Hazard/Risk Assessment BIOTIC LIGAND MODEL OF THE ACUTE TOXICITY OF METALS. 1. TECHNICAL BASIS

TLDR: The biotic ligand model of acute metal toxicity to aquatic organisms is based on the idea that mortality occurs when the metal-biotic ligand complex reaches a critical concentration, a generalization of the free ion activity model that relates toxicity to the concentration of the divalent metal cation.

Abstract: The biotic ligand model (BLM) of acute metal toxicity to aquatic organisms is based on the idea that mortality occurs when the metal-biotic ligand complex reaches a critical concentration. For fish, the biotic ligand is either known or suspected to be the sodium or calcium channel proteins in the gill surface that regulate the ionic composition of the blood. For other organisms, it is hypothesized that a biotic ligand exists and that mortality can be modeled in a similar way. The biotic ligand interacts with the metal cations in solution. The amount of metal that binds is determined by a competition for metal ions between the biotic ligand and the other aqueous ligands, particularly dissolved organic matter (DOM), and the competition for the biotic ligand between the toxic metal ion and the other metal cations in solution, for example, calcium. The model is a generalization of the free ion activity model that relates toxicity to the concentration of the divalent metal cation. The difference is the presence of competitive binding at the biotic ligand, which models the protective effects of other metal cations, and the direct influence of pH. The model is implemented using the Windermere humic aqueous model (WHAM) model of metal-DOM complexation. It is applied to copper and silver using gill complexation constants reported by R. Playle and coworkers. Initial application is made to the fathead minnow data set reported by R. Erickson and a water effects ratio data set by J. Diamond. The use of the BLM for determining total maximum daily loadings (TMDLs) and for regional risk assessments is discussed within a probabilistic framework. At first glance, it appears that a large amount of data are required for a successful application. However, the use of lognormal probability distributions reduces the required data to a manageable amount. Keywords—Bioavailability Metal toxicity Metal complexation Risk assessment