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Assessment of water quality in and around Jia-Bharali river basin, North Brahmaputra Plain, India, using multivariate statistical technique

Khound, Nayan J., Bhattacharyya, Krishna G.
Applied water science 2018 v.8 no.8 pp. 221
World Health Organization, agricultural runoff, anions, bicarbonates, calcium, cations, chemical oxygen demand, chlorides, data collection, fluorides, groundwater, hardness, hydrochemistry, iron, magnesium, multivariate analysis, nitrates, pH, phosphates, potassium, principal component analysis, sodium, sulfates, surface water, total dissolved solids, water quality, water quality standards, water utilities, watersheds, India
The present study envisages the application of multivariate analysis, water utility class and conventional graphical representation to reveal the hidden factor responsible for deterioration of water quality and determine the hydrochemical facies of water sources in Jia-Bharali river basin, North Brahmaputra Plain, India. Fifty groundwater and 35 surface water samples were collected and analyzed for 15 parameters viz pH, TDS, hardness, COD, Ca²⁺, Mg²⁺, Na⁺, K⁺, Fe, HCO₃⁻, Cl⁻, SO₄²⁻, NO₃⁻, PO₄³⁻ and F⁻ for a period of 3 hydrological years (2009–2011) in six different seasons (three wet and three dry). The results were evaluated and compared with WHO and BIS water quality standards. Except Fe (> 0.3 mg/L), all parameters were found well within the desirable limit of WHO and BIS for drinking water. Ca²⁺ and HCO₃⁻ were dominant ions among cations and anions. The piper trilinear diagram classified majority of water samples for both seasons fall in the fields of Ca²⁺–Mg²⁺–HCO₃⁻ water type indicating temporary hardness. Varimax factors extracted by principal component analysis indicates anthropogenic (domestic and agricultural runoff) and geogenic influences on the trace elements. Hierarchical cluster analysis grouped water sources into three statistically significant clusters based on the similarity of water quality characteristics. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex datasets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective water quality management.