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Improving the accuracy of effect-directed analysis: The role of bioavailability


Aquatic ecosystem has been suffering contamination by multiple stressors. Traditional chemical-based risk assessment usually fails to explain toxicity contribution from contaminants which are not regularly monitored or with unknown identity. Diagnosing causes of noted adverse outcome in environment is of great importance in ecological risk assessment and effect-directed analysis (EDA) has been designed to fulfill this purpose. The EDA approach has been increasingly used in aquatic risk assessment owing to its specialty in effect-directed nontarget analysis, yet lack of environmental relevance makes conventional EDA less favorable. In particular, ignoring bioavailability in EDA may cause biased and even erroneous identification of causative toxicants in a mixture. Taking bioavailability into consideration is of great importance to improve the accuracy of EDA diagnosis. The current article reviews the current status and applications of EDA practices which incorporated bioavailability. The use of biological samples is the most obvious way to include bioavailability into EDA applications, but its development is limited due to small sample size and lack of evidence for metabolizable compounds. Bioavailability/bioaccessibility-based extraction (bioaccessibility-directed and partitioning-based extraction) and passive dosing techniques are recommended to be used to integrate bioavailability into EDA diagnosis in abiotic samples. Lastly, future perspectives of expanding and standardizing the use of biological samples and bioavailability-based techniques in EDA are discussed.

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Publication details

The article was received on 14 Aug 2017, accepted on 06 Oct 2017 and first published on 11 Oct 2017

Article type: Critical Review
DOI: 10.1039/C7EM00377C
Citation: Environ. Sci.: Processes Impacts, 2017, Accepted Manuscript
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    Improving the accuracy of effect-directed analysis: The role of bioavailability

    J. You and H. Li, Environ. Sci.: Processes Impacts, 2017, Accepted Manuscript , DOI: 10.1039/C7EM00377C

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