Article(id=1244308290260283893, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1244308288934883825, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-4193.2020.10.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1578499200000, receivedDateStr=2020-01-09, revisedDate=1581523200000, revisedDateStr=2020-02-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1774595814528, onlineDateStr=2026-03-27, pubDate=1603555200000, pubDateStr=2020-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1774595814528, onlineIssueDateStr=2026-03-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774595814528, creator=13701087609, updateTime=1774595814528, updator=13701087609, issue=Issue{id=1244308288934883825, tenantId=1146029695717560320, journalId=1149651085930835976, year='2020', volume='42', issue='10', pageStart='1', pageEnd='154', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1774595814212, creator=13701087609, updateTime=1774597088429, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1244313633455452997, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1244308288934883825, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1244313633455452998, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1244308288934883825, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=37, endPage=46, ext={EN=ArticleExt(id=1244308290562273786, articleId=1244308290260283893, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Influence of submarine groundwater discharge in the blue carbon budget of typical mangrove: A case study from the Zhenzhu Bay, Guangxi, columnId=1194652705852465724, journalTitle=Haiyang Xuebao, columnName=Article, runingTitle=null, highlight=null, articleAbstract=

As one of the forms of land-ocean interactions, submarine groundwater discharge (SGD) can release solutes into the coastal sea and has a significant impact on the nutrients budget in coastal seawater. Here, using 222Rn tracer, the SGD and the associated dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) inputted to the Zhenzhu Bay, a typical mangrove-dominated bay, were quantified. The results show that the average concentrations of 222Rn, DIC and DOC in groundwater are relatively higher than those in river water and surface sea water. A 222Rn mass balance implies that SGD rate is (0.36±0.36) m/d during January 2019. And SGD-derived DIC and DOC fluxes are estimated to be (2.41±2.63)×107 mol/d and (1.96±2.20)×106 mol/d. It confirmed that SGD-derived carbon is the most important carbon source in this bay, with 91% DIC and 89% DOC of the total input fluxes by SGD, respectively. Our results highlight the importance of groundwater-derived carbon fluxes in the Zhenzhu Bay, especially in the blue carbon assessments and biogeochemical process in tidal zones such as mangrove ecosystems.

, correspAuthors=Fenfen Zhang, authorNote=null, correspAuthorsNote=null, copyrightStatement=Haiyang Xuebao, 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=Yali Wang, Fenfen Zhang, Xiaogang Chen, Linwei Li, Xilong Wang, Yanling Lao, Jinzhou Du), CN=ArticleExt(id=1244308293271794205, articleId=1244308290260283893, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=海底地下水排放对典型红树林蓝碳收支的影响, columnId=1149698756456657529, journalTitle=海洋学报, columnName=论文, runingTitle=null, highlight=null, articleAbstract=

海底地下水排放(Submarine Groundwater Discharge,SGD)是陆海相互作用的重要表现形式之一,其携带的物质对近岸海域生源要素的收支有重要影响。本文利用222Rn示踪技术估算了我国典型红树林海湾—广西珍珠湾在2019年枯季(1月)SGD携带的碳通量。调查发现,地下水中222Rn活度、溶解无机碳(DIC)和溶解有机碳(DOC)的平均浓度均高于河水和湾内表层海水。利用222Rn质量平衡模型估算得到珍珠湾SGD速率为(0.36±0.36) m/d,SGD输入到珍珠湾的DIC和DOC通量分别为(2.41±2.63)×107 mol/d和(1.96±2.20)×106 mol/d。珍珠湾溶解碳的源汇收支表明,SGD携带的DIC和DOC分别占珍珠湾总DIC和总DOC来源的91%和89%。因此,SGD携带的DIC和DOC是珍珠湾DIC和DOC的主要来源,是海岸带蓝碳收支和生物地球化学循环过程中的重要组成。

, correspAuthors=张芬芬, authorNote=null, correspAuthorsNote=
*张芬芬,副教授,主要从事碳循环、海洋生物地球化学研究。E-mail:
, copyrightStatement=版权所有©《海洋学报》编辑部 2023, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=8/9Lilvx32B0pIAF9uB4dQ==, magXml=r6tL4q/pnmPexHls0+0vig==, pdfUrl=null, pdf=q/h7Mpks2RsbliIaTg7ZQA==, pdfFileSize=8239355, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=pz5AoDS//T3nqbRULuoxcQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=JkVoibbDullBKuPbNakzSg==, mapNumber=null, authorCompany=null, fund=null, authors=

王亚丽(1994-),女,河南省长葛市人,主要从事近海生物地球化学研究。E-mail:

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王亚丽(1994-),女,河南省长葛市人,主要从事近海生物地球化学研究。E-mail:

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王亚丽(1994-),女,河南省长葛市人,主要从事近海生物地球化学研究。E-mail:

