We developed and evaluated a total toxic units modeling approach for predicting mean toxicity as measured in laboratory tests for Great Lakes sediments containing complex mixtures of environmental contaminants (e.g., polychlorinated biphenyls, polycyclic aromatic hydrocarbons, pesticides, chlorinated dioxins, and metals). The approach incorporates equilibrium partitioning and organic carbon control of bioavailability for organic contaminants and acid volatile sulfide (AVS) control for metals, and includes toxic equivalency for planar organic chemicals. A toxic unit is defined as the ratio of the estimated pore-water concentration of a contaminant to the chronic toxicity of that contaminant, as estimated by U.S. Environmental Protection Agency Ambient Water Quality Criteria (AWQC). The toxic unit models we developed assume complete additivity of contaminant effects, are completely mechanistic in form, and were evaluated without any a posteriori modification of either the models or the data from which the models were developed and against which they were tested. A linear relationship between total toxic units, which included toxicity attributable to both iron and un-ionized ammonia, accounted for about 88% of observed variability in mean toxicity; a quadratic relationship accounted for almost 94%. Exclusion of either bioavailability components (i.e., equilibrium partitioning control of organic contaminants and AVS control of metals) or iron from the model substantially decreased its ability to predict mean toxicity. A model based solely on un-ionized ammonia accounted for about 47% of the variability in mean toxicity. We found the toxic unit approach to be a viable method for assessing and ranking the relative potential toxicity of contaminated sediments.
Additional publication details
|Publication Subtype||Journal Article|
|Title||Estimating aquatic toxicity as determined through laboratory tests of great lakes sediments containing complex mixtures of environmental contaminants|
|Series title||Environmental Monitoring and Assessment|
|Other Geospatial||Great Lakes region|
|Google Analytic Metrics||Metrics page|