27±0.812.19±0.412.87±1.25
OC31.72±0.552.02±0.863.55±0.203.38±1.25
OC41.43±0.471.41±0.431.62±0.362.25±1.87
EC13.36±1.283.33±0.932.89±0.813.68±1.58
EC21.25±0.611.06±0.490.65±0.220.55±0.40
EC30.21±0.120.26±0.010.21±0.180.36±0.42
OPC0.75±0.530.82±0.201.36±0.421.30±0.66
), ArticleFig(id=1234106407017042727, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Table 3, caption=

Spearman analysis of the exit of Wujing Road tunnel

, figureFileSmall=null, figureFileBig=null, tableContent=
物质MTBTMBT2-NH2-BT2-OH-BTBT
MTBT1.000.63**0.400.87**0.83**
MBT1.000.470.57*0.34
2-NH2-BT1.000.61**0.16
2-OH-BT1.000.75**
BT1.00
), ArticleFig(id=1234106407184814900, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=表3, caption=

五经路隧道出口Spearman分析

, figureFileSmall=null, figureFileBig=null, tableContent=
物质MTBTMBT2-NH2-BT2-OH-BTBT
MTBT1.000.63**0.400.87**0.83**
MBT1.000.470.57*0.34
2-NH2-BT1.000.61**0.16
2-OH-BT1.000.75**
BT1.00
), ArticleFig(id=1234106407331615552, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=EN, label=Table 4, caption=

Comparison of emission factors between Wujing Road Tunnel and other tunnels (mg/(km· vehicles))

, figureFileSmall=null, figureFileBig=null, tableContent=
数据来源OCECPM2.5重型车比例(%)行驶速度(km/h)
本研究2.801.6013.770.1(不足)40
深圳隧道[32]9.6820.1863.9620.18~33.860
香港隧道[39]13130~6060~70
维也纳隧道[40]5.417.8264.2~12.680(限速)
印度隧道[41]12.69.8452060
韩国隧道[42]101044/54
), ArticleFig(id=1234106407474221896, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106390378238655, language=CN, label=表4, caption=

五经路隧道与其他隧道排放因子比较(mg/(km⋅辆))

, figureFileSmall=null, figureFileBig=null, tableContent=
数据来源OCECPM2.5重型车比例(%)行驶速度(km/h)
本研究2.801.6013.770.1(不足)40
深圳隧道[32]9.6820.1863.9620.18~33.860
香港隧道[39]13130~6060~70
维也纳隧道[40]5.417.8264.2~12.680(限速)
印度隧道[41]12.69.8452060
韩国隧道[42]101044/54
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隧道机动车源中苯并噻唑及其衍生物排放特征
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魏文迪 , 王婷 * , 常俊雨 , 毛洪钧
中国环境科学 | 大气污染与控制 2025,45(6): 2992-3000
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中国环境科学 | 大气污染与控制 2025, 45(6): 2992-3000
隧道机动车源中苯并噻唑及其衍生物排放特征
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魏文迪 , 王婷* , 常俊雨, 毛洪钧
作者信息
  • 南开大学环境科学与工程学院，天津市城市交通污染防治研究重点实验室，国家环境保护城市空气颗粒物污染防治重点实验室，天津 300071

    • 魏文迪(2001-)，女，山东滨州人，南开大学硕士研究生，主要从事机动车排放与暴露评估研究.发表论文1篇..

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* 责任作者，副研究员，
The emission characteristics of benzothiazoles and their derivatives from motor vehicles in tunnels
Wen-di WEI , Ting WANG* , Jun-yu CHANG, Hong-jun MAO
Affiliations
  • Tianjin Key Laboratory of Traffic Pollution Control, State Key Laboratory of Particulate Air Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
出版时间: 2025-06-20
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摘要
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以天津市五经路隧道为例，探讨了隧道环境中颗粒态，气态污染物及道路尘中苯并噻唑及其衍生物(BTs)的排放特征.结果表明，隧道环境中，颗粒物，气态污染物及道路尘中的BTs浓度变化呈现出规律性，尤其是二羟基苯并噻唑(2-OH-BT)和苯并噻唑(BT)的浓度变化显著.由于在封闭的隧道环境中BTs主要来源于机动车轮胎磨损颗粒物，因此2-OH-BT和BT可作为识别机动车非尾气排放的重要标识物.隧道实验期间日车流量为11972~16157辆/d，总碳(TC)浓度为10.85~15.75 µg/m3，BTs浓度为3.33~8.41ng/m3，隧道中2-巯基苯并噻唑(MBT)，2-OH-BT和BT气粒比值普遍高于受体环境的气粒比值，而2-OH-BT和BT为主要气态BTs的优势组分，表明机动车轮胎磨损源产生的MBT，2-OH-BT和BT大部分以气态挥发，因此气相中BTs不可被忽视.对于机动车排放因子计算，五经路隧道机动车OC，EC，PM2.5平均排放因子分别为2.80，1.60，13.77mg/(km·辆).健康风险评价模型中通过摄食途径摄入BTs的日暴露量最高，儿童和成人的日摄入量为12.03和1.29ng/(kg·d)，儿童的总日暴露量是成人的9倍以上，因此儿童可能会比成人遭受更大的交通污染健康威胁.

