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Your Environment. Your Health.

Dataset Details

Superfund Research Program

Title: Dataset describing polychlorinated biphenyl (PCB) congener accumulation on polyurethane foam (PUF) and solid-phase microextraction (SPME) passive samplers in sediment slurry bioreactors bioaugmented with Paraburkholderia xenovorans LB400

Accession Number: doi: 10.25820/data.006160

Link to Dataset: https://iro.uiowa.edu/esploro/outputs/9984200704902771

Repository: Iowa Research Online

Summary: This dataset describes the time-series accumulation of polychlorinated biphenyl (PCB) congeners on two seperate types of passive sampling media deployed over 35-days in laboratory-scale bioreactors containing PCB-contaminated sediment slurry gathered from a field site as part of a modifed biodegradation assay experiment. The two types of passive sampling media used in the experiment are polyurethane foam (PUF) plugs inserted in the necks of the bioreactors and solid-phase microoextraction (SPME) fibers coated with polydimethylsiloxane (PDMS) directly immersed in the sediment slurry. The experimental treatment conditions included bioaugmentation with Paraburkholderia xenovorans LB400 and the addition of the phytogenic biosurfactant, saponin. Non-bioaugmented and surfactant-free controls were also included in the experiment and this dataset. The experiment was designed to understand how biodegradation by aerobic, PCB-degrading microorganisms can mitigate the mass flux of PCB congeners from contaminated sediment to air through biodegradation of freely-dissolved PCB congeners as they desorb from sediment particles, over time. The data resulting from these experiments can inform future experimental design and be reused by other researchers for further analysis and / or interpretive insights.

Publication(s) associated with this dataset:
  • Bako CM, Martinez A, Ewald JM, Hua J, Ramotowski DJ, Dong Q, Schnoor JL, Mattes TE. 2022. Aerobic bioaugmentation to decrease polychlorinated biphenyl (PCB) emissions from contaminated sediments to air. Environ Sci Technol 56(20):14338-14349. doi:10.1021/acs.est.2c01043 PMID:36178372 PMCID:PMC9583607
Project(s) associated with this dataset:
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