A Stochastic Method for Characterizing Ground‐Water Contamination

It is becoming widely recognized that field-scale ground-water contaminant plumes are irregular and difficult to predict. Factors which complicate the characterization of such plumes include geological variability, data limitations, and uncertainties about the source of contamination. This paper describes a new approach to site characterization which accounts for variability and uncertainty in a systematic way. The site characterization procedure extracts more information from limited data by combining field measurements with predictions from a stochastic ground-water model. The model provides prior estimates of the mean and standard deviation of solute concentration throughout a contaminated site. These estimates are updated whenever new measurements of hydraulic conductivity, head, and/or concentration become available. The updated concentration standard deviation estimates may be used to guide the placement of sampling wells and to evaluate the accuracy of the site characterization. If updating and data collection are carried out sequentially, over a series of discrete sampling rounds, the sampling network can evolve in response to new information. A case study described in the paper illustrates how the characterization procedure can be applied to a typical field site.

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