๐Ÿ‡บ๐Ÿ‡ธConceptual Bayesian Networks for Groundwater Remediation and Assessment

John Carriger, Ph.D., U.S. Environmental Protection Agency

To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.

Abstract

Causal structural models are used to capture knowledge of a problem domain through framing potential events as random variables and connections as causal arcs. Moreover, the properties of causal structural models foster additional insights on causal and inferential interactions among variables from interventions and observations. Conceptual site models are commonly used in environmental assessments for capturing the knowledge of the fate, transport, and risks at contaminated sites and form the basis for simulation models. The usage of causal structural models with conceptual site modeling may provide additional value for site remediation and assessments. We call this combination conceptual Bayesian networks (CBNs) and explore their application potential in contaminated site management for assessing the subsurface movement of contaminated plumes. Once constructed, the CBN can capture the hypothesized locations and movements of a plume as well as critical zones of offsite flux. Causal pathway identification can examine offsite transport pathways and the potential effects of remediation decisions that intervene on those pathways. Interventions for containing or removing subsurface contamination and breaking the transport pathways are graphically represented as decision nodes. Finally, measurement node types can explicitly include lines of evidence for subsurface processes in the CBN. Acausal pathways from influence paths provide additional information on statistical inferences when lines of evidence are observed individually or in conjunction. The CBN concept may provide additional insights beyond traditional conceptual site models and could be a valuable component in a site managerโ€™s toolbox.

The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Authors

John F. Carriger1^1, Michael C. Brooks2^2, Carolyn Acheson3^3, Ronald Herrmann1^1, Lee Rhea2^2

1^1US Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation and Technology Division, Cincinnati, OH

2^2US Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Groundwater Characterization and Remediation Division, Ada, OK

3^3US Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation and Technology Division, Cincinnati, OH (retired)

About the Presenter

John Carriger is a research scientist at the U.S. Environmental Protection Agencyโ€™s Office of Research and Development in Cincinnati, Ohio. John has a marine science Ph.D. from the College of William and Mary. Johnโ€™s research interests include applying risk assessment, decision analysis, and weight of evidence tools to environmental problems.

John Carriger, Ph.D.

Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:

Previous Conference Presentations

๐Ÿ‡บ๐Ÿ‡ธpageA Probabilistic Community Analysis for Coral Reef Ecosystems in Puerto Rico๐Ÿ‡บ๐Ÿ‡ธpageAssessing Coral Reef Condition Indicators for Local and Global Stressors Using Bayesian Networks๐Ÿ‡บ๐Ÿ‡ธpageA Bayesian network analysis of the Federal Employee Viewpoint Survey (FEVS) for the U.S. EPA

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