苯并噻唑及其衍生物  /  隧道测试  /  排放因子  /  排放特征  /  碳分析

This study takes the Wujing Road Tunnel in Tianjin as an example to explore the emission characteristics of benzothiazoles (BTs) in particulate, gaseous pollutants, and road dust. The results show that the concentrations of particulate matter, gaseous pollutants, and BTs in road dust exhibit regular patterns, especially the concentration changes of 2-hydroxybenzothiazole (2-OH-BT) and benzothiazole (BT). Since BTs in the enclosed tunnel environment mainly originate from tire wear particles of motor vehicles, 2-OH-BT and BT can serve as important tracers for identifying non-exhaust emissions from motor vehicles. During the tunnel experiment, the daily traffic volume ranged from 11,972 to 16, 157 vehicles per day, the total carbon (TC) concentration was between 10.85and 15.75μg/m3, and the BTs concentration was between 3.33 and 8.41ng/m3. The gas-particle ratio values of 2-mercaptobenzothiazole (MBT), 2-OH-BT, and BT in the tunnel were generally higher than those in the receptor environment, and 2-OH-BT and BT were the dominant gaseous BTs components. This indicates that most MBT, 2-OH-BT, and BT generated from tire wear sources of motor vehicles are released in the gaseous phase, so the gaseous BTs should not be overlooked. For the calculation of motor vehicle emission factors, the average emission factors of organic carbon (OC), elemental carbon (EC), and PM2.5 in the Wujing Road Tunnel were 2.80, 1.60, and 13.77mg/(km⋅vehicle), respectively. In the health risk assessment model, the daily exposure to BTs through ingestion was the highest. The daily intake for children and adults was 12.03 and 1.29ng/(kg⋅d), respectively. The total daily exposure for children was more than nine times that of adults, indicating that children may face a greater health threat from traffic pollution than adults.

