Article(id=1234106386687259396, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1234106384963400440, 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=1731600000000, receivedDateStr=2024-11-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772163491174, onlineDateStr=2026-02-27, pubDate=1750348800000, pubDateStr=2025-06-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772163491174, onlineIssueDateStr=2026-02-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772163491174, creator=13701087609, updateTime=1772163491174, updator=13701087609, issue=Issue{id=1234106384963400440, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='6', pageStart='2961', pageEnd='3552', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1772163490763, creator=13701087609, updateTime=1772163969484, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1234108392948682946, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1234106384963400440, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1234108392948682947, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1234106384963400440, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3311, endPage=3320, ext={EN=ArticleExt(id=1234106390327915339, articleId=1234106386687259396, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Spatiotemporal distributions of dissolved oxygen and its driving factors in gate-controlled seagoing rivers, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

This study analysed the spatiotemporal characteristics of dissolved oxygen (DO) concentrations upstream and downstream of sluices during dry and wet years, using data from three automated water quality monitoring stations and field measurements along the Huangjiang River in Guangdong Province. Multiple statistical methods were employed to identify the relative contributions of key influencing factors to DO variability across years under different precipitation conditions. Upstream DO concentrations were generally higher in dry years ((8.02±0.10) mg/L) than in wet years ((7.26±0.08) mg/L). In contrast, DO levels in the downstream tidal section increased from (4.45±0.10) mg/L (dry year) to (7.33±0.09) mg/L (wet year), primarily due to improved water quality. Periodic fluctuations were observed in both years, with higher DO levels during the flood season and lower levels during the non-flood season throughout the river channel. Influenced by the gate control and different external inputs, DO fluctuations upstream and downstream were driven by different factors. Rainfall and water temperature explained 44% to 87% of the DO variability upstream. While ammonia nitrogen, and CODMn were the most influential factors downstream, accounting for 53% to 75% of the variability. Furthermore, the “lacustrine” upstream section was especially sensitive to climate variations. In this area, the loss of phytoplankton biomass caused by stormwater runoff was a major factor contributing to DO difference during dry and wet years. While DO downstream is more easily influenced by water pollutants, especially the significant decrease in oxygen-demanding substances.

, correspAuthors=Ju HUANG, Zhen WANG, 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=Xiao-yue JIN, Ju HUANG, Zhong-ya FAN, Lu HUANG, Qian-li LUO, Zhen WANG), CN=ArticleExt(id=1234106397395316967, articleId=1234106386687259396, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=闸控入海河流DO时空分布特征及其驱动因素, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

本文以广东省黄江河为研究对象,基于沿程3个水质自动监测站数据,分析枯水年(2021年)、丰水年(2023年)黄江河挡潮闸闸上与闸下河段DO的时空分布特征,并采用统计学方法,研究了DO变化的驱动因素及相对贡献.结果表明:闸上河段因水环境相对稳定,西闸断面DO呈现枯水年((8.02±0.10) mg/L)>丰水年((7.26±0.08) mg/L)特征;而闸下河段因水质改善,DO呈现枯水年((4.45±0.10) mg/L)<丰水年((7.33±0.09) mg/L)特征;枯、丰水年年内全河段DO均呈现汛期高、非汛期低的周期性变化规律.受闸坝作用及外源输入差异影响,闸上河段DO变化的主要驱动因素为降雨和水温,贡献度达到44%~87%;闸下河段DO变化的主要驱动因素则是氨氮和高锰酸盐指数,贡献度达到53%~75%.闸上“湖泊化”缓流河段DO更易受气候变化影响,尤其是暴雨径流引起的浮游植物流失是导致枯、丰水年DO差异的直接原因.而枯水年闸下感潮河段DO受外源污染输入及滞留影响显著,丰水年闸下河段耗氧物质的减少是DO提升的重要原因.

, correspAuthors=黄菊, 王振, authorNote=null, correspAuthorsNote=
* 责任作者,工程师,
** 教授,
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金晓玥(1999-),女,山东淄博人,汕头大学硕士研究生,从事流域水生态环境治理研究..

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金晓玥(1999-),女,山东淄博人,汕头大学硕士研究生,从事流域水生态环境治理研究..

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金晓玥(1999-),女,山东淄博人,汕头大学硕士研究生,从事流域水生态环境治理研究..

