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Conceptual Bayesian Networks for Supporting Contaminated Site Ecological Risk Assessments

Conceptual Bayesian Networks for Supporting Contaminated Site Ecological Risk Assessments

Abstract

In contaminated site assessments, knowledge of the direct and indirect factors related to stressor impacts on individuals, populations, and communities of organisms is used for designing alternatives that manage or remediate ecological risks. The ecological risk assessment (ERA) framework (USEPA, 1998) provides a logical approach that can help generate this type of information. One key product of the problem formulation step in an ERA is the conceptual site model (CSM). The CSM is a graphical depiction of the risk environment that traces the fate and transport pathways of contaminants from sources of contamination (e.g., a leaking storage tank) to receptors (i.e., the ecological endpoints of concern in the risk problem). The CSM guides the development of methods for assessing ecological risk scenarios and for remediation alternative design. The qualitative and quantitative aspects of Bayesian networks may support CSM development and risk characterization. In particular, the diagrammatic representation from the qualitative aspects of causal Bayesian networks (i.e., the directed acyclic graphs) adds explanatory depth for developing the evidence base for risk characterization and remediation interventions. The components of conceptual Bayesian networks can be used to represent the characteristics and measures of the risk factors. The connections help to decompose, piece together, and explore the potential relationships that bring about high-risk scenarios. Causal pathway analysis of the conceptual Bayesian networks provides visualizations of potential exposure pathways from initial and intermediate sources to receptors. Remediation options that would break the transport of contaminants to ecological receptors can then be identified from the causal pathways. The quantitative aspects of Bayesian networks support the propagation of uncertainties in the exposure relationships, the effects of exposure, and the effectiveness of risk management interventions. It is necessary to adequately estimate the uncertainties for improved causal inferences and judgments about ecological risks and the effectiveness of remediation. Even if the conceptual network is not quantified, the structures support mechanistic and statistical designs for risk characterization. These and other largely unexplored benefits of conceptual Bayesian networks for assessing and managing contaminated sites will be discussed in this presentation.

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EPA Disclaimer: The findings and conclusions in this abstract have not been formally disseminated by the U.S. EPA and should not be construed to represent any agency determination or policy.

About the Presenter

John Carriger is a researcher with the U.S. Environmental Protection Agency’s Office of Research and Development. John received his Ph.D. in Marine Science from the College of William & Mary in 2009. His research interests are developing and applying causal modeling, decision analysis, and risk assessment tools to diverse environmental problems. John lives and works in Cincinnati, OH, USA.


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