title: Regression models to predict water-soil heavy metals partition coefficients in risk assessment studies.
abstract: Risk assessment studies apply fate and transport models to predict the behaviour of chemicals in the environment. The definition of physico-chemical properties is crucial to predict the mobility of pollutants and heavy metals in particular within the environmental compartments. The conservative approach normally adopted at a screening level in attributing a value to the K(d) value, results in an extremely variable mobility in soil. In this paper a regression model to estimate rapidly the K(d) for heavy metals is proposed and applied to Pb, allowing a considerable reduction (3-4 orders of magnitude) of the estimation uncertainty. The application of a stepwise forward multiple regression to literature data provided a pH-dependent regression equation of the soil-water distribution coefficient (K(d)) for Pb: log K(d)=1.99+0.42 pH.
authors.affiliation: Department of Environmental Sciences, University of Venice, Calle Larga Sta Marta, 2137, I-30123, Venice, Italy.
authors.forename: C
authors.initials: C
authors.lastname: Carlon
authors.forename: M
authors.initials: M
authors.lastname: Dalla Valle
authors.forename: A
authors.initials: A
authors.lastname: Marcomini
mesh_terms: D004787:Environmental Pollution / Q000706:statistics & numerical data*; D007854:Lead / Q000633:toxicity; D019216:Metals, Heavy / Q000633:toxicity*; D008962:Models, Theoretical; D012044:Regression Analysis; D018570:Risk Assessment; D012989:Soil Pollutants; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data
chemical_list: D019216:Metals, Heavy; D012989:Soil Pollutants; D007854:Lead