Stochastic Framework for Addressing Chemical Partitioning and Bioavailability in Contaminated Sediment Assessment and Management.

Autor: Brennan AA; Water Resources Science, University of Minnesota Duluth, Duluth, Minnesota 55812, United States., Mount DR; Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Duluth, Minnesota 55804, United States., Johnson NW; Water Resources Science, University of Minnesota Duluth, Duluth, Minnesota 55812, United States.; Department of Civil Engineering, University of Minnesota, Duluth, Duluth, Minnesota 55812, United States.
Jazyk: angličtina
Zdroj: Environmental science & technology [Environ Sci Technol] 2021 Aug 17; Vol. 55 (16), pp. 11040-11048. Date of Electronic Publication: 2021 Jul 26.
DOI: 10.1021/acs.est.1c01537
Abstrakt: Passive sampling to quantify net partitioning of hydrophobic organic contaminants between the porewater and solid phase has advanced risk management for contaminated sediments. Direct porewater ( C free ) measures represent the best way to predict adverse effects to biota. However, when the need arises to convert between solid-phase concentration ( C total ) and C free , a wide variation in observed sediment-porewater partition coefficients ( K TOC ) is observed due to intractable complexities in binding phases. We propose a stochastic framework in which a given C total is mapped to an estimated range of C free through variability in passive sampling-derived K TOC relationships. This mapping can be used to pair estimated C free with biological effects data or inversely to translate a measured or assumed C free to an estimated C total . We apply the framework to both an effects threshold for polycyclic aromatic hydrocarbon (PAH) toxicity and an aggregate adverse impact on an assemblage of species. The stochastic framework is based on a "bioavailability ratio" (BR), which reflects the extent to which potency-weighted, aggregate PAH partitioning to the solid-phase is greater than that predicted by default, K OW -based K TOC values. Along a continuum of C total , we use the BR to derive an estimate for the probability that C free will exceed a threshold. By explicitly describing the variability of K TOC and BR, estimates of risk posed by sediment-associated contaminants can be more transparent and nuanced.
Databáze: MEDLINE
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