Article(id=1241408713366492058, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, 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=1726934400000, receivedDateStr=2024-09-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773904501525, onlineDateStr=2026-03-19, pubDate=1745078400000, pubDateStr=2025-04-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773904501525, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773904501525, creator=13701087609, updateTime=1773904501525, updator=13701087609, issue=Issue{id=1241408710791189399, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='4', pageStart='1777', pageEnd='2368', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773904500911, creator=13701087609, updateTime=1773904624658, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241409229878259747, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241409229878259748, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1833, endPage=1843, ext={EN=ArticleExt(id=1241408713823671200, articleId=1241408713366492058, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Analysis of the spatiotemporal characteristics and external source contributions of dust events in Inner Mongolia based on Lidar Network, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

To investigate the spatial and temporal distribution characteristics of dust events in Inner Mongolia and their exogenous contributions, high-precision aerosol vertical distribution profiles were obtained using seven multi-band lidars deployed in Inner Mongolia. In addition, the three-dimensional dynamic evolution, atmospheric circulation and exogenous dust sources of summer dust storms in Inner Mongolia were further analyzed by combining ground station observations, atmospheric chemistry models, meteorological reanalysis data and backward trajectory models. It is found that the overall number of dust days in Inner Mongolia from 2014 to 2024 shows a fluctuating upward trend, and spring is the peak season for dust events, with the cumulative number of dust days reaching 117. In the past five years, the change of the number of dust days in spring was relatively stable, but since 2020, the number of dust days in summer began to show an increasing trend. During 20~25July 2024, a serious dust event occurred in Inner Mongolia. The dynamic evolution of the dust event from high altitude to near-surface was effectively monitored by the networked LiDAR in Inner Mongolia. on 20 July, the dust first appeared in the west-central part of Inner Mongolia and the China-Mongolia border, and then spread eastward under the combined effect of the northwesterly winds behind the low-pressure trough and the surface gales. The intensity of the dust weakened somewhat on 22 July. However, the low-pressure trough reappeared on 23 July, and the northerly flow behind the trough guided the cold air southward to form strong winds, which rapidly blew up the surface dust particles, leading to a general increase in dust uptake in west-central Inner Mongolia and transporting them downstream through the northwesterly wind behind the trough. During this dust event, the Sino-Mongolian border became the main potential source area of PM10 during the dust event, contributing most significantly to the PM10 mass concentration. This indicates that the Sino-Mongolian border area is gradually becoming an important source of dust in northern China.

, correspAuthors=Yu GU, Tian-shu ZHANG, 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, authorCompany=null, fund=null, authors=null, authorsList=Yuan-zhu DONG, Yu GU, Xue-chun BAI, Xing-jun ZHOU, Ying PAN, Zi-huai YI, Ze-nan WANG, Guang-qiang FAN, Xi MU, Yan XIANG, Tian-shu ZHANG, Wen-qing LIU), CN=ArticleExt(id=1241408719037190245, articleId=1241408713366492058, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于组网激光雷达的内蒙古沙尘事件时空特征与外源贡献分析, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

为揭示内蒙古地区沙尘事件的时空分布特征及其外源贡献,利用部署在内蒙古地区的七台三波段激光雷达,获取了高精度气溶胶垂直分布廓线.此外,进一步结合地面站点观测数据、大气化学模式、气象再分析数据及后向轨迹模型,深入分析了内蒙古夏季沙尘暴的三维动态演变、大气环流结构及外来沙尘源区. 研究发现,2014~2024年内蒙古地区的沙尘日数整体呈波动上升趋势,春季为沙尘事件的高发季节,累计沙尘日数达到117d. 近5年来,春季沙尘日数的变化相对稳定,但自2020年起,夏季沙尘出现日数开始呈增长趋势,5年增长率约为18.9%. 2024年7月20~25日,内蒙古地区发生了一次严重的沙尘暴事件. 通过组网激光雷达,有效监测了沙尘暴从高空至近地面的动态演变过程. 沙尘首先出现在阿拉善盟策克口岸,随后在低压槽后西北风及地表大风的共同作用下向东扩散. 随后2d,沙尘强度有所减弱. 然而,之后低压槽再次出现,槽后偏北气流引导冷空气南下,形成强风,迅速吹起地表沙尘,导致内蒙古中西部地区起沙量普遍增加,并将沙尘通过槽后西北风向下游输送. 此次沙尘事件中,中蒙边境成为沙尘天气PM10的主要潜在源区,对PM10质量浓度的贡献最为显著. 这表明,中蒙边境地区正逐渐成为中国北方重要的沙尘源.

, correspAuthors=谷雨, 张天舒, authorNote=null, correspAuthorsNote=
* 责任作者,教授级高级工程师,;
** 研究员,
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=EQj3GGNN3qR2HCvFCiBGIA==, magXml=0upYijzGWUWf87WHv61PVA==, pdfUrl=null, pdf=Mbz325RowPLMPkCYn3mm6A==, pdfFileSize=9745777, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=NUt/kIXjUQxKitpLdSvtig==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=xEd2Nv37+HYQNU2e0IFAGg==, mapNumber=null, authorCompany=null, fund=null, authors=

董元柱(1998-),男,河南信阳人,硕士,主要从事沙尘事件时空特征及归因研究.发论文3篇..

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董元柱(1998-),男,河南信阳人,硕士,主要从事沙尘事件时空特征及归因研究.发论文3篇..

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董元柱(1998-),男,河南信阳人,硕士,主要从事沙尘事件时空特征及归因研究.发论文3篇..

