Assessment of Groundwater Chemistry and Status in a Heavily Used Semi-Arid Region with Multivariate Statistical Analysis

Abstract

This hydrogeological study assessed the quality of phreatic water supplies across the semi-arid, traditional agricultural region of the Yinchuan region in northwest China, near the upper reaches of the Yellow River. We analyzed the chemical characteristics of water collected from 39 sampling stations before the 2011 summer-autumn irrigation period, using multivariate statistical analysis and geostatistical methods. We determined which factors influence the composition of groundwater, using principal component analysis (PCA) and two modes of cluster analysis. PCA showed that the most important variables in the study area were the strong evaporation effect caused by the dry climate, dissolution of carbonate minerals and those containing F − and K − , and human activity including the treatment of domestic sewage and chemical fertilization. The Q-mode of cluster analysis identified three distinct water types that were distinguished by different chemical compositions, while the R-mode of analysis revealed two distinct clusters of sampling stations that appeared to be influenced by distinct sets of natural and/or anthropogenic factors.

Evaluation of seasonal variation of water quality using multivariate statistical analysis and irrigation parameter indices in Ajakanga area, Ibadan, Nigeria

“…PCA is a multivariate statistical procedure which is used to diminish the dimensionality of the original data set consisting of a large number of interrelated variables while still retaining the inherent dependencies existed in the data set (Jianqin et al 2010). Cluster analysis (CA) is a statistical technique that classifies water samples quality parameters into cluster whereby samples/variables within a particular cluster are similar to each other, but dissimilar from other clusters (Zhang et al 2014;Sundaray 2010). CA was performed based on agglomerative schedule using a combination of Ward's linkage method (Ward 1963) and squared Euclidean distances as a measure of similarity between samples and/or parameters (Zhang et al 2014) while PCA extract factor with eigenvalue > 1 which explained more total variation in the data set.…”

“…Cluster analysis (CA) is a statistical technique that classifies water samples quality parameters into cluster whereby samples/variables within a particular cluster are similar to each other, but dissimilar from other clusters (Zhang et al 2014;Sundaray 2010). CA was performed based on agglomerative schedule using a combination of Ward's linkage method (Ward 1963) and squared Euclidean distances as a measure of similarity between samples and/or parameters (Zhang et al 2014) while PCA extract factor with eigenvalue > 1 which explained more total variation in the data set. Only component (factor) with eigenvalue > 1 were retained and later subjected to varimax rotation (Kaiser 1958;Vega et al 1998;Usman et al 2014) before being used for interpretation.…”

“…The suitability of groundwater resources for irrigation purpose was also studied (Sujatha and Reddy 2003;Sadashivaiah et al 2008;Ramesh and Bhuvana 2012). Several published research studies have employed the use of multivariate statistical analysis in the interpretation of groundwater quality data obtained from various sources (Sundaray 2010;Singh et al, 2008;Uddamari et al 2014;Oketola et al 2013;Molla et al 2015;Arslan 2013;Zhang et al 2014;Majolagbe et al 2016;Markic et al 2015;Razmkhah et al 2010). Scientists have also employed the use of principal component analysis (PCA) to study soil physicochemical properties and its geochemical constituents, identification of heavy metals pollutants in soil, analysis of heavy metals presence in dust and evaluation of influence of seasons on air pollution (Adhikari et al 2003;Ma et al 2016;Satyanarayanan et al 2016;Gergen and Harmanescu 2012;Iwara et al 2014;Lu et al 2010;Burt et al 2014;Benhaddya and Hadjel 2014;Abdul Raheem et al 2008).…”

Assessment of Long-Term Evolution of Groundwater Hydrochemical Characteristics Using Multiple Approaches: A Case Study in Cangzhou, Northern China

“…Principal component analysis (PCA) and correlation test (CT) have been conducted by researchers worldwide to make groundwater management decisions [43][44][45][46]. For instance, Guo [44] used PCA to identify the relationship among heavy metals of flood slack water deposits and their possible sources; and Zhang [46] used PCA to determine factors that influence the composition of groundwater.…”

“…For instance, Guo [44] used PCA to identify the relationship among heavy metals of flood slack water deposits and their possible sources; and Zhang [46] used PCA to determine factors that influence the composition of groundwater. In this study, PCA and CT are used to identify the possible sources of major ions in groundwater and hydrogeological reactions that may occur in the study area.…”