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Z1,Z2,Z3分别代表中游站,西闸站,西闸下游站

, figureFileSmall=ouYYOkdXp7b14787LC1wBQ==, figureFileBig=jRj/bHZUCAkkE2ITKM8qXg==, tableContent=null), ArticleFig(id=1234153441887580637, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=EN, label=Fig.2, caption=Average of dissolved oxygen in the dry-wet year and stage, figureFileSmall=I44ImXpRn9/RFOjLWlGZFA==, figureFileBig=/E1HMgjfi5lMt8QjlaHPMA==, tableContent=null), ArticleFig(id=1234153441958883806, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=CN, label=图2, caption=各站点DO枯、丰水年均值变化与汛期-非汛期变化

柱状图上字母标记表示组内及组间显著性差异,相同字母表示无显著性差异,不同字母表示存在显著性差异

, figureFileSmall=I44ImXpRn9/RFOjLWlGZFA==, figureFileBig=/E1HMgjfi5lMt8QjlaHPMA==, tableContent=null), ArticleFig(id=1234153442025992671, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=EN, label=Fig.3, caption=Average of environmental parameters in the dry and wet years, figureFileSmall=Z5Uwn64TWudRk3bKD+jMvg==, figureFileBig=xbbw/LHNeZpdaXo009HdfA==, tableContent=null), ArticleFig(id=1234153442088907232, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=CN, label=图3, caption=各站点各环境要素枯、丰水年均值变化

柱状图上字母标记表示组内及组间显著性差异,相同字母表示无显著性差异,不同字母表示存在显著性差异

, figureFileSmall=Z5Uwn64TWudRk3bKD+jMvg==, figureFileBig=xbbw/LHNeZpdaXo009HdfA==, tableContent=null), ArticleFig(id=1234153442181181921, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=EN, label=Fig.4, caption=Correlation coefficient between DO and environmental parameters, figureFileSmall=Tiq8mtfy+x6ce04dk8JFlw==, figureFileBig=NsAZ0HzvncGEMrjlpVwqGA==, tableContent=null), ArticleFig(id=1234153442269262306, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=CN, label=图4, caption=DO与各要素之间的相关性系数, figureFileSmall=Tiq8mtfy+x6ce04dk8JFlw==, figureFileBig=NsAZ0HzvncGEMrjlpVwqGA==, tableContent=null), ArticleFig(id=1234153443657576931, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=EN, label=Table 1, caption=

Average of DO and environmental parameters in wet and dry years

, figureFileSmall=null, figureFileBig=null, tableContent=
站点年份DO(mg/L)总氮(mg/L)氨氮(mg/L)总磷(mg/L)CODMn(mg/L)水温(℃)pH值电导率(µS/cm)浊度(NTU)降雨(mm)降雨天数(d)降雨天数(>10mm)(d)
黄江中游20217.410.350.060.044.5026.337.23462.5817.051751.4413258
20236.361.240.190.093.9326.067.40573.2635.603003.6212071
黄江西闸20218.020.920.030.044.2626.217.98940.6218.591751.4413258
20237.261.070.090.033.0425.607.37490.8325.403003.6212071
黄江西闸20214.452.850.970.176.0026.057.6922673.1214.171751.4413258
下游20237.333.620.260.034.1526.517.7414523.4425.043003.6212071
), ArticleFig(id=1234153443733074404, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=CN, label=表1, caption=

丰枯水年DO与其他因素均值

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站点年份DO(mg/L)总氮(mg/L)氨氮(mg/L)总磷(mg/L)CODMn(mg/L)水温(℃)pH值电导率(µS/cm)浊度(NTU)降雨(mm)降雨天数(d)降雨天数(>10mm)(d)
黄江中游20217.410.350.060.044.5026.337.23462.5817.051751.4413258
20236.361.240.190.093.9326.067.40573.2635.603003.6212071
黄江西闸20218.020.920.030.044.2626.217.98940.6218.591751.4413258
20237.261.070.090.033.0425.607.37490.8325.403003.6212071
黄江西闸20214.452.850.970.176.0026.057.6922673.1214.171751.4413258
下游20237.333.620.260.034.1526.517.7414523.4425.043003.6212071
), ArticleFig(id=1234153443825349093, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=EN, label=Table 2, caption=

Multiple Linear Regression for each site in wet and dry years

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参数黄江中游枯水年黄江中游丰水年黄江西闸枯水年黄江西闸丰水年黄江西闸下游枯水年黄江西闸下游丰水年
贡献度(%)P贡献度(%)P贡献度(%)P贡献度(%)P贡献度(%)P贡献度(%)P
降雨42.73<0.0013.140.03234.44<0.00131.08<0.0016.490.0012.300.014
氨氮2.28<0.0014.290.01212.74<0.00123.52<0.00127.10<0.0014.130.001
总磷0.28<0.00118.34<0.0014.23<0.0012.20<0.0012.840.0220.140.542
水温43.14<0.00148.21<0.00125.23<0.00113.29<0.00137.54<0.0018.44<0.001
pH值10.84<0.00112.98<0.00121.03<0.00119.06<0.0010.230.5096.31<0.001
CODMn0.180.0041.730.1110.260.0350.180.02223.15<0.00152.03<0.001
总氮0.070.0807.190.0010.0020.8570.410.0010.00030.98118.58<0.001
电导率0.220.0012.410.0600.150.10510.12<0.0012.730.0250.750.158
浊度0.260.0011.720.1111.91<0.0010.150.0370.0030.9327.32<0.001
), ArticleFig(id=1234153443909235174, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=CN, label=表2, caption=

