title: [Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].
abstract: Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.
authors.forename: Xiang
authors.initials: X
authors.lastname: Liu
authors.forename: Ling-Peng
authors.initials: LP
authors.lastname: Guo
authors.forename: Fei-Yun
authors.initials: FY
authors.lastname: Zhang
authors.forename: Jie
authors.initials: J
authors.lastname: Ma
authors.forename: Shu-Yong
authors.initials: SY
authors.lastname: Mu
authors.forename: Xin
authors.initials: X
authors.lastname: Zhao
authors.forename: Lan-Hai
authors.initials: LH
authors.lastname: Li
mesh_terms: D016000:Cluster Analysis; D004784:Environmental Monitoring; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D045483:Rivers / Q000737:chemistry*; D012621:Seasons; D012914:Snow / Q000737:chemistry; D062206:Spatial Analysis; D014874:Water Pollutants, Chemical / Q000032:analysis; D060753:Water Quality
chemical_list: D014874:Water Pollutants, Chemical