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Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India
Authors:Kunwar P. Singh   Amrita Malik   Vinod K. Singh   Dinesh Mohan  Sarita Sinha
Affiliation:

aEnvironmental Chemistry Section, Industrial Toxicology Research Centre, Post Box 80, MG Marg, Lucknow 226 001, Uttar Pradesh, India

bNational Botanical Research Institute, Lucknow, Uttar Pradesh, India

Abstract:Water quality data set from the alluvial region in the Gangetic plain in northern India, which is known for high fluoride levels in soil and groundwater, has been analysed by chemometric techniques, such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares (PLS) in order to investigate the compositional differences between surface and groundwater samples, spatial variations in groundwater composition and influence of natural and anthropogenic factors. Trilinear plots of major ions showed that the groundwater in this region is mainly of Na/K-bicarbonate type. PCA performed on complete data matrix yielded six significant PCs explaining 65% of the data variance. Although, PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types (dug well, hand-pump and surface water). However, a visible differentiation between the water samples pertaining to two watersheds (Khar and Loni) was obtained. DA identified six discriminating variables between surface and groundwater and also between different types of samples (dug well, hand pump and surface water). Distinct grouping of the surface and groundwater samples was achieved using the PLS technique. It further showed that the groundwater samples are dominated by variables having origin both in natural and anthropogenic sources in the region, whereas, variables of industrial origin dominate the surface water samples. It also suggested that the groundwater sources are contaminated with various industrial contaminants in the region.
Keywords:Alluvial aquifer   Groundwater   Chemometric techniques   Principal component analysis   Discriminant analysis   Partial least squares
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