- Program Areas
- Installation Energy and Water
- Environmental Restoration
- Munitions Response
- Resource Conservation and Resiliency
- Weapons Systems and Platforms
Integrated Passive Sampler-Food Web Modeling Framework for Monitoring Remedy Effectiveness
Dr. Philip Gschwend | Massachusetts Institute of Technology
The objective of this project is to demonstrate the accuracy of food web models (FWMs) when accurate exposure data from passive samplers are used. Polyethylene (PE) passive samplers will be deployed in both the water column and the sediment bed of a polychlorinated biphenyl (PCB)-contaminated lake to obtain surface water and porewater concentrations of individual PCB congeners in that ecosystem. Subsequently, these concentration data will be used as inputs to drive the FWM, assuming lower trophic levels are equilibrated with the water in their surroundings and higher trophic levels reflect the combined influences of their diets.
This project will use PE passive sampler data from an aquatic ecosystem contaminated with PCBs, together with an uncalibrated, spatially explicit FWM, to demonstrate the utility and accuracy of these tools for reliably predicting the expected long-term impacts of low-level PCB exposures at Department of Defense (DoD) and other contaminated sediment sites. Poor bioaccumulation model performance in the past is believed to be largely a function of poorly characterized exposure concentration inputs (as opposed to deficiencies in the model framework) and as such success of the approach relies chiefly upon a better characterization of exposure concentrations used as inputs to FWMs. The passive sampler technology simply entails deploying strips of polyethylene in the water column and sediment bed of the lake, and allowing this polymeric medium to accumulate PCBs from the surroundings. Subsequent analysis of the polyethylene will reveal the samplers' PCB concentrations, and these will be translated to give the water and porewater concentrations of PCBs. Such water concentrations can then be used to calculate the PCB concentrations in fauna and plankton assuming lipid-water equilibrations. And finally, the FWM can calculate the higher trophic organism body burdens of the PCBs using knowledge of those animals' diets.
The demonstration will be conducted at both a relatively contaminated part of the lake and at cleaner parts of the lake in two different seasons (spring/summer vs. fall/winter). In addition to PE sampling, the team will collect organisms at these times so as to compare predictions to observed biota concentrations. They will also collect sediment samples and analyze them for PCBs so the data can be used as done in current practice to demonstrate the increased effectiveness of the new approach (use of passive samplers). This current practice approach will assume porewater concentrations can be calculated by normalizing the bulk sediment concentrations with focKoc, the product of the sediment's organic carbon contents and the PCB's organic carbon-normalized partition coefficient. Since the Kow values of these PCB congeners are known, these inputs to the FWM are fixed. The team will also collect gut contents and del15N data to check assumptions about the dietary preferences for each organism. Using existing model parameterization, and providing additional lipid content data for the fish, the team will be able to assess the accuracy of the FWM using the passive sampler-derived inputs.
DoD faces legacy contamination at approximately 6,000 sediment sites nationwide and will benefit from an integrated framework for characterizing exposures and resulting concentrations throughout the aquatic food web. This project will demonstrate the feasibility of combining more reliable exposure profiles as obtained from PE samplers with a spatially- and temporally-explicit FWM to better predict potential impacts of remedial activities or monitored natural attenuation. The PE sampler driven spatially-explicit FWM provides a set of robust tools for supporting cost-effective management of contaminated sediment sites. (Anticipated Project Completion - 2018)