These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


PUBMED FOR HANDHELDS

Search MEDLINE/PubMed


  • Title: Assessing the effects of field-relevant pesticide mixtures for their compliance with the concentration addition model - An experimental approach with Daphnia magna.
    Author: Schell T, Goedkoop W, Zubrod JP, Feckler A, Lüderwald S, Schulz R, Bundschuh M.
    Journal: Sci Total Environ; 2018 Dec 10; 644():342-349. PubMed ID: 29981982.
    Abstract:
    The environmental risk assessment of pesticides is mainly performed on individual active ingredients. In surface waters within the agricultural landscape, however, contamination is usually characterized by complex pesticide mixtures. To estimate the joint effects caused by these complex mixtures, mathematical models have been proposed. Among these, the model of concentration addition (CA) is suggested as default model for the risk assessment of chemical mixtures as it is considered protective for mixtures composed of similar and dissimilar acting substances. Here we assessed the suitability of CA predictions for seven field relevant pesticide mixtures using acute (immobility) and chronic (reproduction) responses of the standard test species Daphnia magna. Pesticide mixtures indicated largely additive or less than additive effects when using CA model predictions as a reference. Moreover, we revealed that deviations from CA predictions are lower for chronic (up to 3.2-fold) relative to acute (up to 7.2-fold) response variables. Additionally, CA predictions were in general more accurate for complex mixtures relative to those composed of only a few pesticides. Thus, this study suggests CA models as largely protective for the risk assessment of pesticide mixtures justifying its use as default model. At the same time, extrapolating conclusions about the joint effects of pesticides from acute to chronic responses is uncertain, due to partly large discrepancies with regards to the deviation of model prediction and observed effects between exposure scenarios.
    [Abstract] [Full Text] [Related] [New Search]