This is a past event.

Mugdha Priyadarshini (Graduate Student), Noel Urban, and Judith Perlinger, Civil & Environmental Engineering, Michigan Technological University

Can Management of PCBs in Atmospheric Deposition Achieve Reductions of PCB Concentrations in Lake Superior Trout?

Abstract:

Polychlorinated biphenyl (PCB) compounds are a class of persistent, bioaccumulative and toxic organic pollutant that is produced as complex mixtures (containing more than 80 congeners) called Aroclors. Chronic PCB exposure can cause major health effects in humans including reproductive effects like reduced conception and birth rates, effects on neurological, immune and endocrine systems, and cancer. Atmospheric deposition has been considered to be the major input of PCBs to Lake Superior. The objective of this project was to analyze the congener distributions in fish to identify the source of PCBs to Lake Superior. If the atmosphere is the major source of PCBs to the lake, then a more aggressive removal of PCB sources to the atmosphere would help to achieve more rapid reductions in fish PCB content. Positive Matrix Factorization (PMF) was used to analyze the air and fish PCB concentrations.

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Ashley Hendricks (Graduate Student) and Noel Urban, Civil & Environmental Engineering, Michigan Technological University

Model Validation as a Critical Component for the Management of Mercury Contamination in Lakes

Abstract:

Mercury (Hg) is a toxic contaminant that bioaccumulates in fish. Fish consumption advisories have been set for the Great Lakes and inland lakes, however, many lakes do not have specific advisories set due to lack of measurements. Models have the poten-tial to be used as an alternative to measurements; models are relatively inexpensive and are not as time intensive. Several of the Hg models that have been published neglect to validate their model, particularly in estimating uncertainty in predictions. It is critical to quantify this uncertainty, as models are often over-parameterized, constrained by few measurements, and often are not generalized for all lakes. As an approach, the Markov Chain Monte Carlo (MCMC) Bayesian method was used for uncertainty analysis. This approach consists of compiling model parameter values cited in literature, and using this “prior” information to estimate the posterior distribution of the model parameters. Model validation also consisted of calibration and sensitivity analy-sis of the model. Model validation allows for regulators to be able to make more informed and confident decisions when using models in water quality management.