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Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches

Farley, Kevin J.; Meyer, Joseph S.; Balistrieri, Laurie S.; De Schamphelaere, Karel A.C.; Iwasaki, Yuichi; Janssen, Colin R.; Kamo, Masashi; Lofts, Stephen ORCID: https://orcid.org/0000-0002-3627-851X; Mebane, Christopher A.; Naito, Wataru; Ryan, Adam C.; Santore, Robert C.; Tipping, Edward ORCID: https://orcid.org/0000-0001-6618-6512. 2015 Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches. Environmental Toxicology and Chemistry, 34 (4). 741-753. 10.1002/etc.2820

Abstract
As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.
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Programmes:
CEH Science Areas 2013- > Pollution & Environmental Risk
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