Marine microplastic pollution is one of the biggest environmental problems we face. The growth of plastic production has not ceased since the 1950s and it is currently estimated that 368 tons of plastic were produced in 2019 (PlascticsEurope, 2020). Geyer et al. (2017) estimate that 79% of the plastic produced in the world still remains in the environment; this plastic due to the effect of degradation and subsequent fragmentation, is present in the form of microplastics in all oceans and, due to its small size can be ingested by fish and filter-feeding organisms. In addition, microplastics have additives and chemical contaminants associated with them, and the potential effect of microplastic ingestion on marine organisms, and through them, the potential risk to humans, is unknown. In the present study, European seabass (Dicentrarchus labrax) were fed for 60 days with three treatments: Control (feed), MP (feed with 10% virgin microplastics) and EMP (feed with 10% environmental microplastics), being the first study to evaluate long-term accumulation of contaminants due to ingestion of environmental microplastics (EMP) in fish. Both plastic additives such as PBDEs, and chemical contaminants adsorbed from the environment such as PCBs and DDE, were analyzed in the EMP, feed and liver. The concentration of microplastics in the feed was calculated based on the MPs/zooplankton wet weight (WW) ratio of 0.1 found in an area of maximum accumulation in the Canary Islands. Therefore, it is an experiment that simulates real conditions, but in the worst-case scenario, using both, concentrations based on data obtained in oceanographic campaigns and microplastics collected from the environment. Our results show that in this scenario, additives and chemical contaminants adsorbed on EMPs bioaccumulate in fish liver due to long-term ingestion of microplastics.

The management of plastic debris is a serious issue due to its durability. Unfortunately, million tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to monitor the amount of plastic in beaches is to collect samples and visually count and sort the plastic particles present in them. This is a very time-consuming task. In this work, we present a Computer Visionbased system which is able to automatically count and classify microplastic particles (1-5 mm) into five different visual classes. After cleaning a collected sample in the lab, the proposed system makes use of a pair of its images with different characteristics. The procedure includes a segmentation step, which is based on the Sauvola thresholding method, followed by a feature extraction and classification step. Different features and classifiers are evaluated as well as a deep learning approach. The system is tested on 12 different beach samples with a total of 2507 microplastic particles. The particles of each sample were manually counted and sorted by an expert. This data represents the ground truth, which is compared later with the results of the automatic processing proposals to evaluate their accuracy. The difference in the number of particles is 34 (1.4%) and the error in their classification is less than 4% for all types except for the line shapes particles. These results are obtained in less than half of the time needed by the human expert doing the same task manually. This implies that it is possible to process more than twice as many samples using the same time, allowing the biologists to monitor wider areas and more frequently than doing the process manually.