"A model to predict concentrations and uncertainty for mercury species " by Ashley Hendricks

Date of Award

2018

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Environmental Engineering (MS)

Administrative Home Department

Department of Civil and Environmental Engineering

Advisor 1

Noel Urban

Committee Member 1

Min Wang

Committee Member 2

Cory McDonald

Abstract

To increase understanding of mercury cycling, a seasonal mass balance model was developed to predict mercury concentrations in lakes and fish. Results indicate that seasonality in mercury cycling is significant and is important for a northern latitude lake. Models, when validated, have the potential to be used as an alternative to measurements; models are relatively inexpensive and are not as time intensive. Previously published mercury models have neglected to perform a thorough validation. Model validation allows for regulators to be able to make more informed, confident decisions when using models in water quality management. It is critical to quantify uncertainty; models are often over-parameterized and constrained by few measurements. As an approach, the Markov Chain Monte Carlo (MCMC) Bayesian method was used for uncertainty analysis. The uncertainty analysis provided a better means for calibration, helpful insight on the distribution of model parameter values, and the uncertainty in model predictions.

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