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dc.contributor.advisorContractor, Dinshaw N.en_US
dc.contributor.authorElmore, Andrew Curtis.
dc.creatorElmore, Andrew Curtis.en_US
dc.date.accessioned2011-10-31T17:45:56Z
dc.date.available2011-10-31T17:45:56Z
dc.date.issued1991en_US
dc.identifier.urihttp://hdl.handle.net/10150/185706
dc.description.abstractCharacterization of the effectiveness of ground water contamination remediation alternatives is complex due to uncertainties associated with the ground water system. This dissertation presents a Monte Carlo simulation model for stochastic characterization of the maximum concentration of contaminant remaining in an aquifer after the application of pump and treat remedial alternatives. The model is written in FORTRAN 77 for the Convex 240. The model uses a publicly available finite difference code for flow analysis and a commercially available method of characteristics transport code. Hydraulic conductivity fields are randomly generated using the turning bands method; initial concentration fields are conditionally simulated on measured and estimated concentration values; and retardation coefficient fields are negatively correlated to hydraulic conductivity using partition coefficients sampled from a log normal distribution. The model was applied to three pump and treat alternatives selected for consideration at a Nebraska contamination site. Two dimensional analysis of flow and transport was performed. Special treatment of flow boundary conditions was necessary due to site conditions and model restrictions. The probabilistic analyses of the resulting maximum concentration ensembles were used to demonstrate decision analysis at the site. Beta probability distributions were fitted to the maximum output ensembles. The decision tree model incorporated monetary values, human health considerations, and regulatory issues as well as probabilistic considerations. Illustration of the decision analysis procedure showed that the choice of the optimal remedial alternative was dependent on the monetary value assigned to noncarcinogenic and carcinogenic adverse human health risks.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectDissertations, Academicen_US
dc.subjectCivil engineering.en_US
dc.titleMonte Carlo simulation of ground water remediation at a Nebraska contamination site.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.identifier.oclc712050073en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberHaldar, Achintyaen_US
dc.contributor.committeememberYeh, Tian-Chyi J.en_US
dc.contributor.committeememberChow, Kwok W.en_US
dc.identifier.proquest9210312en_US
thesis.degree.disciplineCivil Engineering and Engineering Mechanicsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
dc.description.noteThis item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu.
dc.description.admin-noteOriginal file replaced with corrected file August 2023.
refterms.dateFOA2018-04-24T17:42:58Z
html.description.abstractCharacterization of the effectiveness of ground water contamination remediation alternatives is complex due to uncertainties associated with the ground water system. This dissertation presents a Monte Carlo simulation model for stochastic characterization of the maximum concentration of contaminant remaining in an aquifer after the application of pump and treat remedial alternatives. The model is written in FORTRAN 77 for the Convex 240. The model uses a publicly available finite difference code for flow analysis and a commercially available method of characteristics transport code. Hydraulic conductivity fields are randomly generated using the turning bands method; initial concentration fields are conditionally simulated on measured and estimated concentration values; and retardation coefficient fields are negatively correlated to hydraulic conductivity using partition coefficients sampled from a log normal distribution. The model was applied to three pump and treat alternatives selected for consideration at a Nebraska contamination site. Two dimensional analysis of flow and transport was performed. Special treatment of flow boundary conditions was necessary due to site conditions and model restrictions. The probabilistic analyses of the resulting maximum concentration ensembles were used to demonstrate decision analysis at the site. Beta probability distributions were fitted to the maximum output ensembles. The decision tree model incorporated monetary values, human health considerations, and regulatory issues as well as probabilistic considerations. Illustration of the decision analysis procedure showed that the choice of the optimal remedial alternative was dependent on the monetary value assigned to noncarcinogenic and carcinogenic adverse human health risks.


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