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Science of the Total Environment2021758,143714., articleTitle=Identifying a transport mechanism of dust aerosols over South Asia to the Tibetan Plateau: A case study, refAbstract=null)], funds=[Fund(id=1241460615861039760, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, awardId=2022YFC3704000; 2022YFC3700400, language=CN, fundingSource=国家重点研发计划(2022YFC3704000; 2022YFC3700400), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241460606734234150, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, xref=1., ext=[AuthorCompanyExt(id=1241460606746817063, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460606734234150, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.Institute of Environment Hefei Comprehensive National Science Center, Hefei 230088, China), AuthorCompanyExt(id=1241460606767788584, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460606734234150, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.合肥综合性国家科学中心环境研究院,安徽 合肥 230088)]), AuthorCompany(id=1241460606927172137, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, xref=2., ext=[AuthorCompanyExt(id=1241460606939755050, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460606927172137, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.Inner Mongolia Autonomous Region Environmental Monitoring Station, Hohhot 010011, China), AuthorCompanyExt(id=1241460606948143659, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460606927172137, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.内蒙古自治区环境监测总站,内蒙古 呼和浩特 010011)]), AuthorCompany(id=1241460607019446828, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, xref=3., ext=[AuthorCompanyExt(id=1241460607032029741, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460607019446828, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China), AuthorCompanyExt(id=1241460607040418350, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460607019446828, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.中国科学院合肥物质科学研究院安徽光学精密机械研究所环境光学与技术重点实验室,安徽 合肥 230031)]), AuthorCompany(id=1241460607115915823, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, xref=4., ext=[AuthorCompanyExt(id=1241460607128498736, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460607115915823, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China), AuthorCompanyExt(id=1241460607136887345, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, companyId=1241460607115915823, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.安徽大学物质科学与信息技术研究院,安徽 合肥 230601)])], figs=[ArticleFig(id=1241460614099432058, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.1, caption=The spatial distribution of ambient air quality monitoring stations and LiDAR network sites in Inner Mongolia, figureFileSmall=EdzXifjsPSTS/qm+qy+ejA==, figureFileBig=PfHJa7vKhl0NCx97M8rrsw==, tableContent=null), ArticleFig(id=1241460614195901051, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图1, caption=内蒙古地区国控环境空气质量监测点(国控点)及组网激光雷达点位的空间分布

图中黑点表示国控点的位置,紫色三角形表示激光雷达站点的位置:(a):阿尔山;(b):阿拉善盟策克口岸;(c):阿拉善右旗;(d):鄂尔多斯市;(e):甘其毛都口岸;(f):洪格尔苏木;(g):镶黄旗

, figureFileSmall=EdzXifjsPSTS/qm+qy+ejA==, figureFileBig=PfHJa7vKhl0NCx97M8rrsw==, tableContent=null), ArticleFig(id=1241460614288175740, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.2, caption=The simulation area for the WRF-Chem model, figureFileSmall=KoXBNwPkFPXSCdtr0xqGIQ==, figureFileBig=oIDc7F7OGB+0FxLM8OlGXQ==, tableContent=null), ArticleFig(id=1241460614380450429, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图2, caption=WRF-Chem模式的模拟区域图, figureFileSmall=KoXBNwPkFPXSCdtr0xqGIQ==, figureFileBig=oIDc7F7OGB+0FxLM8OlGXQ==, tableContent=null), ArticleFig(id=1241460614464336510, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.3, caption=Results of daily PM10 (a), hourly PM10 (b), pressure (c), temperature (d), humidity (e) wind direction (f) and wind speed (g)observations in Hohhot from 1 to 30 July 2024 compared with WRF-Chem and WRF simulations, figureFileSmall=bki5q7AzBNs3WlSjs7bG5Q==, figureFileBig=q9V9mueLxA8KWwAgpbXRTw==, tableContent=null), ArticleFig(id=1241460614539833983, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图3, caption=2024年7月1~30日呼和浩特市逐日PM10 (a)、逐小时PM10 (b)、气压(c)、温度(d)、湿度(e)、风向(f)和风速(g)观测值与WRF-Chem及WRF模拟的对比结果, figureFileSmall=bki5q7AzBNs3WlSjs7bG5Q==, figureFileBig=q9V9mueLxA8KWwAgpbXRTw==, tableContent=null), ArticleFig(id=1241460614615331456, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.4, caption=Daily variation of PM10 concentration and interannual variation of dust days in Inner Mongolia from 2014 to 2024, figureFileSmall=vqg2Jl3vPuV+akqPldmxHg==, figureFileBig=lVm8hhDuwU53moG5NsE0fQ==, tableContent=null), ArticleFig(id=1241460614690828929, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图4, caption=2014~2024年内蒙古地区PM10浓度的日变化及沙尘日数的年际变化

