• 关键词：水质传感器  富营养化  营养盐  多元逐步回归分析  高频自动监测

• 基金项目：国家自然科学基金（No.51279194）；中国科学院“一三五”项目（No.NIGLAS2012135002）；Gordon and Betty Moore基金会项目（No.1182）；淳安县环保局项目

• 崔扬
• 1. 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008;2. 中国科学院大学, 北京 100049

• 朱广伟
• 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008

• 张运林
• 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008

• 朱梦圆
• 1. 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008;2. 中国科学院大学, 北京 100049

• 许海
• 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008

• 施坤
• 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008

• 李未
• 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008

• 秦伯强
• 中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008

• 摘要：湖泊和水库中蓝藻水华等富营养化灾害的形成往往只需几天的时间，因此，在富营养化水体的水质管理上需要进行高频的水质指标监测.本研究以新安江水库（千岛湖）为例，基于水质传感器探头现场获取的水质参数，采用多元逐步回归分析方法，获得总氮（TN）、总磷（TP）、高锰酸盐指数（COD_Mn）、叶绿素a（Chl）、营养状态指数（TSI）、透明度（SD）等指示湖库富营养化状况的关键水质指标与水体藻蓝素（PC）、浊度（TURB）、有色可溶性有机物（CDOM）、电导率（EC）、溶解氧（DO）等现场水质参数之间的定量关系，以满足高频监测湖泊富营养化关键指标的需要. 结果表明，2013年调查期间，新安江水库各湖区水质差异较大，调查的54个点位中，SD介于1.10~8.60 m之间，TN介于0.78~1.68 mg·L^-1之间，TP介于7.90~71.1 μg·L^-1之间，具有较为宽泛的代表性.相关分析表明，CDOM与TN、TP、COD_Mn、Chl、TSI、SD均存在显著的相关关系，可以作为新安江水库水质富营养化状况的一个重要自动监测指标；TURB与TP、Chl、SD、TSI之间也显著相关，PC则与TN、COD_Mn相关，而探头获得的叶绿素浓度值（Chl_s）与TN、SD显著负相关. 通过与实测值比较表明，统计分析建立的富营养化指标多元回归方程估算值与实测值吻合度较高，能够满足水体管理的需要. 本研究为湖泊和水库的富营养化灾害监控、预警提供了理论依据.

• Abstract：Formation of algal bloom in lakes and reservoirs takes several days, leading to eutrophication disaster. Therefore, it is necessary to set up real-time water quality monitoring systems for eutrophication water quality management. In the present study, we took Xin'anjiang Reservoir (Qiandao lake) as an example to investigate the quantitative relationships between key eutrophication indices including total nitrogen (TN), total phosphorus (TP), permanganate index (COD_Mn), chlorophyll a (Chl), trophic state index (TSI), transparency (SD) and in-situ water quality parameters such as phycocyanobilin (PC), turbidity (TURB), chromophoric dissolved organic matter (CDOM), electric conductivity (EC), and dissolved oxygen (DO), by in-situ high frequency water quality monitoring for better predicting the risk of eutrophication disaster. The results indicated that water quality varied substantially in different regions in Xin'anjiang Reservoir. The range of SD, TN, and TP were from 1.10 m to 8.60 m, 0.78 mg·L^-1 to 1.68 mg·L^-1, and 7.90 μg·L^-1 to 71.1 μg·L^-1, respectively, over 54 sampling sites in 2013 which provided a broad representativeness of water quality in the region. Correlation analysis showed that CDOM was significantly correlated with TN, TP, COD_Mn, Chl, TSI, and SD, illustrating CDOM as an important automatic monitoring parameter for eutrophication conditions. TURB was closely related to TP, Chl, SD and TSI, and PC was significantly correlated with TN and COD_Mn. Similar relationship held for Chl_s obtained by sensor on TN,SD. Values estimated from multiple regression analysis for eutrophication indices fit well with the observed values, which could meet the demand of water management. The results are of great importance for eutrophication disaster monitoring and warning in lakes and reservoirs.

• Key words：water quality sensor  eutrophication  nutrients  multiple stepwise regression analysis  high frequency automatic monitoring

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