各站点丰枯水年多元线性回归模型

, figureFileSmall=null, figureFileBig=null, tableContent=
参数黄江中游枯水年黄江中游丰水年黄江西闸枯水年黄江西闸丰水年黄江西闸下游枯水年黄江西闸下游丰水年
贡献度(%)P贡献度(%)P贡献度(%)P贡献度(%)P贡献度(%)P贡献度(%)P
降雨42.73<0.0013.140.03234.44<0.00131.08<0.0016.490.0012.300.014
氨氮2.28<0.0014.290.01212.74<0.00123.52<0.00127.10<0.0014.130.001
总磷0.28<0.00118.34<0.0014.23<0.0012.20<0.0012.840.0220.140.542
水温43.14<0.00148.21<0.00125.23<0.00113.29<0.00137.54<0.0018.44<0.001
pH值10.84<0.00112.98<0.00121.03<0.00119.06<0.0010.230.5096.31<0.001
CODMn0.180.0041.730.1110.260.0350.180.02223.15<0.00152.03<0.001
总氮0.070.0807.190.0010.0020.8570.410.0010.00030.98118.58<0.001
电导率0.220.0012.410.0600.150.10510.12<0.0012.730.0250.750.158
浊度0.260.0011.720.1111.91<0.0010.150.0370.0030.9327.32<0.001
), ArticleFig(id=1234153443993121255, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=EN, label=Table 3, caption=

DO under different rainfall at the Huangjiang West Sluice station in wet and dry years

, figureFileSmall=null, figureFileBig=null, tableContent=
项目晴天小雨中雨大雨暴雨大暴雨
枯水年天数(d)15774421141
期间西闸断面DO(mg/L)9.077.036.356.226.165.06
丰水年天数(d)17549372095
期间西闸断面DO(mg/L)7.937.015.815.615.445.09
枯丰水年西闸断面平均DO(mg/L)8.477.026.105.915.805.10
枯丰水年西闸断面平均浊度(NTU)17.7419.6227.5027.7731.3133.57
), ArticleFig(id=1234153444064424424, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1234106386687259396, language=CN, label=表3, caption=

枯丰水年黄江西闸站不同降雨程度下DO浓度

, figureFileSmall=null, figureFileBig=null, tableContent=
项目晴天小雨中雨大雨暴雨大暴雨
枯水年天数(d)15774421141
期间西闸断面DO(mg/L)9.077.036.356.226.165.06
丰水年天数(d)17549372095
期间西闸断面DO(mg/L)7.937.015.815.615.445.09
枯丰水年西闸断面平均DO(mg/L)8.477.026.105.915.805.10
枯丰水年西闸断面平均浊度(NTU)17.7419.6227.5027.7731.3133.57
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闸控入海河流DO时空分布特征及其驱动因素
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金晓玥 1, 2 , 黄菊 2, * , 范中亚 2 , 黄露 2 , 罗千里 2 , 王振 1, **
中国环境科学 | 环境生态 2025,45(6): 3311-3320
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中国环境科学 | 环境生态 2025, 45(6): 3311-3320
闸控入海河流DO时空分布特征及其驱动因素
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金晓玥1, 2 , 黄菊2, * , 范中亚2, 黄露2, 罗千里2, 王振1, **
作者信息
  • 1.汕头大学,省市共建海洋灾害预警与防护广东省重点实验室,广东 汕头 515063
  • 2.生态环境部华南环境科学研究所,广东省水与大气污染防治重点实验室,国家水环境模拟与污染控制重点实验室,广东 广州 510535
  • 金晓玥(1999-),女,山东淄博人,汕头大学硕士研究生,从事流域水生态环境治理研究..

