Four sandy beaches on the island of Malta in the Central Mediterranean were regularly sampled for Large MicroPlastic (LMP) particles having a diameter between 1 mm and 5 mm, at stations located at the waterline and 10 m inshore. The 10975 extracted LMP particles were characterised (dimensions, surface roughness, colour) through unaided visual observation, microscopic analyses, and an algorithm developed within the current study. Two-thirds of the isolated particles were smooth and the majority of these belonged to the grey-white colour category, with a low degree of opaqueness and discolouration, and a high degree of transparency, suggesting that these were dominated by low-density polyethylene LMPs. Conflicting evidence concerning the relative residence time of the isolated LMPs within seawater emerged from the colour and contour roughness determination, although an abundance of primary LMPs (production pellets) within our sample might have been responsible for the low contour roughness results obtained. Roughly six times as many particles were recorded within the inshore sampling stations as the particles recorded at the waterline stations. The developed algorithm performed very well when the dimension and colour parameter values it delivered were compared with those obtained by microscopic analyses. Highlights ► No universal methodology of high validity for analysing isolated microplastics. ► Use of image processing techniques to automatically extract parameters for LMPs. ► Make the process less timeconsuming and removes subjectivity. ► Samples collected from a number of popular beaches around the island of Malta. ► Algorithm performed well in determining the dimensions and colour of the LMPs.

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In recent years, citizen science campaigns have provided a very good platform for widespread data collection. Within the marine domain, jellyfish are among the most commonly deployed species for citizen reporting purposes. The timely validation of submitted jellyfish reports remains challenging, given the sheer volume of reports being submitted and the relative paucity of trained staff familiar with the taxonomic identification of jellyfish. In this work, hundreds of photos that were submitted to the “Spot the Jellyfish” initiative are used to train a group of region-based, convolution neural networks. The main aim is to develop models that can classify, and distinguish between, the five most commonly recorded species of jellyfish within Maltese waters. In particular, images of the Pelagia noctiluca, Cotylorhiza tuberculata, Carybdea marsupialis, Velella velella and salps were considered. The reliability of the digital architecture is quantified through the precision, recall, f1 score, and κ score metrics. Improvements gained through the applicability of data augmentation and transfer learning techniques, are also discussed. Very promising results, that support upcoming aspirations to embed automated classification methods within online services, including smart phone apps, were obtained. These can reduce, and potentially eliminate, the need for human expert intervention in validating citizen science reports for the five jellyfish species in question, thus providing prompt feedback to the citizen scientist submitting the report.

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High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.