灰色柱为PM10浓度的日变化,实线为沙尘日数年际变化,虚线表示识别沙尘日数的阈值

, figureFileSmall=vqg2Jl3vPuV+akqPldmxHg==, figureFileBig=lVm8hhDuwU53moG5NsE0fQ==, tableContent=null), ArticleFig(id=1241460614766326402, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.5, caption=The seasonal distribution of dust days in Inner Mongolia from 2014 to 2024, figureFileSmall=WuFjOktnKprKP0MNJLnvzg==, figureFileBig=W/mOfhum+B3V1ZkrxOfnHw==, tableContent=null), ArticleFig(id=1241460614841823875, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图5, caption=2014~2024年内蒙古地区沙尘日数的季节分布, figureFileSmall=WuFjOktnKprKP0MNJLnvzg==, figureFileBig=W/mOfhum+B3V1ZkrxOfnHw==, tableContent=null), ArticleFig(id=1241460614913127044, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.6, caption=Changes in PM10 concentrations observed at ambient air quality monitoring stations in Inner Mongolia from January to July 2024, figureFileSmall=YHUhNhCIbEpoy0wvKQWBvg==, figureFileBig=d08op5YImm9WJvpsMTBXnQ==, tableContent=null), ArticleFig(id=1241460614988624517, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图6, caption=2024年1~7月内蒙古地区国控环境空气质量监测点观测的PM10浓度变化, figureFileSmall=YHUhNhCIbEpoy0wvKQWBvg==, figureFileBig=d08op5YImm9WJvpsMTBXnQ==, tableContent=null), ArticleFig(id=1241460615055733382, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.7, caption=The spatial distribution of the dust emission during the dust event based on the WRF-Chem model, figureFileSmall=dJpOOSop241egKkVShhMmQ==, figureFileBig=Ecgm6Rxh2GCVmTXcQK54qA==, tableContent=null), ArticleFig(id=1241460615135425159, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图7, caption=基于WRF-Chem模式模拟的沙尘事件期间起沙量空间分布, figureFileSmall=dJpOOSop241egKkVShhMmQ==, figureFileBig=Ecgm6Rxh2GCVmTXcQK54qA==, tableContent=null), ArticleFig(id=1241460615261254280, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.8, caption=The 355nm extinction coefficient, 532nm extinction coefficient, 355nm depolarization ratio and 532nm depolarization ratio observed during the dust event based on the LiDAR network, figureFileSmall=RcOebxAOs8SiWnorrIVLSw==, figureFileBig=DXrbq6O3JJv673PK78rkAw==, tableContent=null), ArticleFig(id=1241460615319974537, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图8, caption=基于组网激光雷达观测的沙尘事件期间355nm消光系数、532nm消光系数、355nm退偏振比和532nm退偏振比的垂直特征

(a):阿尔山;(b):阿拉善盟策克口岸;(c):阿拉善右旗;(d):鄂尔多斯市;(e):甘其毛都口岸;(f):洪格尔苏木;(g):镶黄旗

, figureFileSmall=RcOebxAOs8SiWnorrIVLSw==, figureFileBig=DXrbq6O3JJv673PK78rkAw==, tableContent=null), ArticleFig(id=1241460615382889098, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.9, caption=The weather pattern at 500 hPa at 12:00 during the dust event, figureFileSmall=nf/vHHLOHbn0mAcuW5vDUg==, figureFileBig=78xqF+obU5pgDLW3p7SkyQ==, tableContent=null), ArticleFig(id=1241460615454192267, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图9, caption=沙尘事件期间12:00时500hPa的天气形势场

温度场(填色,单位:℃)、500hPa位势高度场(黑色等值线,单位:gpm)和10m风场(箭矢,单位:m/s)

, figureFileSmall=nf/vHHLOHbn0mAcuW5vDUg==, figureFileBig=78xqF+obU5pgDLW3p7SkyQ==, tableContent=null), ArticleFig(id=1241460615512912524, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Fig.10, caption=The cluster analysis of 48-h backward trajectories during the dust even and the concentration of PM10 obtained from WCWT analysis, figureFileSmall=OjVPD6SYNXVZYx0GkhrlgQ==, figureFileBig=EYVZbTO2UG1dNDRBbxtp5g==, tableContent=null), ArticleFig(id=1241460615580021389, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=图10, caption=沙尘事件期间48h后向轨迹聚类和基于WCWT分析得到的PM10浓度空间分布

彩色线表示气流轨迹,数字表示气流轨迹的百分比;填色为PM10浓度空间分布(μg/m3)

, figureFileSmall=OjVPD6SYNXVZYx0GkhrlgQ==, figureFileBig=EYVZbTO2UG1dNDRBbxtp5g==, tableContent=null), ArticleFig(id=1241460615647130254, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=EN, label=Table 1, caption=

WRF-Chem configuration options for physical and chemical parameterizations

, figureFileSmall=null, figureFileBig=null, tableContent=
物理化学过程设置
微物理方案Lin et al.’s scheme
长波辐射方案RRTMG
短波辐射方案RRTMG
陆面方法Noah land surface model
边界层方案Yonsei University scheme
沙尘排放方案GOCART
气溶胶方案CBMZ
), ArticleFig(id=1241460615722627727, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408713366492058, language=CN, label=表1, caption=

