Search Results (41)

Expanded polystyrene litter in marine environments harbors diverse and distinct microbial communities, referred to as the plastisphere. This study aimed to investigate the monthly dynamics of bacterial and potentially pathogenic bacterial (PPB) communities on expanded polystyrene over one year. Vibrio species dominated the PPB community, cooccurring at consistently higher abundances on expanded polystyrene than in the surrounding seawater, particularly under higher temperatures and low dissolved organic carbon (DOC) levels. At a temperature threshold of 16 °C, the abundance of zoonotic species, such as Vibrio parahaemolyticus and Vibrio alginolyticus, increased significantly. Some psychrotrophic Vibrio spp. were detected under moderately eutrophic conditions, suggesting that expanded polystyrene may also serve as a dispersal vector facilitating their transport to more favorable habitats. Multivariate analyses, including partial least squares path modeling, revealed temperature and DOC as the primary environmental factors influencing PPB community composition. However, environmental responses varied by taxonomic groups, with different preferences observed under varying eutrophic conditions. In conclusion, these findings demonstrate that expanded polystyrene litter supports a selective and environmentally responsive bacterial population, highlighting the potential role of plastic debris in promoting pathogenic bacterial persistence and spread in marine ecosystems, particularly under conditions associated with climate change, including warming and eutrophication. Full article

The PLASTRON (Reuse of plastic from the sea using additive manufacturing as a strategy for the challenges of tourism supply chains and business resilience (Italian acronym: riuso della PLAstica dal mare usando la manifattura additiva come Strategia per le sfide delle filiere del TuRismO e la resilieNza delle imprese)) project aims to enhance the sustainability of coastal communities by improving plastic waste management and fostering the transition to efficient circular economy models, aligned with the European Green Deal. A Franco-Italian multidisciplinary team is testing low-investment local initiatives for collecting plastics from coasts, ports, and the sea. The project develops protocols to integrate waste into the recycling chain and create value-added goods through additive manufacturing. Special focus is given to degraded marine litter and mixed plastics, exploring their reuse via blending with other materials and natural additives. The focus was on the characterisation of two material blends, polyolefin mix (MPO) and Polyethylene terephthalate (PET), both with plastic marine litter. The processability of the MPO blend was comparable to that of commercial recycled MPO. The differences observed between 3D printing and injection moulding for the MPO derived from marine litter were negligible, unlike those found in the commercial MPO. The PET, modified with 0.8% chain extender additive, exhibited performance equivalent to—or in some cases even superior to—that of virgin commercial PET. However, 3D printing processing induced a certain brittleness in the material. Full article

Accumulation of plastic litter in the marine environment is a pressing global concern. To study this issue, we use the Blue2 Modelling Framework (Blue2MF), an integrated modelling tool developed by the Joint Research Centre (JRC) of the European Commission. Our study uses the Lagrangian model LTRANS-Zlev (LTRANS) in the Blue2MF to simulate the trajectories, distribution, and accumulation of macroplastics in five European marine basins: the Baltic Sea, Black Sea, Mediterranean Sea, Atlantic Northwest European Shelf, and Atlantic Southwest European Shelf. By incorporating model-estimated macroplastic inputs from land and estimations of maritime (fishing) sources, we simulate distribution patterns of marine macroplastics between 2016 and 2018. Our study addresses the challenges involved in modelling the spatial distribution and abundances of macroplastics with the LTRANS model and the factors that may condition the estimation of the model accuracy when model results are compared/validated with marine litter observations available. We compare our model results with available observations, achieving a good agreement between predicted and observed macroplastic distributions and abundances and estimating the model accuracy for both beached and floating macroplastics. Our study provides a basis for future forecast runs to evaluate the impact of policy/management options on marine macroplastic pollution in European Seas. Full article

Monitoring marine litter in the Arctic is crucial for environmental assessment, yet automated methods are needed to process large volumes of visual data. This study develops and compares two distinct machine learning approaches to automatically detect floating marine litter, birds, and other anomalies from ship-based optical imagery captured in the Barents and Kara seas. We evaluated a supervised Visual Object Detection (VOD) model (YOLOv11) against a self-supervised classification approach that combines a Momentum Contrast (MoCo) framework with a ResNet50 backbone and a CatBoost classifier. Both methods were trained and tested on a dataset of approximately 10,000 manually annotated sea surface images. Our findings reveal a significant performance trade-off between the two techniques. The YOLOv11 model excelled in detecting clearly visible objects like birds with an F1-score of 73%, compared to 67% for the classification method. However, for the primary and more challenging task of identifying marine litter, which demonstrates less clear visual representation in optical imagery, the self-supervised approach was substantially more effective, achieving a 40% F1-score, versus the 10% obtained for the VOD model. This study demonstrates that, while standard object detectors are effective for distinct objects, self-supervised learning strategies can offer a more robust solution for detecting less-defined targets like marine litter in complex sea-surface imagery. Full article

