2018
DOI: 10.1007/s11356-018-1519-z
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Spatial variation, source identification, and quality assessment of surface water geochemical composition in the Indus River Basin, Pakistan

Abstract: The Indus River Basin (IRB) with an area of 139,202 km is the lifeline river basin of Pakistan. An intensive study was conducted in six subcatchments of the IRB with five in the Upper Indus Basin (UIB) and one of the Lower Indus Basin (LIB; between Tarbela Dam and Panjand), i.e., the Gilgit River Basin (UIB-I), Hunza River Basin (UIB-II), UIB-III, UIB-IV and UIB-V, and LIB. A total of 84 surface water samples were collected from main stream and tributaries from June to August, 2016. The pH, electric conductivi… Show more

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Cited by 51 publications
(21 citation statements)
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“…Spearman's rank correlation was used to assess the potential associations between the TEs as data were not normally distributed [32]. Hierarchical agglomerative cluster analysis (CA) was performed to assess the variability of the TEs on the dataset and to group the similar sampling sites [33,34]. Also, principal component analysis (PCA) was applied to identify the potential pollutants' sources in the lake water [35].…”
Section: Discussionmentioning
confidence: 99%
“…Spearman's rank correlation was used to assess the potential associations between the TEs as data were not normally distributed [32]. Hierarchical agglomerative cluster analysis (CA) was performed to assess the variability of the TEs on the dataset and to group the similar sampling sites [33,34]. Also, principal component analysis (PCA) was applied to identify the potential pollutants' sources in the lake water [35].…”
Section: Discussionmentioning
confidence: 99%
“…The mean Cu concentrations recorded in the five rivers in the study did not exceed the EQS for water Principal component analysis (PCA) is a powerful technique that allows the dimensionality of a data set to be reduced in such a way as to highlight similarities and differences [34,35]. PCA generated five main components and showed that only two factors were necessary for the Eigen values, with values above 0.5, which represents 83.8% of the total variance.…”
Section: Analysis and Distribution Of Heavy Metals And Arsenic In Surface Watermentioning
confidence: 93%
“…Among these compounds, ethyl 2-methylpropanoate (0.12 mg/kg), ethyl butanoate (0.59 mg/kg), ethyl 2-methylbutyrate (0.24 mg/kg), 3-methylbutyl acetate (0.19 mg/kg), E-2-hexenal (1.78 mg/kg), ethyl hexanoate (2.21 mg/kg), 3,7-dimethyl-1,3,6-Octatriene (0.49 mg/kg), 2-Heptanol (1.06 mg/kg), ethyl heptanoate (0.02 mg/kg), ethyl E-2-hexenoate (0.04 mg/kg), and etheyl octanoat (0.54 mg/kg) were present at relatively low concentrations (<1 mg/kg). Despite their low levels, the OAVs of these compounds were above 10, so these compounds might significantly contribute to the aroma of RRT fruits [20]. Ethyl 2-methylpropanoate (OAV of 1167), ethyl butanoate (OAV of 3279), ethyl 2-methylbutyrate (OAV of 811), and ethyl hexanoate (OAV of 2205) were present with much higher OAVs than other compounds.…”
Section: (B)mentioning
confidence: 98%