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Combining multivariate statistical analysis with geographic information systems mapping: a tool for delineating groundwater contamination

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Abstract

Multivariate Statistical Analysis (MSA) has successfully been coupled with geographic information system (GIS) mapping tools to delineate zones of aquifer contamination potential. While delineating contaminants is key to site remediation, it is often compromised by a poor understanding of hydrogeologic conditions, and by uncertainties in contaminant observations. MSA provides improved estimates of contamination potential by augmenting observed contaminant concentrations with auxiliary information from other water-quality parameters. GIS is useful for organizing and managing water-quality information by visually communicating large amounts of information. The proposed method first establishes appropriate areal extents, GIS coverages, and scales for displaying groundwater contamination concentrations of tritium and the volatile organic contaminants trichloroethylene (TCE) and tetrachloroethylene (PCE) at the Savannah River Site, South Carolina, USA. Principal components analysis is used to group variables that are most indicative of contamination potential. Tritium contamination potential is best represented as the combination of tritium with the cations Al, Mg, Na and total dissolved solids, while PCE contamination potential is predicted using PCE and Cl. Maps of contamination potentials for 1993–1995 geochemical data compare favorably with measured contaminant concentrations during 1999. Cluster analysis of water-quality data groups geochemical and contaminant concentrations into zones of homogeneous behavior.

Résumé

L’Analyse Statistique Multivariée (ASM) a été couplée avec succès avec les outils cartographiques des Sytèmes d’Information Géographique (SIG), afin de délimiter les zones à potentiel de contamination des aquifères. Alors que la connaissance de l’extension des contaminants est primordiale pour la réhabilitation des sites, elle est fréquemment compromise par une compréhension limitée du contexte hydrogéologique, et par les incertitudes sur l’observation des contaminants. L’ASM fournit une meilleure estimation du potentiel de contamination, en complétant les concentrations observées en contaminants par des informations annexes issues d’autres paramètres qualitatifs. Les SIG, par leurs capacités à conjuguer visuellement de grandes quantités d’information, sont utiles pour organiser et gérer les informations sur la qualité de l’eau. La première étape de la méthode proposée consiste à définir les limites spatiales appropriées, les couvertures SIG, ainsi que les échelles de visualisation des concentrations en contaminants des eaux souterraines, soit le tritium et les composés organiques volatiles trichloroéthylène et tetrachloroéthylène, sur le site de la rivière Savannah (Californie du Sud, USA). L’analyse en composantes principales est utilisée afin de regrouper les variables caractérisant de manière optimale le potentiel de contamination. Le potentiel de contamination en tritium est représenté le plus significativement par la combinaison du tritium, des cations Al, Mg et Na, et des solides dissous totaux (TDS). Le potentiel de contamination en tétrachloroéthylène est quant à lui estimé en utilisant le tetrachloroéthylène et Cl. Les cartes des potentiels de contamination établies sur les données géochimiques de la période 1993–1995 renvoient une image comparable aux concentrations en contaminants effectivement mesurées en 1999. Une analyse de groupement des données sur la qualité de l’eau rassemble les concentrations géochimiques et en contaminants dans des zones aux comportements homogènes.

Resumen

El Análisis Estadístico Multivariado (AEM) se ha acoplado con éxito con herramientas cartográficas del Sistema de Información Geográfico (SIG), para delinear zonas de potencial contaminación de acuíferos. En tanto que la delimitación de contaminantes es importante para recuperación del sitio, ella está a menudo amenazada por una comprensión pobre de las condiciones hidrogeológicas, y por las incertidumbres en las observaciones del contaminante. El AEM proporciona estimaciones mejoradas de contaminación potencial al aumentar las concentraciones observadas del contaminante, con la información auxiliar de otros parámetros de calidad de agua. El SIG es útil para organizar y manejar la información de calidad de agua, a través de la comunicación visual de cantidades grandes de información. El método propuesto establece primero magnitudes de área apropiadas, el área cubierta por el SIG, y las escalas para mostrar las concentraciones de contaminación del agua subterránea, a partir de tritio y de contaminantes orgánicos volátiles como tricloroetileno (TCE) y tetracloroetileno (PCE) en el sitio del Río al Savannah, Carolina del Sur, EE.UU. El análisis de componentes principales se usa para agrupar variables que son más indicativas de contaminación potencial. El potencial de contaminación Tritio, se representa mejor como la combinación de tritio con los cationes Al, Mg, Na y los sólidos disueltos totales, mientras la contaminación potencial de PCE se predice usando PCE y Cl. Los mapas de potenciales de contaminación para los datos geoquímicos de 1993–1995 se comparan favorablemente con las concentraciones medidas del contaminante durante 1999. El análisis de racimo hecho en datos de calidad de agua, agrupa concentraciones geoquímicas y concentraciones del contaminante en las zonas de comportamiento homogéneo.