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Scientia Sinica: Terrae, 2015, 45(11): 1641−1648., articleTitle=null, refAbstract=null)], funds=[Fund(id=1246523616091857275, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, awardId=null, language=CN, fundingSource=广西科技计划项目(2017AB43024);国家自然科学基金项目(41976041);广西高校中青年教师基础能力提升项目(2018KY0616)。, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1246523607376093235, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, xref=1, ext=[AuthorCompanyExt(id=1246523607388676149, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607376093235, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China), AuthorCompanyExt(id=1246523607397064758, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607376093235, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 华东师范大学 河口海岸学国家重点实验室,上海 200241)]), AuthorCompany(id=1246523607489339448, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, xref=2, ext=[AuthorCompanyExt(id=1246523607606779962, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607489339448, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China), AuthorCompanyExt(id=1246523607619362876, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607489339448, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 北部湾大学 广西北部湾海洋灾害研究重点实验室,广西 钦州 535011)]), AuthorCompany(id=1246523607732609089, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, xref=3, ext=[AuthorCompanyExt(id=1246523607753580611, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607732609089, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 College of Resource and Environment, Beibu Gulf University, Qinzhou 535011, China), AuthorCompanyExt(id=1246523607761969221, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607732609089, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 北部湾大学 资源与环境学院,广西 钦州 535011)]), AuthorCompany(id=1246523607858438219, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, xref=4, ext=[AuthorCompanyExt(id=1246523607866826829, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607858438219, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4 Institute of Eco-Chongming, Shanghai 202162, China), AuthorCompanyExt(id=1246523607871021136, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, companyId=1246523607858438219, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4 崇明生态研究院,上海 202162)])], figs=[ArticleFig(id=1246523612971295008, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Fig. 1, caption=Study area (a), sampling stations (b), and the continuous monitoring system in the field (c), figureFileSmall=rQOh7Et4ArBxGWlO4PvNxA==, figureFileBig=GsJ34VhSSk0gOISxPN/BwQ==, tableContent=null), ArticleFig(id=1246523613059375399, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=图1, caption=研究区域(a),采样站位(b)和连续观测站现场图(c), figureFileSmall=rQOh7Et4ArBxGWlO4PvNxA==, figureFileBig=GsJ34VhSSk0gOISxPN/BwQ==, tableContent=null), ArticleFig(id=1246523613168427311, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Fig. 2, caption=The conceptual model of the 222Rn mass balance used to estimate submarine groundwater discharge in coastal zones

Red arrows: sources, black arrows: sinks; modified from Burnett and Dulaiova[28]

, figureFileSmall=TsgsFo01PaUHYdVeDlD+Sw==, figureFileBig=owpxh3J13dR73RyBS2WPnQ==, tableContent=null), ArticleFig(id=1246523613306839352, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=图2, caption=沿海地区SGD通量的222Rn质量平衡模型

红色箭头代表源项,黑色箭头代表汇项;修改自Burnett和Dulaiova[28]

, figureFileSmall=TsgsFo01PaUHYdVeDlD+Sw==, figureFileBig=owpxh3J13dR73RyBS2WPnQ==, tableContent=null), ArticleFig(id=1246523614850343229, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Fig. 3, caption=Temporal variation of 222Rn activities, temperature, salinity, DIC and DOC concentrations versus water depth during the time series observation, figureFileSmall=QGSflNaPdb6JOpqyphusZw==, figureFileBig=x3cTQyKh48u6U4vOW61qTA==, tableContent=null), ArticleFig(id=1246523614930035009, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=图3, caption=连续观测期间222Rn活度、温度、盐度、DIC和DOC浓度及水深随时间变化, figureFileSmall=QGSflNaPdb6JOpqyphusZw==, figureFileBig=x3cTQyKh48u6U4vOW61qTA==, tableContent=null), ArticleFig(id=1246523615030698312, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Fig. 4, caption=Net 222Rn flux (rectangles) and mixing loss of 222Rn (dotted line) versus time based on continuous 222Rn observation, figureFileSmall=/W/6lVgIBSzmqDINiTXrAQ==, figureFileBig=5FdsqNpYPGgrRHdau6qjpw==, tableContent=null), ArticleFig(id=1246523615127167312, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=图4, caption=连续观测期间222Rn的净通量Fnet(灰色柱状图)和混合损失Fmix(蓝色虚线)随时间变化, figureFileSmall=/W/6lVgIBSzmqDINiTXrAQ==, figureFileBig=5FdsqNpYPGgrRHdau6qjpw==, tableContent=null), ArticleFig(id=1246523615211053396, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Fig. 5, caption=DIC (a) and DOC (b) budgets in the Zhenzhu Bay, figureFileSmall=/DlFCsbUdaoXz/ts6dG37A==, figureFileBig=5tjN+CUEgwTSdekpioTVrQ==, tableContent=null), ArticleFig(id=1246523615286550876, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=图5, caption=珍珠湾DIC(a)和DOC(b)收支, figureFileSmall=/DlFCsbUdaoXz/ts6dG37A==, figureFileBig=5tjN+CUEgwTSdekpioTVrQ==, tableContent=null), ArticleFig(id=1246523615395602785, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Table 1, caption=

The salinity, 222Rn activities, DIC and DOC concentrations in groundwater and river water collected along the coast of the Zhenzhu Bay

, figureFileSmall=null, figureFileBig=null, tableContent=
站位经纬度盐度水深/m离海距离/m222Rn/Bq·m−3DOC/mol·m−3DIC/mol·m−3
  注:−表示无数据。
PW121.617 8°N,108.252 8°E27.00.501 083±1750.161.50
PW221.584 4°N,108.137 8°E28.80.501 064±1690.191.23
PW321.559 2°N,108.140 0°E19.30.602 410±2560.060.82
PW421.053 2°N,108.186 4°E1.003 565±2920.082.37
GW21.508 6°N,108.221 4°E0.31.51008 050±4720.081.02
RW21.594 2°N,108.221 4°E0.6640±1220.080.13
), ArticleFig(id=1246523615504654696, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=表1, caption=

珍珠湾沿岸地下水和河水的盐度,222Rn活度,DIC和DOC浓度

, figureFileSmall=null, figureFileBig=null, tableContent=
站位经纬度盐度水深/m离海距离/m222Rn/Bq·m−3DOC/mol·m−3DIC/mol·m−3
  注:−表示无数据。
PW121.617 8°N,108.252 8°E27.00.501 083±1750.161.50
PW221.584 4°N,108.137 8°E28.80.501 064±1690.191.23
PW321.559 2°N,108.140 0°E19.30.602 410±2560.060.82
PW421.053 2°N,108.186 4°E1.003 565±2920.082.37
GW21.508 6°N,108.221 4°E0.31.51008 050±4720.081.02
RW21.594 2°N,108.221 4°E0.6640±1220.080.13
), ArticleFig(id=1246523615588540778, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Table 2, caption=

