Jermilova, Una

Fish datapoints

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Names:
Creator (cre): Jermilova, Una
Abstract:
2023

Freshwater datapoints

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Names:
Creator (cre): Jermilova, Una
Abstract:
2023

Netica model for GSL region

Type:
Names:
Creator (cre): Jermilova, Una
Abstract:
2023

Netica model for GBS region

Type:
Names:
Creator (cre): Jermilova, Una
Abstract:
2023

Bayesian Network Model of Mercury Exposure to Aquatic Ecosystems of the Mackenzie Watershed

Type:
Names:
Creator (cre): Jermilova, Una, Thesis advisor (ths): Hintelmann, Holger, Thesis advisor (ths): Kirk, Jane L, Degree committee member (dgc): Hintelmann, Holger, Degree committee member (dgc): Kirk, Jane L, Degree committee member (dgc): Landis, Wayne, Degree committee member (dgc): Buell, Mary-Claire, Degree granting institution (dgg): Trent University
Abstract:

A significant portion (15-20%) of mercury (Hg) in the Arctic Ocean is believed to originate from Arctic rivers, such as the Mackenzie River watershed in the NWT. Recent (2005- 2020) Hg monitoring data of freshwater and fish tissue and environmental model outputs were compiled and used to develop a Bayesian Network Relative Risk model (BN-RRM), a probabilistic model capable of analyzing causal relationships. The objectives of the model were to estimate the risk posed to fish health and the subsequent dietary Hg-exposure to humans; to compare the relative risks between regions of the watershed; and to identify the influential Hg sources. The output of the BN-RRMs differed significantly throughout the watershed, with atmospheric Hg deposition and soil erosion Hg release consistently flagged as important explanatory variables. Analysis of the endpoint uncertainties revealed gaps in knowledge and in Hg datasets, which should be the focus of study for future monitoring programs.

Author Keywords: Aquatic Ecosystems, Arctic, Bayesian Network, Mercury, Risk Assessment, Toxicology

2023