通讯作者:

* 责任作者,工程师,
Spatiotemporal distributions of dissolved oxygen and its driving factors in gate-controlled seagoing rivers
Xiao-yue JIN1, 2 , Ju HUANG2, * , Zhong-ya FAN2, Lu HUANG2, Qian-li LUO2, Zhen WANG1, **
Affiliations
  • 1.Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China
  • 2.Guangdong Key Laboratory of Water and Air Pollution Control, National Key Laboratory of Water Environment Simulation and Pollution Control, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510535, China
出版时间: 2025-06-20
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本文以广东省黄江河为研究对象,基于沿程3个水质自动监测站数据,分析枯水年(2021年)、丰水年(2023年)黄江河挡潮闸闸上与闸下河段DO的时空分布特征,并采用统计学方法,研究了DO变化的驱动因素及相对贡献.结果表明:闸上河段因水环境相对稳定,西闸断面DO呈现枯水年((8.02±0.10) mg/L)>丰水年((7.26±0.08) mg/L)特征;而闸下河段因水质改善,DO呈现枯水年((4.45±0.10) mg/L)<丰水年((7.33±0.09) mg/L)特征;枯、丰水年年内全河段DO均呈现汛期高、非汛期低的周期性变化规律.受闸坝作用及外源输入差异影响,闸上河段DO变化的主要驱动因素为降雨和水温,贡献度达到44%~87%;闸下河段DO变化的主要驱动因素则是氨氮和高锰酸盐指数,贡献度达到53%~75%.闸上“湖泊化”缓流河段DO更易受气候变化影响,尤其是暴雨径流引起的浮游植物流失是导致枯、丰水年DO差异的直接原因.而枯水年闸下感潮河段DO受外源污染输入及滞留影响显著,丰水年闸下河段耗氧物质的减少是DO提升的重要原因.

闸控  /  入海河流  /  溶解氧  /  枯丰水年  /  环境要素

This study analysed the spatiotemporal characteristics of dissolved oxygen (DO) concentrations upstream and downstream of sluices during dry and wet years, using data from three automated water quality monitoring stations and field measurements along the Huangjiang River in Guangdong Province. Multiple statistical methods were employed to identify the relative contributions of key influencing factors to DO variability across years under different precipitation conditions. Upstream DO concentrations were generally higher in dry years ((8.02±0.10) mg/L) than in wet years ((7.26±0.08) mg/L). In contrast, DO levels in the downstream tidal section increased from (4.45±0.10) mg/L (dry year) to (7.33±0.09) mg/L (wet year), primarily due to improved water quality. Periodic fluctuations were observed in both years, with higher DO levels during the flood season and lower levels during the non-flood season throughout the river channel. Influenced by the gate control and different external inputs, DO fluctuations upstream and downstream were driven by different factors. Rainfall and water temperature explained 44% to 87% of the DO variability upstream. While ammonia nitrogen, and CODMn were the most influential factors downstream, accounting for 53% to 75% of the variability. Furthermore, the “lacustrine” upstream section was especially sensitive to climate variations. In this area, the loss of phytoplankton biomass caused by stormwater runoff was a major factor contributing to DO difference during dry and wet years. While DO downstream is more easily influenced by water pollutants, especially the significant decrease in oxygen-demanding substances.

