Article(id=1314917549981135362, 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=1733846400000, receivedDateStr=2024-12-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430373901, onlineDateStr=2026-10-08, pubDate=1752940800000, pubDateStr=2025-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430373901, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430373901, creator=13701087609, updateTime=1791430373901, 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=3935, endPage=3943, ext={EN=ArticleExt(id=1314917550186656259, articleId=1314917549981135362, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=The temporal and spatiotemporal variation of CO2 concentration in river and groundwater in typical agricultural watersheds, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

From October 2023 to September 2024, regular monitoring of water quality parameters and CO2 concentrations in river water and groundwater was conducted in the upstream watershed of the Nantiao River in Lin'an, to quantitatively investigate their spatiotemporal variability. Results showed that both riverine and groundwater CO2 concentrations exhibited pronounced temporal fluctuations. On a monthly scale, peak CO2 concentrations in both river water and groundwater occurred in July, whereas the minima were temporally asynchronous, observed in December for rivers and January for groundwater, respectively. And seasonally, CO2 concentrations in both river water and groundwater followed the order:summer > spring > autumn > winter. Overall, groundwater CO2 concentrations averaged (152.04 ± 35.15) μmol/L, approximately 4.57 times higher than those in river water (33.24 ± 6.19μmol/L), indicating that groundwater may represent a significant potential source of aquatic CO2 emissions in this region. CO2 levels were markedly elevated in agricultural zones (river:68.47μmol/L; groundwater: 306.09μmol/L) compared to forested areas (river: 20.64μmol/L; groundwater: 73.93μmol/L). Significant positive correlations (P < 0.01) were observed between CO2 concentrations and agricultural-related water quality indicators (e.g., TN, TP, DOC), whereas negative correlations were found with DO, pH, and water temperature. Multiple linear regression revealed that the optimal spatial scales of land-use type combination influence on CO2 concentrations were 750m for river water (R2 = 0.88) and 1250m for groundwater (R2 = 0.58). Within these spatial buffers, riverine CO2 concentrations were most strongly associated with residential land cover (r = 0.90, P < 0.01), while groundwater CO2 was more influenced by cropland proportion (r = 0.83, P < 0.05). These findings suggest that intensive agricultural activities may significantly contribute to CO2 enrichment in groundwater, which in turn serves as an important source of aquatic CO2 emissions in the upstream watershed of the Nantiao River in Lin'an.

, authors=Tao-min MU1, Kai-yue MA1, Peng RUAN1, Xin-rui TAN1, Jian-hong WU1, 2, Fang-fang ZHANG1, Sheng-jia HE1, 2, *, Pei-kun JIANG1, 2, authorsList=Tao-min MU, Kai-yue MA, Peng RUAN, Xin-rui TAN, Jian-hong WU, Fang-fang ZHANG, Sheng-jia HE, Pei-kun JIANG, authorCompany=null, correspAuthors=Sheng-jia HE, 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=1314917550555755012, articleId=1314917549981135362, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=典型农业流域河流和地下水CO2浓度时空变异, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

于2023年10月~2024年9月在临安南苕溪上游流域定期开展常规水质及河流和地下水CO2浓度监测,定量研究该地区河流水体、地下水CO2浓度时空变异特征.结果表明:河流水体和地下水CO2浓度均呈现显著的时间变化.在月尺度上,两者的峰值均出现于7月,但其最小值并不同步,分别出现在12月和1月;就季节尺度而言,河流水体和地下水CO2浓度由高到低依次为:夏季 > 春季 > 秋季 > 冬季.整体来看,地下水CO2浓度均值高达(152.04±35.15)μmol/L,是河流浓度均值(33.24±6.19)μmol/L)的4.57倍,表明地下水是该地区水体CO2排放一个重要的潜在“源”.农业活动密集区水体CO2浓度(河流68.47μmol/L,地下水306.09μmol/L)与林地区域(河流20.64μmol/L,地下水73.93μmol/L)相比普遍较高,且河流和地下水CO2浓度与农业相关水质指标(如TN、TP、DOC)均呈显著正相关(P<0.01),而与DO、pH值和WT呈显著负相关(P<0.01).基于多元线性回归方法,得到南苕溪上游流域不同土地利类型组合(面积占比)对该地区河流水体、地下水CO2浓度的最佳影响半径分别为750m(R2 = 0.88)和1250m(R2 = 0.58).相关性分析结果表明,尽管在最佳影响半径内河流水体CO2浓度与人居地相关性最强(r = 0.90,P<0.01),但地下水CO2浓度变化更易受耕地占比的影响(r = 0.83,P<0.05).由此推断集约化农业生产可能是导致农业流域地下水CO2的“富集”的一个重要原因,而CO2浓度较高的地下水将成为临安南苕溪上游流域水体CO2排放的一个重要来源.

