L Jay Field | PubMed Health

PubMed2003

Predicting sediment toxicity using logistic regression: a concentration-addition approach.

The question posed in this article is how useful the chemical concentration measurements for predicting the outcome of sediment toxicity tests are. Using matched data on sediment toxicity and sediment chemical concentrations from a number of studies, we investigated several approaches for predicting toxicity based on multiple logistic regression with concentration-addition models. Three models were found to meet criteria for acceptability. The first model uses individual chemicals selected using stepwise selection. The second uses derived variables to reflect combined metal contamination, polycyclic aromatic hydrocarbon (PAH) contamination, and the interaction between metals and PAHs. The third and final model is a separate species model with derived variables. Overall, these models suggest that toxicity may be correctly predicted approximately 77% of the time, although prediction is better for samples identified as nontoxic than for those known to be toxic.

Animals↗

PubMed2002

Predicting amphipod toxicity from sediment chemistry using logistic regression models.

Individual chemical logistic regression models were developed for 37 chemicals of potential concern in contaminated sediments to predict the probability of toxicity, based on the standard 10-d survival test for the marine amphipods Ampelisca abdita and Rhepoxynius abronius. These models were derived from a large database of matching sediment chemistry and toxicity data, which includes contaminant gradients from a variety of habitats in coastal North America. Chemical concentrations corresponding to a 20, 50, and 80% probability of observing sediment toxicity (T20, T50, and T80 values) were calculated to illustrate the potential for deriving application-specific sediment effect concentrations and to provide probability ranges for evaluating the reliability of the models. The individual chemical regression models were combined into a single model, using either the maximum (P(Max) model) or average (P(Avg) model) probability predicted from the chemicals analyzed in a sample, to estimate the probability of toxicity for a sample. The average predicted probability of toxicity (from the P(Max) model) within probability quartiles closely matched the incidence of toxicity within the same ranges, demonstrating the overall reliability of the P(Max) model for the database that was used to derive the model. The magnitude of the toxic effect (decreased survival) in the amphipod test increased as the predicted probability of toxicity increased. Users have a number of options for applying the logistic models, including estimating the probability of observing acute toxicity to estuarine and marine amphipods in 10-d toxicity tests at any given chemical concentration or estimating the chemical concentrations that correspond to specific probabilities of observing sediment toxicity.

Amphipoda↗