Monte Carlo analysis and Bayesian decision theory for assessing the effects of waste sites on groundwater, II: Applications

Medina, Miguel Á.; Butcher, Jonathan B.; Marín, Carlos M.

doi:10.1016/0169-7722(89)90003-x

Cited by 27 publications

(12 citation statements)

References 5 publications

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“…Finding optimal distribution functions for model transport parameters or solving the inverse problem for the contaminant transport equation has two major problems Bayes' theorem, we have -P(IAI A)P(A)dA [14] where P(AIIA) is the updated or posterior distribution of the random transport variable A that predicts contaminant concentrations at a pumping well that are very close to actual measured concentrations. Figure 5 represents the steps in the development of transport models for a contaminated aquifer and how distribution functions for model transport variables are adjusted by Bayesian updating to provide better estimates of these variables for a particular contaminated aquifer (53)(54)(55)(56)(74)(75)(76)(77) [15] for noncarcinogens R (D) = 0, for D< Dt [16] where D£ is the threshold dose below which there is no observed adverse effect. In actuality, zero exposure doses cannot be achieved, and risk assessment is concerned with the determination of safe exposure doses or the distribution of safe exposure doses that are greater than zero but produce a negligible increase in cancer incidence over background.…”

Section: Contaminant Transport Modelsmentioning

confidence: 99%

Evaluation of Health Risks for Contaminated Aquifers

Piver¹,

Jacobs²,

Medina³

1997

Environmental Health Perspectives

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Section: Contaminant Transport Modelsmentioning

confidence: 99%

Evaluation of Health Risks for Contaminated Aquifers

Piver¹,

Jacobs²,

Medina³

1997

Environmental Health Perspectives

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“…Incorporating spatial variability into Bayesian decision analysis is important for handling data worth problems in many realistic contamination situations. Marin et al [1989] and Medina et al [1989] outlined a Bayesian risk methodology for sampling cont•tmination in spatially heterogeneous aquifers for permitting of waste disposal sites. However, the worth of a measurement was quantified by the degree that it increased the precision of an estimate of contaminant concentration rather than in monetary terms.…”

Section: Previous Workmentioning

confidence: 99%

When enough is enough: The worth of monitoring data in aquifer remediation design

James¹,

Gorelick²

1994

Water Resources Research

135

101

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Given the high cost of data collection at groundwater contamination remediation sites, it is becoming increasingly important to make data collection as costeffective as possible. A Bayesian data worth framework is developed in an attempt to carry out this task for remediation programs in which a groundwater contaminant plume must be located and then hydraulically contained. The framework is applied to a hypothetical contamination problem where uncertainty in plume location and extent are caused by uncertainty in source location, source loading time, and aquifer heterogeneity. The goal is to find the optimum number and the best locations for a sequence of observation wells that minimize the expected cost of remediation plus sampling. Simplifying assumptions include steady state heads, advective transport, simple retardation, and remediation costs as a linear function of discharge rate. In the case here, an average of six observation wells was needed. Results indicate that this optimum number was particularly sensitive to the mean hydraulic conductivity. The optimum number was also sensitive to the variance of the hydraulic conductivity, annual discount rate, operating cost, and sample unit cost. It was relatively insensitive to the correlation length of hydraulic conductivity. For the case here, points of greatest uncertainty in plume presence were on average poor candidates for sample locations, and randomly located samples were not cost-effective. 1Now at Oak Ridge National Laboratory, Oak Ridge, Tennessee.Copyright !994 by the American Geophysical Union.Paper number 94WR01972. 0043-1397/94/94 WR-01972505.00 in reducing remediation costs. Second, collection of data should cease when its cost of acquisition is greater than its benefit in reducing remediation cost.We develop a Bayesian data worth framework for the case of aquifer remediation in which the location and extent of a contaminant plume are both uncertain, yet the plume must be contained hydraulically by pumping. Consider the hypothetical contamination problem displayed in Figures 1 and 2. Here, there exists a single plume which must be prevented from reaching a compliance surface. Although we display a plume in Figures 1 and 2, there is uncertainty about its location and extent because of three major factors: (1) the source location is only vaguely known, (2) the time that the source has been active is uncertain, and (3) the spatial variability of the hydraulic conductivity field is unknown. Given this uncertainty, in order to prevent with certainty the plume from reaching the compliance surface, a width much larger than the actual plume width must be contained. This results in excessively high remediation costs. Fortunately, uncertainty can be reduced by searching for the plume by installing monitoring wells and collecting water quality samples. The worth of installing a monitoring well is linked to how much the information that it provides can be used to reduce the width of the containment zone and hence the expected remediation cost. The foundation o...

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“…This model has been modified to run successive Monte Carlo simulations to account for subsurface heterogeneity (12,14,15 (18), but with the second moment method, other types of distributions can be easily accommodated.…”

Section: Model Simulationsmentioning

confidence: 99%

“…The initial distribution function for the hydraulic conductivity was determined from hydrodynamic data from the contaminated aquifer. This initial distribution function was altered (optimized) using Bayesian updating to obtain a distribution function that best approximated transport behavior for this particular aquifer (12,14,15,19). With this updated distribution function, 100 realizations of the hydraulic conductivity field were generated using a Sequential Gaussian Simulation (SGSIM) routine from the Geostatistical Software Library (20).…”

Section: Model Simulationsmentioning

confidence: 99%

Second moment method for evaluating human health risks from groundwater contaminated by trichloroethylene.

Jacobs

Warmerdam²,

Medina³

et al. 1996

Environ Health Perspect

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Pollutants in groundwater aquifers may constitute a significant human health risk. A large variation in response may result among human populations experiencing the same level and duration of exposure to pollutants. Variability in response, as a result of exposure to a carcinogenic contaminant such as trichloroethylene (TCE), can be represented by a distribution function of safe doses. Spatial variability in aquifer characteristics and contaminant transport parameters requires the use of stochastic transport models to quantify variability in exposure concentrations. A second moment method is used to evaluate the probability of exceeding safe dose levels for a contaminated aquifer. The name of this method stems from the fact that the formulation is based on the first and second moments of the random variables. With this method, the probability is a function of the variability of contaminant concentration (which incorporates variability in hydrogeologic parameters such as hydraulic conductivity) and the variability in response in the human population. In this manner, the severity of the health risk posed by a contaminated aquifer and the evaluation of appropriate strategies and technologies for aquifer remediation are a function of contaminant concentrations and human health risks. The applicability and limitations of this method are demonstrated with data on groundwater contaminated by TCE at Hill Air Force Base, Utah. Images Figure 1. Figure 2. Figure 3. Figure 4.

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Monte Carlo analysis and Bayesian decision theory for assessing the effects of waste sites on groundwater, II: Applications

Cited by 27 publications

References 5 publications

Evaluation of Health Risks for Contaminated Aquifers

Evaluation of Health Risks for Contaminated Aquifers

When enough is enough: The worth of monitoring data in aquifer remediation design

Second moment method for evaluating human health risks from groundwater contaminated by trichloroethylene.

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