The sources and sinks of 222Rn in the Zhenzhu Bay during January 2019

, figureFileSmall=null, figureFileBig=null, tableContent=
222Rn通量/Bq·m−2·h−1各项贡献
源项
河流输入0.24±0.040.30%
涨潮输入29.52±14.0937.00%
溶解226Ra贡献0.09±0.050.11%
底部沉积物扩散0.76±0.010.95%
SGD输入49.16±49.0961.63%
汇项
退潮输出38.83±21.9143.27%
大气逃逸1.32±0.791.47%
222Rn衰变损失0.02±0.010.02%
混合损失49.57±37.3955.24%
), ArticleFig(id=1246523615697592684, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=表2, caption=

2019年1月珍珠湾222Rn的源汇收支

, figureFileSmall=null, figureFileBig=null, tableContent=
222Rn通量/Bq·m−2·h−1各项贡献
源项
河流输入0.24±0.040.30%
涨潮输入29.52±14.0937.00%
溶解226Ra贡献0.09±0.050.11%
底部沉积物扩散0.76±0.010.95%
SGD输入49.16±49.0961.63%
汇项
退潮输出38.83±21.9143.27%
大气逃逸1.32±0.791.47%
222Rn衰变损失0.02±0.010.02%
混合损失49.57±37.3955.24%
), ArticleFig(id=1246523615806644591, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=EN, label=Table 3, caption=

SGD rates and associated DIC and DOC fluxes from previous study in typical mangroves ecosystems worldwide

, figureFileSmall=null, figureFileBig=null, tableContent=
研究区域SGD
/cm·d−1
SGD输送的DIC
通量/mol·m−2·d−1
SGD输送的DOC
通量/mol·m−2·d−1
  注:−表示无数据。
澳大利亚摩尔顿湾[7]0.250.024
澳大利亚华斯顿和加拿曼湾[16]6.7~270.13~0.450~0.025
澳大利亚摩尔顿
[14]
4.40.160.036
澳大利亚珊瑚溪[3]35.5
澳大利亚哈特角河口[15]470.690.54
中国广西茅尾海[8]20~360.25~0.700.25~0.31
越南芹椰县红树林
潮溪[40]
3.1~7.10.35~0.680.021~0.068
帕劳巴贝达奥普火山岛
红树林溪[41]
3.30.0790.035
6.20.010.008
中国广西珍珠湾360.500.04
), ArticleFig(id=1246523615903113587, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1244308290260283893, language=CN, label=表3, caption=

全球典型红树林生态系统SGD速率及其携带的DIC和DOC通量

, figureFileSmall=null, figureFileBig=null, tableContent=
研究区域SGD
/cm·d−1
SGD输送的DIC
通量/mol·m−2·d−1
SGD输送的DOC
通量/mol·m−2·d−1
  注:−表示无数据。
澳大利亚摩尔顿湾[7]0.250.024
澳大利亚华斯顿和加拿曼湾[16]6.7~270.13~0.450~0.025
澳大利亚摩尔顿
[14]
4.40.160.036
澳大利亚珊瑚溪[3]35.5
澳大利亚哈特角河口[15]470.690.54
中国广西茅尾海[8]20~360.25~0.700.25~0.31
越南芹椰县红树林
潮溪[40]
3.1~7.10.35~0.680.021~0.068
帕劳巴贝达奥普火山岛
红树林溪[41]
3.30.0790.035
6.20.010.008
中国广西珍珠湾360.500.04
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海底地下水排放对典型红树林蓝碳收支的影响
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王亚丽 1 , 张芬芬 1, * , 陈小刚 1 , 李林蔚 1 , 王希龙 2 , 劳燕玲 2, 3 , 杜金洲 1, 4
海洋学报 | 论文 2020,42(10): 37-46
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海洋学报 | 论文 2020, 42(10): 37-46
海底地下水排放对典型红树林蓝碳收支的影响
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王亚丽1 , 张芬芬1, * , 陈小刚1, 李林蔚1, 王希龙2, 劳燕玲2, 3, 杜金洲1, 4
作者信息
  • 1 华东师范大学 河口海岸学国家重点实验室,上海 200241
  • 2 北部湾大学 广西北部湾海洋灾害研究重点实验室,广西 钦州 535011
  • 3 北部湾大学 资源与环境学院,广西 钦州 535011
  • 4 崇明生态研究院,上海 202162
  • 王亚丽(1994-),女,河南省长葛市人,主要从事近海生物地球化学研究。E-mail:

通讯作者:

*张芬芬,副教授,主要从事碳循环、海洋生物地球化学研究。E-mail:
Influence of submarine groundwater discharge in the blue carbon budget of typical mangrove: A case study from the Zhenzhu Bay, Guangxi
Yali Wang1 , Fenfen Zhang1, * , Xiaogang Chen1, Linwei Li1, Xilong Wang2, Yanling Lao2, 3, Jinzhou Du1, 4
Affiliations
  • 1 State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China
  • 2 Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China
  • 3 College of Resource and Environment, Beibu Gulf University, Qinzhou 535011, China
  • 4 Institute of Eco-Chongming, Shanghai 202162, China
出版时间: 2020-10-25 doi: 10.3969/j.issn.0253-4193.2020.10.004
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海底地下水排放(Submarine Groundwater Discharge,SGD)是陆海相互作用的重要表现形式之一,其携带的物质对近岸海域生源要素的收支有重要影响。本文利用222Rn示踪技术估算了我国典型红树林海湾—广西珍珠湾在2019年枯季(1月)SGD携带的碳通量。调查发现,地下水中222Rn活度、溶解无机碳(DIC)和溶解有机碳(DOC)的平均浓度均高于河水和湾内表层海水。利用222Rn质量平衡模型估算得到珍珠湾SGD速率为(0.36±0.36) m/d,SGD输入到珍珠湾的DIC和DOC通量分别为(2.41±2.63)×107 mol/d和(1.96±2.20)×106 mol/d。珍珠湾溶解碳的源汇收支表明,SGD携带的DIC和DOC分别占珍珠湾总DIC和总DOC来源的91%和89%。因此,SGD携带的DIC和DOC是珍珠湾DIC和DOC的主要来源,是海岸带蓝碳收支和生物地球化学循环过程中的重要组成。

