RESUMEN
A probabilistic risk assessment was conducted to characterize risks to a representative piscivorous mammal (mink, Mustela vison) and a representative carnivorous mammal (short-tailed shrew, Blarina brevicauda) exposed to PCBs, dioxins, and furans in the Housatonic River area downstream of the General Electric (GE) facility in Pittsfield, Massachusetts. Contaminant exposure was estimated using a probabilistic total daily intake model and parameterized using life history information of each species and concentrations of PCBs, dioxins, and furans in prey collected in the Housatonic River study area. The effects assessment preferentially relied on dose-response curves but defaulted to benchmarks or other estimates of effect when there were insufficient toxicity data. The risk characterization used a weight of evidence approach. Up to 3 lines of evidence were used to estimate risks to the selected mammal species: 1) probabilistic exposure and effects modeling, 2) field surveys, and 3) species-specific feeding or field studies. The weight of evidence assessment indicated a high risk for mink and an intermediate risk for short-tailed shrew.
Asunto(s)
Dioxinas/envenenamiento , Exposición a Riesgos Ambientales/efectos adversos , Contaminantes Ambientales/envenenamiento , Furanos/envenenamiento , Visón/fisiología , Bifenilos Policlorados/envenenamiento , Musarañas/fisiología , Animales , Monitoreo del Ambiente/métodos , Massachusetts , Reproducción/fisiología , Medición de Riesgo , RíosRESUMEN
Some regulatory programs rely on quantitative structure-activity relationship (QSAR) models to predict toxic effects to biota. Many currently existing QSAR models can predict the effects of a wide range of substances to biota, particularly aquatic biota. The difficulty for regulatory programs is in choosing the appropriate QSAR model or models for application in their new and existing substances programs. We evaluated model performance of six QSAR modeling packages: Ecological Structure Activity Relationship (ECOSAR), TOPKAT, a Probabilistic Neural Network (PNN), a Computational Neural Network (CNN), the QSAR components of the Assessment Tools for the Evaluation of Risk (ASTER) system, and the Optimized Approach Based on Structural Indices Set (OASIS) system. Using a testing data set of 130 substances that had not been included in the training data sets of the QSAR models under consideration, we compared model predictions for 96-h median lethal concentrations (LC50s) to fathead minnows to the corresponding measured toxicity values available in the AQUIRE database. The testing data set was heavily weighted with neutral organics of low molecular weight and functionality. Many of the testing data set substances also had a nonpolar narcosis mode of action and/or were chlorinated. A variety of statistical measures (correlation coefficient, slope and intercept from a linear regression analysis, mean absolute and squared difference between log prediction and log measured toxicity, and the percentage of predictions within factors of 2, 5, 10, 100, and 1,000 of measured toxicity values) indicated that the PNN model had the best model performance for the full testing data set of 130 substances. The rank order of the remainder of the models depended on the statistical measure employed. TOPKAT also had excellent model performance for substances within its optimum prediction space. Only 37% of the substances in the testing data set, however, fell within this optimum prediction space.