A. Herrera

Microplastic and tar pollution on three Canary Islands beaches: An annual study

Marine debris accumulation was analyzed from three exposed beaches of the Canary Islands (Lambra, Famara and Las Canteras). Large microplastics (1-5mm), mesoplastics (5-25mm) and tar pollution were assessed twice a month for a year. There was great spatial and temporal variability in the Canary Island coastal pollution. Seasonal patterns differed at each location, marine debris concentration depended mainly of local-scale wind and wave conditions. The most polluted beach was Lambra, a remote beach infrequently visited. The types of debris found were mainly preproduction resin pellets, plastic fragments and tar, evidencing that pollution was not of local origin, but it cames from the open sea. The levels of pollution were similar to those of highly industrialized and contaminated regions. This study corroborates that the Canary Islands are an area of accumulation of microplastics and tar rafted from the North Atlantic Ocean by the southward flowing Canary Current.

Novel methodology to isolate microplastics from vegetal-rich samples

Microplastics are small plastic particles, globally distributed throughout the oceans. To properly study them, all the methodologies for their sampling, extraction, and measurement should be standardized. For heterogeneous samples containing sediments, animal tissues and zooplankton, several procedures have been described. However, definitive methodologies for samples, rich in algae and plant material, have not yet been developed. The aim of this study was to find the best extraction protocol for vegetal-rich samples by comparing the efficacies of five previously described digestion methods, and a novel density separation method. A protocol using 96% ethanol for density separation was better than the five digestion methods tested, even better than using H2O2 digestion. As it was the most efficient, simple, safe and inexpensive method for isolating microplastics from vegetal rich samples, we recommend it as a standard separation method.

Automatic Counting and Classification of Microplastic Particles.

Microplastic particles have become an important ecological problem due to the huge amount of plastics debris that ends up in the sea. An additional impact is the ingestion of microplastics by marine species, and thus microplastics enter into the food chain with unpredictable effects on humans. In addition to the exploration of their presence in fishes, researchers are studying the presence of microplastics in coastal areas. The workload is therefore time consuming, due to the need to carry out regular campaigns to quantify their presence in the samples. So, in this work a method for automatic counting and classifying microplastic particles is presented. To the best of our knowledge, this is the first proposal to address this challenging problem. The method makes use of Computer Vision techniques for analyzing the acquired images of the samples; and Machine Learning techniques to develop accurate classifiers of the different types of microplastic particles that are considered. The obtained results show that making use of color based and shape based features along with a Random Forest classifier, an accuracy of 96.6% is achieved recognizing four types of particles: pellets, fragments, tar