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Assessing mercury exposure to water and fish of the Mackenzie watershed using a Bayesian network analysis.

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Title: Assessing mercury exposure to water and fish of the Mackenzie watershed using a Bayesian network analysis.
Authors: Jermilova, Una1 (AUTHOR), Kirk, Jane L2 (AUTHOR), Moe, S Jannicke3 (AUTHOR), Landis, Wayne G4 (AUTHOR), Sharpe, Emma4 (AUTHOR), McGovern, Maeve5 (AUTHOR), Braaten, Hans Fredrik Veiteberg6 (AUTHOR), Gundersen, Cathrine Brecke6 (AUTHOR), Dastoor, Ashu P7 (AUTHOR), Schaefer, Kevin8 (AUTHOR), Hintelmann, Holger H1 (AUTHOR)
Source: Integrated Environmental Assessment & Management. Mar2025, Vol. 21 Issue 2, p396-413. 18p.
Subject Terms: *Mercury poisoning, *Lakes, *Freshwater ecology, *Watersheds, *Environmental monitoring, Bayesian analysis, Keystone species, Risk assessment
Geographic Terms: Mackenzie River (N.W.T.)
Abstract: Bayesian Network Relative Risk Models (BN-RRM) were developed to assess recent (2005–2020) risk of mercury (Hg) exposure to the freshwater ecosystems of Great Slave Lake (GSL) and the Mackenzie River Basin (MRB) in the Canadian Northwest Territories. Risk is defined as the probability of a specified adverse outcome; here the adverse outcome was the probability of environmental Hg concentrations exceeding the Hg regulatory guidelines (thresholds values) established to protect the health of humans and aquatic biota. Environmental models and Hg monitoring studies were organized into a probabilistic (Bayesian network) model which considered six Hg input pathways, including atmospheric Hg deposition, Hg release from permafrost thaw, terrestrial to aquatic Hg transfer via soil erosion, and the proximity to mining, fossil fuel developments, and retrogressive permafrost thaw slumps (RPTS). Sensitivity analysis was used to assess spatial trends in influence of the sources to Hg concentrations in freshwater and in the tissue of five keystone fish species (lake whitefish, lake trout, northern pike, walleye, and burbot) which are essential for the health and food security of the people in the MRB. The risk to the health of keystone fish species, defined by toxicological dose-response curves, was generally low but greatest in GSL where fish size, mine proximity, and soil erosion were identified to be important explanatory variables. These BN-RRMs provide a probabilistic framework to integrate advances in Hg cycling modeling, identify gaps in Hg monitoring efforts, and calculate risk to environmental endpoints under alternative scenarios of mitigation measures. For example, the models predicted that the successful implementation of the Minamata Treaty, corresponding to 35%–60% reduction in atmospheric Hg deposition, would translate to a ∼1.2-fold reduction in fish Hg concentrations. In this way, these models can form the basis for a decision-support tool for comparing and ranking risk-reduction initiatives. [ABSTRACT FROM AUTHOR]
Copyright of Integrated Environmental Assessment & Management is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Assessing mercury exposure to water and fish of the Mackenzie watershed using a Bayesian network analysis.
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  Data: <searchLink fieldCode="AR" term="%22Jermilova%2C+Una%22">Jermilova, Una</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kirk%2C+Jane+L%22">Kirk, Jane L</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moe%2C+S+Jannicke%22">Moe, S Jannicke</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Landis%2C+Wayne+G%22">Landis, Wayne G</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sharpe%2C+Emma%22">Sharpe, Emma</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McGovern%2C+Maeve%22">McGovern, Maeve</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Braaten%2C+Hans+Fredrik+Veiteberg%22">Braaten, Hans Fredrik Veiteberg</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gundersen%2C+Cathrine+Brecke%22">Gundersen, Cathrine Brecke</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dastoor%2C+Ashu+P%22">Dastoor, Ashu P</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schaefer%2C+Kevin%22">Schaefer, Kevin</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hintelmann%2C+Holger+H%22">Hintelmann, Holger H</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Integrated+Environmental+Assessment+%26+Management%22">Integrated Environmental Assessment & Management</searchLink>. Mar2025, Vol. 21 Issue 2, p396-413. 18p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Mercury+poisoning%22">Mercury poisoning</searchLink><br />*<searchLink fieldCode="DE" term="%22Lakes%22">Lakes</searchLink><br />*<searchLink fieldCode="DE" term="%22Freshwater+ecology%22">Freshwater ecology</searchLink><br />*<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Keystone+species%22">Keystone species</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Mackenzie+River+%28N%2EW%2ET%2E%29%22">Mackenzie River (N.W.T.)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Bayesian Network Relative Risk Models (BN-RRM) were developed to assess recent (2005–2020) risk of mercury (Hg) exposure to the freshwater ecosystems of Great Slave Lake (GSL) and the Mackenzie River Basin (MRB) in the Canadian Northwest Territories. Risk is defined as the probability of a specified adverse outcome; here the adverse outcome was the probability of environmental Hg concentrations exceeding the Hg regulatory guidelines (thresholds values) established to protect the health of humans and aquatic biota. Environmental models and Hg monitoring studies were organized into a probabilistic (Bayesian network) model which considered six Hg input pathways, including atmospheric Hg deposition, Hg release from permafrost thaw, terrestrial to aquatic Hg transfer via soil erosion, and the proximity to mining, fossil fuel developments, and retrogressive permafrost thaw slumps (RPTS). Sensitivity analysis was used to assess spatial trends in influence of the sources to Hg concentrations in freshwater and in the tissue of five keystone fish species (lake whitefish, lake trout, northern pike, walleye, and burbot) which are essential for the health and food security of the people in the MRB. The risk to the health of keystone fish species, defined by toxicological dose-response curves, was generally low but greatest in GSL where fish size, mine proximity, and soil erosion were identified to be important explanatory variables. These BN-RRMs provide a probabilistic framework to integrate advances in Hg cycling modeling, identify gaps in Hg monitoring efforts, and calculate risk to environmental endpoints under alternative scenarios of mitigation measures. For example, the models predicted that the successful implementation of the Minamata Treaty, corresponding to 35%–60% reduction in atmospheric Hg deposition, would translate to a ∼1.2-fold reduction in fish Hg concentrations. In this way, these models can form the basis for a decision-support tool for comparing and ranking risk-reduction initiatives. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Integrated Environmental Assessment & Management is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1093/inteam/vjae011
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 396
    Subjects:
      – SubjectFull: Mercury poisoning
        Type: general
      – SubjectFull: Lakes
        Type: general
      – SubjectFull: Freshwater ecology
        Type: general
      – SubjectFull: Watersheds
        Type: general
      – SubjectFull: Environmental monitoring
        Type: general
      – SubjectFull: Bayesian analysis
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      – SubjectFull: Keystone species
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Mackenzie River (N.W.T.)
        Type: general
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      – TitleFull: Assessing mercury exposure to water and fish of the Mackenzie watershed using a Bayesian network analysis.
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              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
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