gate-controlled  /  seagoing river  /  dissolved oxygen  /  dry and wet year  /  key environmental parameter
金晓玥, 黄菊, 范中亚, 黄露, 罗千里, 王振. 闸控入海河流DO时空分布特征及其驱动因素. 中国环境科学, 2025 , 45 (6) : 3311 -3320 .
Xiao-yue JIN, Ju HUANG, Zhong-ya FAN, Lu HUANG, Qian-li LUO, Zhen WANG. Spatiotemporal distributions of dissolved oxygen and its driving factors in gate-controlled seagoing rivers[J]. China Environmental Science, 2025 , 45 (6) : 3311 -3320 .
水体中的溶解氧(DO)来源于大气中的氧气通过表面交换进入水体,以及水中植物和浮游生物的光合作用释放的氧气[1-3].水体低氧问题通常由高温、富营养化和水动力条件等因素导致,进而导致鱼类窒息死亡,对水生态系统结构与安全产生严重威胁[4-5].
河流DO分布特征会表现出明显的时空差异性[6-8].在时间上,DO含量呈现出明显的汛期-非汛期变化,这主要受到温度变化和生物活动强度影响[9-10].在空间上,上游水体DO通常高于下游水体[11].而影响河流DO时空分布的因素却错综复杂,从复氧和耗氧过程来看,大气复氧和光合作用是主要的氧气供应源,而有机物降解、硝化作用和生物呼吸则是主要的氧气消耗过程.当耗氧速率超过复氧速率时,DO可能显著下降.不同区域水体耗氧机制存在差异,以珠江和长江下游为代表的工业化影响区域,水体盐度升高、流速减缓及人类活动加剧是溶解氧偏低的主要驱动因素[6];而在海河塘沽口等污染集中河段,以NH4+-N为代表的还原性污染物引发的化学耗氧过程则成为缺氧现象的核心成因[12].此外,气候、水文条件等因素也会对河流DO产生显著影响.强降雨和洪水过程会导致浮游植物量骤减,抑制水体复氧能力[13],极端降水事件更易触发溶解氧骤降[14].另外,闸坝建设会改变河流水文连通性,使闸上闸下河段水生态环境存在显著差异[15-18].其阻隔效应不仅会改变藻类群落功能,将以硅藻、绿藻为主的河流群落特征转变为以蓝藻、隐藻为主的湖泊群落特征[19-20]还会引发氮磷与溶解氧的耦合波动[21].现阶段国内外研究多关注水利设施(闸坝等)对河流水文、水体富营养化的影响,而对于不同降雨强度(枯、丰水年)下,闸控入海河流DO的变化及其影响因素的研究仍缺少关注,黄江河是广东省海丰县三大河系之一,属于典型的中小型闸控入海河流.本文以黄江河为研究对象,通过采集沿程水质自动站在线监测数据和补充监测数据,利用多元统计分析方法,探究枯、丰水年闸控河流DO的时空分布特征及驱动要素,以期为同类型小流域的水环境治理与管控提供参考.
黄江河发源于广东莲花山脉上腊烛山,干流河道总长70.16km(其中公平水库以下黄江干流42.6km),平均坡降1.1‰,天然落差1054.4m,流域面积约1121km2[22].黄江河处于南亚热带海洋性气候区,阳光充足,风力强劲,降雨主要集中在每年5~9月份,为汛期,其他月份为非汛期;全年平均气温均超过20oC,且夏季温高雨多,冬季稍冷雨少[23].据统计,黄江河流域多年平均降雨量2174.10mm,2021年流域降雨量为1751.44mm,较平均值少19%,为枯水年份;2023年流域降雨量为3003.62mm,较平均值高28%,为丰水年份.黄江河干流距离入海口12km处建有1座黄江西闸挡潮闸,将黄江河一分为二[23-25].从污染源分布来看,闸上河段汇水范围内以农业面源为主,水质相对较好;闸下河段污染源以支流输入的城市面源为主,水质相对较差.
采用黄江河干流沿程自上而下的黄江中游自动站(中游站,Z1)、黄江西闸自动站(西闸站,Z2)、黄江西闸下游自动站(西闸下游站,Z3)3个自动水质监测站逐时数据进行研究分析,其中中游站、西闸站分别位于黄江西闸挡潮闸上游约7.7km、0.7km河段,西闸下游站则位于黄江西闸下游1.1km河段,具体分布位置如图1所示.于课题组研发的黄江河水环境综合治理平台(https://www.sciestech.com:1004/)获取自动站逐时频次数据.由于站点维护调试等因素,获取数据时间段为2021年与2023年,包括DO、氨氮、总氮、总磷、水温、pH、CODMn、电导率、浊度9项数据.降雨量数据来源于中国气象数据网(https://data.cma.cn/).