海底地下水排放  /  氡-222  /  红树林  /  碳收支  /  蓝碳

As one of the forms of land-ocean interactions, submarine groundwater discharge (SGD) can release solutes into the coastal sea and has a significant impact on the nutrients budget in coastal seawater. Here, using 222Rn tracer, the SGD and the associated dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) inputted to the Zhenzhu Bay, a typical mangrove-dominated bay, were quantified. The results show that the average concentrations of 222Rn, DIC and DOC in groundwater are relatively higher than those in river water and surface sea water. A 222Rn mass balance implies that SGD rate is (0.36±0.36) m/d during January 2019. And SGD-derived DIC and DOC fluxes are estimated to be (2.41±2.63)×107 mol/d and (1.96±2.20)×106 mol/d. It confirmed that SGD-derived carbon is the most important carbon source in this bay, with 91% DIC and 89% DOC of the total input fluxes by SGD, respectively. Our results highlight the importance of groundwater-derived carbon fluxes in the Zhenzhu Bay, especially in the blue carbon assessments and biogeochemical process in tidal zones such as mangrove ecosystems.

submarine groundwater discharge  /  222Rn  /  mangrove  /  carbon budget  /  blue carbon
王亚丽, 张芬芬, 陈小刚, 李林蔚, 王希龙, 劳燕玲, 杜金洲. 海底地下水排放对典型红树林蓝碳收支的影响. 海洋学报, 2020 , 42 (10) : 37 -46 . DOI: 10.3969/j.issn.0253-4193.2020.10.004
Yali Wang, Fenfen Zhang, Xiaogang Chen, Linwei Li, Xilong Wang, Yanling Lao, Jinzhou Du. Influence of submarine groundwater discharge in the blue carbon budget of typical mangrove: A case study from the Zhenzhu Bay, Guangxi[J]. Haiyang Xuebao, 2020 , 42 (10) : 37 -46 . DOI: 10.3969/j.issn.0253-4193.2020.10.004
“蓝碳”是指在植被丰富的沿海生态系统中固存的碳,尤其是海岸带的红树林、海草床和盐沼生态系统[1]。作为地球上最富碳的生态系统之一,红树林在沿海水域的生物地球化学过程中发挥着重要作用[2-3]。它可以从大气中吸收碳,将大量碳储存在地下,为碳从陆地到海洋的运输提供重要途径[4]。许多研究对红树林土壤的碳固存率进行了量化[5-6],当海水遇到富含有机物的潮间带沉积物时,土壤呼吸过程被加速,在波浪和潮汐等的作用下,红树林的土壤碳呼吸产物仍然容易通过海底地下水排放(Submarine Groundwater Discharge,SGD)或孔隙水交换输出到沿海水域[7-8]
SGD是指在不考虑水体成分和驱动力的情况下,通过陆海界面由海底排放到近岸海域的所有水流,它包括陆源淡水和再循环海水[9-10]。微生物作用、与沉积物成分反应等生物地球化学作用可以使溶解无机碳(Dissolved Inorganic Carbon,DIC)和溶解有机碳(Dissolved Organic Carbon,DOC)等在沿岸地下水中富集[9-13],在某些封闭或半封闭海湾,SGD携带的碳通量与沿岸河流输入相当,甚至可能超过河流[14-15],使SGD成为溶解碳由陆向海输出的主要途径之一,影响河口和海湾环境的碳循环[16-17]。红树林区域SGD输送的DIC和DOC通量是其生态系统蓝碳收支源项中重要但被忽视的组成部分,并且可能对红树林生态系统中蓝碳收支的汇项评估产生重要影响[7-8]。如Maher等[7]指出摩尔顿(Moreton)湾红树林系统中93%~99%的DIC和89%~92%的DOC输出是由SGD驱动。在广西茅尾海红树林海湾,SGD输送的DIC和DOC通量至少是沿岸河流输入的两倍,占茅尾海DIC和DOC总来源的70%以上[8]。相比于美国和澳大利亚[7, 12, 14-15],国内也开展了一些红树林SGD的研究,但主要集中在营养盐通量的工作,对于碳的关注较少[18-20]
