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Article(id=1314917794341286165, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917704910340427, 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=1736179200000, receivedDateStr=2025-01-07, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430432161, onlineDateStr=2026-10-08, pubDate=1755619200000, pubDateStr=2025-08-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430432161, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430432161, creator=13701087609, updateTime=1791430432161, updator=13701087609, issue=Issue{id=1314917704910340427, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='8', pageStart='4145', pageEnd='4736', issueExtLink='null', onlineDate='null', pubDate='1755619200000', pubDateStr='2025-08-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430410840, creator='13701087609', updateTime=1791431909627, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314923991341555972, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917704910340427, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314923991345750277, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314917704910340427, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4482, endPage=4490, ext={EN=ArticleExt(id=1314917794584555798, articleId=1314917794341286165, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Water quality assessment and driving factors identification from a water quality-algae coupling perspective: A case study of the upper Xin'an River Basin in Anhui Province, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

From the perspective of water quality-algae coupling, this study investigates the evolution of water quality and algal responses in the upper Xin'an River Basin in Anhui Province from August 2022 to April 2023. Multivariate statistical analyses identified the key drivers of water quality changes, and the relationships between water quality characteristics and algal indicators were explored. A composite algal indicator was developed to serve as an indicator of water ecological health, which includes [algal turbidity (AT) and algal dominance index (ADI)], elucidating its mechanisms in water quality assessment. Furthermore, a conceptual model integrating water quality and algal responses was proposed for comprehensive water quality identification. The results show that: (1) Water quality in the study area was primarily classified as Category Ⅲ (67.9%), with an average water quality index (WQI) of 81.61 (ranging from 55.42 to 90.83), signifying good quality overall. However, significant deterioration occurred between February and April 2023, with the WQI dropping to 76.09, highlighting the need for improved identification and protection measures. (2) Water temperature (WT), dissolved oxygen (DO), and nutrient concentrations were the primary factors driving water quality changes, with Spearman correlation coefficients reaching 0.72 (P≤0.01). (3) AT and ADI were effective composite indicators in rapidly identifying water quality degradation and ecosystem health. (4) The conceptual water quality identification model, constructed based on sensory characteristics, organic matter characteristics, and algal response, addresses the limitations of existing water quality standards, which often overlook biological indicators and degradation processes. These findings provide a scientific basis for identification and protecting the aquatic ecological environment of the Xin'an River Basin and offer insights into improving and innovating traditional monitoring systems.

, authors=Jin-song ZHANG1, 2, 3, Xin-yue JU1, 2, Jie ZHENG4, Peng HUANG1, Xiang HU1, Ming DONG5, Renqing JIA1, Xiang WANG6, Mingjun MA1, Nanjing ZHAO1, 2, 5, Gaofang YIN1, 2, 5, *, authorsList=Jin-song ZHANG, Xin-yue JU, Jie ZHENG, Peng HUANG, Xiang HU, Ming DONG, Renqing JIA, Xiang WANG, Mingjun MA, Nanjing ZHAO, Gaofang YIN, authorCompany=null, correspAuthors=Gaofang YIN, 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=1314917794903322903, articleId=1314917794341286165, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=水质-藻类耦合视角下的水质评估与驱动因子——以新安江上游安徽段流域为例, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

以新安江上游安徽段流域为例，从水质-藻类耦合视角研究了2022年8月~2023年4月期间其水质演变过程与藻类响应特征；利用多元统计分析探究了水质演变的关键驱动因子，在此基础上辨识水质演变特征与藻类指标之间的潜在关联性，开发了具备水生态健康指示效应的关键藻复合指标：单位质量藻表观浊度（AT）和总藻浓度梯度下典型藻优势度（ADI），揭示了其在水质评估中的作用机制．最后，提出了集水质和藻类响应特征于一体的综合水质识别概念模型．结果表明，（1）研究区水质评价以Ⅲ类为主（占比为67.9%），水质综合指数（WQI）平均值为81.61（范围为55.44~90.83），达到良好等级，在2023年2月~2023年4月水质出现明显恶化，WQI平均值降至76.09，特别需要更好的识别和保护；（2）水温（WT）、溶解氧（DO）及营养盐浓度是导致水质演变的重要驱动因子，Spearman相关系数最高可达0.72（P≤0.01）；（3）藻复合指标AT和ADI作为表征水质和生态系统状态的综合指标可快速识别水体恶化；（4）基于感官特征、有机物特征和藻类响应特征构建的水质识别概念模型有望解决现有水质评价标准忽略水质恶化和生物指标的问题．研究结果可为新安江水源地水生态环境的识别和保护以及传统监测系统的提升与革新提供科学理论依据．

, authors=张劲松1, 2, 3, 巨昕玥1, 2, 郑洁4, 黄朋1, 胡翔1, 董鸣5, 贾仁庆1, 王翔6, 马明俊1, 赵南京1, 2, 5, 殷高方1, 2, 5, *, authorsList=张劲松, 巨昕玥, 郑洁, 黄朋, 胡翔, 董鸣, 贾仁庆, 王翔, 马明俊, 赵南京, 殷高方, authorCompany=null, correspAuthors=殷高方, authorNote=

张劲松（1975-），男，重庆梁平人，高级工程师，学士，主要从事环境空气和水质自动监测以及水环境管理．发表论文10余篇．.

