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Probabilistic data integration for characterization of spatial distribution of residual LNAPL

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Abstract

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.

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Acknowledgements

The first author would like to thank the Alberta Ingenuity Fund for providing partial support for this study. Data for the contaminated site was obtained from the University of Alberta Consortium for Research on Natural Attenuation (CORONA) program, jointly sponsored by NSERC and the oil and gas industry.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada

    Amir H. Hosseini & Clayton V. Deutsch

  2. BGC Engineering Inc., Edmonton, AB, Canada

    Kevin W. Biggar

  3. Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB, Canada

    Carl A. Mendoza

Authors
  1. Amir H. Hosseini
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  2. Clayton V. Deutsch
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  3. Kevin W. Biggar
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  4. Carl A. Mendoza
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Correspondence to Amir H. Hosseini.

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Hosseini, A.H., Deutsch, C.V., Biggar, K.W. et al. Probabilistic data integration for characterization of spatial distribution of residual LNAPL. Stoch Environ Res Risk Assess 24, 735–749 (2010). https://doi.org/10.1007/s00477-009-0360-9

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  • DOI: https://doi.org/10.1007/s00477-009-0360-9

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