广西珍珠湾湾内有着中国大陆海岸红树林连片面积最大的海湾红树林[21],有关SGD输出的碳通量还未见报道。本文基于222Rn的质量平衡模型,估算了SGD的速率及其携带的DIC和DOC通量,并对珍珠湾溶解碳的源汇收支进行评估,以期对典型红树林区域蓝碳收支循环提供基础的科学数据。
珍珠湾位于广西防城港市防城区(图1),海湾呈漏斗状,口门西起万尾岛的东沙头,东至江山半岛的白龙台,南部开口与北部湾相连,口门宽约3.5 km,海岸线长46 km,海湾面积94.2 km2。珍珠湾的潮汐类型以正规全日潮为主,多年平均潮差2.2 m,最大潮差5.1 m。珍珠湾属于热带海洋季风气候,年平均气温22.5℃,年平均降雨量为2 220 mm[22]。港湾主要有江平江注入,年平均径流量为7.67亿m3。珍珠湾是国家级红树林生态系统自然保护区北仑河口保护区的重要区域之一,湾内有着中国大陆海岸红树林连片面积最大的海湾红树林。该地区有红树林10.68 km2,占北仑河口保护区红树林面积的83.8%[21]
本研究选取珍珠湾内蚝排建筑作为表层海水中222Rn活度连续观测站(TS,21°33′28.5″N,108°09′29.6″E,图1c)。连续观测时间从2019年1月19日10:50到1月20日15:20,海水通过固定在水面下0.5 m左右的潜水泵不断泵入RAD-AQUA系统中,两个串联的RAD7探测器每30 min自动获取一次计数(图1c),以减小随机误差的影响。与此同时,温度、盐度等参数由放置在潜水泵附近的多参数水质测量仪(TROLL 600,Aqua)每15 min自动获取数据,水深和风速(DCFM8906,General Tools & Instruments)每小时手动测量。当水气平衡后,通过将气相中222Rn活度与水/空气分配系数α相乘得到海水中222Rn活度,计算过程如下:
${{C}}_{{t}}{=}{\alpha }{{C}}_{{g-t}}{,}$
式中,Cgtt时刻气相中222Rn活度,该值由RAD7仪器直接导出;Ctt时刻海水中222Rn活度(单位:Bq/m3),α222Rn在水/空气的分配系数,与盐度和温度有关[23],计算过程如下:
${\alpha}{=}{\beta }{×}{T}{/273.15}, $
式中,T表示水温(单位:K),β为Bunsen系数,它是温度和盐度的函数,计算过程详见Schubert等[23]文章中的方程(3)。
本研究共采集间隙水(PW)4个,井水(GW)和河水(RW)各1个(图1b),采样信息详见表1。用Push-point采样器在0.5~1.5 m深度通过溢流法将间隙水缓慢注入250 mL玻璃瓶中,防止222Rn逃逸到空气中[8, 20, 24]。井水和河水样品通过有机玻璃采水器采集,将装满水样的采水器底部的蠕动管插入氡瓶底部,同样用溢流法收集。采集于250 mL玻璃瓶中的222Rn样品通过RAD7测氡仪(Durridge)及其RAD AQUA配件完成分析。
222Rn连续监测期间每隔3 h采集水样50 L进行226Ra分析,将水样以小于1 L/min的流速通过锰纤维柱,使Ra充分富集到锰纤维上,用Milli-Q水洗去颗粒物和盐分,控制含水量约为75%[25],然后将锰纤维柱密封保存大于20 d,确保222Rn及其子体与226Ra达到平衡,利用RAD7进行测量[26]。为了提高测试结果的准确性,测量时间设置在10 h以上。
将低潮时在连续观测站点附近采集海底沉积物100 g和500 mL海水置于锥形瓶密封,培养30 d以确保沉积物间隙水中的222Rn和上覆水中的222Rn达到平衡[27],然后将上覆水转移到250 mL玻璃瓶并用RAD7测氡仪立刻进行测量,从而估算222Rn从沉积物向上覆水的扩散通量。
表层海水(每隔3 h一次),地下水和河水等均采集DIC和DOC样品,样品现场处理,冷冻保存后带回实验室分析。DIC样品通过UIC库伦仪测量,DOC样品通过总有机碳分析仪(TOC-VCPH,岛津)进行测定[8]
222Rn的质量平衡模型如图2所示,通过计算近岸水体中222Rn所有的源项和汇项得到222Rn的SGD通量。222Rn在水体中的质量平衡方程如下[20, 29](式中所有源汇项的单位均为Bq/(m2·h)):
${{F}}_{{{\rm{SGD}}}}{+}{{F}}_{{{\rm{riv}}}}{+}{{F}}_{{{\rm{in}}}}{+}{{F}}_{{226}}{+}{{F}}_{{{\rm{sed}}}}{-}{{F}}_{{{\rm{out}}}}{-}{{F}}_{{{\rm{atm}}}}{-}{{F}}_{{{\rm{dec}}}}{-}{{F}}_{{{\rm{mix}}}}{=}{\Delta F} ,$
式中,FrivFSGD分别表示河流和SGD的输入,F226Fsed分别表示溶解态226Ra和沉积物扩散的贡献,FinFout分别表示涨潮时外海水的输入和退潮时湾内海水的输出,FdecFatm分别表示自身放射性衰变和大气逃逸的损失,Fmix表示与近海低浓度水域混合损失,ΔF表示相邻两个时间段222Rn库的差异。
${\Delta F}{=}\frac{{{I}}_{{t}{+}{\Delta t}}{-}{{I}}_{{t}}}{{\Delta t}},$
式中,Δt是时间间隔(单位:h),I222Rn库存,定义为222Rn活度(单位:Bq/m3)和水深H(单位:m)的乘积。
${{I}}_{{t}}{=}A_{222}\left({t}\right){×}{H}\left({t}\right),$
式中,A222t)和Ht)分别表示t时刻222Rn活度和水深。
通过将FSGD除以地下水端元中222Rn活度,计算得到SGD速率(单位:m/d):
${Q}{=}\frac{{{F}}_{{{\rm{SGD}}}}}{{{C}}_{{{\rm{GW}}}}}.$
