Article(id=1314923013921927463, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, 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=1754582400000, receivedDateStr=2025-08-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791431676606, onlineDateStr=2026-10-08, pubDate=1773936000000, pubDateStr=2026-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791431676606, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791431676606, creator=13701087609, updateTime=1791431676606, updator=13701087609, issue=Issue{id=1314922986466009203, tenantId=1146029695717560320, journalId=1234093305789726721, year='2026', volume='46', issue='3', pageStart='1185', pageEnd='1776', issueExtLink='null', onlineDate='null', pubDate='1773936000000', pubDateStr='2026-03-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791431670061, creator='13701087609', updateTime=1791431955369, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314924183251935526, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314924183251935527, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1278, endPage=1287, ext={EN=ArticleExt(id=1314923014190362920, articleId=1314923013921927463, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Simulation study on PM_2.5 concentration in subway system based on network model, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

A network model based on mass conservation was established to simulate PM_2.5 dispersion in subway systems, integrating a source model for train-induced dust in tunnels. The model was applied to a representative line in southern China and validated with field measurements, showing errors within 10%. Results indicate that station PM_2.5 concentration increases nonlinearly with train frequency (approximately +86% for a +78% frequency increase) and exhibits a near-constant differential relative to outdoor concentration, suggesting a linear additive effect. Notably, for lines operating at over 24 trains per hour during peak periods, station PM_2.5 levels consistently exceeded the indoor air quality standard (50μg/m³), even when outdoor concentrations met the WHO guideline (15μg/m³).

, authors=Chun-wang WANG^{1, 2, 3}, Xiao-feng LI^{4, *}, Lin-quan TONG^{2, 3}, Zhong-bin ZhANG^{2, 3}, Jing-guang FAN^{2, 3}, Long-zhe JIN¹, authorsList=Chun-wang WANG, Xiao-feng LI, Lin-quan TONG, Zhong-bin ZhANG, Jing-guang FAN, Long-zhe JIN, authorCompany=null, correspAuthors=Xiao-feng LI, 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=1314923014509130025, articleId=1314923013921927463, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于网络模型的地铁系统PM_2.5浓度模拟研究, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

本文依据质量守恒原理建立了地铁系统PM_2.5浓度的网络模型，并建立了列车在隧道内运行阶段的PM_2.5产尘量模型，从而模拟地铁系统各区域的PM_2.5浓度变化．应用该模型对中国南方某典型地铁线路进行了模拟分析，并开展现场实验进行了验证．结果表明，模拟结果误差在10%以内．定量分析了发车对数及室外浓度对地铁站PM_2.5浓度的影响：地铁站PM_2.5浓度随着发车对数的增加呈现非线性增加趋势，当发车对数增加78%，车站PM_2.5浓度增加约86%；地铁站PM_2.5浓度与室外PM_2.5在一定范围内浓度差值为一定值，与室外PM_2.5浓度成线性叠加关系．对于高峰期发车对数达到24对/h以上的地铁线路，即使室外浓度达到WHO推荐值（24h均值15μg/m³），地铁站运营期间PM_2.5浓度仍难以达到室内空气质量标准的要求（24h均值50μg/m³）．

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王春旺（1989-），男，河南周口人，博士（后），主要从事轨道交通系统细颗粒物的传播规律及通风系统控制研究．发表论文20余篇．wcw0502@ustb.edu.cn.

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王春旺（1989-），男，河南周口人，博士（后），主要从事轨道交通系统细颗粒物的传播规律及通风系统控制研究．发表论文20余篇．wcw0502@ustb.edu.cn.

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