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

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

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (8) : 4482-4490.

环境生态

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

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 +

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

ZHANG Jin-song^1,2,3, JU Xin-yue^1,2, ZHENG Jie⁴, HUANG Peng¹, HU Xiang¹, DONG Ming⁵, JIA Renqing¹, WANG Xiang⁶, MA Mingjun¹, ZHAO Nanjing^1,2,5, YIN Gaofang^1,2,5

Author information +

文章历史 +

摘要

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.

Abstract

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.

导出引用

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[J]. 中国环境科学. 2025, 45(8): 4482-4490

ZHANG Jin-song, JU Xin-yue, ZHENG Jie, HUANG Peng, HU Xiang, DONG Ming, JIA Renqing, WANG Xiang, MA Mingjun, ZHAO Nanjing, YIN Gaofang. 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

中图分类号： X522

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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(HYKYTD2024004);中国仪器仪表学会科学仪器托举计划项目(CISTJ2024)