在我们的连续观测期间,表层海水的温度、盐度、水深和222Rn活度变化如图3a所示。其中,温度变化范围为15.2~17.4℃,由于太阳辐照,海水较高温度主要出现在12:00−16:00。盐度的变化范围为14.1~30.4,涨潮时盐度从14.1增长到30.4,退潮时盐度从30.4下降到17.7。222Rn的活度范围为50~403 Bq/m3(平均值:(151±84) Bq/m3),涨潮时活度降低,退潮时活度增加。表层海水中DIC和DOC浓度的变化范围分别为0.79~1.41 mol/m3和0.09~0.13 mol/m3222Rn活度和DOC浓度与水位呈负相关,表明明显受潮汐驱动的DOC浓度低的外海水与DOC浓度高的河水及SGD输入混合为主要因素。而DIC与水位趋势大致一致,表明可能存在其他来源,如海湾附近的贝类养殖以及可能存在碳酸盐岩基质,涨潮时被外海水带入湾内。在其他红树林生态系统的相关SGD中也观察到了相似的变化趋势[15, 30]
珍珠湾沿岸地下水和河水的各参数如表1所示。地下水的盐度变化范围为0.3~28.8,222Rn活度范围为1.06×103~8.05×103 Bq/m3,平均活度(3.23±2.89)×103 Bq/m3,地下水中222Rn活度显著高于表层海水和河水,表明SGD是珍珠湾222Rn的重要来源。地下中DIC和DOC的平均浓度分别为(1.39±0.60) mol/m3和(0.11±0.06) mol/m3,远高于河水中DIC(0.13 mol/m3)和DOC浓度(0.08 mol/m3)。
广西区域内河流的径流主要由降雨补给,径流分布变化与降雨分布变化基本一致,汛期(4−9月)径流量约占全年径流量的75%~85%[31]。江平江年平均径流量为7.67亿m3,与江平江最近的防城港市在采样季节降雨量约占全年降雨量的20%(http://tjj.gxzf.gov.cn/tjsj/tjnj/2019/indexch.htm),因此计算出采样期间河流径流量为9.73 m3/s。将河流流量与盐度接近零时河水中222Rn的活度相乘计算得到Friv为(0.24±0.04) Bq/(m2·h)。
海水中222Rn活度受潮汐强烈的影响,222Rn在落潮时随海水从湾内向外输送,反之,涨潮时随着海水进入湾内水体。由潮汐迁移控制的222Rn通量可由下式计算[29]
${{F}_{{\rm{in}}}}=\frac{{{H}_{t+\Delta t}}-{{H}_{t}}}{\Delta {{t}}}\left( {{b}}\overline{{{{C}}_{\rm{w}}}}+\left( 1-{{b}} \right){{{C}}_{\rm{off}}} \right),$
${{F}}_{{{\rm{out}}}}{=}\frac{{{H}}_{{t}}{-}{{H}}_{{t+}{\Delta }{t}}}{{\Delta }{{{t}}}}{{C}}_{{t}} ,$
式中,Ht+ΔtHt分别是ttt时刻的水深,$ \overline{{{{C}}_{\rm{w}}}} $Coff分别表示水柱中222Rn的平均活度和外海水中222Rn的活度,Ct是连续观测期间每个时间间隔下水体中222Rn的活度,b是回流因子,Δt是时间间隔。
根据一个潮周期内222Rn的连续观测,湾内水体中222Rn的平均活度为(151±84) Bq/m3。基于Moore等[32]的假设,b值可近似看作水体中外海水端元在研究区域内的贡献比例,可近似处理为表层海水盐度的平均值22.4除以外海水盐度30.4[33],因此b=0.74。以最高潮时水体中222Rn活度50 Bq/m3代表外海222Rn活度值,根据式(7)和式(8),FinFout分别为3.74~49.90 Bq/(m2·h)和1.96~75.54 Bq/(m2·h)。
222Rn是一种微溶于水的气体,当两相处于不平衡状态时,可以在水−空气界面上交换。大气逃逸通量(Fatm)可以由下式描述[34]
${{F}}_{{{\rm{atm}}}}{=}{k}{(}{{C}}_{{{\rm{w}}}}{-}{α}{{C}}_{{{\rm{air}}}}{)},$
式中,k是气体传输速度(单位:m/s),由示踪实验所得经验公式确定[34-35]CwCair分别是水体和空气中222Rn的活度。水−空气界面的扩散通量Fatm主要受气体输送速度k222Rn活度变化的影响。本次连续观测过程中风速均在1 m/s左右,当风速小于1.5 m/s时,k为常数0.91 cm/s[35],基于式(9),本研究中Fatm变化范围为0.49~3.18 Bq/(m2·h)。
222Rn通过沉积物–水界面的扩散通量(Fsed)可以由下式获得[27]
${{F}_{{\rm{sed}}}}={{(\textit{λ} {\rm{\varphi}}{{D}_{{\rm{m}}}})}^{0.5}}({{{C}}_{\rm{eq}}}-{{C}_{0}}),$
${{D}}_{\rm{m}}{=}{{10}}^{{-}\left(\frac{{980}}{T{+}{2}{73}}{+1.59}\right)} ,$
式中,λ222Rn的衰变常数(0.181 d−1),φ是海底沉积物孔隙率,Dm是分子扩散系数,Ceq是由平衡实验得到的海底沉积物孔隙水中222Rn的活度,C0是上覆水中222Rn的活度,其值为实验观测期间海水活度的实测值,T是水温(单位:°C)。海底沉积物的平均孔隙率φ是0.38,Dm变化范围为1.02×10−5~1.15×10−5 cm2/s,沉积物平衡实验结束时水体中222Rn的活度为(236±60) Bq/m3,计算得到沉积物孔隙水中222Rn的活度为(7 453±1 895) Bq/m3[36]。基于式(10)得到Fsed的变化范围是0.74~0.77 Bq/(m2·h)。
来自溶解226Ra支持的222Rn通量F226可由下式计算:
${{F}}_{{226}}{=}\lambda {{×}{C}}_{{{\rm{Ra}}-226}}{×}{H}.$
连续观测中226Ra活度变化为3.41~8.18 Bq/m3,因此226Ra贡献为0.04~0.18 Bq/(m2·h)。
根据衰变方程,222Rn衰变损失量可按下式计算:
