Article(id=1314917499678851963, 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=1732464000000, receivedDateStr=2024-11-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430361908, onlineDateStr=2026-10-08, pubDate=1752940800000, pubDateStr=2025-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430361908, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430361908, creator=13701087609, updateTime=1791430361908, 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=3622, endPage=3633, ext={EN=ArticleExt(id=1314917499943093116, articleId=1314917499678851963, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Inversion of atmospheric CO₂ emissions in Yangtze River Delta based on WRF-STILT model, columnId=1234106386020365051, journalTitle=China Environmental Science, columnName=Air Pollution Control, runingTitle=null, highlight=null, articleAbstract=

Accurate estimation of CO₂ emissions at the regional scale, particularly in large urban agglomerations, is essential for effective greenhouse gas mitigation. In this study, the WRF-STILT model, combined with three prior anthropogenic CO₂ emission inventories (EDGAR v6.0, an improved version of EDGAR v6.0 integrated with GCG v1.0, and the ODIAC inventory) was used to simulate atmospheric CO₂ concentrations in the Yangtze River Delta during winter 2018. Observational data from a 70-meter tower in Quanjiao, Anhui province, served as the reference. A scale factor Bayesian inversion method was applied to optimize the simulation results, allowing estimation of anthropogenic CO₂ emission fluxes in the region. The results indicate that the CO₂ concentrations simulated by the WRF-STILT model effectively reflect the emission characteristics of the Yangtze River Delta. In winter, simulations based on the improved inventory aligned more closely with observations than those using EDGAR v6.0alone. The posterior CO₂ emission fluxes estimated using the EDGAR and improved inventories were (0.199 ± 0.005) and (0.200 ± 0.007) mg/(m²·s), respectively—representing decreases of 0.02 and 0.01mg/(m²·s) compared to the prior flux estimates. The Bayesian inversion method significantly optimized the prior emissions from the EDGAR inventory. When calculating total CO₂ emissions for the region using the improved inventory, power generation and industrial sectors were identified as the primary sources of uncertainty. Additionally, due to the low nocturnal boundary layer height, the model tended to overestimate the contribution of elevated emission sources near the observation site. Future simulations should ensure the WRF model accurately captures hourly boundary layer heights, and that vertical emission profiles of point sources are incorporated into emission inventories.

, authors=Ying YANG^{1, 2}, Xin-yi MA³, Wen-jing HUANG³, Cheng HU⁴, Ning HU², Mi ZHANG¹, Chang CAO¹, Yi-bo LIU¹, Wei XIAO^{2, *}, authorsList=Ying YANG, Xin-yi MA, Wen-jing HUANG, Cheng HU, Ning HU, Mi ZHANG, Chang CAO, Yi-bo LIU, Wei XIAO, authorCompany=null, correspAuthors=Wei XIAO, 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=1314917500253471613, articleId=1314917499678851963, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于WRF-STILT模式的长三角大气CO₂排放反演, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

准确估算区域尺度特别是大城市群的CO₂排放对温室气体减排工作至关重要，利用WRF-STILT模式结合三种先验人为CO₂排放清单（EDGAR v6.0、EDGAR v6.0与GCG v1.0相结合的改进清单、ODIAC清单）模拟2018年冬季长三角地区大气CO₂浓度，并以安徽全椒70m高塔的大气CO₂浓度观测数据作为参考值，通过比例因子贝叶斯反演的方法对模拟结果进行优化，实现了长三角区域人为CO₂排放通量的估算．结果表明：WRF-STILT模式模拟的CO₂浓度能够较好地显示长三角的CO₂排放特征．冬季，改进清单模拟的CO₂浓度值较仅使用EDGAR v6.0模拟的CO₂浓度值更接近于观测值；基于EDGAR清单和改进清单估算的后验CO₂排放通量分别为（0.199±0.005）和（0.200±0.007）mg/（m²·s），相较于这两个清单的先验CO₂排放通量，后验排放通量分别下降了0.02和0.01mg/（m²·s），比例因子贝叶斯反演法对基于EDGAR清单先验排放的优化幅度较大，用改进清单计算长三角CO₂排放总量时电力与工业排放是不确定性的最大来源；夜晚边界层高度较低，模型在模拟时将边界层外的排放计算进来导致模拟值的高估．在未来进行模拟时首先应确保WRF模型模拟的夜晚小时边界层高度是准确的，其次排放清单产品在制作过程中还应考虑垂直方向上不同排放源的高度信息．

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杨樱（1998-），女，山西太原人，南京信息工程大学硕士研究生，研究方向为城市温室气体观测与反演．发表论文2篇．Yyange1@163.com.

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杨樱（1998-），女，山西太原人，南京信息工程大学硕士研究生，研究方向为城市温室气体观测与反演．发表论文2篇．Yyange1@163.com.

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