WRF-Chem主要物理化学参数化方案的设置

, figureFileSmall=null, figureFileBig=null, tableContent=
物理化学过程设置
微物理方案Lin et al.’s scheme
长波辐射方案RRTMG
短波辐射方案RRTMG
陆面方法Noah land surface model
边界层方案Yonsei University scheme
沙尘排放方案GOCART
气溶胶方案CBMZ
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基于组网激光雷达的内蒙古沙尘事件时空特征与外源贡献分析
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董元柱 1 , 谷雨 2, * , 白雪椿 2 , 周兴军 2 , 潘颖 3 , 易子淮 4 , 王泽楠 4 , 范广强 3 , 穆溪 3 , 项衍 4 , 张天舒 1, 3, ** , 刘文清 1, 3
中国环境科学 | 大气污染与控制 2025,45(4): 1833-1843
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中国环境科学 | 大气污染与控制 2025, 45(4): 1833-1843
基于组网激光雷达的内蒙古沙尘事件时空特征与外源贡献分析
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董元柱1 , 谷雨2, * , 白雪椿2, 周兴军2, 潘颖3, 易子淮4, 王泽楠4, 范广强3, 穆溪3, 项衍4, 张天舒1, 3, ** , 刘文清1, 3
作者信息
  • 1.合肥综合性国家科学中心环境研究院,安徽 合肥 230088
  • 2.内蒙古自治区环境监测总站,内蒙古 呼和浩特 010011
  • 3.中国科学院合肥物质科学研究院安徽光学精密机械研究所环境光学与技术重点实验室,安徽 合肥 230031
  • 4.安徽大学物质科学与信息技术研究院,安徽 合肥 230601
  • 董元柱(1998-),男,河南信阳人,硕士,主要从事沙尘事件时空特征及归因研究.发论文3篇..

通讯作者:

* 责任作者,教授级高级工程师,;
** 研究员,
Analysis of the spatiotemporal characteristics and external source contributions of dust events in Inner Mongolia based on Lidar Network
Yuan-zhu DONG1 , Yu GU2, * , Xue-chun BAI2, Xing-jun ZHOU2, Ying PAN3, Zi-huai YI4, Ze-nan WANG4, Guang-qiang FAN3, Xi MU3, Yan XIANG4, Tian-shu ZHANG1, 3, ** , Wen-qing LIU1, 3
Affiliations
  • 1.Institute of Environment Hefei Comprehensive National Science Center, Hefei 230088, China
  • 2.Inner Mongolia Autonomous Region Environmental Monitoring Station, Hohhot 010011, China
  • 3.Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
  • 4.Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
出版时间: 2025-04-20
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为揭示内蒙古地区沙尘事件的时空分布特征及其外源贡献,利用部署在内蒙古地区的七台三波段激光雷达,获取了高精度气溶胶垂直分布廓线.此外,进一步结合地面站点观测数据、大气化学模式、气象再分析数据及后向轨迹模型,深入分析了内蒙古夏季沙尘暴的三维动态演变、大气环流结构及外来沙尘源区. 研究发现,2014~2024年内蒙古地区的沙尘日数整体呈波动上升趋势,春季为沙尘事件的高发季节,累计沙尘日数达到117d. 近5年来,春季沙尘日数的变化相对稳定,但自2020年起,夏季沙尘出现日数开始呈增长趋势,5年增长率约为18.9%. 2024年7月20~25日,内蒙古地区发生了一次严重的沙尘暴事件. 通过组网激光雷达,有效监测了沙尘暴从高空至近地面的动态演变过程. 沙尘首先出现在阿拉善盟策克口岸,随后在低压槽后西北风及地表大风的共同作用下向东扩散. 随后2d,沙尘强度有所减弱. 然而,之后低压槽再次出现,槽后偏北气流引导冷空气南下,形成强风,迅速吹起地表沙尘,导致内蒙古中西部地区起沙量普遍增加,并将沙尘通过槽后西北风向下游输送. 此次沙尘事件中,中蒙边境成为沙尘天气PM10的主要潜在源区,对PM10质量浓度的贡献最为显著. 这表明,中蒙边境地区正逐渐成为中国北方重要的沙尘源.

沙尘传输  /  组网激光雷达  /  大气化学模式  /  时空特征  /  外来源区

To investigate the spatial and temporal distribution characteristics of dust events in Inner Mongolia and their exogenous contributions, high-precision aerosol vertical distribution profiles were obtained using seven multi-band lidars deployed in Inner Mongolia. In addition, the three-dimensional dynamic evolution, atmospheric circulation and exogenous dust sources of summer dust storms in Inner Mongolia were further analyzed by combining ground station observations, atmospheric chemistry models, meteorological reanalysis data and backward trajectory models. It is found that the overall number of dust days in Inner Mongolia from 2014 to 2024 shows a fluctuating upward trend, and spring is the peak season for dust events, with the cumulative number of dust days reaching 117. In the past five years, the change of the number of dust days in spring was relatively stable, but since 2020, the number of dust days in summer began to show an increasing trend. During 20~25July 2024, a serious dust event occurred in Inner Mongolia. The dynamic evolution of the dust event from high altitude to near-surface was effectively monitored by the networked LiDAR in Inner Mongolia. on 20 July, the dust first appeared in the west-central part of Inner Mongolia and the China-Mongolia border, and then spread eastward under the combined effect of the northwesterly winds behind the low-pressure trough and the surface gales. The intensity of the dust weakened somewhat on 22 July. However, the low-pressure trough reappeared on 23 July, and the northerly flow behind the trough guided the cold air southward to form strong winds, which rapidly blew up the surface dust particles, leading to a general increase in dust uptake in west-central Inner Mongolia and transporting them downstream through the northwesterly wind behind the trough. During this dust event, the Sino-Mongolian border became the main potential source area of PM10 during the dust event, contributing most significantly to the PM10 mass concentration. This indicates that the Sino-Mongolian border area is gradually becoming an important source of dust in northern China.