, authors=穆涛敏1, 马凯月1, 阮鹏1, 谈欣睿1, 邬建红1, 2, 张方方1, 何圣嘉1, 2, *, 姜培坤1, 2, authorsList=穆涛敏, 马凯月, 阮鹏, 谈欣睿, 邬建红, 张方方, 何圣嘉, 姜培坤, authorCompany=null, correspAuthors=何圣嘉, authorNote=

穆涛敏(1998-),女,贵州遵义人,浙江农林大学硕士研究生,研究方向为农业非点源污染及气候变化对水文水质过程影响.发表论文1篇..

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* 责任作者,副教授,
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穆涛敏(1998-),女,贵州遵义人,浙江农林大学硕士研究生,研究方向为农业非点源污染及气候变化对水文水质过程影响.发表论文1篇..

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穆涛敏(1998-),女,贵州遵义人,浙江农林大学硕士研究生,研究方向为农业非点源污染及气候变化对水文水质过程影响.发表论文1篇..

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典型农业流域河流和地下水CO2浓度时空变异
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穆涛敏 1 , 马凯月 1 , 阮鹏 1 , 谈欣睿 1 , 邬建红 1, 2 , 张方方 1 , 何圣嘉 1, 2, * , 姜培坤 1, 2
中国环境科学 | 环境生态 2025,45(7): 3935-3943
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中国环境科学 |环境生态 2025 , 45 (7) : 3935 -3943
典型农业流域河流和地下水CO2浓度时空变异
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穆涛敏(1998-),女,贵州遵义人,浙江农林大学硕士研究生,研究方向为农业非点源污染及气候变化对水文水质过程影响.发表论文1篇..

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穆涛敏(1998-),女,贵州遵义人,浙江农林大学硕士研究生,研究方向为农业非点源污染及气候变化对水文水质过程影响.发表论文1篇..

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穆涛敏1 , 马凯月1, 阮鹏1, 谈欣睿1, 邬建红1, 2, 张方方1, 何圣嘉1, 2, * , 姜培坤1, 2
作者信息
  • 1.浙江农林大学环境与资源学院,碳中和学院,浙江 杭州 311300
  • 2.浙江农林大学,省部共建亚热带森林培育国家重点实验室,浙江 杭州 311300
通讯作者:
* 责任作者,副教授,
作者简介:

穆涛敏(1998-),女,贵州遵义人,浙江农林大学硕士研究生,研究方向为农业非点源污染及气候变化对水文水质过程影响.发表论文1篇..

The temporal and spatiotemporal variation of CO2 concentration in river and groundwater in typical agricultural watersheds
Tao-min MU1 , Kai-yue MA1, Peng RUAN1, Xin-rui TAN1, Jian-hong WU1, 2, Fang-fang ZHANG1, Sheng-jia HE1, 2, * , Pei-kun JIANG1, 2
Affiliations
  • 1.Faculty of Environment and Resources, Faculty of Carbon Neutrality, Zhejiang A & F University, Hangzhou 311300, China
  • 2.State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou 311300, China
出版时间: 2025-07-20
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于2023年10月~2024年9月在临安南苕溪上游流域定期开展常规水质及河流和地下水CO2浓度监测,定量研究该地区河流水体、地下水CO2浓度时空变异特征.结果表明:河流水体和地下水CO2浓度均呈现显著的时间变化.在月尺度上,两者的峰值均出现于7月,但其最小值并不同步,分别出现在12月和1月;就季节尺度而言,河流水体和地下水CO2浓度由高到低依次为:夏季 > 春季 > 秋季 > 冬季.整体来看,地下水CO2浓度均值高达(152.04±35.15)μmol/L,是河流浓度均值(33.24±6.19)μmol/L)的4.57倍,表明地下水是该地区水体CO2排放一个重要的潜在“源”.农业活动密集区水体CO2浓度(河流68.47μmol/L,地下水306.09μmol/L)与林地区域(河流20.64μmol/L,地下水73.93μmol/L)相比普遍较高,且河流和地下水CO2浓度与农业相关水质指标(如TN、TP、DOC)均呈显著正相关(P<0.01),而与DO、pH值和WT呈显著负相关(P<0.01).基于多元线性回归方法,得到南苕溪上游流域不同土地利类型组合(面积占比)对该地区河流水体、地下水CO2浓度的最佳影响半径分别为750m(R2 = 0.88)和1250m(R2 = 0.58).相关性分析结果表明,尽管在最佳影响半径内河流水体CO2浓度与人居地相关性最强(r = 0.90,P<0.01),但地下水CO2浓度变化更易受耕地占比的影响(r = 0.83,P<0.05).由此推断集约化农业生产可能是导致农业流域地下水CO2的“富集”的一个重要原因,而CO2浓度较高的地下水将成为临安南苕溪上游流域水体CO2排放的一个重要来源.

