Mathematical Geosciences

, Volume 41, Issue 3, pp 307–322 | Cite as

An Integrated Geostatistical Approach for Contaminated Site and Soil Characterisation

  • D. D’Or
  • H. Demougeot-Renard
  • M. Garcia
Special Issue


For several years, abandoned or stopped industrial sites increasingly arouse the interest of politics and investors. Having a high social and economic estate value, these sites generally reveal contaminated soils that must be remediated first before receiving any new use. Due to financial, environmental or human health stakes, heuristic methods appear inappropriate because they do not provide reliable estimations of contaminated soil volumes and ignore spatial uncertainties. Problems at hand may be very complex, involving multiple correlated contaminants for which spatially varying pollutant grades are to be estimated and confronted to various regulatory thresholds, depending on redevelopment target areas. In such conditions, geostatistics provides effective methods to quantify local and global uncertainties about soil contamination and contaminated soil volumes. By quantifying uncertainties, geostatistical models are useful as support for decision-making about redevelopment scenarios or remediation techniques. Specific approaches are required, however, to overcome particular modelling issues as related to the skewness of pollutant grade distributions or change of support. Making use of our practical experience, such an integrated geostatistical approach is proposed for modelling contaminated sites. It is illustrated by application to a recent actual case study.


Change of support Skewness Multivariate Uncertainty assessment Health 


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  1. Caers J, Zhang T (2004) Multiple-point geostatistics: A quantitative vehicle for integrating geologic analogs into multiple reservoir models. In: Integration of outcrop and modern analogs in reservoir modeling. AAPG memoirs, vol 80, pp 383–394 Google Scholar
  2. Carlon C, Critto A, Marcomini A, Nathanail P (2001) Risk based characterisation of contaminated industrial site using multivariate and geostatistical tools. Environ Pollut 111:417–427 CrossRefGoogle Scholar
  3. Carlon C, Pizzol L, Critto A, Marcomini A (2008) A spatial risk assessment methodology to support the remediation of contaminated land. Environ Int 34:397–411 CrossRefGoogle Scholar
  4. Cattle J, McBratney A, Minasny B (2002) Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination. J Environ Qual 31:1576–1588 Google Scholar
  5. De Oliveira V (2004) A simple model for spatial rainfall fields. Stoch Environ Res Risk Assess 18:131–140 CrossRefGoogle Scholar
  6. Demougeot-Renard H, De Fouquet C (2004) Geostatistical approach for assessing soil volumes requiring remediation: Validation using lead-polluted soils underlying a former smelting works. Environ Sci Technol 38:5120–5126 CrossRefGoogle Scholar
  7. Demougeot-Renard H, D’Or D, Garcia M (to appear) Heavy metal soil characterisation—combining geostatistics and in situ FPXRF analysis for a real time adaptative sampling program. In: geoEnV VII—geostatistics for environmental applications, 8–10 September 2008, Southampton (UK) Google Scholar
  8. Demougeot-Renard H, De Fouquet C, Renard P (2004) Forecasting the number of soil samples required to reduce remediation cost uncertainty. J Environ Qual 33:1694–1702 CrossRefGoogle Scholar
  9. Diggle P, Ribeiro P (2007) Model-based geostatistics. Springer, New York Google Scholar
  10. D’Or D (2005) Towards a real-time multi-phase sampling strategy optimization. In: Renard P, Demougeot-Renard H, Froidevaux R (eds) geoENV V—geostatistics for environmental applications. Springer, New York Google Scholar
  11. D’Or D, Demougeot-Renard H, Garcia M (2008a) Geostatistics for contaminated sites and soils: some pending questions. In: Soares A, Pereira M, Dimitrakopoulos R (eds) geoEnV VI—geostatistics for environmental applications. Quantitative geology and geostatistics. Springer, New York, Google Scholar
