Modeling polycyclic aromatic hydrocarbon bioaccumulation and metabolism in time-variable early life-stage exposures.

Recent laboratory investigations into the bioaccumulation and toxicity of polycyclic aromatic hydrocarbons (PAH) have focused on low-level, time-variable exposures to early life-stage fish. Polycyclic aromatic hydrocarbon body-burden residues reported in these studies were lower than critical body-burden residues predicted by the target lipid model (TLM). To understand this discrepancy, a time-variable uptake and depuration model of PAH bioaccumulation was developed. Kinetic constants were fit using measured exposure and tissue concentrations. The resulting lipid-water partition coefficients (K(LW)) were uncorrelated with the octanol-water partition coefficient (K(OW))--a qualitatively unrealistic finding considering that numerous studies have reported a positive correlation between the two. Because PAHs are known to be metabolized, the comparison of K(LW) with K(OW) suggests that metabolism may be occurring in early life-stage fish. Therefore, the uptake and depuration model was modified to include metabolism while assuming linearity of K(LW) with K(OW). Calculated metabolism rates were positively correlated with K(OW)--a finding qualitatively similar to those of other studies. The present study provides a reasonable explanation for the discrepancy between the TLM predictions and the measured toxic effect levels. Given the time-variable exposure concentrations, the maximum measured body burdens used to relate to toxic effects may be underestimated. In addition, the maximum body burden of parent PAH plus metabolites may be a better measure in relating tissue concentrations to toxic effects. Incorporating these refinements in relating body burdens to toxic effects may result in a better comparison between TLM predictions and measured effect levels.

Predicting sediment metal toxicity using a sediment biotic ligand model: methodology and initial application.

An extension of the simultaneously extracted metals/acid-volatile sulfide (SEM/AVS) procedure is presented that predicts the acute and chronic sediment metals effects concentrations. A biotic ligand model (BLM) and a pore water-sediment partitioning model are used to predict the sediment concentration that is in equilibrium with the biotic ligand effects concentration. This initial application considers only partitioning to sediment particulate organic carbon. This procedure bypasses the need to compute the details of the pore-water chemistry. Remarkably, the median lethal concentration on a sediment organic carbon (OC)-normalized basis, SEM*(x,OC), is essentially unchanged over a wide range of concentrations of pore-water hardness, salinity, dissolved organic carbon, and any other complexing or competing ligands. Only the pore-water pH is important. Both acute and chronic exposures in fresh- and saltwater sediments are compared to predictions for cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) based on the Daphnia magna BLM. The SEM*(x,OC) concentrations are similar for all the metals except cadmium. For pH = 8, the approximate values (micromol/gOC) are Cd-SEM*(xOC) approximately equal to 100, Cu-SEM*(x,OC) approximately equal to 900, Ni-SEMoc approximately equal to 1,100, Zn-SEM*(x,OC) approximately equal to 1,400, and Pb-SEM*(x,OC) approximately equal to 2,700. This similarity is the explanation for an empirically observed dose-response relationship between SEM and acute and chronic effects concentrations that had been observed previously. This initial application clearly demonstrates that BLMs can be used to predict toxic sediment concentrations without modeling the pore-water chemistry.

Application of the biotic ligand model to predicting zinc toxicity to rainbow trout, fathead minnow, and Daphnia magna.

The Biotic Ligand Model has been previously developed to explain and predict the effects of water chemistry on the toxicity of copper, silver, and cadmium. In this paper, we describe the development and application of a biotic ligand model for zinc (Zn BLM). The data used in the development of the Zn BLM includes acute zinc LC50 data for several aquatic organisms including rainbow trout, fathead minnow, and Daphnia magna. Important chemical effects were observed that influenced the measured zinc toxicity for these organisms including the effects of hardness and pH. A significant amount of the historical toxicity data for zinc includes concentrations that exceeded zinc solubility. These data exhibited very different responses to chemical adjustment than data that were within solubility limits. Toxicity data that were within solubility limits showed evidence of both zinc complexation, and zinc-proton competition and could be well described by a chemical equilibrium approach such as that used by the Zn BLM.

Extension of the biotic ligand model of acute toxicity to a physiologically-based model of the survival time of rainbow trout (Oncorhynchus mykiss) exposed to silver.

Chemical speciation controls the bioavailability and toxicity of metals in aquatic systems and regulatory agencies are recognizing this as they develop updated water quality criteria (WQC) for metals. The factors that affect bioavailability may be quantitatively evaluated with the biotic ligand model (BLM). Within the context of the BLM framework, the 'biotic ligand' is the site where metal binding results in the manifestation of a toxic effect. While the BLM does account for the speciation and complexation of dissolved metal in solution, and competition among the free metal ion and other cations for binding sites at the biotic ligand, it does not explicitly consider either the physiological effects of metals on aquatic organisms, or the direct effect of water chemistry parameters such as pH, Ca(2+)and Na(+) on the physiological state of the organism. Here, a physiologically-based model of survival time is described. In addition to incorporating the effects of water chemistry on metal availability to the organism, via the BLM, it also considers the interaction of water chemistry on the physiological condition of the organism, independent of its effect on metal availability. At the same time it explicitly considers the degree of interaction of these factors with the organism and how this affects the rate at which cumulative damage occurs. An example application of the model to toxicity data for rainbow trout exposed to silver is presented to illustrate how this framework may be used to predict survival time for alternative exposure durations. The sodium balance model (SBM) that is described herein, a specific application of a more generic ion balance model (IBM) framework, adds a new physiological dimension to the previously developed BLM. As such it also necessarily adds another layer of complexity to this already useful predictive framework. While the demonstrated capability of the SBM to predict effects in relation to exposure duration is a useful feature of this mechanistically-based framework, it is envisioned that, with suitable refinements, it may also have utility in other areas of toxicological and regulatory interest. Such areas include the analysis of time variable exposure conditions, residual after-effects of exposure to metals, acclimation, chronic toxicity and species and genus sensitivity. Each of these is of potential utility to longer-term ongoing efforts to develop and refine WQC for metals.