benzothiazole and its derivatives  /  tunnel testing  /  emission factors  /  emission characteristics  /  carbon analysis
魏文迪, 王婷, 常俊雨, 毛洪钧. 隧道机动车源中苯并噻唑及其衍生物排放特征. 中国环境科学, 2025 , 45 (6) : 2992 -3000 .
Wen-di WEI, Ting WANG, Jun-yu CHANG, Hong-jun MAO. The emission characteristics of benzothiazoles and their derivatives from motor vehicles in tunnels[J]. China Environmental Science, 2025 , 45 (6) : 2992 -3000 .
正文
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基于受体源解析结果，机动车移动源对城市颗粒物贡献达到20%~50%以上，且呈持续上升趋势.传统的燃油汽车产生的环境污染物一部分来自尾气排放，还有一部分来自非尾气排放.非尾气排放包括轮胎磨损，道路扬尘，刹车片磨损，路面磨损等[1-3].目前，对于非尾气颗粒物排放对城市环境影响的分担率研究相对较少.非尾气排放中的磨损排放颗粒物含有丰富的金属元素[4]和有机成分[2](多环芳烃，苯并噻唑BT等)[3,5-9].然而随着机动车第六阶段(国Ⅵ)排放标准推行以及新能源汽车的不断推广，机动车尾气排放量呈现下降趋势，而非尾气排放所占比例逐渐增大.但机动车排放颗粒物的研究和政策法规主要集中在尾气排放方面，对于非尾气排放，在国内外尤其是我国大气环境领域尚不多见.轮胎磨损是重要的非尾气排放形式之一，占非尾气排放的50%以上.轮胎磨损是城市空气环境中BTs的主要来源之一.
BTs是一类由1，3-噻唑环与苯环熔合而成的杂环芳香族化合物，是一种微黄色液体状的化合物，呈喹啉似气味，几乎不溶于水，溶于乙醇、丙酮和二硫化碳，常被用在橡胶产品生产过程中作为橡胶硫化促进剂，在工业中应用广泛，还可以用作金属表面的缓蚀剂和汽车用油的防冻剂[10].其中BT常应用于偶氮染料光敏剂[11]，农药[12]，杀菌剂[13]，除草剂[14]的化学中间体和紫外线稳定剂[15]，被斯洛伐克共和国(欧盟2013)定为重点研究物质之一. MBT经常作为轮胎主要硫化促进剂，能与贵金属及Zn、A1、Cu、Hg等形成稳定的配合物，广泛作为贵金属的沉淀剂及萃取剂[16-17]，BTs因具有应用广泛，持久难降解，生物毒性等特征而引起了广泛关注[12，18-21].常见的BTs有以下几种，二氨基苯并噻唑(2-NH2-BT)，MBT，2-OH-BT，2-甲硫基苯并噻唑(MTBT)和N-环己基-2-苯并噻唑次磺酰胺(CBS)等.
相关研究[17]通过测定轮胎磨损颗粒和道路扬尘中的BT及其6种衍生物，在17个不同轮胎品牌的轮胎磨损颗粒样品中发现了7种BTs，其中7种BTs的质量分数达46.93~215mg/g不等，平均浓度为99.32mg/g，BT和2-OH-BT为主要化合物，占总化合物质量分数的56%~89%.在36个道路扬尘样品中也检测到7种BTs，在道路扬尘中测得BTs(26.62mg/g)浓度最高.通过计算人体对道路扬尘健康风险评估，发现儿童接触道路扬尘时可能比成人遭受更多的健康风险.流行病学研究表明，橡胶厂工人特别是那些暴露于MBT的工人患膀胱癌､肺癌和白血病癌症的风险更高[22].研究指出，BTs与微生物的致突变性及人类的致癌性之间存在显著关联[23-24].不同国家的多项调查均在人体尿液样本中检测到BTs的存在[10]，说明其在一般人群中已形成一定程度的暴露.近期研究结果显示，高水平BTs暴露与肺癌发病风险的升高可能存在相关性[25].本研究通过对五经路隧道机动车排放的碳组分和BTs的浓度分析，揭示交通源BTs的分布，气粒分配特征，评价其作为机动车非尾气排放标识物的潜力，并通过计算BTs对人体的日暴露量，明确机动车源BTs的贡献及环境危害，旨在为制定更精准的机动车污染控制策略以及评估机动车对人体健康的潜在风险提供参考.
1　材料与方法
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1.1　主要试剂与材料
