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  • Title: Enhancing the environmental monitoring of pesticide residues through Apis mellifera colonies: Honey bees versus passive sampling.
    Author: Murcia-Morales M, Vejsnæs F, Brodschneider R, Hatjina F, Van der Steen JJM, Oller-Serrano JL, Fernández-Alba AR.
    Journal: Sci Total Environ; 2023 Aug 01; 884():163847. PubMed ID: 37127158.
    Abstract:
    The use of apicultural matrices for the environmental monitoring of pesticides is a widely employed approach that facilitates to a great extent the sampling procedures. Honey bees are one of the most commonly employed matrices in these studies due to their abundance in the colonies and their direct contact with the beehive and the environment. However, the analysis of this matrix is associated to a lack of representativity of the contaminants accumulated within the beehive, due mainly to the limited number of honey bees that are sampled and analyzed compared to the population in a hive. This small proportion of organisms which are sampled from the colony may lead to underestimations or overestimations of the total pesticide load, depending on the specific individuals that are included in the analysis. In the present work, the passive, non-invasive APIStrip-based sampling approach is compared to active bee sampling with a total of 240 samples taken from 15 apiaries from Austria, Denmark and Greece over a two-month period in 2022. The APIStrips have been found to provide a more comprehensive image of the pesticide residues accumulated in the beehive in terms of number of identified residues and robustness of the results. A total of 74 different pesticide residues were detected: the use of APIStrips allowed to detect 66 pesticides in the three countries, compared to 38 residues in honey bees. The use of APIStrips also resulted in a higher percentage of positive samples (containing at least one pesticide residue). The results provided by the passive sampling approach were also more consistent among the replicates and over time, which reveals an increased sampling robustness.
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