Article(id=1314918405040337505, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, 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=1741190400000, receivedDateStr=2025-03-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430577764, onlineDateStr=2026-10-08, pubDate=1763568000000, pubDateStr=2025-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430577764, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430577764, creator=13701087609, updateTime=1791430577764, updator=13701087609, issue=Issue{id=1314918356222829028, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='11', pageStart='5921', pageEnd='6512', issueExtLink='null', onlineDate='null', pubDate='1763568000000', pubDateStr='2025-11-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430566125, creator='13701087609', updateTime=1791431596967, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922679942078535, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922679942078536, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918356222829028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=5941, endPage=5949, ext={EN=ArticleExt(id=1314918405229081186, articleId=1314918405040337505, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Multivariate analysis of aerosol extinction coefficients in Chengdu City, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

Using the hourly atmospheric visibility and relative humidity observations in Chengdu from October to December 2017, along with simultaneous data of black carbon (BC), particulate matter (PM), and nitrogen dioxide (NO₂) mass concentrations, the response of aerosol extinction coefficients to the changes of multiple factors was analyzed through correlation analysis, variance inflation factor analysis and the GAM model. The results revealed that the explanatory variable set for aerosol extinction coefficients included ρPM_2.5, RH, ρBC/ρPM_2.5, ρPM₁/ρPM_2.5, and ρPM_2.5/ρPM₁₀ (where ρBC, ρPM₁, ρPM_2.5, and ρPM₁₀ represent the mass concentrations of BC, PM₁, PM_2.5, and PM₁₀, respectively). Each variable demonstrated a significant nonlinear relationship with aerosol extinction coefficients. Compared to traditional bivariate models, the multivariate GAM model incorporating ρPM_2.5, RH, ρBC/ρPM_2.5, ρPM₁/ρPM_2.5, and ρPM_2.5/ρPM₁₀ as explanatory variables significantly improved simulation accuracy, particularly in reducing high-humidity-related biases. The adjusted R² values of the GAM models were 0.659, 0.683, 0.738, and 0.938 for slight, light, moderate, and heavy haze conditions, respectively. The coefficient of determination (R²) values were 0.686, 0.737, 0.777, and 0.961, with mean relative errors (MRE) of 0.091, 0.041, 0.071, and 0.016. The above results revealed the complexity of the multifactorial influence of the aerosol extinction coefficient and its intrinsic relationship with the change of haze intensity.

, authors=Ze FAN^{1, 2}, Xin-yi LI³, Jia-yuan MI⁴, Na LI^{1, 2}, Chao-yong TU^{1, 2}, Chang-jian NI^{1, 2, *}, authorsList=Ze FAN, Xin-yi LI, Jia-yuan MI, Na LI, Chao-yong TU, Chang-jian NI, authorCompany=null, correspAuthors=Chang-jian NI, 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=1314918405476545123, articleId=1314918405040337505, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=成都市气溶胶消光系数多变量解析, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

基于成都市2017年10~12月逐时大气能见度和相对湿度观测资料,结合同时次黑碳（BC）、颗粒物（PM）以及二氧化氮（NO₂）质量浓度数据,利用相关性分析、方差膨胀因子法以及GAM模型分析了气溶胶消光系数对多因素变化的响应关系.结果表明:气溶胶消光系数的解释变量集包括ρPM_2.5、RH、ρBC/ρPM_2.5、ρPM₁/ρPM_2.5和ρPM_2.5/ρPM₁₀（ρBC、ρPM₁、ρPM_2.5和ρPM₁₀分别为BC、PM₁、PM_2.5和PM₁₀的质量浓度）,各解释变量与气溶胶消光系数均呈现出显著的非线性关系（通过α=0.001的显著性检验）.相比于传统气溶胶消光系数双变量模型,以ρPM_2.5、RH、ρBC/ρPM_2.5、ρPM₁/ρPM_2.5和ρPM_2.5/ρPM₁₀为解释变量的气溶胶消光系数多变量GAM模型显著提升了对气溶胶消光系数的模拟效果,降低了气溶胶消光系数在高湿气象条件下的模拟偏差.对应轻微、轻度、中度和重度霾,GAM模型的调整判定系数（R²）分别为0.659、0.683、0.738和0.938,决定系数（R²）分别为0.686、0.737、0.777和0.961,平均相对误差（MRE）分别为0.091、0.041、0.071和0.016.上述成果揭示了气溶胶消光系数多因素影响的复杂性及其与霾强度变化的内在联系.

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范泽（2001-），女，吉林长春人，成都信息工程大学硕士研究生，主要从事大气物理学与大气环境方面研究．1040920134@qq.com.

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范泽（2001-），女，吉林长春人，成都信息工程大学硕士研究生，主要从事大气物理学与大气环境方面研究．1040920134@qq.com.

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