dust transport  /  LiDAR network  /  atmospheric chemical modelling  /  spatial and temporal characteristics  /  exogenous dust source
董元柱, 谷雨, 白雪椿, 周兴军, 潘颖, 易子淮, 王泽楠, 范广强, 穆溪, 项衍, 张天舒, 刘文清. 基于组网激光雷达的内蒙古沙尘事件时空特征与外源贡献分析. 中国环境科学, 2025 , 45 (4) : 1833 -1843 .
Yuan-zhu DONG, Yu GU, Xue-chun BAI, Xing-jun ZHOU, Ying PAN, Zi-huai YI, Ze-nan WANG, Guang-qiang FAN, Xi MU, Yan XIANG, Tian-shu ZHANG, Wen-qing LIU. Analysis of the spatiotemporal characteristics and external source contributions of dust events in Inner Mongolia based on Lidar Network[J]. China Environmental Science, 2025 , 45 (4) : 1833 -1843 .
沙尘暴是干旱半干旱地区常见的灾害性天气现象,其形成主要是由于强风将干燥、裸露地表的大量沙尘气溶胶卷入大气层.由于沙尘暴具备持续时间短、强度大的特征,往往能在短时间内对人体健康和生态环境造成严重威胁,并对社会经济和居民生活带来巨大损失[1-2].因此,沙尘暴已成为许多沙尘源区附近或下游地区和国家面临的严峻环境问题[3-5].沙尘暴发生时,大量沙尘气溶胶被释放至大气中[6],这些气溶胶不仅通过吸收和散射太阳短波辐射而加热大气,直接影响地气系统的辐射能量平衡[7-9],还能够作为云凝结核和冰核参与云宏微观物理过程,间接影响地球辐射收支[10-12].因此,沙尘暴对区域及全球的环境与气候变化有重大影响,对沙尘暴变化特征和来源贡献的研究具有重要意义.
中国北方的沙尘源地主要分布在河西走廊、内蒙古中西部以及塔克拉玛干沙漠等沙漠或干旱半干旱地区,其中塔克拉玛干沙漠和内蒙古地区的沙尘暴尤为频繁[13-14].内蒙古自治区位于中国北部边疆,大部分地区属于半干旱与半湿润气候区,降水相对集中且气候干燥.由于靠近冬季风源地,冬春季节大风天气频繁,降水稀少,土壤容易被强风卷起,形成风沙天气[15].同时,无节制的放牧导致草场退化和土壤沙化,加剧了风沙现象的发生.综合以上因素,最终引发沙尘暴[16].此外,内蒙古中西部地区分布着大量沙漠与戈壁,夏季气温普遍较高且降水量偏少,这加速了土壤水分的蒸发,使得地表土壤干燥,植被稀少,最终导致沙尘天气的爆发[17].近几十年,受沙尘源地地表风速减小、植被覆盖、降水和土壤湿度增加等因素影响,中国北方春季沙尘事件的发生频率呈现下降趋势[18-21].中国北方春季沙尘暴日数占全年沙尘暴日数的比例从2000年的72.14%下降到2019年的46.88%,下降率为7.3%/10a.与此相对,中国北方夏季沙尘暴日数呈上升趋势,从2010年的16.15%增加至2019年的34.92%,增长率为12.4%/10a[22].然而,内蒙古并非中国北方唯一的沙尘源地,其沙尘事件的时空特征仍不明确.此外,目前针对内蒙古地区沙尘事件的研究大多集中于单一激光雷达站对沙尘事件过程的分析,这仅能提供局部站点的污染变化信息,基于综合组网观测和模式模拟方法分析明显不足,亟需开展相关研究.
本文利用2014~2024年国控环境空气质量监测点(国控点)的PM10数据,通过设定阈值识别了内蒙古地区该时段内发生的沙尘事件,并分析了其年际及季节变化特征.进而结合组网激光雷达、大气化学模式模拟、气象再分析数据以及后向轨迹模型,对发生在内蒙古地区的一次夏季沙尘暴的三维演变过程、高低空环流配置和外源贡献进行了全面分析.本研究旨在深入理解内蒙古地区夏季沙尘事件的演化过程、输送特征及源区贡献.
内蒙古自治区位于中国北部,地处97°~126°E,37°~53°N之间,东接黑龙江、吉林、辽宁,南邻河北、山西、陕西和宁夏,西与甘肃相连,北与蒙古国和俄罗斯接壤,呈狭长状分布(图1).内蒙古总面积约为118.3万km2,占全国总面积的12.3%.自治区地势西高东低,平均海拔约为1000m.气候类型主要为中温带大陆性气候,具有显著的季风特征,冬季寒冷漫长,夏季温暖短促,气候干燥,多风沙.年平均气温在-1~10℃之间,年降水量从东部的500mm逐渐减少到西部的100mm,东部降水相对丰富,西部则干旱少雨.这种气候特征导致内蒙古地区沙尘事件频发[17].自治区中西部地区生态环境较为脆弱,是中国干旱和沙尘灾害的重灾区,主要沙尘源地包括巴丹吉林沙漠、腾格里沙漠、乌兰布和沙漠及库布齐沙漠等.
