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Probabilistic Assessment of Ground‐Water Contamination: 2. Results of Case Study
Author(s) -
Istok Jonathan D.,
Rautman Christopher A.
Publication year - 1996
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.1996.tb02171.x
Subject(s) - contamination , probabilistic logic , environmental science , groundwater , water contamination , statistics , mathematics , geology , geotechnical engineering , biology , ecology
The first paper in this series (Rautman and Istok, 1996) presented a geostatistical framework for obtaining a probabilistic assessment of ground‐water contamination. This paper presents the results of a case study that applies this framework to define the spatial extent and severity of nitrate and Dacthal (dimethyl tetrachloroterephthalate or DCPA, a herbicide) contamination in the unsaturated and saturated zones for a 150 km 2 site near Ontario, Oregon. Sediment samples collected from 35 boreholes were used to compute vertical accumulations of nitrate and DCPA in the unsaturated zone. Measured nitrate and DCPA concentrations in ground‐water samples collected from 42 wells were used to compute vertical accumulations of nitrate and DCPA in the saturated zone. Sample variograms were fit with nugget and spherical models to describe the pattern of spatial continuity of nitrate and DCPA concentrations and accumulations. Conditional, sequential Gaussian simulation was used to generate 100 simulations for each variable on a 0.5 × 0.5 km grid. Probabilistic summaries of these simulations were used to develop (a) maps showing the probability of contamination exceeding specified theshold values, (b) probability distributions for contaminant accumulation and concentration at unsampled locations, (c) probabilistic descriptions for the location of contaminant‐plume boundaries, and (d) probability distributions for the total contaminated area and total contaminant mass. The results demonstrate that interpretations of site characterization data to determine the extent and magnitude of contamination at a site will vary depending upon the level of uncertainty that will be tolerated by the decision maker. This case study also illustrates the potential applicability and utility of the probabilistic approach for the interpretation of site characterization data and the design of future data collection activities.