Citation:

Brennan, A., Dave Mount, AND N. Johnson. Stochastic Framework for Addressing Chemical Partitioning and Bioavailability in Contaminated Sediment Assessment and Management. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 55:11040-11048, (2021). https://doi.org/10.1021/acs.est.1c01537

Impact/Purpose:

Sampling of interstitial water for concentrations of contaminants is a common approach for assessing the bioavailability of chemicals at contaminated sediment sites. While effective for predicting bioavailability, it is common for concentrations in interstitial water to be measured only for a subset of sediments of interest at a site, with concentrations in bulk sediment only measured more broadly. In addition, many remedial designs are focused on chemical concentrations in bulk sediment rather than interstitial water. These situations create a need to relate concentrations in interstitial water and bulk sediments for purposes of risk evaluation and/or remedial design. This paper provides a framework to address this need, using a stochastic approach that relates the likelihood of exceeding defined risk thresholds based on defined (or assumed) variability in sediment partitioning. An example is provided based on data on PAH bioavailability for two sites having different partitioning characteristics. Further examples are given to show how the approach can be adapted to use a species sensitivity distribution, rather than just a single risk threshold, to provide a more nuanced assessment of likely effects.

Description:

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 (Cfree) measures represent the best way to predict adverse effects to biota. However, when the need arises to convert between solid-phase concentration (Ctotal) and Cfree, a wide variation in observed sediment-porewater partition coefficients (KTOC) is observed due to intractable complexities in binding phases. We propose a stochastic framework in which a given Ctotal is mapped to an estimated range of Cfree through variability in passive samplingderived KTOC relationships. This mapping can be used to pair estimated Cfree with biological effects data or inversely to translate a measured or assumed Cfree to an estimated Ctotal. 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, KOW-based KTOC values. Along a continuum of Ctotal, we use the BR to derive an estimate for the probability that Cfree will exceed a threshold. By explicitly describing the variability of KTOC and BR, estimates of risk posed by sedimentassociated contaminants can be more transparent and nuanced.

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