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A Geostatistical Simulation Approach to a Pollution Case in Northeastern Italy

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Abstract

The study zone is located in the city of Padova (the Veneto region, NE Italy). The industrial activities present in this area since 1950 have produced very high concentrations of Pb, Zn, Cu, Pcb and oils until a depth of 7 m. The unsaturated and the saturated zones are both polluted. Because of the abundance of Pb values it was decided to analyze the lead distribution in the studied zone. In many studies of the polluted sites, the geometry of the investigated volume is highly anisotropic. Generally we have an extension of some hectares in the horizontal plane and of a few meters in depth. It is likely that different horizontal spatial continuity structures in pollution distribution are found at different depths both for the layered nature of the medium and for the transition between unsaturated and saturated conditions. In such conditions the decision to divide a 3D problem into 1D and 2D problems can be useful. Initially the studied volume was divided into seven layers up to 5 m in depth; the study was then approached in two phases. First, the Pb values in the vertical direction were analyzed, considering a derive along z, and estimating the values using the Kriging with Trend (KT) method. Thus it was possible to increase the data in the z direction, especially in the deeper layers. Second, 500 realizations of the Pb distribution for each of the seven layers were simulated using the simulated annealing procedure. Finally, results were presented and discussed for each layer in terms of median and probability maps.

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References

  • Carle, S. F., 1997, Implementation scheme for avoid artifact discontinuities in simulated annealing: Math. Geology, v. 29, no. 2, p. 231–244.

    Article  Google Scholar 

  • Chilès, J. P., and Delfiner, P., 1999, Geostatistics: Modelling Spatial Uncertainty: Wiley, New York, 695 p.

    Google Scholar 

  • Desbarats, A. J., 1996, Geostatistical estimation: Kriging, in Rouhani, S., Srivastava, R. M., Desbarats, A. J., Cramer, M. V., and Johnson, A. I., eds., Geostatistics for Environmental and Geotechnical applications: American Society for Testing and Materials, West Conshohocken, Pennsylvania, p. 32–50.

  • Deutsch, C. V., 1989, DECLUS: a Fortran 77 program for determining optimum spatial declustering weights: Computers & Geosciences, v. 15, no. 3, p. 325–332.

    Google Scholar 

  • Deutsch, C. V., and Journel, A. G., 1998, GSLIB Geostatistical software library and user's guide: Oxford University Press, New York, 369 p.

    Google Scholar 

  • Deutsch, C. V., and Journel, A. G., 1994, Integrating well test-derived effective absolute permeabilities in geostatistical reservoir modeling, in Yarus, J. M., and Chambers, R. L., eds., Stochastic modeling and geostatistic: principles, methods, and case studies: Amer. Assoc. Petroleum Geol., Tulsa, Oklahoma, p. 131–142.

  • Deutsch, C. V., 2002, Geostatistical reservoir modeling: Oxford University Press, New York, 376 p.

    Google Scholar 

  • Fetter, C. W., 1999, Contaminant hydrogeology: Prentice Hall, Upper Saddle River, New Jersey, 500 p.

    Google Scholar 

  • Goovaerts, P., 1997, Geostatistics for natural resource evaluation: Oxford University Press, New York, 483 p.

    Google Scholar 

  • Goovaerts, P., 2001, Geostatistical modeling of uncertainty in soil science: Geoderma, v. 103, no. 1, p. 3–26.

    Article  Google Scholar 

  • Isaaks, E. H., and Srivastava, M. R., 1989, An introduction to applied geostatistics: Oxford University Press, New York, 561 p.

    Google Scholar 

  • Journel, A. G., and Huijbregts, C. J., 1978, Mining geostatistics: Academic Press, New York, 600 p.

    Google Scholar 

  • Pannatier, Y., 1996, Variowin: software for spatial data analysis in 2D: Springer, New York, 91 p.

    Google Scholar 

  • Ploner, A., 1999, The use of variogram cloud in geostatistical modeling: Envirometrics, v. 10, no. 4, p. 413–437.

    Article  Google Scholar 

  • Rouhani, S., 1996, Modeling spatial variability using geostatistical simulation, in Rouhani, S., Srivastava, R. M., Desbarats, A. J., Cramer, M. V., and Johnson, A. I., eds., Geostatistics for Environmental and Geotechnical applications: American Society for Testing and Materials, West Conshohocken, Pennsylvania, p. 20–31.

  • Van Laarhoven, P. J. M., and Aarts, E. H. L., 1987, Simulate annealing: theory and application: D. Reidel, Dordrecht, The Netherlands, 186 p.

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Correspondence to Sebastiano Trevisani.

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Fabbri, P., Trevisani, S. A Geostatistical Simulation Approach to a Pollution Case in Northeastern Italy. Math Geol 37, 569–586 (2005). https://doi.org/10.1007/s11004-005-7307-6

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  • DOI: https://doi.org/10.1007/s11004-005-7307-6

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