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基于多元统计分析的汉丰湖水质评估及原因解析
引用本文:乔哲,何丙辉,李天阳,王德宝.基于多元统计分析的汉丰湖水质评估及原因解析[J].农业环境科学学报,2020,37(2):261-269.
作者姓名:乔哲  何丙辉  李天阳  王德宝
作者单位:西南大学资源环境学院/三峡库区生态环境教育部重点实验室, 重庆 400715,西南大学资源环境学院/三峡库区生态环境教育部重点实验室, 重庆 400715,西南大学资源环境学院/三峡库区生态环境教育部重点实验室, 重庆 400715,西南大学资源环境学院/三峡库区生态环境教育部重点实验室, 重庆 400715
基金项目:国务院三峡建设委员会办公室生态与环境系统重点支流水质监测项目(JJ2015-016);中国科学院科技服务网络计划(STS计划)重大项目(KFJ-EW-STS-008)
摘    要:为明确三峡库区最大的库中湖——汉丰湖水质的时空变化特征及其主要影响因素,在汉丰湖来水区、湖区、出水区设置14个采样点,于2015年每月下旬在各点位采集水样并分析主要水质指标,综合运用多种统计评价方法,解析汉丰湖水质的时空变化特征及主导因素。结果表明,汉丰湖水质空间变化可聚类为4个区域:南河来水区(区域1)、东河来水区(区域2)、汉丰湖上游(区域3)和汉丰湖中下游(区域4),其月份变化可聚类为2个时段:蓄水期(1-2月及9-12月)和泄水期(3-8月)。营养状态指数分析显示区域4中的调节坝(HF5)及调节坝下(XJ1)水质呈中营养状态,而其余点位及区域水质均为轻度富营养,各月份水质表现为1月、12月为中营养,10月为中度富营养,其他月份均为轻度富营养。汉丰湖水体中总氮(TN)、铵态氮(NH4+-N)、硝态氮(NO3--N)、总磷(TP)、可溶性磷(DP)、正磷酸盐(PO43--P)、高锰酸盐指数(CODMn)浓度均表现为自上游向下游降低趋势。主成分分析揭示流域水土流失是汉丰湖水质恶化的主要诱因,而三峡库区水位调控也对湖区水质产生重要影响。

关 键 词:水环境,时空变化,富营养化,主成分分析,聚类分析
收稿时间:2019/1/8 0:00:00

Comprehensive assessment of water quality in Hanfeng Lake using multivariate analysis
QIAO Zhe,HE Bing-hui,LI Tian-yang and WANG De-bao.Comprehensive assessment of water quality in Hanfeng Lake using multivariate analysis[J].Journal of Agro-Environment Science( J. Agro-Environ. Sci.),2020,37(2):261-269.
Authors:QIAO Zhe  HE Bing-hui  LI Tian-yang and WANG De-bao
Institution:Key Laboratory of the Three Gorges Reservoir Region''s Eco-Environment, Ministry of Education, College of Resource and Environment, Southwest University, Chongqing 400715, China,Key Laboratory of the Three Gorges Reservoir Region''s Eco-Environment, Ministry of Education, College of Resource and Environment, Southwest University, Chongqing 400715, China,Key Laboratory of the Three Gorges Reservoir Region''s Eco-Environment, Ministry of Education, College of Resource and Environment, Southwest University, Chongqing 400715, China and Key Laboratory of the Three Gorges Reservoir Region''s Eco-Environment, Ministry of Education, College of Resource and Environment, Southwest University, Chongqing 400715, China
Abstract:In this study, we aimed to clarify the temporal and spatial variation and main influencing factors of water quality in Hanfeng Lake, which is the largest lake in the Three Gorges Reservoir(TGR) region of China. A total of 14 sampling sites were studied across different regions, including the inflow, main lake body, and outlet areas of the Hanfeng Lake basin. Water samples were collected at each sampling site at the end of each month in 2015 and water quality parameters were analyzed. Multiple statistical techniques were used to identify spatial and temporal variations in water quality. The water quality of Hanfeng Lake could be clustered into four spatial regions, including the incoming water area of Nan River(Region 1), the incoming water area of East River(Region 2), the upper reaches of Hanfeng Lake (Region 3), and the middle and lower reaches of Hanfeng Lake(Region 4). Variations in water quality could also be grouped into two distinct periods:Storage period(from January to February and September to December) and drainage period(from March to August). Trophic state analysis indicated that water quality in the regulating dam(HF5) and below the regulating dam(XJ1), both of which belonged to Region 4, exhibited moderate eutrophication; Sites in other regions showed slight eutrophication. The eutrophication level was lowest in January and December and highest in October, reaching moderate eutrophication levels; other months showed mild eutrophication levels. The concentrations of total nitrogen(TN), ammonium nitrogen(NH4+-N), nitrate nitrogen(NO3--N), total phosphorus(TP), soluble phosphorus(DP), orthophosphate(PO43--P), and permanganate index(CODMn) showed downward trends from upstream to downstream in the Hanfeng Lake basin. Principal component analysis results implied that soil and water losses contributed most to the deterioration of water quality in the Hanfeng Lake and the regulation of water levels related to the TGR also significantly affected the water quality.
Keywords:water environment  temporal and spatial variation  water eutrophication  principal component analysis  cluster analysis
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