title: Predicting sediment toxicity using logistic regression: a concentration-addition approach.
abstract: 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.
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, Virginia 24061-0439, USA. epsmith@vt.edu
authors.forename: Eric P
authors.initials: EP
authors.lastname: Smith
authors.forename: Tim
authors.initials: T
authors.lastname: Robinson
authors.forename: L Jay
authors.initials: LJ
authors.lastname: Field
authors.forename: Susan B
authors.initials: SB
authors.lastname: Norton
mesh_terms: D000818:Animals; D004784:Environmental Monitoring; D005544:Forecasting; D019015:Geologic Sediments / Q000737:chemistry*; D016015:Logistic Models; D008670:Metals / Q000032:analysis / Q000633:toxicity; D011084:Polycyclic Aromatic Hydrocarbons / Q000032:analysis / Q000633:toxicity; D011336:Probability; D014874:Water Pollutants, Chemical / Q000032:analysis / Q000633:toxicity*
chemical_list: D008670:Metals; D011084:Polycyclic Aromatic Hydrocarbons; D014874:Water Pollutants, Chemical