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题名: 跨流域调水对北京密云库富营养化风险潜在影响研究
作者: 曾庆慧1
学位类别: 博士
答辩日期: 2017-06
授予单位: 中国科学院生态环境研究中心
授予地点: 北京
导师: 李叙勇
关键词: 密云水库,南水北调,EFDC模型,富营养化,氮磷阈值 ; Miyun reservoir, The South-to-North Water Transfer Project, EFDC Model, Eutrophication, Nutrient threshold
其他题名: The potential impact of an inter-basin water transfer project on eutrophication risk of Miyun reservoir, Beijing
学位专业: 环境科学
中文摘要: 密云水库是北京市唯一的地表饮用水水源地。南水北调中线工程通过京密引水渠反向加压,将南水北调来水输送至密云水库。南水北调来水的注入会增加密云水库的蓄水量,但源水区丹江口水库和受水区密云水库在水化学、水生态特征等方面都存在差异,调水可能会对密云水库的富营养化风险产生影响。因此迫切需要全面提升密云水库水质安全保障水平,以确保饮用水水源地的供水安全。 本文首先分析了密云水库主要水环境因子的时空分布特征,客观评估了库区水环境现状和富营养化状况。其次,通过设计室内富营养化阈值实验获得了密云水库富营养化的氮磷阈值,通过设计营养盐添加实验探究了氮磷营养盐对藻类生长的协同作用。再次,通过环境流体动力学模型(EFDC)探究了①不同水文年型调水;②不同入流时间及调水量;③不同入流氮磷浓度这三大类情景下南水北 调来水对密云水库氮磷营养盐浓度及富营养化风险的影响。然后,利用回归树(RT)、随机森林(RF)、支持向量机(SVM)和神经网络(ANN)四个机器学习模型,用容易测量的环境变量预测了密云水库的藻细胞密度,并从中选出最优模型来对调水导致的水质及浮游植物群落组成变化进行预测。最后,形成了综合性可视化的密云水库水环境管理系统,直观、形象和动态地跟踪显示密云水库水环境因子的时空变化以及预测多年调水情景下密云水库富营养化风险的发展趋势。论文的主要结果如下: (1)1990-2011年密云水库水质月监测数据分析结果表明:密云水库属于中营养化水体。白河入库和潮河入库的总氮浓度均呈高度显著上升趋势。高锰酸盐指数无明显升降趋势。总磷和五日生化需氧量呈显著或高度显著下降趋势。与潮白河入库水质变化相比,库区水质变化较小。潮河流域的潜在非点源污染较白河流域多。流量并不是引起水质趋势变化的主要因素,水质的变化主要是由于污染 源变化而引起。 (2)藻类增长潜力实验表明:为了限制藻类的生长,溶解活性磷( SRP)、硝态氮(NO3-N)和铵态氮(NH4-N)的富营养化阈值应该分别设定为低于0.04mg P L-1, 0.5 mg N L-1和 0.3 mg N L-1。相应的总磷(TP)和总氮(TN)的富营养化阈值应分别低于 0.071 mgPL-1和 0.898 mgNL-1。在氮浓度相同的条件下,“NH4-N + SRP”对藻类生长的促进作用大于“NO3-N + SRP”。NH4-N也是浮游植 物生长的一个重要因素,因此为了控制水华的形成要同时减少 NH4-N和 NO3-N的输入。虽然磷负荷的减少非常重要,同时也建议适当减少密云水库上游各种形式的氮输入。 (3)EFDC模型预测结果表明:在丰、平、枯等不同设计水文年型条件下调水,库区内总磷和总氮浓度的变化都主要来源于上游营养物质的输入,调水带来的影响较小。目前调水影响的范围主要集中在白河库区。长时间、均匀分布流量的调水方式比短期内大流量的调水方式对库区藻类生物量的影响更小。磷是密云水库藻类生长的限制性元素,且库区内藻类生长对五月到十月时间段内调水中总 磷浓度的增加更为敏感,当调水中磷浓度增加一倍时,春、夏、秋三个季节入流时库区内叶绿素 a的平均浓度分别增大7.76%、16.67%和 16.45%。研究结果表明应该重点关注五月到十月这段时间内调水中磷浓度的变化情况,以防止水华的发生。 (4)优化后的四个机器学习模型都能通过简单易得的环境变量来模拟藻密 度,预测精度从高到低依次为随机森林模型、神经网络模型、支持向量机模型和回归树模型。此外,通过随机森林模型对密云水库主要优势种群(蓝藻、绿藻和硅藻)进行了预测,测试集的预测精度在 0.824到 0.869范围内。模型预测调水后不同种群浮游植物所占的百分比和调水前相比变化了-8.88%到 9.93%,并且模型预测调水后每个季节的优势种群和调水前保持一致。本研究为预测调水所引起的浮游植物群落组成变化提供了一个有用的方法。可以通过对某一特定区域的相关数据重新建模从而将这个方法推广应用到其他地区。 (5)密云水库水环境管理系统目前已经在密云水库业务化运行。水环境预测管理模块可以预测未来不同水文年型和调水情景下南水北调入库后密云水库库区水质变化状况。结果表明大约到 2020年左右,库区内原本的水体就几乎被南水北调来水和上游来水完全替换。调水后库区内预测的总磷浓度变化幅度不大,库区整体的总氮浓度略有升高,白河库区变化较为明显。库区富营养化风险整体 较调水前降低,尤其是库北和潮河入库等浅水区域的富营养化风险减小。 本研究有助于管理者建立更经济有效的密云水库氮磷营养盐含量控制标准,对决策者确定调水入流时间及调水量具有一定的指导意义。本研究最终的应用成果以密云水库水环境管理系统呈现,有助于我们更好地理解跨流域调水对受水区富营养化风险的潜在影响,为日后有效的进行水源地保护和富营养化管控提供了科学手段。
英文摘要: Miyun reservoir is the most important source of drinking water for Beijing. The middle route of the South-to-North Water Transfer Project (SNWTP) transferred the water to Miyun reservoir through Jingmi diversion canal. The transferred water would increase the water storage capacity of the Miyun reservoir. However, there are differences between the water chemistry and water ecology of Danjingkou reservoir and Miyun reservoir, and water transfer project would cause complex effect on eutrophication risk of Miyun reservoir. It is necessary to provide technical support for the safety of water quality in Miyun reservoir. Based on the analysis of the temporal and spatial distribution of main aquatic environment factors in Miyun reservoir, the present situation of the water environment and eutrophication state were evaluated. Firstly, bioassay experiments were conducted to determine the thresholds of soluble reactive phosphorus (SRP), nitrate-nitrogen (NO3-N), and ammonium-nitrogen (NH4-N) in Miyun Reservoir. A separate nutrient addition bioassay was designed to assess the synergistic interactions between these nutrients. Secondly, to predict the impacts of this long-distance inter-basin water transfer project on the N&P (nitrogen and phosphorus) concentrations and eutrophication risk of the receiving system, an environmental fluid dynamics code (EFDC) model was applied. Three scenarios were defined to fully understand the N&P and chlorophyll a (Chl a) variation among different hydrological years, different quantity and timing of water transfer, and different inflows of N&P concentrations. Thirdly, we used machine learning models to predict the total algal cell densities via utilization of easy to measure hydro-chemical variables. The model performances of four machine learning models, including regression trees (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were evaluated. The best model was selected to predict changes in phytoplankton community composition in Miyun Reservoir caused by the middle route of SNWTP. Finally, a comprehensive and visual water environment management system of Miyun reservoir was formed. The system could show the spatial and temporal variations of