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

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.