Environmental Earth Sciences

, Volume 71, Issue 7, pp 3161–3170 | Cite as

Factorial kriging of a geochemical dataset for heavy-metal spatial-variability characterization

The Wallonian region
Original Article

Abstract

Characterizing the spatial patterns of variability is a fundamental aspect when investigating what could be the causes behind the spatial spreading of a set of variables. In this paper, a large multivariate dataset from the southeast of Belgium has been analyzed using factorial kriging. The purpose of the study is to explore and retrieve possible scales of spatial variability of heavy metals. This is achieved by decomposing the variance-covariance matrix of the multivariate sample into coregionalization matrices, which are, in turn, decomposed into transformation matrices, which serve to decompose each regionalized variable as a sum of independent factors. Then, factorial cokriging is used to produce maps of the factors explaining most of the variance, which can be compared with maps of the underlying lithology. For the dataset analyzes, this comparison identifies a few point scale concentrations that may reflect anthropogenic contamination, and it also identifies local and regional scale anomalies clearly correlated to the underlying geology and to known mineralizations. The results from this analysis could serve to guide the authorities in identifying those areas which need remediation.

Keywords

Factorial kriging analysis Geostatistics Coregionalization Heavy metal contamination Wallonia geochemical data set Belgium 

References

  1. Bartholomé P et al (1977) Métallogénie de la Belgique, des Pays-Bas et du Luxembourg, Rapport nr 1, Belgium, pp 38Google Scholar
  2. Candeias C, Ferreira da Silva E, Salgueiro AR, Pereira HG, Reis AP, Patinha C, Matos JX, Avila PH (2011) The use of multivariate statistical analysis of geochemical data for assessing the spatial distribution of soil contamination by potentially toxic elements in the Aljustrel mining area (Iberian Pyrite Belt, Portugal)Google Scholar
  3. da Silva EF, Avila PF, Salgueiro AR, Candeias C, Pereira HG (2013) Quantitative spatial assessment of soil contamination in S. Francisco de Assis due to mining activity of the Panasqueira mine (Portugal). Environ Sci Pollut Res. doi: 10.1007/s11356-013-1495-2
  4. Delmer A (1912), La question du minerai de fer en Belgique (première partie et deuxiéme partie), Annales des mines de Belgique, Tome XVII", 4ème livraison, 853–940, (1912), and Tome XVIII", 2ème livraison, pp 325–448Google Scholar
  5. Goovaerts P (1997) Geostatistics for natural resources evaluation, 1st ed Oxford university press, Oxford, pp 483Google Scholar
  6. Goovaerts P (1998) Geostatistical tools for characterizing the spatial variability of microbiological and physico-chemical soil properties. Biol Fertility Soils 27(4):315–334CrossRefGoogle Scholar
  7. Goovaerts P (1993) Spatial orthogonality of the principal components computed from coregionalized variables. Mathematical Geolo 25:281–302CrossRefGoogle Scholar
  8. Goovaerts P (1992) Factorial kriging analysis: a useful tool for exploring the structure of multivariate spatial soil information. J Soil Sci 43:597–619CrossRefGoogle Scholar
  9. Goovaerts P (1991) Etude des relations entre propriétés physico-chimiques du sol par la géostatistique multivariable, Cahiers de Géostatistique, In:Compte-rendu des Journées de Géostatistique, Fontainebleau, France 1:247–261Google Scholar
  10. Goulard M, Voltz M (1992) Linear coregionalisation model: tools for estimation and choice of cross-variogram matrix. Math Geol 24(3):269–286CrossRefGoogle Scholar
  11. Guagliardi I, Buttafuoco G, Cicchella D, De Rosa R (2013) A multivariate approach for anomaly separation of potentially toxic trace elements in urban and peri-urban soils: an application in a southern Italy area. J Soils Sediments 13(1):117–128CrossRefGoogle Scholar
  12. Huang L-M, Deng C-B, Huang N, Huang X-J (2013) Multivariate statistical approach to identify heavy metal sources in agricultural soil around an abandoned PbZn mine in Guangxi Zhuang Autonomous Region, China. Environ Earth Sci 68(5):1331–1348CrossRefGoogle Scholar
  13. Khedhiri S, Semhi Kh, Duplay J, Darragi F (2011) Comparison of sequential extraction and principal component analysis for determination of heavy metal partitioning in sediments: the case of protected Lagoon El Kelbia (Tunisia). Environ Earth Sci 62(5):1013–1025CrossRefGoogle Scholar
  14. Keshav Krishna A, Rama Mohan K, Murthy NN, Periasamy V, Bipinkumar G, Manohar K, Srinivas Rao S (2013) Assessment of heavy metal contamination in soils around chromite mining areas, Nuggihalli, Karnataka, India. Environ Earth Sci. doi: 10.1007/s12665-012-2153-6
  15. Liebens J, Mohrherr C J, Ranga Rao K (2012) Trace metal assessment in soils in a small city and its rural surroundings, Pensacola, FL. Environ Earth Sci 65(6):1781–1793CrossRefGoogle Scholar
  16. Maria Astel A, Chepanova L, Simeonov V (2011) Soil contamination interpretation by the use of monitoring data analysis. Water Air Soil Pollut. 216(1–4):375–390CrossRefGoogle Scholar
  17. Matheron G (1982) Pour une analyse krigeante des données régionalisées, Note interne N-732, Centre de Géostatistique, Fontainbleau, FranceGoogle Scholar
  18. Queiroz JCB et al (2008) Geochemical characterization of heavy metal contaminated area using multivariate factorial kriging. Environ Geol 55:95-105Google Scholar
  19. Rodríguez Martín JA, Vázquez de la Cueva A, Grau Corbí JM, López Arias M (2007) Factors Controlling the Spatial Variability of Copper in Topsoils of the Northeastern Region of the Iberian Peninsula, Spain. Water Air Soil Pollut 186(1–4):311–321CrossRefGoogle Scholar
  20. Sondag F, Martin H (1984) Inventaire géochimique des ressources métallifères de la Wallonie.In: Synthèse générale et rapport de fin de recherches, UCL, Projet Ministère de l’économie Wallonne, Belgique, pp 15Google Scholar
  21. Wackernagel H (1988) Geostatistical techniques for interpreting multivariate spatial information.In: Chung CF et al. (eds) Quantitative analysis of mineral and energy resources, Reidel publishing company, Dordrecht, pp 393–409 Google Scholar
  22. Wackernagel H, Butenuth C (1989) Caractérisation d’anomalies géochimiques par la géostatistique multivariable. J Geochem Explor 32:437–444CrossRefGoogle Scholar
  23. Wackernaegel H, Sanguinetti H (1992) Gold prospection with factorial cokriging in the Limousin, France. Document interne, Centre de géostatistique ENSMP, Paris, pp 1–11Google Scholar
  24. Xiao HY , Zhou WB, Zeng FP, Wu DS (2010) Water chemistry and heavy metal distribution in an AMD highly contaminated river. Environ Earth Sci 59(5):1023–1031CrossRefGoogle Scholar
  25. Yeh M-S, Lin Y-P, Chang L-C (2006) Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms. Environ Geol 50(1):101–121CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ahcène Benamghar
    • 1
  • J. Jaime Gómez-Hernández
    • 2
  1. 1.ENSTPAlgiersAlgeria
  2. 2.Research Institute of Water and Environmental EngineeringUniversitat Politècnica de ValènciaValenciaSpain

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