${{F}_{{\rm{dec}}}}=\lambda \times \left[ {{C}_{{\rm{w}}}}\left( 1-{{\rm{e}}^{-{{\text λ}} \Delta {{t}}}} \right) \right]\times H.$
222Rn半衰期相比(3.83 d),由于测量时间间隔较小,通过计算Fdec变化范围也较小为8.47×10−3~3.07×10−2 Bq/(m2·h),通常可以忽略不计。
我们定义通过河流输入,潮汐迁移,大气逃逸,沉积物扩散,溶解态226Ra贡献和222Rn放射性衰变校正后剩余的222Rn为净222Rn通量(Fnet),它应由SGD通量(FSGD)和混合损失(Fmix)来平衡。我们将Fnet的最大负值(图4中所示的虚线)作为相邻时间间隔的混合损失通量Fmix[28, 35],这种情况下Fmix是保守估算的,因此,Fmix为5.93~150.44 Bq/(m2·h)。SGD贡献的222Rn通量为FnetFmix之和,保守估计通过SGD的222Rn通量为0~168 Bq/(m2·h)。
珍珠湾的222Rn质量平衡如表2所示,其中枯季222Rn源项中SGD输入、涨潮时输入、沉积物扩散、河流输入和母体226Ra贡献分别占总来源的61.63%、37.00%、0.95%、0.30%和0.11%;而汇项中混合损失、退潮时输出、大气逃逸和自身衰变损失分别占比55.24%、43.27%、1.47%和0.02%。
地下水端元的选取是地下水示踪研究的一个重要步骤,同时也是估算SGD速率不确定度的主要因素[37]。在先前的研究中,一些区域地下水排放以沉积物缓慢渗流为主导过程,间隙水中222Rn的活度作为端元有利于SGD的估算[29, 38];一些研究为保守估计,将离连续站较近的222Rn活度更高的井水作为端元[39];也有研究将离岸较近的浅层井水和潮间带间隙水均作为端元[8, 20]。本研究中,潮间带间隙水活度在1 064~3 565 Bq/m3之间,井水活度较高为8 050 Bq/m3。沿岸地下水中222Rn活度变化较大的原因可能与离海岸线的距离、取样深度、地质基质和高度动态含水层系统有关[20]。本文将离岸较近的井水和潮间带间隙水均作为地下水端元,通过式(6)计算得出珍珠湾SGD平均速率为(0.36±0.36) m/d。表3归纳了世界其他区域红树林生态系统SGD的相关研究数据,总体上来看,我们的计算结果在全球报道的范围之内(0.03~0.47 m/d),但处于较高的水平。在红树林生态系统中SGD主要是再循环海水主导[3, 42],再循环海水主要受潮汐和波浪的控制[43]。本研究采样期间潮差较大(约4 m)可能是珍珠湾SGD速率较高的原因之一,因较大的潮差可以在更宽的时空尺度上驱动海水进入海岸含水层中,该过程会产生一定水力梯度,进而驱动SGD及其相应的物质输送到近海海域[44]。此外,Santos等[45]发现SGD速率与连续观测期间222Rn的平均活度呈正相关,即连续观测期间222Rn的平均活度越高,SGD速率越大。珍珠湾连续观测期间222Rn平均活度为(151±84) Bq/m3,远高于其他红树林的观测结果,例如芹耶县(Can Gio)红树林小溪[40]((22±10) Bq/m3)和茅尾海[8]((43±26) Bq/m3)等,这也可能是珍珠湾SGD速率较大的原因。
为了评估SGD输送到珍珠湾的DIC和DOC通量,我们将SGD速率(或通量)和地下水端元中DIC和DOC浓度的平均值相乘来估算。进入海湾的SGD通量的大小为SGD速率和海湾的渗流面积的乘积。严格地说,在任何地方进行的单点观测结果都不能代表整个海湾[39]。由于观测站距海岸1.05 km,因此假定渗流区的宽度约为1.05 km,海岸线长度约为46 km,渗流面积约为48.3 km2,由此计算得SGD通量为1.74×107 m3/d,相当于河流总输入量的约21倍。地下水端元中DIC和DOC的浓度范围分别为0.82~2.37 mol/m3和0.06~0.19 mol/m3,经计算,SGD输送的DIC和DOC通量分别为1.39×107~4.12×107 mol/d和1.04×106~3.31×106 mol/d,平均值分别为(2.41±2.63)×107 mol/d[(0.50±0.54)mol/(m2·d)]和(1.96±2.20)×106 mol/d[(0.04±0.05)mol/(m2·d)]。从表3可以看出,珍珠湾SGD携带的DIC和DOC通量的大小在世界其他红树林生态系统报道的范围之内[DIC:0.01~0.70 mol/(m2·d);DOC:0.008~0.54 mol/(m2·d)]。与此同时,河流输入的DIC和DOC通量估算为1.10×105 mol/d和6.98×104 mol/d,SGD携带的DIC通量和DOC通量保守估计分别约是河流输入的126倍和14倍以上。类似现象在其他地区也有相关报道[8, 14]。如Stewart等[14]估算出SGD输入到莫雷顿湾的DIC和DOC通量分别是当地河流的20倍和38倍;Chen等[8]报道SGD输入茅尾海的DIC和DOC通量分别是当地河流的5~11倍和2~6倍。
根据广泛用于近岸生态系统中各生源要素收支平衡的箱式模型[46],珍珠湾DIC和DOC收支可表示如下[8, 47]
${{\rm{DIC}}:}{{I}}_{{{\rm{SGD}}}}{+}{{I}}_{{{\rm{riv}}}}{+}{{I}}_{{{\rm{diff}}}}{=}{{I}}_{{{\rm{mix}}}}{+}{{I}}_{{{\rm{sea\text{-}air}}}}{+}{{I}}_{{{\rm{bio}}}} ,$
${{\rm{DOC}}:}{{O}}_{{{\rm{SGD}}}}{+}{{O}}_{{{\rm{riv}}}}{+}{{O}}_{{{\rm{diff}}}}{+}{{O}}_{{{\rm{pro}}}}{=}{{O}}_{{{\rm{mix}}}}{+}{{O}}_{{{\rm{rem}}}},$
