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Mixture Extrapolation Approaches
Published in Keith R. Solomon, Theo C.M. Brock, Dick de Zwart, Scott D. Dyer, Leo Posthuma, Sean M. Richards, Hans Sanderson, Paul K. Sibley, Paul J. van den Brink, Extrapolation Practice for Ecotoxicological Effect Characterization of Chemicals, 2008
Leo Posthuma, Sean M. Richards, Dick De Zwart, Scott D. Dyer, Paul K. Sibley, Christopher W. Hickey, Rolf Altenburger
First, there are experiment requirements. Organization of treatments is a major critical element of any mixture experiment, as mixture design will necessarily rely on previous experiments and may therefore be prone to error. An example is running the control group of a single-compound toxicity test asynchronous to the test exposure, which is a violation of a basic experiment design rule. Previous experiments should not be used, unless sufficient care is taken to randomize or control error sources. The type and quality of data that are used in a mixture assessment model are critical. For example, index-based approaches such as the toxic unit approach, or the use of toxic equivalence quotients, reduce full dose response curves to just 1 point each (e.g., the EC50), obviating the utility of other effect levels. The findings of a study may or may not be applicable to other exposure and/or effect levels, especially when the interest is in the “tails” of the curves (protection target), whereas experimental data pertain to EC50 levels. It is crucial to clarify the objectives of the study (mechanistic understanding, testing quantitative prediction accuracy of models, etc.) and to design the study in such a way that these targets are reached. For example, if one wants better mechanistic understanding, one should measure target concentrations rather than ambient exposure concentrations.
Distribution and Fate of Organic and Inorganic Contaminants in a River Floodplain-Results of a Case Study on the River Elbe, Germany
Published in Donald L. Wise, Debra J. Trantolo, Edward J. Cichon, Hilary I. Inyang, Ulrich Stottmeister, Remediation Engineering of Contaminated Soils, 2000
Kurt Friese, Barbara Witter, Werner Brack, Olaf Buettner, Frank Krueger, Maritta Kunert, Holger Rupp, Guenter Miehlich, Alexander Groengroeft, Ren Schwartz, Andrea van der Veen, Dieter W. Zachmann
The toxicity identification evaluation (TIE) approach, according to the concept of the U.S. EPA, (47-49) combines the laboratory biotesting of liquid samples such as pore water, elutriates, and extracts and chemical analysis with fractionation procedures focusing on the identification of effective toxicants. In-situ studies and bulk sediment tests are dispensed with. The main advantage of TIE is the identification of real cause-effect relationships and effective toxicants. Samples are sequentially biotested and fractionated, separating a limited number of compounds with defined chemical properties according to the fractionation procedures applied. These chemicals can in a further step be identified analytically and confirmed as toxicants. This confirmation may be done, for example, by applying artificial mixtures to biotesting containing the expected toxicants in concentrations equivalent to those in the fraction, by removing the expected toxicant and testing again, by calculating the additive toxicity of the components using the toxic unit approach, by comparing symptoms caused by the fraction and the individual compounds or the mixture, or by comparing species sensitivity.
Ecological risk of heavy metals in sediment of an urban river in Bangladesh
Published in Human and Ecological Risk Assessment: An International Journal, 2018
Md. Saiful Islam, Ram Proshad, Saad Ahmed
Contamination of heavy metals (Cr, Ni, Cu, As, Cd, and Pb) was investigated in the surface sediments of Buriganga River adjacent to the capital city of Bangladesh. In the present investigation, the comparison results of heavy metals with background and toxicological reference values suggested that the river Buriganga was polluted by heavy metals and might create an adverse effect to the surrounding riverine ecosystems. The PCA indicates that Cr, Ni, Cu, and As were predominantly contributed by anthropogenic activities, whereas Pb was mainly impacted by dust from the lead-acid battery factory. The overall pollution load was slightly higher in winter than in summer season. The contamination factor (CF), pollution load index (PLI), enrichment factor (EF), and toxic unit (TU) revealed that sediments in this study were considerably polluted by the examined heavy metals. Metals in surface sediments of Buriganga River showed high degree of contamination. Considering the individual metal, Cd and Pb had very high potential ecological risk for most of the sites of the study area. To obtain a comprehensive risk assessment, either the biological and toxicological data or bioaccumulation data in the benthic environment of the studied river in Bangladesh should be considered in further studies.