采用R语言(4.3.1)对自动站获取的逐时频次数据进行日均值计算,再使用箱型图(Box-plot)方法识别并删除超出1.5倍四分位距(IQR)范围的异常值;然后对筛选出的数据进行统计分析.因各数据集符合正态分布,数据均以均值±标准误差(SE)表示.DO以及其他环境因素的时空变化特征采用双因素方差分析方法(Two-way ANOVA)分析,并进一步采用Tukey’s post hoc检验各环境因素组间差异,采用Origin 2021软件绘图.
DO与其他各环境因素之间的相关性采用Pearson相关性分析法在R(4.3.1)软件中进行.
使用R(4.3.1)软件进行多元线性回归分析(Multiple Linear Regression)厘清各驱动因素对DO变化的贡献程度.首先,对各因素数据进行对数转换,然后分别计算了各自的回归系数、标准误差、t以及贡献程度.
式中:Y是因变量(DO);X1,X2,…,Xp是自变量(各环境要素);β0是截距,表示当所有自变量X1,X2,…,Xp都为零时,因变量Y的期望值;β1,β2,…,βp是自变量X1,X2,…,Xp的系数,表示它们对因变量Y的贡献程度;ε是误差项,表示模型无法解释的随机误差或扰动.
根据双因素方差分析,黄江河3个站点断面枯、丰水年DO均呈现显著差异(P<0.05).中游站枯水年DO((7.41±0.09)mg/L)较丰水年DO((6.36±0.11)mg/L)高出14%,西闸站枯水年DO((8.02±0.10) mg/L)较丰水年((7.26±0.08) mg/L)高出9%;而西闸下游站枯、丰水年DO变化呈相反,枯水年DO((4.45±0.10) mg/L)显著低于丰水年((7.33±0.09) mg/L),变幅达65%.从不同降水年份来看,黄江河闸上河段DO呈现枯水年高、丰水年低,而闸下河段DO呈现枯水年低、丰水年高的变化特征.
从不同水期来看,因全年80%的降雨量集中在汛期,在年内黄江河DO呈现明显的汛期低、非汛期高的变化特征.由图2e可知,各站点非汛期DO均显著高于汛期(P<0.05).其中,枯水年非汛期中游站、西闸站、西闸下游站3个站点DO均值分别为(8.40±0.40),(8.83±0.13),(4.69±0.14) mg/L,比枯水年汛期(6.04±0.08),(6.90±0.11),(4.11±0.13) mg/L高出2.36,1.93,0.58mg/L;丰水年非汛期3个站点DO均值分别为(6.96±0.19), (8.06±0.81), (7.69±0.13)mg/L,比丰水年汛期(5.53±0.18), (6.13±0.12), (6.86±0.11) mg/L高出1.44,1.93,0.83mg/L.相较丰水年,枯水年汛期与非汛期的DO变化差值更大.
枯水年黄江河沿程各断面DO年均值呈现西闸断面((8.02±0.10) mg/L)>中游断面((7.41±0.09) mg/L)>西闸下游断面((4.45±0.10) mg/L)特征(闸上>闸下);而丰水年呈现中游断面((6.36±0.11)mg/L)<西闸断面((7.26±0.08) mg/L)<西闸下游断面((7.33±0.09) mg/L)特征(闸上<闸下),枯、丰水年沿程DO变化趋势相反.枯水年汛期与非汛期沿程DO均呈现闸上>闸下特征,丰水年非汛期沿程各站点DO接近,差值不大,而汛期则呈现闸上<闸下特征.
图3所示,中游站、西闸站枯水年氨氮浓度分别为(0.06±0.01)、(0.03±0.01)mg/L,丰水年增加到(0.19±0.01)、(0.09±0.01)mg/L,增幅分别为14%、67%,而西闸下游站枯水年氨氮浓度为(0.97±0.006)mg/L,丰水年下降至(0.26±0.01)mg/L,降幅为73%.总体而言,相较于枯水年,丰水年闸上水体氨氮、总氮和浊度显著增加(P<0.05),增幅为14%~70%,CODMn浓度显著下降(P<0.05),降幅为10%~20%;闸下水体氨氮、总磷、CODMn和电导率显著下降(P<0.05),降幅分别73%、82%、31%和36%,浊度和总氮显著上升(P<0.05),增幅分别为76%和21%.各环境要素(除温度、pH值外)均展现出显著的时空差异性.
r>0.3时,DO与其影响因素存在相关关系[26].根据图4可知,黄江河DO与降雨、氨氮、水温和浊度呈显著负相关,与pH值和电导率呈显著正相关.闸上水体DO与各指标的相关性比闸下水体更显著,如西闸站枯水年DO与pH值、电导率和CODMn的相关性系数分别为0.69、0.61和0.38,且均在P<0.001水平下显著,而西闸下游站DO与pH值的相关性不显著,与电导率的相关性在P<0.01水平下显著.同样丰水年西闸站DO与各指标的相关性也基本强于西闸下游站.研究表明[27],在感潮河段,晴天和小雨时潮汐运动对DO的影响会占主导地位,但随着丰水年降雨增多,大量淡水注入河口、海湾等近岸水域,影响潮汐涨落过程中的水量交换,潮汐对DO作用有所减弱,闸下水体DO与其他指标的相关性有所增强.
