Estimating species sensitivity distributions on the basis of readily obtainable descriptors and toxicity data for three species of algae, crustaceans, and fish.

Estimation of species sensitivity distributions (SSDs) is a crucial approach to predicting ecological risks and water quality benchmarks, but the amount of data required to implement this approach is a serious constraint on the application of SSDs to chemicals for which there are few or no toxicity data. The development of statistical models to directly estimate the mean and standard deviation (SD) of the logarithms of log-normally distributed SSDs has recently been proposed to overcome this problem. To predict these two parameters, we developed multiple linear regression models that included, in addition to readily obtainable descriptors, the mean and SD of the logarithms of the concentrations that are acutely toxic to one algal, one crustacean, and one fish species, as predictors.

Quantifying the precision of ecological risk: Conventional assessment factor method vs. species sensitivity distribution method.

In ecological risk assessment, the Predicted No Effect Concentration (PNEC) of a substance is generally derived by one of two methods: either by applying an Assessment Factor (AF) or by using a Species Sensitivity Distribution (SSD). With the AF method, which is the conventional way, the PNEC is determined by dividing the lowest No Observed Effect Concentration (NOEC) by an AF of a certain fixed magnitude. With the SSD method, which is becoming increasingly used in the European Union and the United States, an HC5 value (Hazardous Concentration for 5% of species) is estimated from the NOEC and then divided by an AF to derive the PNEC.