The accumulation of floating marine macro-litter (FMML) poses a major threat to coastal ecosystems, yet its transport dynamics in semi-enclosed seas remain poorly understood. This study establishes the first regional model to simulate the source-to-sink transport processes of FMML in the Bohai and Yellow Seas (BYS). By combining a high-resolution hydrodynamic model with Lagrangian particle tracking, we successfully reproduced observed spatiotemporal distribution patterns and accumulation hotspots. Our simulations reveal that the heterogeneity of FMML distribution is co-regulated by seasonal hydrodynamic variations and anthropogenic activities. We identified two major cross-regional transport pathways originating from Laizhou Bay and the northern Shandong Peninsula. Furthermore, backward particle tracking traced summer FMML hotspots to potential high-emission sources along the northern Jiangsu coast and the Yangtze River estuary. Despite limitations in emission inventories, this study provides a crucial mechanistic framework for FMML management in the BYS and a transferable methodology for other regional seas. Full article

Plastic pollution is an increasing global concern, with estuaries being especially vulnerable as transition zones between freshwater and marine systems. These ecosystems often accumulate large amounts of waste, affecting wildlife and water quality. This study focuses on analysing the circulation patterns of the Ave Estuary, a small, shallow system on Portugal’s north-western coast, and their influence on litter transport and distribution. This site was selected for installing an aquatic litter removal technology under the EU-funded MAELSTROM project. A 2DH hydrodynamic model using Delft3D FM, coupled with the Wflow hydrological model, was implemented and validated. Various scenarios were simulated to assess estuarine dynamics and pinpoint zones prone to litter accumulation and flood risk. The results show that tidal action and river discharge mainly drive the estuary’s behaviour. Under low discharge, floating litter should be mostly transported toward the ocean, while high discharge conditions should result in litter movement at all depths due to stronger currents. High water levels and flooding occur mainly upstream and in specific low-lying areas near the mouth. Low-velocity zones, which can favour litter accumulation, were found around the main channel and on the western margin near the estuary’s mouth, even during high flows. These findings highlight persistent accumulation zones, even under extreme event conditions. Full article

Marine plastic pollution has become a critical transboundary environmental issue, particularly affecting coastal regions with insufficient waste management infrastructure. This study applies a modified Lagrangian hydrodynamic model, TrackMPD v.1, to simulate the movement and accumulation of macroplastics in the West Africa Coastal Area. The research investigates three case studies: (1) the Liberia–Gulf of Guinea region, (2) the Mauritania–Gulf of Guinea coastal stretch, (3) the Cape Verde, Mauritania, and Senegal regions. Using both forward and backward simulations, macroplastics’ trajectories were tracked to identify key sources and accumulation hotspots. The findings highlight the cross-border nature of marine litter, with plastic debris transported far from its source due to ocean currents. The Gulf of Guinea emerges as a major accumulation zone, heavily impacted by plastic pollution originating from West African rivers. Interesting connections were found between velocities and directions of the plastic debris and some of the characteristics of the West African Monson climatic system (WAM) that dominates the area. Backward modelling reveals that macroplastics beached in Cape Verde largely originate from the Arguin Basin (Mauritania), an area influenced by fishing activities and offshore oil and gas operations. Results are visualized through point tracking, density, and beaching maps, providing insights into plastic distribution and accumulation patterns. The study underscores the need for regional cooperation and integrated monitoring approaches, including remote sensing and in situ surveys, to enhance mitigation strategies. Future work will explore 3D simulations, incorporating degradation processes, biofouling, and sinking dynamics to improve the representation of plastic behaviour in marine environments. This research is conducted within the Global Development Assistance (GDA) Agile Information Development (AID) Marine Environment and Blue Economy initiative, funded by the European Space Agency (ESA) in collaboration with the Asian. Development Bank and the World Bank. The outcomes provide actionable insights for policymakers, researchers, and environmental managers aiming to combat marine plastic pollution and safeguard marine biodiversity. Full article

Microplastic pollution presents a significant and rising risk to both ecological integrity and the long-term viability of economic activities reliant on marine ecosystems. The Black Sea, a region sustaining economic sectors such as fisheries, tourism, and maritime transport, is increasingly vulnerable to this form of contamination. Mytilus galloprovincialis, a well-established bioindicator, accumulates microplastics, providing a direct measure of environmental pollution and indicating potential economic consequences deriving from degraded ecosystem services. While previous studies have documented microplastic pollution in the Black Sea, our paper specifically quantified microplastic contamination in M. galloprovincialis collected from four sites along the western Black Sea coast, each characterised by distinct levels of anthropogenic influence: Midia Port, Constanta Port, Mangalia Port, and 2 Mai. We used statistical analysis to quantify site-specific microplastic contamination in M. galloprovincialis and employed machine learning to develop models predicting accumulation patterns based on environmental variables. Our findings demonstrate the efficacy of mussels as bioindicators of marine plastic pollution and highlight the utility of machine learning in developing effective predictive tools for monitoring and managing marine litter contamination in marine environments, thereby contributing to sustainable economic practices. Full article

