Using multivariate statistical analysis of groundwater major cation and trace element concentrations to evaluate groundwater flow in a regional aquifer

Abstract

Abstract:Groundwater samples were collected from 11 springs in Ash Meadows National Wildlife Refuge in southern Nevada and seven springs from Death Valley National Park in eastern California. Concentrations of the major cations (Ca, Mg, Na and K) and 45 trace elements were determined in these groundwater samples. The resultant data were subjected to evaluation via the multivariate statistical technique principal components analysis (PCA), to investigate the chemical relationships between the Ash Meadows and Death Valley spring waters, to evaluate whether the results of the PCA support those of previous hydrogeological and isotopic studies and to determine if PCA can be used to help delineate potential groundwater¯ow patterns based on the chemical compositions of groundwaters. The results of the PCA indicated that groundwaters from the regional Paleozoic carbonate aquifers (all of the Ash Meadows springs and four springs from the Furnace Creek region of Death Valley) exhibited strong statistical associations, whereas other Death Valley groundwaters were chemically dierent. The results of the PCA support earlier studies, where potentiometric head levels, d18 O and dD, geological relationships and rare earth element data were used to evaluate groundwater¯ow, which suggest groundwater¯ows from Ash Meadows to the Furnace Creek springs in Death Valley. The PCA suggests that Furnace Creek groundwaters are moderately concentrated Ash Meadows groundwater, re¯ecting longer aquifer residence times for the Furnace Creek groundwaters. Moreover, PCA indicates that groundwater maȳ ow from springs in the region surrounding Scotty's Castle in Death Valley National Park, to a spring discharging on the valley¯oor. The study indicates that PCA may provide rapid and relatively cost-eective methods to assess possible groundwater¯ow regimes in systems that have not been previously investigated.

Hydrogeochemical Characteristics and Groundwater Quality Evaluation Based on Multivariate Statistical Analysis

“…PCA can be used to simplify data, determine the association between variables and samples, evaluate the clustering or similarity of data [14,[23][24][25], and determine the source of differences between parameters [12]. In PCA, the main components of groundwater data are extracted.…”

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

“…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.…”

Investigation of tectonically affected groundwater systems through a multidisciplinary approach

“…The area is lacking in outcrops and has a large number of springs. To the best of the authors' knowledge, such an integrated approach has never been undertaken to study such a complex area, although examples of the use of PCA for groundwater characterization may be found in the literature (Stetzenbach et al, 1999;Koonce et al, 2006;Dassi, 2011). As a result, this integrated method allows full characterization of mountain aquifers with complex geological conditions and the assessment of the role played by faults and fracture systems on groundwater circulation.…”