Geostatistical Modeling of Uncertainty for the Risk Analysis of a Contaminated Site
Enrico Guastaldi
DOI: 10.4236/jwarp.2011.38066   PDF    HTML     5,154 Downloads   9,997 Views   Citations


This work is a study of multivariate simulations of pollutants to assess the sampling uncertainty for the risk analysis of a contaminated site. The study started from data collected for a remediation project of a steel- works in northern Italy. The soil samples were taken from boreholes excavated a few years ago and analyzed by a chemical laboratory. The data set comprises concentrations of several pollutants, from which a subset of ten organic and inorganic compounds were selected. The first part of study is a univariate and bivariate sta- tistical analysis of the data. All data were spatially analyzed and transformed to the Gaussian space so as to reduce the effects of extreme high values due to contaminant hot spots and the requirements of Gaussian simulation procedures. The variography analysis quantified spatial correlation and cross-correlations, which led to a hypothesized linear model of coregionalization for all variables. Geostatistical simulation methods were applied to assess the uncertainty. Two types of simulations were performed: correlation correction of univariate sequential Gaussian simulations (SGS), and sequential Gaussian co-simulations (SGCOS). The outputs from the correlation correction simulations and SGCOS were analyzed and grade-tonnage curves were produced to assess basic environmental risk.

