title: Using the Bayesian network relative risk model risk assessment process to evaluate management alternatives for the South River and upper Shenandoah River, Virginia.
abstract: We have conducted a series of regional scale risk assessments using the Bayesian Network Relative Risk Model (BN-RRM) to evaluate the efficacy of 2 remediation options in the reduction of risks to the South River and upper Shenandoah River study area. The 2 remediation options were 1) bank stabilization (BST) and 2) the implementation of best management practices for agriculture (AgBMPs) to reduce Hg input in to the river. Eight endpoints were chosen to be part of the risk assessment, based on stakeholder input. Although Hg contamination was the original impetus for the site being remediated, multiple chemical and physical stressors were evaluated in this analysis. Specific models were built that incorporated the changes expected from AgBMP and BST and were based on our previous research. Changes in risk were calculated, and sensitivity and influence analyses were conducted on the models. The assessments indicated that AgBMP would only slightly change risk in the study area but that negative impacts were also unlikely. Bank stabilization would reduce risk to Hg for the smallmouth bass and belted kingfisher and increase risk to abiotic water quality endpoints. However, if care were not taken to prevent loss of nesting habitat to belted kingfisher, an increase in risk to that species would occur. Because Hg was only one of several stressors contributing to risk, the change in risk depended on the specific endpoint. Sensitivity analysis provided a list of variables to be measured as part of a monitoring program. Influence analysis provided the range of maximum and minimum risk values for each endpoint and remediation option. This research demonstrates the applicability of ecological risk assessment and specifically the BN-RRM as part of a long-term adaptive management scheme for managing contaminated sites. Integr Environ Assess Manag 2017;13:100-114. © 2016 SETAC.
authors.affiliation: Environmental Science, Western Washington University, Bellingham, Washington, USA.
authors.forename: Annie F
authors.initials: AF
authors.lastname: Johns
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Scarlett E
authors.initials: SE
authors.lastname: Graham
authors.affiliation: Environmental Science, Western Washington University, Bellingham, Washington, USA.
authors.forename: Meagan J
authors.initials: MJ
authors.lastname: Harris
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: April J
authors.initials: AJ
authors.lastname: Markiewicz
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Jonah M
authors.initials: JM
authors.lastname: Stinson
authors.affiliation: Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
authors.forename: Wayne G
authors.initials: WG
authors.lastname: Landis
mesh_terms: D001499:Bayes Theorem; D003247:Conservation of Natural Resources / Q000379:methods*; D004784:Environmental Monitoring; D008628:Mercury / Q000032:analysis*; D008962:Models, Theoretical; D018570:Risk Assessment; D045483:Rivers; D014768:Virginia; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D014874:Water Pollutants, Chemical; D008628:Mercury
keywords: Adaptive management; Bayesian network relative risk model; Ecological risk assessment; Mercury contamination; Virginia