Article(id=1314917455114363741, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917454552327005, 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=1731427200000, receivedDateStr=2024-11-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430351283, onlineDateStr=2026-10-08, pubDate=1752940800000, pubDateStr=2025-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430351283, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430351283, creator=13701087609, updateTime=1791430351283, updator=13701087609, issue=Issue{id=1314917454552327005, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='7', pageStart='3553', pageEnd='4144', issueExtLink='null', onlineDate='null', pubDate='1752940800000', pubDateStr='2025-07-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430351150, creator='13701087609', updateTime=1791431931754, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314924084186669335, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917454552327005, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314924084186669336, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917454552327005, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3553, endPage=3563, ext={EN=ArticleExt(id=1314917455328273246, articleId=1314917455114363741, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=The causes and control strategies of O₃ pollution in a typical industrial city, columnId=1241049967662518582, journalTitle=China Environmental Science, columnName=Ozone Pollution Control, runingTitle=null, highlight=null, articleAbstract=

Taking Xingtai, a typical industrial city, as the study object, the pollution characteristics of O₃ were analyzed based on air quality monitoring and meteorological data from 2018 to 2022. The geographical detector method was employed to investigate the individual and combined impacts of meteorological factors and precursors on O₃ pollution. An air quality model was used to assess the sensitivity of O₃ generation, and EKMA curves were adopted to determine optimal emission reduction ratios in different control areas. The research revealed that the monthly average O₃ concentrations in Xingtai exhibited an inverted V-shaped pattern, with peak values observed in June (199.28μg/m³) and trough values recorded in January and December (37.16μg/m³). The hourly O₃ concentrations and their variation ranges across seasons followed the order: summer > spring > autumn > winter. Significant seasonal differences were identified in the driving factors affecting surface O₃ concentrations. On an annual scale, meteorological factors were found to exert greater influence on O₃ pollution than precursor pollutants. Temperature and solar radiation were identified as the main positive driving factors for O₃ concentration increases, while humidity and precipitation served as primary negative drivers. CO and NO₂ were positive drivers in spring and summer but showed opposite effects in autumn and winter. The central urban area with surrounding counties, along with Qinghe and Linxi counties, were classified as VOCs control areas. The northwestern and central-eastern counties were designated as synergistic control areas for both VOCs and NO_x. The optimal emission reduction ratios for between VOCs and NO_x were determined to be 1.5:1 and 1:1, respectively, in the different control areas.

, authors=Kai JIANG, Xiao-song HOU, Shui-yuan CHENG^*, authorsList=Kai JIANG, Xiao-song HOU, Shui-yuan CHENG, authorCompany=null, correspAuthors=Shui-yuan CHENG, 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=1314917455705760607, articleId=1314917455114363741, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=典型工业城市O₃污染成因及控制策略, columnId=1241049969449292093, journalTitle=中国环境科学, columnName=臭氧污染与控制, runingTitle=null, highlight=null, articleAbstract=

以典型工业城市邢台市为对象，基于2018~2022年的空气质量监测数据和气象数据，分析了O₃的污染特征．通过地理探测器方法，研究了气象因素与前体物对O₃污染的单独及联合影响．利用空气质量模型评估了O₃生成的敏感性，并采用EKMA曲线确定不同控制区的最佳减排比例．研究表明，邢台市月均浓度呈倒V型，在6月出现O₃浓度高峰期，1月和12月出现O₃浓度低值，变化幅度为37.16~199.28μg/m³，不同季节O₃小时浓度及变化幅度大小均为夏季>春季>秋季>冬季．各驱动因素对地面O₃浓度影响存在显著的季节差异．在年度时间尺度上，气象因素对O₃污染的影响高于前体物．温度和太阳辐射是O₃浓度上升的主要正向驱动因素，而湿度和降水是主要的负向驱动因素．春夏季节，CO和NO₂为正向驱动，秋冬季则相反．邢台市中心城区及周边部分区县，以及清河县和临西县被确定为VOCs控制区，西北部和中东部区县则为VOCs和NO_x协同控制区，VOCs与NO_x的最佳减排分别为1.5：1和1：1．

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蒋凯（2000-），男，河南信阳人，北京工业大学硕士研究生，主要从事大气污染防治及环境规划管理方面研究．jiangkai@emails.bjut.edu.cn.

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蒋凯（2000-），男，河南信阳人，北京工业大学硕士研究生，主要从事大气污染防治及环境规划管理方面研究．jiangkai@emails.bjut.edu.cn.

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