  12. D’Or D, Allard D, Biver P, Froidevaux R, Walgenwitz A (2008b) Simulating categorical random fields using the multinomial regression approach. In: Ortiz JM, Emery X (eds) Proceedings of the eight international geostatistics congress, Santiago de Chile. GECAMIN Ltd Google Scholar
  13. EPA (1989) Methods for evaluating the attainment of cleanup standards. Volume 1: Soils and solid media. Tech Rep EPA/230-02-89-042, United States Environmental Protection Agency. [Electronic version]. Retrieved on September 14, 2007
  14. Gay J, Korre A (2006) A spatially-evaluated methodology for assessing risk to a population from contaminated land. Environ Pollut 142:227–234 CrossRefGoogle Scholar
  15. Glavin R, Hooda P (2005) A practical examination of the use of geostatistics in the remediation of a site with a complex metal contamination history. Soil Sediment Contam 14:155–169 CrossRefGoogle Scholar
  16. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York Google Scholar
  17. Goovaerts P (2001) Geostatistical modelling of uncertainty in soil science. Geoderma 103:3–26 CrossRefGoogle Scholar
  18. Goovaerts P, Webster R, Dubois J (1997) Assessing the risk of soil contamination in the Swiss Jura using indicator geostatistics. Environ Ecol Stat 4:49–64 CrossRefGoogle Scholar
  19. Hooker P, Nathanail C (2006) Risk-based characterisation of lead in urban soils. Chem Geol 226:340–351 CrossRefGoogle Scholar
  20. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic, New York Google Scholar
  21. Juang K, Chen Y, Lee D (2004) Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environ Pollut 127:229–238 CrossRefGoogle Scholar
  22. Komac M, Sajn R (2001) Polluted or nonpolluted—a fuzzy approach determining soil pollution. In: Proceeding of the annual conference of the international association for mathematical geology Google Scholar
  23. Kyriakidis P (1997) Selecting panels for remediation in contaminated soils via stochastic imaging. In: Baafi E, Schofield N (eds) Geostatistics Wollongong ’96, vol. 2. Kluwer Academic, Dordrecht Google Scholar
  24. Lajaunie C, Wackernagel H (2000) Geostatistical approaches to change of support problems. Theoretical framework. Technical report N30/01/G, ENSMP-ARMINES, Centre de Géostatistique Google Scholar
  25. Li W, Zhang C (2007) A random-path Markov chain algorithm for simulating categorical soil variables from random point samples. Soil Sci Soc Am J 71:656–668 CrossRefGoogle Scholar
  26. McGrath D, Zhang C, Carton O (2004) Geostatistical analyses and hazard assessment on soil lead in Silvermines area. Ireland Environ Poll 127:239–248 CrossRefGoogle Scholar
  27. Moore S, McLaughlin D (1980) Mapping contaminated soil plumes by kriging. In: EPA national conference on management of uncontrolled hazardous wastes (Environmental Protection Agency Report) Google Scholar
  28. Saito H, Goovaerts P (2003) Selective remediation of contaminated sites using a two-level multiphase strategy and geostatistics. Environ Sci Technol 37:1912–1918 CrossRefGoogle Scholar
  29. Steiger BV, Webster R, Schulin R, Lehmann R (1996) Mapping heavy metals in polluted soil by disjunctive kriging. Environ Pollut 94:205–215 CrossRefGoogle Scholar
  30. Thayer W, Griffith D, Goodrum P, Diamond G, Hassett J (2003) Application of geostatistics to risk assessment. Int J 23:945–960 Google Scholar
  31. Van Meirvenne M, Goovaerts P (2001) Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold. Geoderma 102:75–100 CrossRefGoogle Scholar
  32. Wackernagel H (2003) Multivariate geostatistics, 2nd edn. Springer, Berlin Google Scholar
  33. Waller L, Gotway C (2004) Applied spatial statistics for public health data. Wiley, New York CrossRefGoogle Scholar
  34. Zhu H, Journel A (1991) Mixture of populations. Math Geol 23:647–671 CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2009

Authors and Affiliations

  1. 1.FSS International r&dChavilleFrance

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