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Modeling PCBs residues in fish tissue based on sediment PCBs concentration
Citation:
Hoffman, J., L. Burkhard, G. Peterson, T. Hollenhorst, M. Pearson, A. Cotter, AND J. Launspach. Modeling PCBs residues in fish tissue based on sediment PCBs concentration. St. Louis River Summit, Duluth, MN, March 03 - 04, 2020.
Impact/Purpose:
In Great Lakes Areas of Concern, an important natural resource management goal is to reduce or eliminate fish consumption advisories by remediating sediments contaminated by polychlorinated biphenyls (PCBs). To understand the potential impact of remediation on fish tissue concentrations, we developed a habitat-specific, geospatial Biota-Sediment Accumulation Factor (BSAF) model that predicts fish tissue residues based on sediment PCBs concentration and habitat quality. We conclude this approach has strong potential to be used for PCBs hot-spot confirmation, estimating remediation project footprints, and to estimate a project’s potential impact to improve the quality of the fishery.
Description:
In the St. Louis River Area of Concern, an important natural resource management goal is to reduce or eliminate fish consumption advisories by remediating contaminant sediments and restoring aquatic habitat. One of the two contaminants of concern is polychlorinated biphenyls (PCBs), which are distributed heterogeneously in sediments throughout the lower St. Louis River. Owing to this heterogenous distribution, it is recognized that PCBs concentration in fish tissue vary widely, presumably due to differences in both habitat use and life history. Therefore, to understand the potential impact of remediation and restoration on fish tissue concentrations, we developed a habitat-specific, geospatial Biota-Sediment Accumulation Factor (BSAF) model that predicts fish tissue residues based on sediment PCBs concentration and habitat quality. We conducted a field validation of the model based on a random, stratified sampling and found the model had a high degree of accuracy for predicting fish tissue residues. We then developed a high-resolution (10 m) version of the model using a Monte Carlo style approach to support decision-making for the Munger Landing remediation. We conclude this approach has strong potential to be used for PCBs hot-spot confirmation, estimating remediation project footprints, and to estimate a project’s potential impact to improve the quality of the fishery.