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洱海流域水质时空变化特征 被引量:24

Spatial and Temporal Variation of Water Quality of Erhai Basin
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摘要 依据2009年洱海流域62个样点的水质调查数据,应用聚类分析对洱海流域水质时空变化特征进行分析,将样点在空间上分为4组,分别为无干扰的苍山林区源头溪流(A1)、受到一定程度人类干扰的流经农业区和城镇区溪流的中上游段及湖泊(A2)、干扰较严重的流经农业区和城镇区溪流的下游段(A3)以及干扰最为严重的洱海岸边带(A4),判别分析结果表明聚类准确率达95.2%。应用主成分分析方法得到影响洱海流域水质的4个主成分为:第1主成分,Alk、Ca2+、Mg2+、HS;第2主成分,TP;第3主成分,TN和NO3--N;第4主成分,DOC和TOC,这些主成分空间差异明显。在时间尺度上,Alk表现为丰水期高于枯水期;Ca2+表现为枯水期高于丰水期;TP没有表现出明显的季节性变化规律;TN在A1和A3组表现为丰水期高于枯水期。 Based on the water quality survey of the Erhai basin in 2009,covering 62 sampling points,cluster analysis was done of the data for characterization of the spatial and temporal variation of water quality of the Erhai basin.Spatially,the sampling points were divided into 4 groups,that are,Group A1,source streams in the interference-free Cangshan forest;Group A2,upper and middle reaches of the streams and lakes in the farming areas and towns that are subjected to some extent of human interference;Group A3,lower reaches of the streams running through the farming areas and towns that are subjected to severe human interference;and Group A4,offshore of the Erhai Lake that is most seriously interfered by mankind.Discriminatory analysis(DA) was used to identify significant indexes,and to reflect results of the spatial cluster analysis,and the results of the analysis show that the clustering reached 95.2% in accuracy.By means of principal component analysis(PCA),four groups of principal components were identified.Principal Component Group 1 included alkalinity,Ca2+,Mg2+ and hardness;Group 2,TP;Group 3,TN and NO-3-N;and Group 4,DOC and TOC.Spatial differences were significant between these groups.On a time scale,Alk was higher in the wet season than in the dry season and Ca2+ the other way round.TP did not vary much,while TN in Group A1 and Group A3 was higher in the wet season than in the dry season.
出处 《生态与农村环境学报》 CAS CSSCI CSCD 北大核心 2011年第4期14-20,共7页 Journal of Ecology and Rural Environment
基金 国家水体污染控制与治理科技重大专项(2008ZX07526-002-07)
关键词 洱海流域 水质时空变化 主成分分析 聚类分析 判别分析 Erhai basin spatial and temporal variations of water quality principal component analysis cluster analysis discriminatory analysis
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