为了提升内蒙古自治区沙尘天气监测网能力,完善沙尘天气监测网,内蒙古自治区环境监测总站在自治区内部署了7台三波长(355,532和1064nm)大气颗粒物激光雷达.大气颗粒物激光雷达点位分布见图1.兴安盟阿尔山市、阿拉善盟额济纳旗策克口岸和阿拉善右旗、鄂尔多斯市东胜区、巴彦淖尔市乌拉特中旗甘其毛都口岸、锡林郭勒盟苏尼特左旗洪格尔苏木和镶黄旗,涵盖内蒙古地区由西到东大部分地区.三波长大气颗粒物激光雷达由合肥中科环光技术有限公司提供,雷达系统由激光发射分系统、接收分系统、光电转换和数据采集分系统、电源分系统、综合控制分系统、外部雷达机柜等组成,包含1064,532,355nm三个波长,以及1064,532,355nm三个米散射通道,532,355nm两个偏振通道和N2拉曼通道.与单波长和双波长颗粒物激光雷达相比,三波长颗粒物激光雷达,能够实现全波段更加准确的消光系数反演[23-24],通过组网信息还能有效辨识粗细颗粒物种类[25].本研究中使用了355,532nm的消光系数及退偏振比来监测发生在内蒙古地区的一次沙尘事件.此外,本文中使用的大气颗粒物数据来自国控环境空气质量监测点,时间分辨率为1h.
欧洲中期天气预报中心发布的第五代大气再分析数据集(ERA-5)提供1979年至今的大气和近地面再分析数据,时间分辨率为1h,空间分辨率为0.25°×0.25°.ERA-5可以很好地捕捉天气过程的细节[26].本文选取ERA-5大气环流数据分析沙尘暴事件期间大气环流驱动因子,采用的具体变量包括500hPa位势高度、温度和10m风场.
GDAS数据来自NCEP全球资料同化系统(ftp.arlhq.noaa.gov/pub/archives/gdasl),其空间分辨率为1°×1°,包括温度、相对湿度、气压、水平风速和垂直风速等气象要素场.GDAS数据按5周的周期进行保存,并每月更新一次.本研究利用该数据来计算后向轨迹.
WRF模式是美国环境预报中心(NCEP)和美国国家大气研发中心(NCAR)共同研发的新一代中尺度天气模式,广泛应用于科研和业务领域.WRF-Chem是在WRF的基础上耦合了化学模块(Chem),用来研究大气中各种化学成分的排放、传输以及污染物之间的一系列复杂的化学反应.本文的WRF-Chem模拟试验中采用的物理化学参数化方案如表1所列.起沙参数化方案为GOCART(Geogria Insititute of Technology-Goddard Global Ozone Chemistry Aerosol Radiationand Transport Model).目前,GOCART已在全球范围内广泛应用[27],在非洲地区[28]、北美地区[29]和东亚地区[30]等的起沙模拟能力均表现优良.本研究中WRF-Chem模式采用两层嵌套,最外层模拟区域为图2中的D01,空间分辨率为81km×81km,网格数为74×61,最内层模拟区域为中国北方和蒙古国,空间分辨率为27km×27km,网格数88×85,模拟时间为2024年6月22日~2024年8月1日,其中前10d为模式初始化时间.人为排放清单来自中国多分辨率排放清单(MEIC)数据,生物排放则通过在线建模系统MEGAN计算得出.
对2024年7月1~30日期间呼和浩特市逐日PM10、逐小时PM10以及温度、湿度、风向、风速和气压的逐小时观测值与WRF模拟结果进行了对比分析,以评估模式的性能(图3).从图中可以看出,各个气象要素和PM10的模拟结果与观测结果基本一致,相关系数(R)均大于0.52.其中,气压和温度的模拟效果最好,R值超过0.9.在同一高度上,WRF略微低估了温度,在近地面(2km以下),WRF高估了湿度、风向和风速,这4个参数的R值最高分别达到0.98,0.75,0.76和0.72.总的来说,这些统计指标表明,模式能够捕捉到这一时期的主要气象特征.
图3(a)(b)还进一步展示了PM10模拟值和观测值的对比结果.尽管由于WRF-Chem模式模拟偏差或排放的不确定性,WRF-Chem对PM10高值有所低估,但逐日PM10和逐小时PM10一致性指数(IOA)和R分别能达到0.73,0.86和0.67,0.77.此外,归一化平均误差(NMB)为-14.87%和-16.08%(-19%~-8%),符合美国环保局建议的标准(EPA,2005).总体而言,这些结果较为可靠,可用于进一步研究.
采用HYSPLIT后向轨迹模型对空气污染物的来源和扩散路径进行了模拟[31].基于给定的气象数据,该模型可以通过计算不同高度和不同起始事件的后向轨迹来分析大气污染物的输送与扩散,在追踪沙尘源地和分析沙尘输送过程中具有广泛的应用.
轨迹聚类分析根据空间相似性对某一研究点位的气团轨迹进行分类,从而确定气流主导方向和潜在污染源.本文中使用了地理信息系统中的TrajStat软件并采用欧拉聚类方法进行聚类分析.根据气流在不同季节的传播速度和方向,将气流轨迹分为三类,从而分析2024年7月20~25日呼和浩特沙尘暴事件的来源和传播路径.
浓度权重轨迹分析法(CWT)可以计算不同区域对污染物浓度的相对贡献.它通过平均经过某网格的相关轨迹的污染物浓度,将停留时间加权浓度分配给每个网格单元.CWT的计算公式如式(1)所示.
式中: Cij表示第(ij)网格单元的加权平均浓度,i为轨迹的指数,m为轨迹的总数,Cl为轨迹l经过网络单元(ij)时对应的污染物浓度,τijl为轨迹l在网格单元(ij)停留的时间.为了考虑轨迹较少的网格单元造成的不确定性,CWT方法需要使用加权函数,得到的修正浓度加权值为WCWT.
