Abstract
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.
Highlights
The availability of good quality water is an indispensible feature for drinking, agriculture, industrial and irrigation purposes (Nagaraju et al 2014) as well as for preventing diseases and improving the quality of life (Nabila et al 2014)
The results showed that the six principle components (PC) accounted for more than 70% of the total variance (73.85% in the dry season and 72.68% in the wet season), which could be usefully applied to identify the main sources of variation in the surface water (SW) chemistry of the study area in both the seasons
This study shows that multivariate analysis is a useful method that could helps in determining the sources and extent of pollution
Summary
The availability of good quality water is an indispensible feature for drinking, agriculture, industrial and irrigation purposes (Nagaraju et al 2014) as well as for preventing diseases and improving the quality of life (Nabila et al 2014). Applied Water Science (2018) 8:221 techniques, such as cluster analysis (CA) and principal component analysis (PCA) are widely used in the interpretation of complex data matrices to evaluate the water quality, ecological status, identification of possible factors that influence water systems and offers a valuable tool for reliable management of water resources as well as rapid solution to pollution problems (Farnham et al 2003; Tanasković et al 2012; Matiatos et al 2014; Kamtchueng et al 2016, Kumar et al 2017) This method helps in the interpretation of natural associations between different variables and highlights the information not available at first glance. The major objectives of this study were to (1) investigate the spatial and temporal variation of water quality parameters of the ground water and the surface water sources and (2) demonstrate the usefulness of the Multivariate statistical analysis to interpret the water quality parameters of the water sources in and around Jia-Bharali river basin of India
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