BT(97%)、2-NH2-BT(98%)、2-OH-BT(98%)、MBT(97%)采购于德国AlfaAesar公司，MTBT(>98%)、CBS(95%)采购于日本TokyoKaseiKogyo公司.内标D4-BT采购于加拿大TorontoResearch Chemicals公司，HPLC级甲醇(CNW)、Poly-Sery HLBSPE小柱(CNW)、9mm棕色螺纹口自动进样瓶(CNW)以及瓶帽､固相萃取小柱连接管(CNW)均采购于上海安谱实验科技股份有限公司.色谱柱(ACQUITYUPLC BEHSHIELDRP 18column,100mm×3mm,1.7µm)和保护柱(BEHC18,5mm×2.1mm,1.7µm)均为美国Waters公司生产.
1.2　样品采集与分析
1.2.1　采样地点与方法
五经路隧道位于天津市中心城区，是连接南北主干道的重要枢纽.该隧道为双向六车道，行驶方向分为自南向北和自北向南两个方向，本论文选取由南向北方向进行研究.选取隧道路段约1200m作为实验路段，隧道宽12m，行车净高4.5m，限速40km/h，五经路隧道作为机动车源采样点.考虑到隧道出入口有通风区段以及隧道出口受上坡坡度和内灌风影响较大，采样点设在距隧道入口30m处和距出口550m的中点处，仪器放置于同侧路边，采样高度为人体呼吸带，距离地面约1.5m.为研究不同时期BTs排放特征的变化趋势，分别于2017年8月11日~17日，2019年8月29日~9月6日进行了连续观测.此外，采样分类为白天和晚上两个时间段，早上6:30~下午21:30共15h归属于白天范畴，下午22:00~第二天早上6:00共8h属于晚上范畴.隧道中交通车队的小型车比例稳定，所选车辆的轮胎品牌、类型、尺寸和成分并未发生显著变化.基于这一因素，研究结果在一定程度上仍具有代表性，所采集的样本能反映长期以来的隧道排放特征.
实验期间利用Vaisala WXT520气象变送器对五经路隧道的气象数据进行实时连续观测，气象参数包括温度、相对湿度、大气压、风速和风向等，时间分辨率为1min.同时利用路测激光分型交通调查仪获取测试期间内车流量和车速数据，交通调查仪将车辆分为电动车、小型客车、微型货车、中型客车、微型客车、大型客车、轻型货车、中型货车、重型货车共9个车型.实验使用石英纤维滤膜(φ90mm)和青岛恒远的中流量采样器、切割头采集PM2.5颗粒物，每个样品以100L/min的速率采集24h30min，在采样之前石英纤维滤膜置于马弗炉内600℃烘焙2h，然后放在恒温(20℃,50% RH)的天平室平衡48h以上，用十万分之一天平称重三次取均值，误差在0.00005之内.采样后使用同样的方法平衡滤膜并称重，滤膜采样前后保存于冰箱(约4℃).使用北京奥泽尔交通调查仪自动采集隧道全天过往车型和车流量数据.
1.2.2　样品的前处理和分析方法
取二分之一采后的石英滤膜样品放置于15mL的带盖玻璃离心管中，并每隔10个样品加入1个空白对照样品，在每一个样品中添加10ng D4-BT内标，室内平衡至内标溶剂挥发.每个样品添加8mL(5mL甲醇+3mL纯水)的提取剂，密封后置于超声波水浴槽中超声60min，用离心机4000r/min离心10min，使用0.45µm孔径的尼龙有机相滤头和一次性注射器提取至40mL棕色EPA样品瓶中，然后加入8mL提取剂重复上述过程一次.每个样品得到约16mL溶剂，添加超纯水配置成甲醇：水(体积比1:3)溶液，用poly-SeryHLB固相萃取小柱(3mL、60mg)净化样品.先用5mL甲醇和5mLHPLC级纯水对小柱进行活化和平衡，使用真空泵抽取，样品以4mL/min上样.上样结束后用10mL甲醇水溶液(20%)进行洗杂，真空泵抽真空干燥小柱，最后用4mL甲醇洗脱目标物，取1.5mL于2mL棕色进样瓶进样.
使用美国Waters公司生产的超高液相色谱仪(WatersACQUITYUPLC)，串联三重四极杆质谱仪XevoTQ-S(ESI-MS/MS)对标准溶液和样品进行分离测试.采用HPLC级甲醇作为流动相的有机相(A)和含0.1% HPLC级甲酸的HPLC级纯水作为流动相的水相(B)，以0.3L/min的流速对各目标物质进行梯度分离.流动相梯度变化程序如下：有机相比例在前0.5min内由20%降至10%，在1.5min之内升高至100%，并保持2.5min至第4min，之后降至20%并保持1min至第6min，连续自动进样量为10µL.质谱在电喷雾电离正离子模式(ESI+)和多反应监测(MRM)模式下进行目标物的测定.毛细管电压为3.0kV，离子源温度为150℃，脱溶剂气温度为350℃，锥孔反吹气流量为150L/h，脱溶剂气流量为800L/h.
1.2.3　质量控制
