Article(id=1314917874314076718, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917704910340427, 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=1735315200000, receivedDateStr=2024-12-28, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430451228, onlineDateStr=2026-10-08, pubDate=1755619200000, pubDateStr=2025-08-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430451228, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430451228, creator=13701087609, updateTime=1791430451228, updator=13701087609, issue=Issue{id=1314917704910340427, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='8', pageStart='4145', pageEnd='4736', issueExtLink='null', onlineDate='null', pubDate='1755619200000', pubDateStr='2025-08-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430410840, creator='13701087609', updateTime=1791431909627, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314923991341555972, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917704910340427, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314923991345750277, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917704910340427, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4207, endPage=4215, ext={EN=ArticleExt(id=1314917874548957743, articleId=1314917874314076718, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Tunnel ventilation methods based on baseline emission factors for new energy vehicles, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

To explore the impact of the increase in new energy vehicles, such as LNG heavy-duty trucks and hybrid vehicles, on tunnel ventilation and to address the issue of ventilation flow calculation discrepancies caused by insufficient consideration of their emission characteristics in current design codes, this study builds a tunnel vehicle baseline emission factor library and a refined tunnel ventilation calculation method based on the emission characteristics of new energy vehicles. Factors such as slope and vehicle fleet composition are taken into account. The study uses the Huayan Tunnel as a case study and calculates the required ventilation flow for different penetration rates of new energy vehicles based on the updated emission factor library. The results are then compared with those obtained using the existing ventilation calculation methods. The results indicate that the refined method significantly improves the accuracy of the required ventilation flow calculation. When the design year is set to 2030, the required ventilation flow for NO₂ dilution is 305.5% higher than that calculated using the International Tunnel Ventilation Report (PIARC 2019) method, while the required ventilation flow for CO and VI dilution decreases by 10% and 6.5%, respectively. Additionally, for every 10% increase in LNG heavy-duty truck penetration, the required ventilation flow for NO₂ dilution increases by approximately 55%. In contrast, the impact of hybrid and pure electric vehicles on the required ventilation flow is negligible. The traditional design codes, which focus on CO and VI as the main control indicators, are no longer applicable to fleet structures with a rapidly increasing proportion of new energy vehicles. The required ventilation flow for NO₂ dilution will become a key control indicator for future tunnel ventilation design.

, authors=Xin WANG¹, Zhao-wen QIU^{1, *}, Cheng LI¹, Jian-xun CHEN^{2, **}, Wei-wei LIU², Yan-bin LUO², authorsList=Xin WANG, Zhao-wen QIU, Cheng LI, Jian-xun CHEN, Wei-wei LIU, Yan-bin LUO, authorCompany=null, correspAuthors=Zhao-wen QIU, Jian-xun CHEN, 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, fund=null), CN=ArticleExt(id=1314917874880307760, articleId=1314917874314076718, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=考虑新能源汽车基准排放的隧道通风计算方法, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

为探究液化天然气（LNG）重型车、混合动力汽车等新能源车辆的增加对公路隧道通风的影响，并解决现行设计规范因未充分考虑其排放特性而导致需风量计算偏差的问题，基于新能源汽车排放特性，考虑坡度、车队构成等因素更新了隧道车辆基准排放因子库，并据此构建了精细化隧道通风计算方法；以华岩隧道为案例，对不同新能源汽车渗透率下的隧道需风量进行了测算．结果表明，精细化方法显著提高了需风量计算的准确性．当设计年份为2030年时，稀释NO₂的需风量较国际通风报告（PIARC 2019）方法增加305.5%，而稀释CO和VI的需风量分别减少10%和6.5%．LNG重型车渗透率每增加10%，NO₂稀释需风量增加约55%．相较之下，混合动力汽车和纯电动汽车对需风量的影响可以忽略．传统设计规范以CO和VI作为主要控制指标，已不适用于新能源车辆比例快速增长的车队结构，NO₂稀释需风量将成为未来隧道通风设计的关键控制指标．

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王鑫（1994-），男，陕西神木人，长安大学博士研究生，研究方向为交通环境污染．发表论文3篇．wxin@chd.edu.cn.

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王鑫（1994-），男，陕西神木人，长安大学博士研究生，研究方向为交通环境污染．发表论文3篇．wxin@chd.edu.cn.

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