Date of Award

Summer 8-2016

Embargo Period

1-12-2017

Degree Type

Dissertation (CMU Access Only)

Degree Name

Doctor of Philosophy (PhD)

Department

Civil and Environmental Engineering

Advisor(s)

Mitchell J. Small

Abstract

Conventional experimental techniques are sometimes limited in their ability to assess the actual risk of chemical exposures. Therefore, there is a rising awareness of mathematical, computational, and statistical approaches to provide insight into the adverse effects of environmental contaminants. Richard Bach once wrote: “Any powerful idea is absolutely fascinating and absolutely useless until we choose to use it.” Likewise, any data may be viewed as absolutely fascinating and absolutely useless until we choose to understand and use it. Recent advances in science and technology provide alternative paths to develop effective risk-assessment methods for environmental contaminants. Moreover, these methods are more efficient in terms of time and cost. Therefore, I develop three Chapters to show the importance of statistical methods in environmental-health risk assessment, and highlight the potency of data-driven knowledge and multidisciplinary research for the future of environmental science and engineering. In Chapter 1, I review the potential risks of missing chemical data and concentration variability on mixture toxicity by developing 27 occurrence scenarios based on data from the literature. The @RISK software simulates random concentrations, assuming multivariate lognormal distributions for the mixture components. In Chapter 2, I demonstrate how a performance analysis can be implemented for a Bayesian Network (BN) representation of a dose-response relationship. I explore the effect of different sample sizes on predicting the strength of the relationship between true responses and true doses of environmental toxicants. In Chapter 3, I characterize the risk factors of a prenatal arsenic exposure network by using Bayesian Network (BN) modeling as a tool for health risk assessment.

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