为了检验前处理和检测方法的可靠性，通过向空白滤膜样品中添加20×10-9和50×10-9浓度的标准溶液和10ng内标，每个样品分做3个平行样，并完整的进行提取-纯化-浓缩定容等过程，经内标校正后，除2-NH2-BT外，其他5种物质的加标回收率在92%~125.7%，各物质的标准曲线的相关系数R2均大于0.99，将每一种目标物信噪比的3倍作为其检出限(LOD)，信噪比的10倍作为其定量限(LOQ).通过空白样品来评估来自实验室耗材和试剂的质量控制，每隔10个样品加一个甲醇空白样品来评估仪器的状态，结果发现，空白样品中BTs的浓度均低于样品浓度多个数量级.
1.3　碳分析方法
采用热/光碳分析仪(DRI2001A型)测定样品中的碳组分，本研究采用OC/EC最小比值法估算二次有机碳(SOC)的浓度.EC具有化学惰性和热稳定性，所以可作为污染物指示物，常用间接方法估算SOC.本文采用SOC经验公式如下：
式中：(OC/EC)min为样品中OC/EC最小值.
其中OC1主要来源于生物质燃烧，OC2主要来源于煤烟尘，EC2和EC3主要来源于柴油车尾气排放，OC3，OC4和EC1主要来源于汽油车尾气排放[26].
1.4　机动车排放因子计算
隧道试验是评估机动车排放因子(EFs)应用非常广泛的方法[27-31].其基本原理是通过测定出隧道内污染物的浓度，扣除进入隧道空气中污染物的环境本底浓度，则可计算得出机动车污染物排放因子，用式(2)[29,32-34]计算隧道内BTs的平均排放因子：
式中：EFs是混合车型的平均排放因子，ng/(km·辆);Cout是隧道出口BTs的浓度，(ng/m3);Cin是隧道入口BTs的浓度，(ng/m3);A是隧道横截面积，m2;v是隧道内的空气流速，m/s;t是采样时间，s;N是采样时间内的车流量，辆;L是隧道入口与出口的距离，km.
1.5　健康风险评价方法
人体主要通过3种途径暴露于道路尘中：摄食、皮肤接触和呼吸.本文选用美国EPA的健康风险评价模型，3种暴露途径的非致癌日均暴露量的计算公式分别如公式(3)、(4)、(5)，人体对道路空气中BTs的呼吸日暴露量计算公式如公式(6).通过计算人体对BTs的日均暴露量，研究隧道环境对人体健康的潜在风险.
式中：DIIng、DIInh和DIDermal分别是摄食、呼吸和皮肤接触的日暴露量;C为BTs道路尘浓度mg/kg;Cair为滤膜和PUF中BTs浓度之和;其他参数含义及单位见表1[35-36].对于五经路隧道颗粒物暴露，每日暴露时间(ET)取2h.
2　结果与分析
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2.1　气象数据及交通车流量
利用路测激光分型交通调查仪获取测试期间内车流量和车速数据，交通调查仪将车辆分为9个车型.五经路隧道两次实验期间，平均日车流量分别为(16157±335)辆/d和(11972±955)辆/d，其中重型货车占到总流量的比例不足0.1%，货车占总流量的21%~26%.五经路隧道由原来以小型客车和微型货车为主要车型的交通流，转变为以小型客车，微型客车和微型货车为主要车型的交通流.2019年隧道实验与2017年隧道实验相比，平均温度增加3.6℃，平均湿度降低30%，平均风速增加0.6m/s，日车流量降低约5000辆/d，自行车和电动车数量大大降低，其原因可能居民改变了出行方式，自行车和电动车的交通工具逐渐被地铁､公交车等代替.
在2019年五经路隧道实验期间，由于部分车型数量较少，故在此将车辆分为3大类，交通调查仪统计出两个时段的具体车流量如图1所示，白天通行总车辆数为(10775±926)辆，是晚上通行总车辆数的8.9倍.白天交通流中居前3位车型分别为微型客车、小型客车、微型货车，占总车辆数的90.6%以上，晚上交通流中车型分布与白天一致.
2.2　碳组分排放特征
2.2.1　碳组分浓度水平
由表2可见，2017年隧道实验入口和出口的总碳(TC)分别为(10.85±2.51)µg/m3和(12.19±2.33) µg/m3,2019年隧道实验入口和出口的TC分别为(12.59±2.92) µg/m3,(15.75±7.85)µg/m3，五经路隧道入口和出口点位OC、EC分别增长了48.62%和33.68%.2017年五经路隧道碳组分浓度分布为：OC>EC>EC1>OC2>OC4>EC2>OC1>EC3,2019年五经路隧道样品中碳组分浓度水平分布为：OC>EC>EC1>OC3>OC2>OC4>EC2>OC1>EC3.
2.2.2　OC/EC特征及相关性
