Evaluation of microplastic pollution using bee colonies : An exploration of various sampling methodologies

Recent research has highlighted the potential of honeybees and bee products as biological samplers for monitoring xenobiotic pollutants. However, the effectiveness of these biological samplers in tracking microplastics (MPs) has not yet been explored. This study evaluates several methods of sampling MPs, using honeybees, pollen, and a novel in-hive passive sampler named the APITrap. The collected samples were characterized using a stereomicroscopy to count and categorise MPs by morphology, colour, and type. To chemical identification, a micro-Fourier transform-infrared (FTIR) spectroscopy was employed to determine the polymer types. The study was conducted across four consecutive surveillance programmes, in five different apiaries in Denmark. Our findings indicated that APITrap demonstrated better reproducibility, with a lower variation in results of 39%, compared to 111% for honeybee samples and 97% for pollen samples. Furthermore, the use of APITrap has no negative impact on bees and can be easily applied in successive samplings. The average number of MPs detected in the four monitoring studies ranged from 39 to 67 in the APITrap, 6 to 9 in honeybee samples, and 6 to 11 in pollen samples. Fibres were the most frequently found, accounting for an average of 91% of the total MPs detected in the APITrap, and similar values for fragments (5%) and films (4%). The MPs were predominantly coloured black, blue, green and red. Spectroscopy analysis confirmed the presence of up to five different synthetic polymers. Polyethylene terephthalate (PET) was the most common in case of fibres and similarly to polypropylene (PP), polyethylene (PE), polyacrylonitrile (PAN) and polyamide (PA) in non fibrous MPs. This study, based on citizen science and supported by beekeepers, highlights the potential of MPs to accumulate in beehives. It also shows that the APITrap provides a highly reliable and comprehensive approach for sampling in large-scale monitoring studies.