environmental factors, and predict the eutrophication risk under water diversion scenarios for ages. The results showed the following: (1) Miyun reservoir is a mesotrophic reservoir. The nitrogen concentration of Baihe inflow and Chaohe inflow have highly significant upward trends. The trends of CODMn concentration in Baihe inflow and Chaohe inflow are not obvious. The trends of TN and BOD5 have significant or highly significant downward trends. Compared with water quality variation in Baihe inflow and Chaohe inflow, the water quality variation in the reservoir is smaller. Flow is not the main effect of water quality, and the change of water quality is mainly caused by the change of pollution sources. (2) Nutrient threshold bioassay indicated that eutrophication thresholds of SRP, NO3-N and NH4-N should be targeted at below 0.04 mg P L , 0.5 mg N L-1 and 0.3 mg-1 N L -1 , respectively, to limit the growth of phytoplankton. The corresponding thresholds of TP and TN should be targeted at below 0.071 mg P L-1 and 0.898 mg N L-1 . The stimulatory effect of “NH4-N plus P” on phytoplankton biomass was greater than “NO3- N plus P” at the same N concentration. Ammonium was an important factor for the growth of phytoplankton and inputs of both NH4-N and NO3-N should be reduced to control bloom formation. Although P load reduction is important, appropriate reductions of all forms of N in watershed is recommended in the nutrient management strategy for Miyun Reservoir. (3) The water transfer project would not result in a substantial difference to the trophic state of the Miyun reservoir in any of the hydrological years. The area affected by the water transfer did not involve the entire reservoir. To minimize the impact of water transfer on N&P nutrients and Chl a, water should be transferred as uniform as possible with small discharge. The variation in Chl a was more sensitive to an increase in P than an increase in N for the transferred water. The increased percentages of the average Chl a concentration when water was transferred in the spring, summer and autumn were 7.76%, 16.67% and 16.45%. The results imply that special attention should be given to prevent P increment of the transferred water from May to October to prevent algal blooms. (4) Total algal densities could be predicted with easily measured hydro-chemical variables using all four machine learning models. The predictive accuracies (Pearson’s correlation coefficient) of four machine learning models in descending order were RF, ANN, SVM, and RT model. Furthermore, the predicted accuracies of the RF model for dominant phytoplankton phyla (Cyanophyta, Chlorophyta, and Bacillariophyta) in Miyun Reservoir ranged from 0.824 to 0.869 in the testing step. The predicted proportions with water transfer of the different phytoplankton phyla ranged from -8.88% to 9.93%, and the predicted dominant phyla with water transfer in each season remained unchanged compared to the phytoplankton succession without water transfer. The results of the present study provide a useful tool for predicting the changes in phytoplankton community caused by water transfer. The method is transferrable to other locations via establishment of models with relevant data to a particular area. (5) The water environment management system of Miyun reservoir has been operated in Miyun reservoir. The module of water environment prediction and management can predict the eutrophication risk under different water diversion scenarios for ages. The original water in Miyun reservoir would be almost replaced by transferred water and upstream inflow around 2020. Compared to the water quality without water transfer, the predicted concentration of TP would not change significantly. The predicted concentration of TN would increase slightly. The eutrophication risk would be reduced, especially in shallow water area such as Kubei and Chaohe. This study will help managers to establish more practical and economical nutrient criteria for Miyun Reservoir. The results provide useful information for decision makers about the quantity and timing of water transfers. Our findings help better understanding the possible changes in water quality and aquatic ecosystems influenced by inter-basin water transfer. The content of this study is applied in the way of Water Environment Management System of Miyun Reservoir, which provides scientific method for effective water source protection and water resources management in the future.
内容类型: 学位论文
URI标识: http://ir.rcees.ac.cn/handle/311016/38603
Appears in Collections:城市与区域生态国家重点实验室_学位论文

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