河流  /  地下水  /  CO2溶存浓度  /  时空变异  /  土地利用

From October 2023 to September 2024, regular monitoring of water quality parameters and CO2 concentrations in river water and groundwater was conducted in the upstream watershed of the Nantiao River in Lin'an, to quantitatively investigate their spatiotemporal variability. Results showed that both riverine and groundwater CO2 concentrations exhibited pronounced temporal fluctuations. On a monthly scale, peak CO2 concentrations in both river water and groundwater occurred in July, whereas the minima were temporally asynchronous, observed in December for rivers and January for groundwater, respectively. And seasonally, CO2 concentrations in both river water and groundwater followed the order:summer > spring > autumn > winter. Overall, groundwater CO2 concentrations averaged (152.04 ± 35.15) μmol/L, approximately 4.57 times higher than those in river water (33.24 ± 6.19μmol/L), indicating that groundwater may represent a significant potential source of aquatic CO2 emissions in this region. CO2 levels were markedly elevated in agricultural zones (river:68.47μmol/L; groundwater: 306.09μmol/L) compared to forested areas (river: 20.64μmol/L; groundwater: 73.93μmol/L). Significant positive correlations (P < 0.01) were observed between CO2 concentrations and agricultural-related water quality indicators (e.g., TN, TP, DOC), whereas negative correlations were found with DO, pH, and water temperature. Multiple linear regression revealed that the optimal spatial scales of land-use type combination influence on CO2 concentrations were 750m for river water (R2 = 0.88) and 1250m for groundwater (R2 = 0.58). Within these spatial buffers, riverine CO2 concentrations were most strongly associated with residential land cover (r = 0.90, P < 0.01), while groundwater CO2 was more influenced by cropland proportion (r = 0.83, P < 0.05). These findings suggest that intensive agricultural activities may significantly contribute to CO2 enrichment in groundwater, which in turn serves as an important source of aquatic CO2 emissions in the upstream watershed of the Nantiao River in Lin'an.

rivers  /  groundwater  /  CO2 dissolved concentration  /  spatio-temporal variation  /  land use
穆涛敏, 马凯月, 阮鹏, 谈欣睿, 邬建红, 张方方, 何圣嘉, 姜培坤. 典型农业流域河流和地下水CO2浓度时空变异. 中国环境科学, 2025 , 45 (7) : 3935 -3943 .
Tao-min MU, Kai-yue MA, Peng RUAN, Xin-rui TAN, Jian-hong WU, Fang-fang ZHANG, Sheng-jia HE, Pei-kun JIANG. The temporal and spatiotemporal variation of CO2 concentration in river and groundwater in typical agricultural watersheds[J]. China Environmental Science, 2025 , 45 (7) : 3935 -3943 .
  • 国家自然科学基金项目(42277045; 42201122)
  • 浙江农林大学科研发展基金项目(2024LFR075)
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  • 接收时间:2024-12-11
  • 首发时间:2026-10-08
  • 出版时间:2025-07-20
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  • 收稿日期:2024-12-11
基金
国家自然科学基金项目(42277045; 42201122)
浙江农林大学科研发展基金项目(2024LFR075)
作者信息
    1.浙江农林大学环境与资源学院,碳中和学院,浙江 杭州 311300
    2.浙江农林大学,省部共建亚热带森林培育国家重点实验室,浙江 杭州 311300

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* 责任作者,副教授,
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