Article(id=1314922124976939940, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922077824578469, 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=1751385600000, receivedDateStr=2025-07-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791431464666, onlineDateStr=2026-10-08, pubDate=1771516800000, pubDateStr=2026-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791431464666, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791431464666, creator=13701087609, updateTime=1791431464666, updator=13701087609, issue=Issue{id=1314922077824578469, tenantId=1146029695717560320, journalId=1234093305789726721, year='2026', volume='46', issue='2', pageStart='593', pageEnd='1184', issueExtLink='null', onlineDate='null', pubDate='1771516800000', pubDateStr='2026-02-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791431453424, creator='13701087609', updateTime=1791431520751, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922360277398376, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922077824578469, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922360277398377, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922077824578469, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=618, endPage=626, ext={EN=ArticleExt(id=1314922125312484261, articleId=1314922124976939940, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Chemical composition and spatio-temporal variability of pollutant aerosols over the Himalayas using AERONET data, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

This study established a five-component aerosol model (including Black Carbon (BC), Brown Carbon (BrC), Ammonium Sulfate (AS), Dust (DU), and Water (AW)) utilizing AERONET data, to perform inversion calculations on the aerosol components variability in the Himalayas and surrounding regions. The results indicated that, on a multi-year average, the proportion of the mass concentration of each component at various monitoring sites was similar, with AS, DU, AW, BrC, and AW accounting for 53.49%, 29.33%, 10.63%, 5.27%, and 1.28%, respectively. The aerosol mass concentration in the southern slope of the Himalayas (annual average of 390mg/m²) was significantly higher than that in the northern slope (annual average of 49mg/m²), primarily due to the substantial obstruction of South Asian pollutant aerosols by the high-altitude mountains during transboundary transport crossed the Himalayas (with only about 13% transported over the Himalayas). Generally, the aerosol mass concentration in the Himalayan region was highest in spring (multi-year average), while it saw significant reductions in summer, autumn, and winter. This variation was mainly attributed to the cumulative effect of atmospheric pollutant emissions prior to the South Asian Monsoon (SAM) and the substantial transport of dust aerosols from South Asian deserts, which were greatly diminished during the summer monsoon period due to heavy precipitation. Interannually, the average aerosol mass concentration from 2015 to 2024 (with southern slope average of 370.83mg/m²) was slightly lower than that during the earlier period from 2006 to 2015 (with southern slope average of 453.26mg/m²), reflecting an improvement in recent pollution levels in South Asia. However, the overall atmospheric pollution remained extremely serious. Given that the absorbing aerosols like black carbon and brown carbon can significantly accelerate the melting of glacial snow, the persistent high emissions of polluted aerosols in the South Asian region may continue affecting the accelerated glacial changes in the Himalayas in the future.

, authors=Ao-nan ZHANG¹, Zhi-wen DONG^{1, *}, Fang-zhou LI², Xiao-yu JIAO¹, Cong JIANG³, authorsList=Ao-nan ZHANG, Zhi-wen DONG, Fang-zhou LI, Xiao-yu JIAO, Cong JIANG, authorCompany=null, correspAuthors=Zhi-wen DONG, 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=1314922127187338150, articleId=1314922124976939940, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于AERONET的喜马拉雅地区污染物气溶胶组成及时空变化研究, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

本文建立了气溶胶5成分模型（黑碳BC、吸收性有机碳BrC、硫酸铵AS、粉尘DU和水分AW），利用AERONET数据对喜马拉雅山及周边地区的气溶胶组分进行了反演计算．结果显示：多年平均来看，各个站点成分柱质量浓度占比相似，其中AS、DU、AW、BrC和AW依次占比53.49%、29.33%、10.63%、5.27%、1.28%．同时，喜马拉雅山南坡地区的气溶胶柱质量浓度（年平均390mg/m²）显著高于北坡（年平均49mg/m²），主要原因是高海拔山脉大量阻挡了南亚污染物气溶胶跨越喜马拉雅的传输（仅通过约13%）．总体上，喜马拉雅地区春季（多年平均）气溶胶柱质量浓度最高，而夏，秋，冬季表现出大幅降低，这主要是由于春季季风前大气污染物排放的不断累积效应，以及南亚沙漠释放的粉尘气溶胶大量传输造成的，而在夏天季风期随着大量降水的淋洗而大量降低．年际变化来看，2015~2024期间的气溶胶平均柱质量浓度（南坡平均370.83mg/m²）略低于较早的2006~2015年期间（南坡平均453.26mg/m²），这反映出南亚地区近期污染状况有所改善，然而总体大气污染状况仍然十分严重．由于黑碳和棕碳等吸收性气溶胶可以显著影响冰川积雪的加速消融，南亚地区持续的高污染气溶胶排放，预估未来会继续影响喜马拉雅山冰川变化．

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张傲楠（2000-），女，湖北荆州人，中国地质大学（武汉）硕士研究生，主要从事大气遥感方面研究．发表论文1篇．zhangaonan@cug.edu.cn.

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张傲楠（2000-），女，湖北荆州人，中国地质大学（武汉）硕士研究生，主要从事大气遥感方面研究．发表论文1篇．zhangaonan@cug.edu.cn.

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