OC/EC被用来表征碳组分来源以及是否会产生二次污染，对于控制气溶胶有机污染物有着重要作用，当OC/EC>2时，表明OC中存在SOC.OC和EC的比值为1.0~4.2表明有柴油和汽油车的尾气排放，比值为16.8~40.0表明生物质燃烧排放，2.5~10.5表明燃煤排放，32.9~81.6表明烹调排放，13.1为地面扬尘排放，12.7为家庭天然气排放[37].五经路隧道实验样品中OC/EC均值分布：2019年隧道实验(4.45)>2017年隧道实验(2.53)>2，因此，五经路隧道有SOC产生.根据公式(1)估算五经路隧道PM2.5样品中的SOC，五经路隧道中SOC的浓度水平高低顺序为：2019年隧道出口>2019年隧道入口>2017年隧道出口>2017年隧道入口.五经路隧道出口SOC浓度水平比隧道入口高，由于五经路隧道多年机动车污染物的积累和机动车碳组分排放增大，导致2019年隧道实验检测到比2017更多的SOC,2019年五经路隧道的出口SOC浓度水平高达4.45µg/m3，是隧道入口SOC浓度水平的1.2倍.
考虑分析OC和EC是否来自同一排放源，若相关性良好，则说明OC、EC的污染来源相似或较为一致，使用JMP分析软件对五经路隧道样品OC和EC进行Spearman相关性分析，具体结果见图2.五经路隧道入口PM2.5样品中碳组分OC与EC的相关性只有0.41，且P>0.05，表明在入口点位OC和EC排放源复杂.隧道出口样品中碳组分OC与EC的相关性R高达0.85，具有很好的相关性，表明两者来源于同一污染源或类似源.隧道出口OC3与OC4之间的R为0.66，高于隧道入口OC3与OC4的相关性，表明有污染源排放.
2.3　BTs的排放特征
2.3.1　BTs的排放水平
从图3(a)可看出，除CBS外，其他BTs成分的检出率为100%.五经路隧道出口颗粒态ΣBTs浓度水平大于入口，五经路隧道出口各BTs浓度水平分布为：2-OH-BT>BT>MTBT>MBT>2-NH2-BT. 2-OH-BT在2019年五经路隧道出口测得最高值为(2.58±0.92) ng/m3，在2019年隧道出口测的ΣBTs的浓度为(8.41±2.13) ng/m3.BTs更易于富集在较细颗粒物中或轮胎磨损排放的细颗粒较多[38].由于机动车源是隧道中唯一的排放源，BTs主要来源于机动车非尾气排放(轮胎磨损)，表明五经路隧道中机动车是BTs的主要贡献者.使用JMP软件对BTs进行Spearman相关性分析，结果见表3.五经路隧道入口处BTs浓度水平低，一般作为隧道里面背景浓度，所以只分析五经路隧道出口的Spearman分析.五经路隧道出口MTBT和MBT相关性R为0.63,2-OH-BT和BT之间具有很好的相关性，在0.01(双侧)上极显著相关.
从图3(b)可以看出，在样品中除CBS和2-NH2-BT的其他BTs检出率为100%.五经路隧道PUF样品中各BTs浓度大小顺序为：BT>MTBT>2-OH-BT>MBT>2-NH2-BT，五经路隧道气体环境中BT浓度高于2-OH-BT，而在PM2.5中2-OH-BT浓度高于BT.五经路隧道气态ΣBTs浓度高于滤膜样品中ΣBTs浓度，五经路隧道出口ΣBTs浓度为(12.81±2.42)ng/m3，是隧道入口BT浓度的1.59倍.气态MBT与2-NH2-BT浓度在隧道出口和入口相差较小，MTBT,2-OH-BT和BT的浓度有显著的增加.
如图4(c)所示，相较于隧道入口，隧道出口中5种污染物浓度显著升高，尤其是2-OH-BT和BT浓度变化最大，由于隧道是封闭性的，以隧道入口的BTs浓度作为背景值，可以发现2-NH2-BT在道路尘中积累较少，而BTs在道路尘中积累最多，MTBT和MBT浓度相差不大.
在隧道环境中，颗粒物、气态污染物及道路尘中BTs污染物变化表现出一定规律性，其中2-OH-BT和BT浓度在隧道入口处的变化尤为显著.由于隧道结构的封闭性，其内部污染物浓度受机动车排放的影响尤为突出，因此这些污染物的显著浓度变化与机动车源密切相关.特别是BTs主要来源于机动车轮胎磨损，这表明2-OH-BT和BT浓度的积累主要来自于机动车的非尾气排放(即轮胎磨损)，可作为识别机动车非尾气源的重要标识物.
2.3.2　BTs的气粒分配
在五经路隧道中得到不同BTs的气粒比值范围，MTBT、MBT、2-OH-BT、BT的气粒比值范围分别为0.17~0.26，0.86~1.28，0.99~1.10，3.65~4.19，MTBT的气粒比值小于0.5，表明存在气态中的MTBT较少.其中BT的气粒比值高达3.65~4.19.五经路隧道中2-OH-BT和BT为主要气态颗粒物BTs的优势组分.说明机动车轮胎磨损源产生的MBT、2-OH-BT和BT，大部分以气态挥发，其含量高于可悬浮颗粒物中的含量，在研究BTs时，气相中BTs不可被忽视.
2.4　机动车排放因子计算
