Presentation Title:
Evaluating Key Decisions in Modeling the Anacostia River Sediment Chemical Isolation Layer

Topic Area: 4e. Cap Modeling

Formatted Title:

Evaluating Key Decisions in Modeling the Anacostia River Sediment Chemical Isolation Layer

Background/Objectives:

The District of Columbia Department of Energy and Environment selected an interim remedy for remediation of contaminated sediment in the tidal Anacostia River in Washington, DC. This is documented in the 2020 Interim Record of Decision and subsequent Explanation of Significant Differences. The interim remedy targets four constituents of concern (COCs) in sediment: total PCB congeners, dioxin toxic equivalent (TEQ), chlordane, and dioxin-like PCB TEQ. Elevated total PCB concentrations generally overlap with elevated concentrations of the other COCs. Early action areas within three operable units were identified for remediation of surface sediment containing total PCB concentrations greater than 600 micrograms per kilogram. The interim remedy involves selective dredging, subaqueous capping, and in situ treatment of the early action areas which are located in diverse physical settings from waterfronts to wetlands. As part of the remedial design, contaminant transport modeling via CapSim (Shen et al., 2018) was used to develop chemical isolation layer designs and estimate their performance. The objective of the cap design was to prevent contaminated porewater discharge into the benthic zone, and subsequently into the food chain, exceeding the porewater target concentration for at least 100 years. The application of the CapSim model to the Anacostia River remedial design required three key decisions: identification of the design COCs, selection of an appropriate sorption isotherm for activated carbon, and determination of modeled contamination depth.

Approach/Activities:

The CapSim model was used to simulate contaminant transport through the cap using input values from a combination of site-specific data, groundwater modeling results, and literature values. To model COC groups (such as PCBs), a surrogate chemical was used to represent the sum of the constituents of the group. The surrogate was selected based on factors such as predominance in the group, mobility, and available transport parameters. To identify the design COCs, “worst case” scenarios were simulated for each COC using surrogate chemicals. Due to the relatively low PCB porewater concentrations in the study area, the model default activated carbon isotherms did not fit the data. Therefore, the isotherms used in these simulations were sourced from other studies that had developed isotherms for the range of PCB porewater concentrations and dissolved organic carbon concentrations expected during the life of the cap. The effect on cap performance from contaminated porewater in deeper subsurface sediment was modeled by assigning 10 times higher COC concentrations to deeper sediment layers.

Results/Lessons Learned:

Several key decisions were useful in developing the design of the cap and in situ treatment at the early action areas. Total PCBs were selected as the design COC and modeled using congener PCB-52 as a surrogate, owing to this congener’s predominance and above-average mobility. This approach optimized modeling efforts while allowing for a conservative design that would be applicable to all COCs. For amended cap scenarios, a linear isotherm from the Lower Passaic River Treatability Study was used to simulate sorption to activated carbon. For in situ treatment scenarios, a modified Freundlich isotherm was fit to the range of expected porewater concentrations. The linear isotherm was not used for in situ areas because it was overconservative and predicted unreasonably high activated carbon doses. Lastly, sensitivity analyses showed that deep contamination made no significant difference to simulated cap performance. Therefore, cap design scenarios did not include porewater concentrations deeper than 1 foot below sediment surface.

Chit Christian

Affiliation

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Group 1, Poster Board #56: Cap Modeling (Poster)