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

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.