由表4可见，隧道机动车OC、EC、PM2.5平均排放因子分别为2.80,1.60,13.77mg/(km·辆).不同研究中隧道实验PM2.5的平均排放因子排列顺序为：香港隧道，深圳隧道，印度隧道，维也纳隧道，五经路隧道，韩国隧道.本研究的五经路隧道机动车OC、EC平均排放因子均低于其他隧道排放因子，这是因为五经路隧道中车辆行驶速度低，重型车在车流量中所占比例不足0.1%，远小于其他隧道.在重型车比例很高的深圳隧道中，EC的浓度大于OC的浓度，表明重型车排放更多的EC组分.重型车在车队中的组成占比对隧道环境中污染物排放因子有很大影响.
2.5　人体对BTs暴露健康风险评价
如图4所示，3种暴露途径中，经摄食途径的日摄入量最高，儿童和成人的日摄入量分别为12.03,1.29ng/(kg·d)，其次是皮肤接触，呼吸暴露最低.道路尘TSP中BT贡献最大，其次是2-OH-BT.儿童的总日暴露量是成人的9倍以上，这说明儿童可能会比成人遭受更大的健康威胁.
考虑到BTs主要通过呼吸途径进入人体，而且主要引起肺部疾病，所以本研究也考虑呼吸途径的健康风险.如图4所示，儿童和成人的呼吸日暴露量分别为0.457,0.098ng/(kg·d)，儿童的总日暴露量是成人的4倍以上，人体对BT的暴露量最大，其对总暴露量的贡献在37%以上，其次是2-OH- BT、MTBT和MBT,2-NH2-BT的贡献最小，均小于3%.
3　结论
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3.1　基于五经路隧道中碳组分OC/EC比值介于1~4.2，说明五经路隧道中存在SOC的生成，同时对OC和EC的相关性分析表明，五经路隧道OC与EC集中在同一或类似的污染源范围内.
3.2　五经路隧道中2-OH-BT和BT为主要气态颗粒物BTs的优势组分.机动车轮胎磨损源产生的MBT､2-OH-BT和BT，大部分以气态挥发，其含量高于颗粒物中的含量，在研究BTs时，气相中BTs不可被忽视.隧道环境中颗粒物､气态污染物及道路尘中2-OH-BT和BT浓度显著变化，可将其作为机动车非尾气源的标识物.
3.3　与其他隧道对比可知，五经路隧道的排放因子整体低于香港、深圳、印度及维也纳隧道等，其主要原因在于五经路隧道行驶速度较低，而且天津市的交通限行政策导致重型车辆数量较少，使本研究隧道中重型车比例不足0.1%，显著小于其他隧道.
3.4　采用美国EPA推荐的人体暴露健康风险评价模型分析发现，在摄食、皮肤接触和呼吸3种暴露途径中，日摄入量最高的是摄食途径，其次为皮肤接触，呼吸暴露最低.道路尘TSP中BT贡献最大，其次为2-OH-BT，提示二者对人体健康风险可能具有较高相关性，其潜在健康风险值得重点关注.
基金
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  • 中央高校基本科研业务费资助项目(63241318; 63241322; 63243126)
文献
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2025年第45卷第6期
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  • 接收时间：2024-11-17
  • 首发时间：2026-02-27
  • 出版时间：2025-06-20
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  • 收稿日期：2024-11-17
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中央高校基本科研业务费资助项目(63241318; 63241322; 63243126)
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    南开大学环境科学与工程学院，天津市城市交通污染防治研究重点实验室，国家环境保护城市空气颗粒物污染防治重点实验室，天津 300071

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2种不同金属材料的力学参数
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genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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