Stranded beach litter is a ubiquitous issue. Manual monitoring and retrieval can be cost and labour intensive. Therefore, automatic litter monitoring and retrieval is an essential mitigation strategy. In this paper, we present important foundational blocks that can be expanded into an autonomous monitoring-and-retrieval pipeline based on drone surveys and object detection using deep learning. Drone footage collected on the islands of Malta and Gozo in Sicily (Italy) and the Red Sea coast was combined with publicly available litter datasets and used to train an object detection algorithm (YOLOv5) to detect litter objects in footage recorded during drone surveys. Across all classes of litter objects, the 50%–95% mean average precision (mAP50-95) was 0.252, with the performance on single well-represented classes reaching up to 0.674. We also present an approach to geolocate objects detected by the algorithm, assigning latitude and longitude coordinates to each detection. In combination with beach morphology information derived from digital elevation models (DEMs) for path finding and identifying inaccessible areas for an autonomous litter retrieval robot, this research provides important building blocks for an automated monitoring-and-retrieval pipeline.

Beach pollution through litter leads to various negative effects and mitigation and cleanup efforts are required to prevent accumulation. Automated monitoring is an important step towards this goal and has become much more feasible with recent developments in drone technology and artificial intelligence / object detection. To assess the potential of artificial intelligence for litter monitoring on Maltese beaches, two deep learning algorithms (YOLOv5 and Faster R-CNN) were trained on drone footage of beach litter and their detection performance compared. With a mean average precision (mAP) of 0.542 YOLOv5 outperformed Faster R-CNN (mAP: 0.328). In addition, detected litter objects were geolocated so that their position could be estimated with an average error of 3.7 meters.

Shipping releases hazardous substances into marine environments through, e.g., scrubber effluents, bilge-, ballast-, and greywater, sewage, and biocides from antifouling paints. Effective regulation requires identifying these substances, their concentrations, source volumes, and resulting annual loads, to assess potential environmental hazards of individual waste streams relative to land-based sources. Focusing on the North-East Atlantic and the years 2018 and 2023, total loads of hazardous substances from shipping, and the cumulative hazard posed by individual waste streams were quantified. These ship-related sources were compared to inputs from rivers, and coastal industries and wastewater treatment. Substance-specific hazard was assessed by dividing waste-stream concentrations by each substance's environmental threshold value. Mixture toxicity was accounted for through concentration addition, using annual volumes to scale and comparing hazard indices (HI). Results show that all loads from shipping increased between 2018 and 2023. Copper and zinc from antifouling paints dominated total loads, with copper loads surpassing combined land-based copper inputs by 2023. HI from shipping increased even more than loads and shipping contributed more than half to the total HI by 2023 based on the available data. Antifouling paints and scrubbers dominated HI, marking them as the strongest contributors to hazard from shipping. Notably, scrubbers showed the highest relative increase in HI of all waste streams between 2018 and 2023. These findings highlight priority areas for regulation and monitoring within the OSPAR region. Targeted regulation on antifouling paints and scrubbers could substantially reduce the marine environmental impacts from shipping, improving the industry's environmental sustainability.