为了分析大气颗粒物PM10平均浓度的时间变化,本文统计了2014~2024年内蒙古地区的日均PM10的浓度(图4).数据来源于内蒙古境内所有的国控环境空气质量监测点,站点的具体位置如图1所示.从图2中可以看出,内蒙古地区每年PM10的日均浓度呈现周期性高峰和低谷特征.在春季初期和冬季中后期,PM10日平均浓度会显著升高,而在夏季和秋季,PM10日平均浓度则明显下降,通常维持在1000μg/m3以下.值得注意的是,2021年春季期间,PM10日平均浓度达到了2014~2024年间的最大值,为9999μg/m3(达到了仪器监测的最大值).这一极大值的出现主要是由于当年3月发生了近十年来最强的沙尘暴事件[32-33].
大量研究表明,PM10浓度显著受到沙尘天气的影响.当沙尘天气发生时,会向大气中释放大量的沙尘气溶胶,导致大气中的PM10浓度急剧上升.基于这一认识,本文依据《受沙尘天气过程影响城市空气质量评价补充规定》,将PM10日平均浓度为1000μg/m3设为沙尘天气的判别阈值(即PM10日平均浓度大于阈值则记为一个沙尘日),从而识别了2014~2024年期间内蒙古地区的沙尘日数(图4).从图中可以发现,2014~2024年期间内蒙古地区的沙尘日数整体呈现波动上升的趋势.此外,统计结果显示,沙尘日数在2014年达到11年以来最低,为7d.在随后的几年里沙尘日数逐渐增加,直到2018年升至最高,为45d; 2018~2020年,沙尘日数迅速下降,从45d降至20d; 2021~2024年,沙尘日数基本保持稳定,整体的特征为出现的频次高且增加缓和,平均沙尘日数为34.5d.
本文不仅研究了沙尘日数的年际变化,还分析了内蒙古地区沙尘日数的季节分布特征.图5展示了2014~2024年内蒙古地区沙尘日数的季节分布.研究结果表明,内蒙古地区的沙尘日数具有明显的季节变化特征,其中春季为沙尘天气的高发季节,沙尘日数高达177d.相比之下,夏季、秋季和冬季的沙尘日数较少,分别为52,24和40d.虽然春季依然是内蒙古地区沙尘天气的主要发生时期,但自2021年以来,春季沙尘日数的变化趋于平稳,分别为20,20,22和20d.相反,自2020年以来,该地区夏季沙尘日数呈上升趋势,从2020年的5d增加到2024年的10d,5年增长率约为18.9%.
由3.1节可知,近年来内蒙古地区的沙尘事件在夏季明显增加,夏季沙尘事件对当地天气、气候及生态环境的影响愈发不可忽视.从图6中可以发现,在2024年冬末和春季多次爆发了沙尘事件,PM10日均值最大时可以超过8000μg/m3.与此同时,夏季7月20~25日内蒙古地区同样爆发了严重的沙尘暴事件,多个城市的PM10日均值超过了2000μg/m3,最大时可超过6000μg/m3.为了进一步研究内蒙古夏季沙尘暴的时空特征,选择该事件作为研究对象.
图7中可以看出,2024年7月20日,沙尘天气首先出现在内蒙古的中西部及中蒙边境,两地起沙量的最大值可以达到1000g/m2.随后,沙尘向东扩散,内蒙古的中西部的起沙量逐渐减少,而中蒙边境起沙量增加,最大起沙量超过了1000g/m2.7月22日,沙尘天气强度有所减弱,主要受影响地区仍位于中蒙边境,起沙量已经降至800g/m2左右.值得注意的是,在随后一天,沙尘事件急剧增强,内蒙古中西部地区都出现了大范围的起沙,大部分区域的起沙量超过了800g/m2.大量的起沙为沙尘事件的发生发展提供了大量的沙尘颗粒,当有大风来临时,会极大程度地促进沙尘事件的发生.7月23~25日,起沙量逐渐减少,沙尘天气消失.这次沙尘事件严重恶化了当地空气质量,对人们的健康构成了威胁,并给经济和社会活动带来巨大的冲击.
为更好地理解沙尘事件的传输过程,本研究还利用组网激光雷达提供的355,532nm消光系数和退偏振比来研究事件期间沙尘的垂直分布.2024年7月20日01:00时阿拉善盟策克口岸最先监测到高空4000m以上沙尘层入境(图8b),8h后影响近地面,持续3h,沙尘分布在高空3400m以下;阿拉善右旗20日02:00时监测到沙尘自低空500m以下入境,沙尘分布高度逐渐升高,最高达5000m以上(21日04:00时),沙尘影响持续时间为31h,直至7月21日09:00时结束;鄂尔多斯市21日11:00时监测到沙尘自低空2000m以下入境(图8d),沙尘影响持续时间为14h(22日01:00时结束),沙尘分布在低空4500m以下.2024年7月23日沙尘事件急剧增强,整个内蒙古中西部地区都出现了大范围的起沙,23日17:00时阿拉善盟策克口岸最先监测到低空1000m以上沙尘层入境,4h后影响近地面,持续2h,沙尘主要分布在高空1400m以下(图8b);阿拉善右旗23日18:00时监测到低空1400m以下沙尘层入境,沙尘层分别于2.5h后影响近地面,并于23日21:00时结束污染,持续时间为2h,沙尘分布在低空800m以下(图8c);甘其毛都23日20:26监测到沙尘自低空400m以下入境,沙尘影响持续时间为2.5h,直至23日21:00时沙尘事件结束(图8e).在此次沙尘事件中,阿尔山、洪格尔苏木和镶黄旗站点未观测到明显的沙尘层(图8a、8f和8g),主要原因是此次事件的影响范围集中在内蒙古中西部地区(图7),而这些站点距离受影响区域较远.