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* 责任作者，研究员，
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张劲松（1975-），男，重庆梁平人，高级工程师，学士，主要从事环境空气和水质自动监测以及水环境管理．发表论文10余篇．.

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张劲松（1975-），男，重庆梁平人，高级工程师，学士，主要从事环境空气和水质自动监测以及水环境管理．发表论文10余篇．.

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水质-藻类耦合视角下的水质评估与驱动因子——以新安江上游安徽段流域为例
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张劲松 1, 2, 3 , 巨昕玥 1, 2 , 郑洁 4 , 黄朋 1 , 胡翔 1 , 董鸣 5 , 贾仁庆 1 , 王翔 6 , 马明俊 1 , 赵南京 1, 2, 5 , 殷高方 1, 2, 5, *
中国环境科学 | 环境生态 2025,45(8): 4482-4490
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中国环境科学 |环境生态 2025 , 45 (8) : 4482 -4490
水质-藻类耦合视角下的水质评估与驱动因子——以新安江上游安徽段流域为例
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张劲松（1975-），男，重庆梁平人，高级工程师，学士，主要从事环境空气和水质自动监测以及水环境管理．发表论文10余篇．.

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张劲松（1975-），男，重庆梁平人，高级工程师，学士，主要从事环境空气和水质自动监测以及水环境管理．发表论文10余篇．.

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张劲松1, 2, 3 , 巨昕玥1, 2, 郑洁4, 黄朋1, 胡翔1, 董鸣5, 贾仁庆1, 王翔6, 马明俊1, 赵南京1, 2, 5, 殷高方1, 2, 5, *
作者信息
  • 1.中国科学院合肥物质科学研究院，安徽光学精密机械研究所，中国科学院环境光学与技术重点实验室，安徽省环境光学监测技术重点实验室，安徽 合肥 230031
  • 2.中国科学技术大学，安徽 合肥 230026
  • 3.安徽省生态环境监测中心，安徽 合肥 230071
  • 4.安徽省黄山生态环境监测中心，黄山 245499
  • 5.合肥综合性科学中心环境研究院，安徽 合肥 230071
  • 6.安徽工程大学集成电路学院，安徽 芜湖 241000
通讯作者:
* 责任作者，研究员，
作者简介:

张劲松（1975-），男，重庆梁平人，高级工程师，学士，主要从事环境空气和水质自动监测以及水环境管理．发表论文10余篇．.

Water quality assessment and driving factors identification from a water quality-algae coupling perspective: A case study of the upper Xin'an River Basin in Anhui Province
Jin-song ZHANG1, 2, 3 , Xin-yue JU1, 2, Jie ZHENG4, Peng HUANG1, Xiang HU1, Ming DONG5, Renqing JIA1, Xiang WANG6, Mingjun MA1, Nanjing ZHAO1, 2, 5, Gaofang YIN1, 2, 5, *
Affiliations
  • 1.Key Laboratory of Environmental Optics and Technology, Key Laboratory of Environmental Optical Monitoring Technology Chinese Academy of Sciences, Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
  • 2.University of Science and Technology of China, Hefei 230026, China
  • 3.Anhui Provincial Environmental Monitoring Center, Hefei 230071, China
  • 4.Huangshan Ecological and Environmental Monitoring Center, Huangshan 245499, China
  • 5.Hefei Comprehensive Science Center, Institute of Environmental Research, Hefei 230071, China
  • 6.School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
出版时间: 2025-08-20
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以新安江上游安徽段流域为例，从水质-藻类耦合视角研究了2022年8月~2023年4月期间其水质演变过程与藻类响应特征；利用多元统计分析探究了水质演变的关键驱动因子，在此基础上辨识水质演变特征与藻类指标之间的潜在关联性，开发了具备水生态健康指示效应的关键藻复合指标：单位质量藻表观浊度（AT）和总藻浓度梯度下典型藻优势度（ADI），揭示了其在水质评估中的作用机制．最后，提出了集水质和藻类响应特征于一体的综合水质识别概念模型．结果表明，（1）研究区水质评价以Ⅲ类为主（占比为67.9%），水质综合指数（WQI）平均值为81.61（范围为55.44~90.83），达到良好等级，在2023年2月~2023年4月水质出现明显恶化，WQI平均值降至76.09，特别需要更好的识别和保护；（2）水温（WT）、溶解氧（DO）及营养盐浓度是导致水质演变的重要驱动因子，Spearman相关系数最高可达0.72（P≤0.01）；（3）藻复合指标AT和ADI作为表征水质和生态系统状态的综合指标可快速识别水体恶化；（4）基于感官特征、有机物特征和藻类响应特征构建的水质识别概念模型有望解决现有水质评价标准忽略水质恶化和生物指标的问题．研究结果可为新安江水源地水生态环境的识别和保护以及传统监测系统的提升与革新提供科学理论依据．

