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Geostatistical modelling of ground conditions to support the assessment of site contamination

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Abstract

The pattern of ground contamination across a site depends on the historical pattern of contaminant releases at the surface and the redistribution and the fate of contaminants below the surface. Using these concepts a new site assessment approach (assessment modelling) is proposed based on the development of three stochastic models: a model of the physical structure of the ground materials beneath the site; a model of the distribution of surface contaminant spills; and a model of the flow and transport of spilled material into the heterogeneous underlying ground to construct alternative, equally likely, present day contaminant distributions. Combining the models within a Monte Carlo framework can, in principle, improve the understanding of the potential for excess contamination across the site and improve decisions on remediation options and locations. A trial application has been undertaken in the UK using a particular site to assess the approach. The conditions at the site used for the trial and the first of the stochastic model developments, the geostatistical modelling of the soil heterogeneity, are presented in this paper. Non-parametric and parametric geostatistics have been employed to formulate the geostatistical models of the site soils using lithological information from 146 trial pits and boreholes. The approach to the soil modelling and the verification and validation of the results are described. The heterogeneity of the subsurface is complicated by the presence of made-ground, comprised of various inert building wastes, and the non-stationarity of the heterogeneity of the natural ground. This paper is the first of three describing the assessment modelling methodology and its trial application to the site.

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Acknowledgements

The authors would like to express their gratitude to the site owners, their consultants and contractors for providing access to the site and for providing the data from the site that permitted the development and testing of the stochastic methodology.

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Authors and Affiliations

  1. Centre for Land Use and Water Resources Research (CLUWRR), University of Newcastle, Newcastle upon Tyne, NE1 7RU, United Kingdom

    J.A. Morakinyo

  2. Earth Sciences, The University of Birmingham, Birmingham , B15 2TT, United Kingdom

    R. Mackay

  3. FSC Architects and Engineers, 4910—53rd Street, P.O. Box 1777, Yellowknife, NT, Canada, X1A 2P4

    J.A. Morakinyo

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  1. J.A. Morakinyo
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  2. R. Mackay
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Correspondence to J.A. Morakinyo.

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Morakinyo, J., Mackay, R. Geostatistical modelling of ground conditions to support the assessment of site contamination. Stoch Environ Res Ris Assess 20, 106–118 (2006). https://doi.org/10.1007/s00477-005-0015-4

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  • DOI: https://doi.org/10.1007/s00477-005-0015-4

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