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基于组合赋权贝叶斯模型的平寨水库水质评价
李韶慧1,2, 周忠发1,2, 但雨生1,2, 尹林江1,2
1.贵州师范大学 喀斯特研究院/地理与环境科学学院, 贵州 贵阳 550001;2.贵州省喀斯特山地生态环境国家重点实验室培育基地, 贵州 贵阳 550001
摘要:
[目的] 对黔中水利枢纽的一期工程的核心水源工程——平寨水库的水环境质量进行评价,为该水库水环境质量评价及科学管理提供科学参考。[方法] 于2018年1,5,8月选取了平寨水库7个断面进行水样采集,选择溶解氧(DO),化学需氧量(COD),总氮(TN),氨氮(NH3-N)这4个因子作为水质评价指标。在层次分析法和熵权法组合确定各评价指标权重的基础上,结合贝叶斯模型对平寨水库进行水质评价。[结果] ①从组合权重来看,TN和COD对平寨水库水质影响较大,DO和NH3-N影响较小。②平寨水库丰水期水质最好,其次为枯水期,最次为平水期;各断面中库区中心1(KQ1)、库区中心2(KQ2)以及纳雍河断面(NY)水质最差。③TN和COD是平寨水库的主要污染因子。[结论] 基于组合赋权贝叶斯模型的水质评价方法不仅区分了各评价因子对水质贡献的差异性,同时对水质进行了较为精确的评价。整体而言,平寨水库水质状况达到了贵州省水功能区划的相应要求。
关键词:  组合赋权  贝叶斯模型  水质评价  平寨水库
DOI:10.13961/j.cnki.stbctb.2020.02.031
分类号:X824;X522
基金项目:国家自然科学基金委员会:贵州喀斯特科学研究中心项目"喀斯特筑坝河流水安全评估与调控策"(U1612441);贵州省高层次创新型人才培养计划:"百"层次人才项目(黔科合平台人才[2016]5674);贵州省科技计划项目(黔科合平台[2017]5726-57)
Water Quality Evaluation of Pingzhai Reservoir Based on Combined Weighted Bayesian Model
Li Shaohui1,2, Zhou Zhongfa1,2, Dan Yusheng1,2, Yin Linjiang1,2
1.Department of Geography and Environmental Sciences/Karst Research Institute, Guizhou Normal University, Guiyang, Guizhou 550001, China;2.Guizhou Key Laboratory of Mountain Environment, Guizhou Normal University, Guiyang, Guizhou 550001, China
Abstract:
[Objective] The water environment quality of Pingzhai Reservoir was evaluated for the core water source project of the first phase of the Qianzhong water diversion project, in order to provide scientific reference for the evaluation and scientific management of the water environment quality of the reservoir.[Methods] This paper selected seven sections of Pingzhai Reservoir for water sampling in January, May and August 2018. Dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN) and ammonia nitrogen (NH3-N) factors were selected as water quality evaluation index. Based on the combination of analytic hierarchy process and entropy weight method, the weight of each evaluation index was determined, and the water quality of Pingzhai Reservoir was evaluated by Bayesian model.[Results] ① From the combination weight, TN and COD showed great impact on water quality of Pingzhai Reservoir, while DO and NH3-N showed little impact. ② The water quality of Pingzhai Reservoir was the best in high-flow period, followed by the low-flow period, and then followed by the moderate-flow period. Among the sections, the water quality of reservoir area 1 (KQ1), reservoir area 2 (KQ2) and Nayong river sections (NY) were the worst. ③ TN and COD were the main pollution factors in Pingzhai Reservoir.[Conclusion] The water quality evaluation method based on the combined weighted Bayesian model not only distinguishes the difference of contribution of each evaluation factor to water quality, but also evaluates the water quality accurately. Overall, the water quality of Pingzhai Reservoir meets the corresponding requirements of water function zoning in Guizhou Province.
Key words:  combination weighting  Bayesian model  water quality evaluation  Pingzhai Reservoir