水质-藻类耦合  /  水质评估  /  驱动因子  /  新安江

From the perspective of water quality-algae coupling, this study investigates the evolution of water quality and algal responses in the upper Xin'an River Basin in Anhui Province from August 2022 to April 2023. Multivariate statistical analyses identified the key drivers of water quality changes, and the relationships between water quality characteristics and algal indicators were explored. A composite algal indicator was developed to serve as an indicator of water ecological health, which includes [algal turbidity (AT) and algal dominance index (ADI)], elucidating its mechanisms in water quality assessment. Furthermore, a conceptual model integrating water quality and algal responses was proposed for comprehensive water quality identification. The results show that: (1) Water quality in the study area was primarily classified as Category Ⅲ (67.9%), with an average water quality index (WQI) of 81.61 (ranging from 55.42 to 90.83), signifying good quality overall. However, significant deterioration occurred between February and April 2023, with the WQI dropping to 76.09, highlighting the need for improved identification and protection measures. (2) Water temperature (WT), dissolved oxygen (DO), and nutrient concentrations were the primary factors driving water quality changes, with Spearman correlation coefficients reaching 0.72 (P≤0.01). (3) AT and ADI were effective composite indicators in rapidly identifying water quality degradation and ecosystem health. (4) The conceptual water quality identification model, constructed based on sensory characteristics, organic matter characteristics, and algal response, addresses the limitations of existing water quality standards, which often overlook biological indicators and degradation processes. These findings provide a scientific basis for identification and protecting the aquatic ecological environment of the Xin'an River Basin and offer insights into improving and innovating traditional monitoring systems.

water quality-algal coupling  /  water quality assessment  /  driving factors  /  Xin'an River
张劲松, 巨昕玥, 郑洁, 黄朋, 胡翔, 董鸣, 贾仁庆, 王翔, 马明俊, 赵南京, 殷高方. 水质-藻类耦合视角下的水质评估与驱动因子——以新安江上游安徽段流域为例. 中国环境科学, 2025 , 45 (8) : 4482 -4490 .
Jin-song ZHANG, Xin-yue JU, Jie ZHENG, Peng HUANG, Xiang HU, Ming DONG, Renqing JIA, Xiang WANG, Mingjun MA, Nanjing ZHAO, Gaofang YIN. Water quality assessment and driving factors identification from a water quality-algae coupling perspective: A case study of the upper Xin'an River Basin in Anhui Province[J]. China Environmental Science, 2025 , 45 (8) : 4482 -4490 .
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  • 国家重点研发计划项目(2022YFC3103900; 2021YFC3200100)
  • 国家自然科学基金资助项目(42206198; 62375270)
  • 安徽省科技创新平台重大科技项目(S202305a12020004)
  • 合肥综合性科学中心环境研究院科研团队建设项目(HYKYTD202004)
  • 中国仪器仪表学会科学仪器托举计划项目(CISTJ2024)
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  • 接收时间：2025-01-07
  • 首发时间：2026-10-08
  • 出版时间：2025-08-20
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  • 收稿日期：2025-01-07
基金
国家重点研发计划项目(2022YFC3103900; 2021YFC3200100)
国家自然科学基金资助项目(42206198; 62375270)
安徽省科技创新平台重大科技项目(S202305a12020004)
合肥综合性科学中心环境研究院科研团队建设项目(HYKYTD202004)
中国仪器仪表学会科学仪器托举计划项目(CISTJ2024)
作者信息
    1.中国科学院合肥物质科学研究院，安徽光学精密机械研究所，中国科学院环境光学与技术重点实验室，安徽省环境光学监测技术重点实验室，安徽 合肥 230031
    2.中国科学技术大学，安徽 合肥 230026
    3.安徽省生态环境监测中心，安徽 合肥 230071
    4.安徽省黄山生态环境监测中心，黄山 245499
    5.合肥综合性科学中心环境研究院，安徽 合肥 230071
    6.安徽工程大学集成电路学院，安徽 芜湖 241000

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