采用线性回归分析量化各驱动因素对DO变化的贡献程度.如表2所示,在闸上河段,枯水年降雨、水温和pH值对中游站断面和西闸站断面DO的贡献程度最大,分别为42.73%、43.14%、10.84%和34.44%、25.23%、21.03%,而在丰水年降雨、水温对闸上水体DO的综合贡献程度有所减弱,总磷、氨氮等对DO的综合贡献程度有所增强.
对于闸下河段水体,枯水年水温、氨氮、高锰酸盐指数对DO的贡献程度最大,分别为37.54%、27.1%、23.15%,丰水年则是CODMn、总氮贡献程度最大,分别为50.03%、18.58%.在闸下河段,降雨对水体DO变化的贡献程度远小于闸上河段,氨氮、CODMn等耗氧物质对闸下水体DO变化贡献程度较为突出,并且随着氨氮、总磷浓度显著下降,丰水年CODMn和总氮对DO变化的贡献程度显著增大.
黄江西闸挡潮闸在充分发挥御咸蓄淡作用时,也将黄江河一分为二,使得闸上河段呈现“湖泊化”缓流水体特征,而闸下河段则为感潮河段,受径流、潮汐及下游支流污染物输入影响,水环境状态多变.这导致闸上和闸下河段DO特征变化的驱动因素存在显著差异.
降雨期间河流水体DO浓度会有所下降[28-30],徐闯等[26]发现不同降雨强度下潭水河DO总体下降幅度为2.01%~9.26%.在该研究中,降雨量的增加促使丰水年闸上河段DO较枯水年下降9%~14%.根据统计(表3),枯、丰水年流域内降雨天数分别为132,120d,其中小雨天数分别为74,49d,中雨天数分别为42,37d,大雨天数分别为11,20d、暴雨天数分别为4,9d,大暴雨天数分别为1,5d.对应晴天、小雨、中雨、大雨、暴雨及大暴雨降雨事件期间西闸断面DO均值分别为8.47,7.02, 6.10, 5.91, 5.80, 5.10mg/L,水体DO浓度随降雨量增大而减小,这与余香英等[31]的研究结果一致.小雨对DO浓度影响较小,大雨及暴雨事件频次是黄江河闸上水体DO呈现枯水年高、丰水年低特征的直接驱动因素.
降雨径流会导致大量耗氧污染物质冲刷入河,进一步降低水体中DO浓度[32].丰水年西闸站断面氨氮浓度较枯水年增加71%(表1),其对DO的贡献程度由12.74%上升到23.52%(表2).丰水年中游站断面总磷浓度较枯水年增加56%,其对DO的贡献程度达到了18.34%,仅次于水温的影响(表2).此外,径流冲刷作用会导致河水浊度升高,抑制水生植物的光合作用,导致DO下降.研究表明,当浊度达到30~50NTU时,水生植物光合产氧受到严重抑制[233-37].在该研究中,西闸断面水体浊度随降雨强度的增加逐步升高而DO浓度随之逐渐降低.另外,研究表明水体中60%以上的氧气来源于浮游植物的光合作用[38],但降雨径流会导致水体浮游植物大量流失,藻类密度急剧下降,光合复氧能力减弱,致使DO浓度持续偏低.He等[39]研究发现在降雨事件前水体叶绿素a浓度为250µg/L,但降雨期间叶绿素a浓度下降到200µg/L.在该研究中,降雨开闸泄洪事件前西闸断面浮游植物生物量为3.55mg/L,叶绿素a浓度为26µg/L,DO浓度为6.50mg/L;降雨开闸泄洪事件期间(持续24d)浮游植物生物量减少至0.57mg/L、叶绿素a降低为6µg/L,DO平均浓度降至5.31mg/L,降雨泄洪事件结束闭闸7d后,浮游生物量上升至3.67mg/L、叶绿素a浓度上升至22 µg/L,DO浓度提升至6.02mg/L,基本恢复雨前水平.综上所述,丰水年降雨量增加,尤其是大雨和暴雨频次的增多,导致闸站调度频繁,浮游植物生物量流失加剧,直接导致水体DO浓度长时间偏低.
水温对闸上水体DO变化的贡献程度为13.29%~48.21%.通常水温升高会使DO的饱和度下降[40].流域内汛期水温高(平均30.71℃)、非汛期水温低(平均22.79℃),同时叠加期间降雨作用,促使水体DO年内呈现汛期高、非汛期水低变化规律.根据双因素分析,枯、丰水年闸上河段水温无显著差异,未发生明显变化,年平均值均在26℃左右(图3).由此可知,水温对年内不同水期DO的变化影响显著,但对枯、丰水年DO的分布特征差异影响较小.
在该研究中,pH值与闸上水体DO呈显著正相关,贡献程度为10.84%~21.03%.研究表明,pH值与DO浓度之间存在协同变化,当pH值较低时,H+离子的活性增加,促使其与O2结合成水分子,导致DO浓度因水体稀释而降低,当pH值较高时,H+离子活性较低,从而减少了与O2的结合[41].同时,水生生物的呼吸作用和光合作用促使水体pH值与DO密切关联.水生植物在白天进行光合作用时,会增加水体DO含量,吸收二氧化碳,使水体碳酸平衡向左移动,导致水体pH值升高.而在夜间,水生植物和动物都进行呼吸作用,消耗氧气,释放二氧化碳,使得水体DO降低,同时二氧化碳的增加会使碳酸平衡向右移动,水体pH值降低[42-43].pH对DO的变化贡献显著,主要是因为其与DO的强关联性.pH值并非是枯、丰水年DO变化的主要外部驱动因素.
