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dc.contributor.authorNnaji, Soronadi.
dc.creatorNnaji, Soronadi.en_US
dc.date.accessioned2011-11-28T13:25:23Z
dc.date.available2011-11-28T13:25:23Z
dc.date.issued1981en_US
dc.identifier.urihttp://hdl.handle.net/10150/191065
dc.description.abstractA risk-based approach for addressing several non-structural stream quality management objectives is presented. To estimate risk, the input process, the stream contaminant transport, and the consequence of contamination are modeled mathematically. The transport of soluble contaminant introduced at a point into a turbulent stream medium is modeled as a boundary value problem in which the contaminant satisfies the Kolmogorov forward equation within the medium. Observed properties of turbulence are used to justify the adoption of this equation. The fundamental solution, as the probabilistic response of the stream to an instantaneous unit flux input, is derived and used as the kernel in a stochastic integral representation of the transport problem. The bulk input is used as the forcing function in the integral equation. It is modeled as a sequence of independent pulses with random magnitude and duration and also with random interval between the incidence of adjacent pulses ,. Stochastic simulation is used to construct the moments and the probability distribution of stream concentration and those of several variables associated with the exceedance of the concentration above a specified threshold. The variables include the dosage and the time to the first exceedance. The probability that an observed stream concentration exceeds the threshold within a given interval of time is also constructed. Generalizations of the Chebyshev inequality are extended to the case of a stochastic process. Upper bounds on the constructed probability distributions are calculated using these extensions. Based on previous studies, a rectangular hyperbolic relationship is assumed between dosage and consequence. The relationship is combined with the empirical dosage density function to obtain estimates of value risk of stream concentration for various thresholds. Given an acceptable risk, the corresponding threshold may be used as the stream standard. The reliability function, defined as the complementary density function of exceedance times, may be used as a gauge of the effectiveness of pollution abatement measures. Other illustrated areas of application include the construction of a minimum cost contaminant discharge policy and the determination of the optimal sampling interval for stream surveillance.
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.subjectHydrology.en_US
dc.subjectWater -- Pollution -- Mathematical models.en_US
dc.subjectSediment transport -- Mathematical models.en_US
dc.subjectTurbulence -- Mathematical models.en_US
dc.subjectMonte Carlo method.en_US
dc.subjectChebyshev systems.en_US
dc.titleSimulation of stream pollution under stochastic loadingen_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.typetexten_US
dc.contributor.chairFogel, Martin M.en_US
dc.identifier.oclc212845049en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberSimpson, Eugene S.en_US
dc.contributor.committeememberDuckstein, Lucienen_US
dc.contributor.committeememberDavis, Donald R.en_US
dc.contributor.committeememberDietrich, Duaneen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh. D.en_US
dc.description.notehydrology collectionen_US
refterms.dateFOA2018-06-28T00:30:28Z
html.description.abstractA risk-based approach for addressing several non-structural stream quality management objectives is presented. To estimate risk, the input process, the stream contaminant transport, and the consequence of contamination are modeled mathematically. The transport of soluble contaminant introduced at a point into a turbulent stream medium is modeled as a boundary value problem in which the contaminant satisfies the Kolmogorov forward equation within the medium. Observed properties of turbulence are used to justify the adoption of this equation. The fundamental solution, as the probabilistic response of the stream to an instantaneous unit flux input, is derived and used as the kernel in a stochastic integral representation of the transport problem. The bulk input is used as the forcing function in the integral equation. It is modeled as a sequence of independent pulses with random magnitude and duration and also with random interval between the incidence of adjacent pulses ,. Stochastic simulation is used to construct the moments and the probability distribution of stream concentration and those of several variables associated with the exceedance of the concentration above a specified threshold. The variables include the dosage and the time to the first exceedance. The probability that an observed stream concentration exceeds the threshold within a given interval of time is also constructed. Generalizations of the Chebyshev inequality are extended to the case of a stochastic process. Upper bounds on the constructed probability distributions are calculated using these extensions. Based on previous studies, a rectangular hyperbolic relationship is assumed between dosage and consequence. The relationship is combined with the empirical dosage density function to obtain estimates of value risk of stream concentration for various thresholds. Given an acceptable risk, the corresponding threshold may be used as the stream standard. The reliability function, defined as the complementary density function of exceedance times, may be used as a gauge of the effectiveness of pollution abatement measures. Other illustrated areas of application include the construction of a minimum cost contaminant discharge policy and the determination of the optimal sampling interval for stream surveillance.


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