${{F}}_{{{\rm{mix}}}}{=(}{{C}}_{{{\rm{in}}}}{-}{{C}}_{{{\rm{out}}}}{)×}{V/}{T}_{{\rm{f}}} ,$
式中,ISGDIrivIdiff分别表示DIC源项中SGD、河流和沉积物扩散的输入,OSGDOrivOdif分别表示DOC源项中SGD河流和沉积物扩散的输入。Imix表示DIC汇项中混合损失的部分,Omix表示DOC汇项中混合损失的部分,Isea-airIbio分别是指DIC汇项中从地表水排放到大气的CO2和各生物过程对DIC的利用,OproOrem分别表示DOC源项中初级生产力的输入和DOC汇项中水生动物养殖、微生物降解和颗粒清除等,每一项单位均为mol/d。CinCout分别表示湾内和湾外海水中DIC和DOC的平均浓度(单位:mol/m3),V是珍珠湾水体体积(单位:m3),Tf为平均冲刷时间(单位:d)。
珍珠湾沿岸地下水中DIC浓度(0.82~2.37 mol/m3)在世界其他红树林研究区域报道的浓度范围之内(0.74~13.80 mol/m3[8],利用全球红树林沉积物DIC扩散速率的平均值49 mmol/(m2·d)[5]得到珍珠湾沉积物扩散的DIC通量为2.37×106 mol/d。地下水中DOC浓度与Oh等[17]报道的地下水中DOC浓度范围相似(0.10~0.18 mol/m3),因此利用其文章中的DOC扩散速率2.30 mmol/(m2·d)得到珍珠湾沉积物扩散的DOC通量为1.11×105 mol/d。沉积物扩散的通量分别仅占SGD输入DIC和DOC通量的10%和6%。北部湾北部近岸浅水区1月份的初级生产力为1.43 mmol/(m2·d)[48],假设所有初级生产力最终都转化为DOC,那么珍珠湾初级生产力产生的DOC通量为6.91×104 mol/d。
珍珠湾1月份DIC和DOC平均浓度分别为1.11 mol/m3和0.11 mol/m3,湾外海水DIC和DOC平均浓度分别为1.06 mol/m3[8]和0.09 mol/m3,珍珠湾水体体积为7.16×108 m3,根据经典的纳潮量法[49]估算得到平均冲刷时间7.53 d,因此,由式(16)可以得到珍珠湾枯季DIC和DOC混合损失分别为4.75×106 mol/d和1.90×106 mol/d。该区域表层海水向大气排放CO2速率为1.4~7.5 mmol/(m2·d),平均值为3.9 mmol/(m2·d)[50],因此,珍珠湾水域向大气排放CO2通量为1.88×105 mol/d。根据式(14)和(15),IbioOrem分别为2.17×107 mol/d和3.13×105 mol/d。
从珍珠湾DIC和DOC各来源中可以看出(图5),SGD输送的DIC和DOC分别占总DIC来源和总DOC来源的91%和89%,是珍珠湾DIC和DOC的主要来源,对近海碳库有重要贡献。海岸带系统的碳通过多界面过程交换、传输以及转化,从而决定海岸带系统的碳库收支[51]。Chen等[8]总结全球数据得到通过红树林间隙水输出的碳相当于河流排放的约30%,因此,这部分受潮汐等影响产生的碳交换是碳汇的重要但却易被忽视的组成部分,是蓝碳汇估算中的主要误差和挑战之一[52]。这部分碳作为近海海域最主要的有机和无机碳源,可能会对近海区域碳周转、碳埋藏速率的评估产生重要影响,从而影响海岸带的生物地球化学循环过程[53]
珍珠湾作为我国典型的红树林海湾,湾内有大面积的国家级红树林自然保护区。本论文利用222Rn质量平衡模型估算了珍珠湾SGD速率及其携带的碳通量,并对珍珠湾的碳收支进行了计算。结论如下:
(1)珍珠湾SGD速率为(0.36±0.36) m/d,通过SGD输入珍珠湾的DIC和DOC通量分别为(2.41±2.63)×107 mol/d和(1.96±2.20)×106 mol/d。
(2)SGD携带的DIC和DOC分别占珍珠湾总DIC和DOC来源的91%和89%,是珍珠湾DIC和DOC的主要来源。
(3)对红树林蓝碳收支评估时应加以考虑SGD的贡献,海岸带系统蓝碳通过SGD的输送可能会对近海区域的碳周转、碳埋藏速率等产生重要影响。
  • 广西科技计划项目(2017AB43024);国家自然科学基金项目(41976041);广西高校中青年教师基础能力提升项目(2018KY0616)。
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2020年第42卷第10期
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doi: 10.3969/j.issn.0253-4193.2020.10.004
  • 接收时间:2020-01-09
  • 首发时间:2026-03-27
  • 出版时间:2020-10-25
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  • 收稿日期:2020-01-09
  • 修回日期:2020-02-13
基金
广西科技计划项目(2017AB43024);国家自然科学基金项目(41976041);广西高校中青年教师基础能力提升项目(2018KY0616)。
作者信息
    1 华东师范大学 河口海岸学国家重点实验室,上海 200241
    2 北部湾大学 广西北部湾海洋灾害研究重点实验室,广西 钦州 535011
    3 北部湾大学 资源与环境学院,广西 钦州 535011
    4 崇明生态研究院,上海 202162

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*张芬芬,副教授,主要从事碳循环、海洋生物地球化学研究。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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