2008
DOI: 10.1111/j.1745-6584.2007.00404.x
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Multivariate Analyses of Water Chemistry: Surface and Ground Water Interactions

Abstract: Multivariate statistical methods (MSMs) applied to ground water chemistry provide valuable insight into the main hydrochemical species, hydrochemical processes, and water flowpaths important to ground water evolution. The MSMs of principal component factor analysis (FA) and k-means cluster analysis (CA) were sequentially applied to major ion chemistry from 211 different ground water-sampling locations in the Amargosa Desert. The FA reduces the number of variables describing the system and finds relationships b… Show more

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Cited by 66 publications
(46 citation statements)
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“…The goal of PCA is to describe geochemistry using a small number of linear combinations of the original variables that summarize a majority of the variability contained in the original data set (i.e. Cloutier et al 2008;Mencio and Mas-Pla 2008;Woocay and Walton 2008). We used the PRINCOMP procedure in SAS to do the analysis for the restoration site chemistry data (SAS Institute Inc., Cary, NC).…”
Section: Multivariate Statistical Analysismentioning
confidence: 99%
“…The goal of PCA is to describe geochemistry using a small number of linear combinations of the original variables that summarize a majority of the variability contained in the original data set (i.e. Cloutier et al 2008;Mencio and Mas-Pla 2008;Woocay and Walton 2008). We used the PRINCOMP procedure in SAS to do the analysis for the restoration site chemistry data (SAS Institute Inc., Cary, NC).…”
Section: Multivariate Statistical Analysismentioning
confidence: 99%
“…DO concentrations, chemical species sensitive to biogeochemical cycling or changing redox conditions, and those correlated with these species were incorporated into a square-root-transformed principle component analysis (PCA) Mencio and Mas-Pla, 2008;Woocay and Walton, 2008], or for samples with missing values, probabilistic PCA [Roweis, 1998;Tipping and Bishop, 1999;Porta et al, 2005].…”
Section: Data Analysis Methodsmentioning
confidence: 99%
“…The application of multivariate statistical methods to geo-environmental data sets have facilitated the unveiling of hidden structures in the data sets and assisted in resolving key geo-environmental problems at various scales (Sandaw et al 2012). Multivariate analysis of geochemical data operated on the concept that each aquifer zone has its own unique groundwater quality signature, based upon the chemical makeup of the sediments that comprise it (Fetter 1994;Suk and Lee 1999;Woocay and Walton 2008). The application of statistical analysis thus helps in the interpretation of complex data matrices to better understand the water quality as well as identify the possible factors that influence the water chemistry in a region.…”
Section: Introductionmentioning
confidence: 99%