title: Assessing the fit of biotic ligand model validation data in a risk management decision context.
abstract: Biotic ligand models (BLMs) have advanced the ability to predict the concentrations of metals in surface waters likely to harm aquatic organisms. BLMs have been developed for several metals including Cu, Zn, Cd, and Ag. Additionally, the US Environmental Protection Agency has published guidance on the use of a BLM to develop water quality criteria for Cu. To validate the predictive performance of many BLMs, model predictions based on test water quality have been compared with corresponding laboratory toxicity measurements. Validation results are typically described in the published literature in terms of the proportion of predicted effect concentrations that fall within a factor of 2 of measured values. In this article, an alternative is presented using a receiver operating characteristics approach and regression prediction limit analyses, quantifying the probabilities of true and false predictions of excess toxicity risk based on toxic unit calculations and a risk management threshold of 1. The approaches are applied to a published Zn BLM and 3 simulated data sets that reflect attributes of other published BLM validation data. The overall accuracy of the unified Zn BLM is estimated to be 80% to 90%, and analyses of simulated data suggest a similar level of accuracy for other published BLMs. Further application of these validation methods to other BLMs may provide more complete and transparent information on their possible predictive value when used in the management of risks due to aqueous metals.
authors.affiliation: National Council for Air and Stream Improvement (NCASI), Western Michigan University, Kalamazoo, Michigan, USA.
authors.forename: Douglas B
authors.initials: DB
authors.lastname: McLaughlin
mesh_terms: D059001:Aquatic Organisms; D003661:Decision Support Techniques; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*; D014481:United States; D014484:United States Environmental Protection Agency; D014874:Water Pollutants, Chemical / Q000032:analysis; D060753:Water Quality / Q000592:standards*
chemical_list: D014874:Water Pollutants, Chemical
keywords: Aquatic toxicity prediction; Biotic ligand model; Decision making; Metals; Model validation