Oil spills and marine litter pose significant threats to marine ecosystems, necessitating innovative real-time monitoring solutions. This research presents a novel AI-driven multisensor system that integrates RGB, thermal infrared, and hyperspectral radiometers to detect and classify pollutants in dynamic offshore environments. The system features a dual-unit design: an overview unit for wide-area detection and a directional unit equipped with an autonomous pan-tilt mechanism for focused high-resolution analysis. By leveraging multi-hyperspectral data fusion, this system overcomes challenges such as variable lighting, water surface reflections, and environmental interferences, significantly enhancing pollutant classification accuracy. The YOLOv5 deep learning model was validated using extensive synthetic and real-world marine datasets, achieving an F1-score of 0.89 and an mAP of 0.90. These results demonstrate the robustness and scalability of the proposed system, enabling real-time pollution monitoring, improving marine conservation strategies, and supporting regulatory enforcement for environmental sustainability. Full article

Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments. Full article

This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments. Full article

Marine litter poses a significant global threat to marine ecosystems, primarily driven by poor waste management, inadequate infrastructure, and irresponsible human activities. This research investigates the application of image preprocessing techniques and deep learning algorithms for the detection of seafloor objects, specifically marine debris, using unmanned aerial vehicles (UAVs). The primary objective is to develop non-invasive methods for detecting marine litter to mitigate environmental impacts and support the health of marine ecosystems. Data was collected remotely via UAVs, resulting in a novel database of over 5000 images and 12,000 objects categorized into 31 classes, with metadata such as GPS location, wind speed, and solar parameters. Various image preprocessing methods were employed to enhance underwater object detection, with the Removal of Water Scattering (RoWS) method demonstrating superior performance. The proposed deep neural network architecture significantly improved detection precision compared to existing models. The findings indicate that appropriate databases and preprocessing methods substantially enhance the accuracy and precision of underwater object detection algorithms. Full article

Fishing activities have been recognized as one of the primary contributors to marine environmental pollution. Studies have been conducted on the impact of fishing activities on the accumulation of marine debris, but most of these studies have been conducted at specific points in time. This study collected marine debris data over four years in the coastal area of Korea. Data on the magnitude of nearshore fishing activities during the same period were collected and analyzed. Regression models were constructed to explore the impact of nearshore fishing activities on coastal waste accumulation over time. This research aimed to understand the influence of nearshore fishing activities on the accumulation of ocean-sourced coastal waste, leading to the development of a time series regression model. The results indicated that time series models have substantially more explanatory power compared to conventional models, emphasizing the importance of temporal considerations in quantifying the relationship between fishing activities and coastal litter over time. Full article

Anthropogenic waste deposition in aquatic environments precipitates a decline in water quality, engendering pollution that adversely impacts human health, ecological integrity, and economic endeavors. The evolution of underwater robotic technologies heralds a new era in the timely identification and extraction of submerged litter, offering a proactive measure against the scourge of water pollution. This study introduces a refined YOLOv8-based algorithm tailored for the enhanced detection of small-scale underwater debris, aiming to mitigate the prevalent challenges of high miss and false detection rates in aquatic settings. The research presents the YOLOv8-C2f-Faster-EMA algorithm, which optimizes the backbone, neck layer, and C2f module for underwater characteristics and incorporates an effective attention mechanism. This algorithm improves the accuracy of underwater litter detection while simplifying the computational model. Empirical evidence underscores the superiority of this method over the conventional YOLOv8n framework, manifesting in a significant uplift in detection performance. Notably, the proposed method realized a 6.7% increase in precision (P), a 4.1% surge in recall (R), and a 5% enhancement in mean average precision (mAP). Transcending its foundational utility in marine conservation, this methodology harbors potential for subsequent integration into remote sensing ventures. Such an adaptation could substantially enhance the precision of detection models, particularly in the realm of localized surveillance, thereby broadening the scope of its applicability and impact. Full article

Fish-aggregating devices play a significant role in tuna purse fisheries. The severe marine environment and the large number of non-biodegradable fish-aggregating devices impact structural safety and cause marine litter. Therefore, hydrodynamic performance and the use of biodegradable materials are crucial issues for ensuring the sustainability of fish-aggregating devices. In this study, a type of virtual biodegradable drifting fish-aggregating device (Bio-DFAD) was designed. Numerical simulations were conducted to investigate the motion responses and relative velocities of Bio-DFADs in regular waves (first- and fifth-order waves). The numerical model was applied based on unsteady Reynolds-averaged Navier–Stokes equations with the realizable k–ε model. For different scenarios of modeling, various conditions were modeled, including the relative length, wave steepness, and diameter of the balsa wood, to analyze their effects on the hydrodynamic response of the Bio-DFADs. The results indicated that the increased relative length, wave steepness, and diameter of balsa wood had a significant influence on the motion response amplitude operators (RAOs) and relative velocity of Bio-DFADs. The results suggested that a relative length (L_F/B = 1.5) and smaller diameter (D_F = 30 mm) were recommended for fewer motion responses and relative velocity. The obtained results provide insight for practical engineering applications of the hydrodynamic design of Bio-DFADs. Full article