Probabilistic data integration for characterization of spatial distribution of residual LNAPL

• Published in: Stochastic environmental research and risk assessment 2010-07, Vol.24 (5), p.735-749
• Main Authors: Hosseini, Amir H., Deutsch, Clayton V., Biggar, Kevin W., Mendoza, Carl A.
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Computer simulation; Contaminants; Data integration; Earth and Environmental Science; Earth Sciences; Environment; Math. Appl. in Environmental Science; Mathematical models; Monte Carlo methods; Monte Carlo simulation; Nonaqueous phase liquids; Original Paper; Physics; Probability; Probability theory; Probability Theory and Stochastic Processes; Soil contamination; Soil texture; Solute transport; Spatial distribution; Statistics for Engineering; Stochastic models; Texture; Uncertainty; Waste Water Technology; Water Management; Water Pollution Control; Water table
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-009-0360-9

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.