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Guastaldi, E. (2011) Geostatistical Modeling of Uncertainty for the Risk Analysis of a Contaminated Site. Journal of Water Resource and Protection, 3, 563-583. doi: 10.4236/jwarp.2011.38066.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] P. A. Dowd, “Risk in Mineral Projects: Analysis, Perception and Management” Transactions of the Institution of Mining and Metallurgy (Sect. A: Min. industry), Vol. 106, 1997, pp. A9-A18.
[2] T. M. Burgess and R. Webster, “Optimal Interpolation and Isarithmic Mapping of Soil Properties. I. The Variogram and Punctual Kriging,” Journal of Soil Sciences, Vol. 31, No. 2, 1980a, pp. 315-331. doi:10.1111/j.1365-2389.1980.tb02084.x
[3] T. M. Burgess and R. Webster, “Optimal Interpolation and Isarithmic Mapping of Soil Properties. II. Block Kriging,” Journal of Soil Sciences, Vol. 31, No. 2, 1980b, pp. 333-341. doi:10.1111/j.1365-2389.1980.tb02085.x
[4] T. M. Burgess and R. Webster, “Optimal Interpolation and Isarithmic Mapping of Soil Properties. III. Changing Drift and Universal Kriging,” Journal of Soil Sciences, Vol. 31, No. 3, 1980c, pp. 505-524.
[5] T. M. Burgess, R. Webster and A. B. McBratney, “Optimal Interpolation and Isarithmic Mapping of Soil Properties. VI. Sampling Strategy,” Journal of Soil Sciences, Vol. 32, No. 4, 1981, pp. 643-659.
[6] P. Goovaerts, “Geostatistical Tools for Characterizeing the Spatial Variability of Microbiological and Physico- Chemical Soil Properties,” Journal of Biological Chemistry, Vol, 27, No. 4, 1998, pp. 315-334. doi:10.1007/s003740050439
[7] G. B. M. Heuvelink, “Error Propagation in Environmental Modeling with GIS,” Taylor and Francis, Lon- don, 1998.
[8] P. Goovaerts, “Geostatistical Modelling of Uncertainty in Soil Science,” Geoderma, Vol, 103, No. 1-2, 2001, p. 3-26. doi:10.1016/S0016-7061(01)00067-2
[9] P. Goovaerts, G. Avruskin, J. Meliker, M. Slotnick, G. Jacquez and J. Nriagu, “Modeling Uncertainty about Pollutant Concentration and Human Exposure using Geostatistics and a Space-Time Information System: Application to Arsenic in Groundwater of Southeast Michigan,” In 6th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Portland, Maine, 28 June-1 July 2004.
[10] P. A. Dowd and C. Xu, “GeostatWinTM—User’s Manual,” University of Leeds, Leeds, 2004.
[11] Gc. Bortolami, G. Crema, R. Malaroda, F. Petrucci, R. Sacchi, C. Sturani and S. Venzo, “Carta geologica d’Italia, Foglio 56,” 2nd Edition, Servizio Geologico Italiano (Italian Geological Survey), Roma, Torino, 1969.
[12] G. Braga, E. Carabelli, A. Cerro, A. Colombetti, S. D’ Offizi, F. Francavilla, G. Gasperi, M. Pellegrini, M. Zauli and G. M. Zuppi, “Indagini Idrogeologiche Nella Pianura Padana: Le aree del Piemonte (P01-P02) e della Lombardia (Viadana e San Benedetto Po),” ENEL, Torino, 1988.
[13] C. W. Fetter, “Applied Hydrology. 3rd Edition, Prentice Hall,” Upper Saddle River, New Jersey, 1994.
[14] M.A.T.T. Ministero dell’Ambiente e Tutela del Territorio, Repubblica Italiana, “Environmental Minister Law DM471: Rules on Criteria, Procedures and Way for Environmental Remediation of Contaminated Sites,” 5th February 1997, Ordinary Supplement to Official Gazette of Republic of Italy, Rome, No. 293, 15 December 1999.
[15] F. Owen and R. Jones, “Statistics,” Pitman Publishing, London, 1994, p. 529.
[16] P. A. Dowd, “MINE5280: Non-Linear Geostatistics. MSc in Mineral Resource and Environmental Geostatistics,” University of Leeds, Leeds. 1996, p. 170.
[17] P. Goovaerts, “Geostatistics for Natural Resources Eva- luation. Applied Geostatistics Series,” Oxford University Press, Oxford, Vol. 14, New York, 1997, p. 483.
[18] G. Matheron, “Les Variables Régionalisées et leur estimation: Une Application de la Théorie des Fonctions Aléatoires aux Sciences de la Nature,” Masson, Paris, 1965.
[19] A. G. Journel and C. J. Huijbregts, “Mining Geostatistics,” Academic Press Inc., London, 1978 p. 600.,
[20] J. P. Chiles and P. Delfiner, “Geostatistics: Modeling Spatial Uncertainty,” Wiley, New York, Chichester, 1999.
[21] H. Wackernagel, “Multivariate Geostatistics: An Introduction with Applications,” Springer, Berlin, 2003.
[22] R. Webster and M. A. Oliver, “Geostatistics for Environ- Mental Scientists (Statistics in Practice),” John Wiley & Sons, New York, 2001.
[23] C. Lantuejoul, “Geostatistical Simulation: Models and Algorithms,” Springer Verlag, Berlin, 2002, p.256.
[24] P. J. Ravenscroft, “Conditional Simulation for Mining: Practical Implementation in an Industrial Environment,” In: M. Armstrong and P. A. Dowd, Eds., Geostatistical Simulations, Kluwer Academic Publishers, Dordrecht, 1994, pp. 79-87.
[25] A. G. Journel and F. Alabert, “Non-Gaussian Data Expansion in the Earth Sciences,” Terra Nova, Vol. 1, No. 2, 1989, pp. 123-134. doi:10.1111/j.1365-3121.1989.tb00344.x
[26] C. J. Bleines, F. Deraisme, N. Geffory, S. Jeannee, F. Perseval, D. Rambert, O. Renard, Torres and Y. Touffait, “Isatis Software Manual,” Geovariances & Ecole Des Mines De Paris, Paris, 2004.
[27] R. Dimitrakopoulos and M. B. Fonseca, “Assessing Risk in Grade-Tonnage Curves in a Complex Copper Deposit, Northern Brazil, Based on an Efficient Joint Simulation of Multiple Correlated Variables,” Application of Computers and Operations Research in the Minerals Industries, South African Institute of Mining and Metallurgy, 2003.
[28] I. Clark and W. V. Harper, “Practical Geostatistics,” Ecosse North America Llc., Columbus, Ohio, Vol. 1, 2000, p. 342.

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