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References

  • Aadland RK, Gelici JA, Thayer PA (1995) Hydrogeologic framework of west-central South Carolina. Report 5. South Carolina Department of Natural Resources, Water Resources Division, Columbia, SC

    Google Scholar 

  • Abu-Jaber NS, El Aloosy AS, Jawad Ali A (1997) Determination of aquifer susceptibility to pollution using statistical analysis. Environ Geol 31(1–2):92–106

    Google Scholar 

  • Arnett MW, Karapatakis LK, MA/Mtey AR (1995) Savannah River Site environmental report for 1995. Westinghouse Savannah River Corporation, Savannah River Site, Aiken, SC

    Google Scholar 

  • Bollinger J (1999) ArcView geographic information systems interface to the geochemical information management system. Westinghouse Savannah River Corporation, Savannah River Technology Center, Aiken, SC

    Google Scholar 

  • Burrough PA, McDonnell RA (1998) Principles of geographical information systems for land resources assessment. Oxford University Press, New York

    Google Scholar 

  • Ceron JC, Jimenez-Espinosa R, Pulido-Bosch A (2000) Numerical analysis of hydrogeochemical data: a case study. Appl Geochem 15:1053–1067

    Article  Google Scholar 

  • Fetter CW (1994) Applied hydrogeology, 3rd edn. Prentice Hall, New York

    Google Scholar 

  • Grande JA, Gonzalez A, Beltran R, Sanchez-Rodas D (1996) Application of factor analysis to the study of contamination in the aquifer system of Ayamonte-Huelva (Spain). Ground Water 34(1):155–163

    Article  Google Scholar 

  • Guan W, Chamberlain RH, Sabol BM, Doeringand PH (1999) Mapping submerged aquatic vegetation in the Caloosahatchee Estuary: Evaluation of different interpolation methods. Mar Geod 22:69–91

    Article  Google Scholar 

  • Güler C, Thyne GD, McCray JE, Turner AK (2002) Evaluation of graphical and multivariate statistical methods for classification of water chemistry data. Hydrogeol J 11:607–608

    Article  Google Scholar 

  • Harris MK, Flach GP, Thayer PA (1997) Groundwater flow and tritium migration in coastal plain sediments, Savannah River Site, South Carolina. WSRC-MS-97-0075, Westinghouse Savannah River Corporation, Aiken, SC

    Google Scholar 

  • Kehew AE (2001) Applied chemical hydrogeology. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  • Mathes SE (2002) Geographic information systems (GIS) mapping of groundwater contamination at the Savannah River Site (SRS). MSc Thesis, The University of Georgia, Athens, GA

  • Meng SX, Maynard JB (2001) Use of statistical analysis to formulate conceptual models of geochemical behavior: water chemical data from the Botucatu aquifer in Sao Paulo state, Brazil. J Hydrol 250:78–97

    Article  Google Scholar 

  • Miller RB, Castle JW, Temples TJ (2000) Deterministic and stochastic modeling of aquifer stratigraphy, South Carolina. Ground Water 38(2):284–295

    Article  Google Scholar 

  • Ochsenkühn KM, Kontoyannakos J, Ochsenkühn-Petroulu M (1997) A new approach to a hydrochemical study of groundwater flow. J Hydrol 194:64–75

    Article  Google Scholar 

  • SAS Institute Inc. (2005) Website for SAS Institute Inc., http://www.sas.com. Accessed December 2005

  • SPSS Inc. (2005) Website for SPSS Inc., http://www.spss.com. Accessed December 2005

  • Statsoft Inc. (2002) Electronic statistics textbook, principal components and factor analysis, http://www.statsoftinc.com/textbook/stfacan.html. Accessed August 2005

  • Suk H, Lee KK (1999) Characterization of a ground water hydrochemical system through multivariate analysis: clustering into ground water zones. Ground Water 37(3):358–366

    Article  Google Scholar 

  • Vidal M, Melgar J, Lopez A, Santoalla MC (2000) Spatial and temporal hydrochemical changes in groundwater under the contaminating effects of fertilizer and wastewater. J Environ Manage 60:215–225

    Article  Google Scholar 

  • Zanini L, Novakowski KS, Lapcevic P, Bickerton GS, Voralek J, Talbot C (2000) Ground water flow in a fractured carbonate aquifer inferred from combined hydrogeological and geochemical methods. Ground Water 38(3):350–360

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by a grant from the US Department of Energy, Westinghouse Savannah River Corporation through the Education, Research, and Development Association of Georgia Universities. Technical support was provided by John Reed and Jim Bollinger of the Savannah River Site. Editorial assistance provided by Sue Duncan substantially improved the quality of this manuscript.

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Correspondence to Todd C. Rasmussen.

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Mathes, S.E., Rasmussen, T.C. Combining multivariate statistical analysis with geographic information systems mapping: a tool for delineating groundwater contamination. Hydrogeol J 14, 1493–1507 (2006). https://doi.org/10.1007/s10040-006-0041-4

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