Water Quality Assessment of an Unexplored Tropical Freshwater System in Thiruvananthapuram, India: A Multivariate Statistical Approach

Ajayan, Anila P.; Rijstenbil, J.W.; Kumar, Krishna

doi:10.1007/978-3-319-58538-3_30-1

Cited by 1 publication

(1 citation statement)

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“…The Mthatha River Catchment (MRC) authority has been challenged to address the pollutant issues that are coming from the rampant effluent discharge from the Mthatha Prison and the Efata School for the Blind and Deaf which have caused ineffable damage to the MRC water quality. This has triggered different studies on both preventive and curative ways of regulating and maintaining the river's health for the general populace's uses (Achieng et al, 2017;Ajayan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Surface Water Quality Indices in Mthatha River Using Multivariate Statistical Techniques

Makupula

Taiwo

Abayomi

et al. 2023

indones. j. env. man. sus.

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Mthatha town of Eastern Cape Province, South Africa has been challenged to address the pollutant issues that are coming from rampant densification and effluent concentration discharge from the Mthatha Correctional Services Centre and the Efata School for the Blind and Deaf which have caused ineffable impaired damage to the Mthatha River Catchment (MRC). This paper is aimed at identifying drivers of poor water quality in the catchment and classified the River’s water quality into different cluster groups for proper pollutant source control measures. Water quality parameters data comprising of pH; conductivity; Phosphorus; Ammonia (NH4-N); Feacals; and E-coli covering 95 percent and 105 percent of the upstream and downstream sections of the River were available at ten monitored sites of the river catchment. These datasets covering eight years 2012-2020 were analysed in this study. Factor analysis as a choice of principal component analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) was used to deduce inferences for the pollutants’ subsequent classification. The results classified the catchment into three different clusters of lower pollutant (LP), medium pollutant (MP), and high pollutant (HP) areas, with PC1 accounting for 84.54% of the total variance from the three components classification. Adaptive catchment managers would find usefulness in the employed statistical tools in ensuring real-time measures for river non-point pollutants sources control that could offer additional benefits in maintaining a safe life above and below water in the preservation of their public values benefit. The study recommends the issuance of compliance notices and non-point pollutant source control measures to improve the water quality (WQ) parameters.

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Section: Introductionmentioning

confidence: 99%