Abstract
The probability that the concentrations of toxic substances in soil or other medium exceed tolerablemaxima at any unsampled place can be estimated by indicator geostatistics. The method is developed and used to estimate and map the risk of contamination by cadmium, copper and lead in the topsoil of a 14.5 km 2 region in the Swiss Jura. It combines both direct measurements of metal concentrations and thecalibration of a geological map, and it shows that the risk of toxicity is least on Argovian rocks. Two approaches are proposed to divide a region into safe' and 'hazardous' zones on the basis of probability maps. The first declares as contaminated all places where the risk of contamination exceeds a given threshold. The second approach first evaluates the financial costs that might result from a wrongdeclaration, after which the site is allocated to a class so as to minimize that cost. The risk of exposure for humans and animals is generally greater for contaminated agricultural land than for forest soil, and so land use is taken into account in both procedures.
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References
Atteia, O., Dubois, J.-P. and Webster, R. (1994) Geostatistical analysis of soil contamination in the Swiss Jura. Environmental Pollution, 86, 315–27.
Deutsch, C.V. and Journel, A.G. (1992) GSLIB: Geostatistical Software Library and User's Guide. Oxford University Press, New York.
FOEFL (Swiss Federal Office of Environment, Forest and Landscape) (1987) Commentary on the Ordinance relating to Pollutants in Soil (VSBo of June 9, 1986). FOEFL, Bern.
Goovaerts, P. (1994) Comparative performance of indicator algorithms for modelling conditional prob-ability distribution functions. Mathematical Geology, 26, 389–411.
Goovaerts, P. and Journel, A.G. (1995) Integrating soil map information in modelling the spatial varia-tion of continuous soil properties. European Journal of Soil Science, 46, 397–414.
Isaaks, E. (1990) The application of Monte Carlo methods to the analysis of spatially correlated data. PhD thesis, Stanford University, Stanford, CA.
Journel, A.G. (1983) Non-parametric estimation of spatial distributions. Mathematical Geology, 15, 445–468.
Journel, A.G. (1987). Geostatistics for the environmental sciences. EPA project no CR 811893. Technical report, US Environmental Protection Agency, EMS Laboratory, Las Vegas, NV.
Luenberger, D. (1969) Optimization by Vector Space Methods. Wiley, New York.
Matheron, G. (1976) A simple substitute for the conditional expectation: the disjunctive kriging. In Advanced Geostatistics in the Mining Industry, M. Guarascio, M. David, and C. J. Huijbregts (eds), D. Reidel, Dordrecht, pp. 221–36.
Schulin, R., Webster, R. and Meuli, R. (1994). Technical note on objectives, sampling design, and procedures for assessing regional soil pollution and application of geostatistical analysis in such surveys. In: Regional Soil Contamination Surveying, Environmental Documentation No 25. Swiss Federal Office of Environment, Forests and Landscape (FOEFL), Bern.
Srivastava, R. (1987) Minimum variance or maximum profitability? Canadian Industrial Mining Bulletin, 80, 63–8.
Webster, R. and Oliver, M.A. (1989) Optimal interpolation and isarithmic mapping of soil properties. VI. Disjunctive kriging and mapping the conditional probability. Journal of Soil Science, 40, 497–512.
Webster, R., Atteia, O. and Dubois, J.-P. (1994). Coregionalization of trace metals in the soil in the Swiss Jura. European Journal of Soil Science, 45, 205–18.
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Goovaerts, P., Webster, R. & Dubois, JP. Assessing the risk of soil contamination in the Swiss Jura using indicator geostatistics. Environmental and Ecological Statistics 4, 49–64 (1997). https://doi.org/10.1023/A:1018505924603
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DOI: https://doi.org/10.1023/A:1018505924603