Assessment of surface water quality of Pagladia, Beki and Kolong rivers (Assam, India) using multivariate statistical techniques. Issue 4 (1st October 2020)

• Record Type: Journal Article
• Authors: Singh, Kunwar Raghvendra; Goswami, Ankit Pratim; Kalamdhad, Ajay S; Kumar, Bimlesh
• Is Part Of: International journal of river basin management. Volume 18:Issue 4(2020)
• Page Start: 511
• Page End: 520
• Publication Date: 2020-10-01
• Subjects: Water quality -- Beki river -- Pagladia river -- Kolong river; Watershed management -- Periodicals; Water resources development -- Periodicals; Hydraulic engineering -- Periodicals; Watershed hydrology -- Periodicals; Water resources development; Watershed management; Watersheds; Periodicals; 551.483
• DOI: 10.1080/15715124.2019.1566236
• Languages: English
• ISSNs: 1814-2060

Abstract

ABSTRACT: Surface water quality monitoring and assessment has become a critical issue because it affects human and aquatic life. In this study, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) have been applied to identify the possible pollution sources of the Pagladia, Beki and Kolong rivers. Water samples were collected monthly from 27 sampling sites during May 2016 to April 2017. ANOVA analysis showed that there are no statistically significant differences in pollution status in the Pagladia, Kolong and Beki rivers ( p > .05). CA was carried out to reveal the similarities among the sampling sites. CA grouped all the sampling sites into two clusters. The first cluster corresponded to the less polluted sampling sites and the second group corresponded to the more polluted sampling sites. Backward stepwise DA showed that T and Ca 2+ are the discriminating parameters. PCA was applied on the data set to identify the pollution sources of the surface water. PCA resulted in seven valuable factors for the first cluster, accounting for 90.1% of the total variance, and four valuable factors for the second cluster, accounting for 90.3% of the total variance in water quality data sets. This study illustrated the effectiveness of CA, DA and PCA for a better understanding of large and complex data of surface water quality.