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To address the challenge of partial missing data in atmospheric CO₂ concentration observations, this study systematically evaluated the performance of 12 mainstream machine learning algorithms for data imputation, utilizing CO₂ measurement data collected at the 30m (L1 layer) and 80m (L2 layer) heights of the Longfengshan Regional Atmospheric Background Station over the period from January 2009 to June 2022. An optimized ensemble machine learning method was proposed, with the distance-weighted K-Nearest Neighbors (KNeighborsDist) algorithm as its core framework. This method reconstructs missing data by leveraging synchronous observational records from the two co-located height layers. Validation analysis of the reconstructed dataset yielded excellent performance (R²≥0.98), which confirms a strong correlation between the reconstructed greenhouse gas data and in-situ measurements, as well as the effectiveness and accuracy of the proposed model in greenhouse gas data reconstruction tasks. The method successfully filled the data gaps in the original dataset of Longfengshan Station, generating a continuous 5minute interval CO₂ dataset with a completeness rate of 100%. Additionally, it well preserved the long-term trends and seasonal, monthly, and diurnal variation characteristics of CO₂. The proposed AI-based data reconstruction algorithm is applicable to other atmospheric background stations and can provide high-quality data support for regional climate change analysis, carbon emission accounting, and inversion research.

, authors=Tao-feng GU¹, Hai-yan YUE², Zi-li XIONG¹, Jian-ping LIANG³, Yan-ling WU⁴, Li-xin LIU^{5, *}, authorsList=Tao-feng GU, Hai-yan YUE, Zi-li XIONG, Jian-ping LIANG, Yan-ling WU, Li-xin LIU, authorCompany=null, correspAuthors=Li-xin LIU, 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=1314923010688115019, articleId=1314923009991860553, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于AI的大气二氧化碳数据填补与重构算法研究, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

针对大气CO₂浓度部分观测数据缺失的挑战，利用龙凤山区域大气本底站（简称龙凤山站）2009年1月~2022年6月30m（L1层）与80m（L2层）高度层的CO₂浓度观测数据，评估12种主流机器学习算法在数据填补中的性能，提出以K近邻-距离加权（KNeighborsDist）算法为核心框架的优化组合机器学习方法．该方法利用同址不同高度站点均有数据记录重建了缺失数据．经对重建数据集数据测试分析验证，数据集重建效果优异（R²≥0.98），充分表明使用测试数据集重建的温室气体数据与实际测量数据之间具有良好的相关性，验证了所构建模型在温室气体数据重建任务上的有效性和准确性．利用该方法成功填补了龙凤山站原始数据缺失部分，构建了逐5min连续的CO₂数据集，填补后，龙凤山站数据完整率达到100%，填补方法在保留CO₂长期趋势/季节/月/日变化规律上表现良好．本研究提出的AI数据重构算法可在其他本底站应用推广，并可为区域气候变化特征分析、碳排放核算评估及反演研究提供高质量数据支持．

, authors=顾桃峰¹, 岳海燕², 熊梓立¹, 梁建平³, 吴艳玲⁴, 刘立新^{5, *}, authorsList=顾桃峰, 岳海燕, 熊梓立, 梁建平, 吴艳玲, 刘立新, authorCompany=null, correspAuthors=刘立新, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=llIufuJPvhaNyTPyhGc3qg==, magXml=6WPOuijsJTJsc+09pNtMDA==, pdfUrl=null, pdf=e48sSasVv0US3U81hv7HTQ==, pdfFileSize=2904101, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=30igZdYR2/OjqsHohiIqKA==, mapNumber=null, fund=null)}, authors=[Author(id=1314923011321454940, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923009991860553, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=gutaofeng@gz.gov.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314923011396952414, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923009991860553, authorId=1314923011321454940, language=EN, stringName=Tao-feng GU, firstName=Tao-feng, middleName=null, lastName=GU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Guangzhou Meteorological Comprehensive Guarantee Center, Guangzhou 511430, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314923011468255583, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923009991860553, authorId=1314923011321454940, language=CN, stringName=顾桃峰, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.广州市气象综合保障中心，广东 广州 511430, bio={"content":"

顾桃峰（1982-），江苏苏州人，副研级工程师，硕士，主要从事气象探测技术、气象信息技术的研究．发表论文35篇．gutaofeng@gz.gov.cn.

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顾桃峰（1982-），江苏苏州人，副研级工程师，硕士，主要从事气象探测技术、气象信息技术的研究．发表论文35篇．gutaofeng@gz.gov.cn.

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