综合来看,闸上河段DO与降雨、水温呈显著负相关,且降雨与水温对DO的综合贡献程度达44%~87%,说明降雨、气候等外界环境因素的改变易促使闸上水体DO发生变化.另外,大雨、暴雨径流引起的耗氧物质输入、浊度增加及浮游植物流失对DO的影响作用大,持续时间长是驱动丰水年闸上水体DO浓度降低的直接因素.
枯、丰水年闸上河段水体电导率无显著变化,但闸下河段水体电导率有显著下降.枯水年闸下河段电导率与DO呈显著负相关(表2),对DO的贡献程度为2.7%,而丰水年受降雨淡水汇入影响,闸下河段电导率从22673µS/cm降至14523µS/cm(表1),电导率对DO的贡献程度随之下降至0.7%.研究表明,温度不变情况下,盐度每增加100×10-6,DO降低约1%[44-45].根据换算公式,30℃下,电导率从22673µS/cm(盐度11270×10-6)降至14523µS/cm (盐度5000×10-6),饱和溶解氧可提升约74.8%.在该研究中,闸下感潮河段DO浓度从枯水年4.45mg/L上升到丰水年7.33mg/L,提升65%.丰水年降雨对河水盐度的稀释促进闸下河段水体DO的提升.
表2图4所示,闸下河段DO浓度受营养盐(氨氮、总磷、总氮)和CODMn负面影响显著.枯水年DO与氨氮的负相关性最强(r=-0.39),丰水年DO与CODMn的负相关性最强(r=-0.43).根据多元线性回归模型结果,闸下河段CODMn对DO的贡献程度最为显著,且丰水年贡献程度由枯水年的23.15%上升至52.03%,这与丰水年氨氮、总磷浓度的大幅下降,促使有机质成为主要耗氧物质有关[38,46].闸下河段水质受其支流污染汇入影响显著,随着支流水环境的改善,丰水年黄江河闸下河段氨氮、总磷、CODMn浓度相对于枯水年,分别减少73%,82%,33%,干流水质同步得到改善.支流排口输入的耗氧物质减少,促使闸下水体DO即使在丰水年不利环境下,其浓度仍能大幅度提升,这说明水环境质量的改善对DO提升至关重要.综上所述,闸下感潮河段DO受降雨径流、潮汐动力及支流排污等多重影响,其中氨氮、CODMn等耗氧污染物质是影响DO浓度最主要的因素,综合贡献度达53%~75%,并且随着氨氮浓度显著下降,CODMn和总氮的贡献度显著提升.枯水年降雨径流量小,闸站排水频率低,咸潮盐度对DO作用更为明显,叠加水环境质量差,耗氧物质多,感潮河段污染滞留作用强等因素[47],导致枯水年闸下水体DO浓度远低于闸上水体.而随着丰水年的到来,大量淡水注入河口,对潮汐涨落过程中水量交换产生影响,从而潮汐对DO作用有所减弱,这与薛弘涛[27]和Julian等[36]的研究结果一致.同时降雨稀释及水环境改善作用,促使闸下水体DO浓度在丰水年不降反升.
4.1 枯、丰水年黄江河DO时空分布特征存在显著差异.闸上河段DO呈现枯水年>丰水年特征;而闸下感潮河段因水质显著改善,DO呈现枯水年<丰水年变化特征.年内DO均呈现汛期高、非汛期低变化特征.
4.2 闸上河段DO更易受气候变化影响,降雨、水温对DO变化的综合贡献度达44%~87%.径流冲刷浮游植物流失是造成枯、丰年闸上水体DO特征差异的直接原因.丰水年闸下水体氨氮、CODMn等耗氧物质浓度的显著下降是DO上升的重要原因.
4.3 相较于枯水年,闸下感潮河段DO在丰水年多种负面因素影响下,其仍能有大幅提升,说明水环境质量的改善对河流DO的提升至关重要.
  • 国家重点研发计划资助项目(2022YFC3202200)
  • 国家自然科学基金资助项目(42177264; 42403057)
  • 广东省基础与应用基础研究基金资助项目(2023A1515110183)
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2025年第45卷第6期
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国家重点研发计划资助项目(2022YFC3202200)
国家自然科学基金资助项目(42177264; 42403057)
广东省基础与应用基础研究基金资助项目(2023A1515110183)
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    1.汕头大学,省市共建海洋灾害预警与防护广东省重点实验室,广东 汕头 515063
    2.生态环境部华南环境科学研究所,广东省水与大气污染防治重点实验室,国家水环境模拟与污染控制重点实验室,广东 广州 510535

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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
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栓菌属 Trametes 5 2.39
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