Reactive Cap Modeling – A Tool to Refine Remedy Design and Build Stakeholder Consensus



Reactive Cap Modeling – A Tool to Refine Remedy Design and Build Stakeholder Consensus

Presenter:  Kristen Wright-Ng (Haley & Aldrich, Inc.)
Authors:  Kristen Wright-Ng, Sean Carroll and Bill Haswell (Haley & Aldrich, Inc.)



Numerical modeling of reactive sediment caps can be an effective tool for quantifying the predicted success, or more importantly the predicted failure, of a proposed reactive cap design.  While this quantitative approach may be intimidating, the information it provides makes it an integral tool in the design process: once the basic model has been developed, multiple model iterations can be performed and the modeling results can be used to optimize a design concept.  We will present an example and a framework of using the CapSim 2.7 numerical model (Reible and Lampert, 2014) in support of the reactive cap design for NAPL-impacted sediments in the West Branch of the Grand Calumet River in Hammond, Indiana.  In this Great Lakes National Program Office (GLNPO) multi-agency collaborative project, we used numerical modeling to help answer these questions:

– Which parameters are going to be the critical design drivers?
– How will the cap perform with respect to different contaminants of concern?
– What are the appropriate types and amounts of sorptive media?
– How do conservative design assumptions affect the remedy cost?

We found that the numerical model could be used to reduce uncertainty in the design and help build consensus of the project team (including state and federal regulators) around the proposed remedy design. We will explore the sensitive design input parameters, as indicated by the model results, including the groundwater to surface water flux rate, contaminant concentrations in pore water (most notably benzene and naphthalene), and the specified GAC content in the reactive cap as well as how to collect this site-specific data.  By understanding sensitive design parameters, field programs can be developed to collect site specific data in support of numerical modeling, limiting assumptions and uncertainties with model input parameters.  We will explain the model output and put it into the context of remedy selection.

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