Factor analytical approaches for evaluating groundwater trace element chemistry data

Farnham, Irene; Johannesson, Karen H.; Singh, Ashok K.; Hodge, Vernon F.; Stetzenbach, Klaus J.

doi:10.1016/s0003-2670(03)00350-7

Cited by 224 publications

(105 citation statements)

References 26 publications

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“…Multivariate statistical methods used in this study are not described in detail as these techniques are already well described in literature (Wold et al 1987;Farnham et al 2003;Chen et al 2007). In brief, a multivariate data analysis is a statistical technique to analyze complex data sets with multiple variables.…”

Section: Discussionmentioning

confidence: 99%

“…In order to understand the relationship and underlying common characteristics among trace elements, an appropriate data analysis technique is necessary. Multivariate analyses such as factor analysis and/or cluster analysis have been applied extensively to analyze groundwater quality data including major ions (Stetzenbach et al 1999;Kim et al 2005;Lee et al 2008;Shyu et al 2011) and trace elements (Stetzenbach et al 1999;Farnham et al 2003; Koonce et al 2006;Chen et al 2007). These techniques can simplify and clarify the groundwater quality data by reducing a number of variables into a small number of factors, hereby extracting meaningful information regarding the environmental aquatic chemistry.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate analysis of trace elements in shallow groundwater in Fuchu in western Tokyo Metropolis, Japan

Thuyet

Saito

et al. 2016

Environ Earth Sci

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A rapid increase in the use of ground source heat pump (GSHP) systems for heating and cooling of buildings potentially threatens to harm shallow groundwater quality. As a necessary preliminary step toward evaluating GSHP impact on shallow groundwater quality, this study used multivariate statistical analyses to investigate characteristics of trace elements in groundwater in Fuchu city in western Tokyo Metropolis. Water samples were collected twice a month from two aquifers at 30-and 45-m depths using four observation wells for more than a year. Concentrations of 14 trace elements (Li, B, Al, Cr, Mn, Fe, Ni, Cu, As, Se, Sr, Cd, Sb, and Pb) and Si as well as oxidation-reduction potential (ORP), dissolved oxygen (DO), electrical conductivity (EC), and pH were measured. All elements were under the environmental water quality criteria issued by the Japanese Ministry of the Environment. No clear seasonal trends were observed in the concentrations of 15 elements in groundwater. Principal component analyses showed the element properties were well characterized by three principal components (PC). PC1, which represents the more mobile trace elements in the groundwater, consisted of Li, B, Mn, Fe, As, and Si. PC2 included Al, Ni, Cd, and Sb, and represents ORPinsensitive properties. PC3 consisted of Pb, Cu, and Se, which are less mobile in groundwater. Samples collected from the same aquifer had high similarity in distribution and variation of the trace elements. This study proposes a useful statistical platform for assessing future effects of GSHP systems on shallow groundwater quality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate analysis of trace elements in shallow groundwater in Fuchu in western Tokyo Metropolis, Japan

Thuyet

Saito

et al. 2016

Environ Earth Sci

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“…The loadings were evaluated to determine the chemical components responsible for the observed correlations. The chemical components with the largest positive and negative loadings make the biggest contribution (Farnham et al 2003). The first principal component (PC 1) and second principal component (PC 2) values of groundwaters are shown in Online Resource 3.…”

Section: End-member Mixingmentioning

confidence: 99%

Hydrochemical disturbances measured in groundwater during the construction and operation of a large-scale underground facility in deep crystalline rock in Japan

et al. 2015

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Changes in the hydrochemical conditions of groundwater were evaluated following the construction of a large-scale underground facility at the Mizunami Underground Research Laboratory (MIU), Japan. The facility was constructed to a depth of 500 m in sedimentary and granitic rocks. Drawdown of the groundwater level in the range of several tens to hundreds of meters was observed up to hundreds of meters away from the shafts during the first ten years of facility construction and operation. Subsequent changes in groundwater chemistry occurred due to upconing of high-salinity groundwater from the deepest part of the shaft and the infiltration of low-salinity shallow groundwater. We predict that future deep groundwater chemistry in the vicinity of the MIU facility will resemble that of the present-day shallow groundwater. Multivariate statistical analysis provides fundamental insights into such a site. We found that the extent of hydrochemical variability related to MIU construction and operation was dependent on the distance from the facility shafts and galleries and on hydrogeological compartmentalization resulting from lithological boundaries (such as permeable conglomerates vs. more compact lithological units) and other features (such as faults or clay layers). We conclude that hydrochemical impact assessment of groundwater in low-permeability rock is essential prior to the construction of such a facility. This should include characterization of hydrogeological structures and compartments to propose suitable location of shafts and galleries.

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“…Farnham et al [13] applied principal components and clusters analysis to 2 Geofluids trace element chemistry of groundwater and identify rockwater interaction processes. In the study of Cloutier et al [14], multivariate statistical methods were applied to classify the groundwater samples and to identify geochemical processes controlling groundwater geochemistry.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Origin and Evolution of Formation Water in Buried Hill of Jizhong Depression, China, Using Multivariate Statistical Analysis of Geochemical Data

Zeng

2017

Geofluids

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Groundwater samples from buried hill of Jizhong Depression were evaluated using two statistical analyses: hierarchical cluster analysis (HCA) and principal component analysis (PCA). The samples were classified into four clusters, C1-C4, in HCA and the hydrochemical types of C1-C4 are HCO 3 -Na, Cl⋅HCO 3 -Na, Cl-Na, and Cl-Na⋅Ca. From C1 to C2, C3, and C4, the water-rock interaction becomes increasingly intensive, and rNa/rCl gets lower while total dissolved solids and r(Cl-Na)/rMg get higher. Three components of PCA explain 86.87% of the variance. Component1 (PC1), characterized by highly positive loadings in Na + and Cl − , is related to evaporation concentration. Component2 (PC2) is defined by highly positive loading in HCO 3 − and is related to influence of atmospheric water. With high positive loadings in Ca 2+ and high negative loadings in Na + and SO 4 2− , component3 (PC3) suggests plagioclase albitization. The combination of HCA and PCA within the hydrogeological contexts allowed the division of study area into five dynamic areas. From recharge area to discharge area, the influence of atmospheric water gets weaker and water-rock interactions such as evaporation concentration and plagioclase albitization become intensive. Therefore groundwater in buried hill showed paths of hydrochemical evolution, from C1, to C2, C3, and C4. Buried hill reservoir in Jizhong Depression is mainly distributed in hydrodynamic blocking and discharge area; therefore the two regions can be the favorable areas for petroleum migration.

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Factor analytical approaches for evaluating groundwater trace element chemistry data

Cited by 224 publications

References 26 publications

Multivariate analysis of trace elements in shallow groundwater in Fuchu in western Tokyo Metropolis, Japan

Multivariate analysis of trace elements in shallow groundwater in Fuchu in western Tokyo Metropolis, Japan

Hydrochemical disturbances measured in groundwater during the construction and operation of a large-scale underground facility in deep crystalline rock in Japan

Characterization of Origin and Evolution of Formation Water in Buried Hill of Jizhong Depression, China, Using Multivariate Statistical Analysis of Geochemical Data

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