众多研究已经证实,大气环流通过调节风速、风向、温度和湿度等气象要素,能够在不同程度上影响沙尘暴的产生与演变[34-35].本文通过分析2024年7月19~24日500hPa位势高度场、温度场和10m风场(图9),揭示了大气环流对此次沙尘暴事件输送过程的具体影响.在沙尘暴事件爆发的前一日(图9a),500hPa位势高度场中出现了明显的低压槽,其槽底位于内蒙古中西部地区.低压槽后方部分地区出现大风,局部最大风速可达10m/s,大风天气引发了沙尘天气.随后,低压槽东移至蒙古国东部(图9b),在槽后偏北气流的引导下,极地冷空气向东南方向移动,低压槽在冷空气的堆积作用下不断增强,并伴有较强的冷平流.在这一阶段,内蒙古中西部地区和蒙古国南部主要受到槽后西北气流的控制.同时,该区域出现更为强烈的大风,风速超过10m/s,大风可以将该地区的沙尘迅速抬升,配合上此时高空500hPa槽后的西北大风,被卷起的沙尘迅速跨越国界向下游输送,沙尘事件得到充分的发展.在随后的2d内(图9c9d),低压槽继续向东移动,内蒙古地区转为受到较弱高压脊控制.在高压脊的作用下,大气层结稳定,垂直运动较弱,不利于沙尘的输送和沉降,因此沙尘天气开始减弱.然而,到了7月23日(图9e),500hPa低压槽再次迅速发展,槽后的偏北风最大超过了12m/s,导致极地冷空气进一步入侵,并且贝加尔湖以西形成了−10℃冷中心.低压槽槽后的地面风增强,进一步加剧了沙尘排放(图5).最终,随着低压槽的逐渐减弱(图9f),沙尘天气消散.
基于HYSPLIT模型和轨迹聚类分析,本文还对此次沙尘事件中大气污染物的来源和扩散途径进行了详细分析.由图10可知,呼和浩特的气团轨迹主要源自中蒙边境和受偏南风引起的沙尘回流.具体来说,共有约37.5%的气团来自中蒙边境,而11.11%的气团来自新疆东北部.这表明沙尘主要沿西北路径移动,并在低压槽的影响下,沿西北-东南方向影响内蒙古地区.当气团抵达呼和浩特后,受到偏南风的作用,有约51.39%的沙尘回流至呼和浩特.总的来说,中蒙边境贡献了内蒙古地区42%的PM10浓度,而塔克拉玛干沙漠仅仅贡献了11.11%,中蒙边境是此次沙尘事件的重要源地.CWT越大表示该网格对受体站点污染物质量浓度贡献越大.对于呼和浩特尔而言,沙尘天气PM10的污染主要来自高浓度的外源输送.中蒙边境对呼和浩特PM10质量浓度的贡献超过100μg/m3.此外,呼和浩特西部的库布齐沙漠和乌兰布和沙漠也存在高浓度PM10的输送,对当地的空气质量构成了一定的影响.
综上所述,利用气象模型和组网监测网络能够显著提升沙尘事件的预警能力,并为沙尘事件的实时监测及其动态演变提供有力支持.随着夏季沙尘日数的逐年增加,内蒙古地区的沙尘防治应更加注重跨季节的监测和应对措施.此外,研究还指出,中蒙边境地区作为主要沙尘源,应加强该区域的环境治理和监控,以减少其对中国北方地区的影响.
4.1 2014~2024年内蒙古地区沙尘日数呈波动上升趋势,春季为高发季节,累计沙尘日数达117d,显著高于其他季节.近5年来,内蒙古地区春季沙尘日数变化趋于平稳,但夏季沙尘日数自2020年起出现增长,5年增长率约为18.9%.
4.2 组网激光雷达观测和WRF-Chem模式能有效监测2024年7月20~25日沙尘事件的动态演变过程.7月20日,激光雷达首先在阿拉善盟策克口岸观测到明显的沙尘,随后沙尘在槽后强风控制下向东扩散.随后两天沙尘强度有所降低,但7月23日在低压槽作用下沙尘再次增强,阿拉善盟策克、阿拉善右旗和甘其毛都站点均监测到沙尘层.激光雷达与模型模拟结果高度一致,验证了其对沙尘事件监测的有效性.
4.3 2024年7月的沙尘事件主要受低压槽控制,低压槽后偏北气流可以引导极低冷空气南下,冷空气在槽后堆积,促使低压槽发展,槽后大风增强.同时,地表大风将沙尘吹起,与槽后西北大风共同作用,加速沙尘的跨界输送.中蒙边境为本次沙尘天气PM10的主要源区,且对内蒙古地区PM10质量浓度贡献显著.这表明中蒙边境正逐渐成为中国北方空气质量的重要沙尘源.
  • 国家重点研发计划(2022YFC3704000; 2022YFC3700400)
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2025年第45卷第4期
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  • 接收时间:2024-09-22
  • 首发时间:2026-03-19
  • 出版时间:2025-04-20
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  • 收稿日期:2024-09-22
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国家重点研发计划(2022YFC3704000; 2022YFC3700400)
作者信息
    1.合肥综合性国家科学中心环境研究院,安徽 合肥 230088
    2.内蒙古自治区环境监测总站,内蒙古 呼和浩特 010011
    3.中国科学院合肥物质科学研究院安徽光学精密机械研究所环境光学与技术重点实验室,安徽 合肥 230031
    4.安徽大学物质科学与信息技术研究院,安徽 合肥 230601

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