Search Results (47)

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

Unmanned Aerial Vehicles provide a cost-effective solution for water environment monitoring, yet detecting floating litter remains challenging due to small target scales, complex geometries, and severe surface interferences. To bridge the data deficiency in this domain, this study introduces UAV-Flow, a multi-scenario benchmark dataset wherein small-scale targets constitute 78.9%. Building upon this foundation, we propose the Floating Litter Detection Network (FLD-Net), a lightweight, real-time detection framework tailored for edge deployment. Adopting a progressive optimization paradigm, FLD-Net integrates three cascaded enhancement modules to achieve holistic performance gains across feature extraction, cross-scale fusion, and noise suppression. Specifically, the Deformation Feature Extraction Module (DFEM) enhances backbone adaptability to small targets and non-rigid deformations; the Dynamic Cross-scale Fusion Network (DCFN) facilitates efficient cross-scale semantic fusion via content-aware upsampling and an asymmetric topology; and the Dual-domain Anti-noise Attention (DANA) mechanism achieves discriminative decoupling between target semantics and structural noise through spatial-channel interaction. Experimental results on UAV-Flow demonstrate that FLD-Net achieves an mAP₅₀ of 80.47%. Compared to the YOLOv11s baseline, it improves Recall and mAP₅₀ by 11.66% and 8.51%, respectively, with only 9.9 M parameters. Furthermore, deployment on the NVIDIA Jetson Xavier NX yields an inference latency of 14 ms and an energy efficiency of 4.80 FPS/W, confirming the system’s robustness and viability for automated pollution monitoring. Full article

Urban river degradation demands remediation strategies that are both environmentally sustainable and technically feasible. This study evaluated the performance of Floating Treatment Wetlands (FTWs) vegetated with Chrysopogon zizanioides (vetiver) and incorporating four substrate configurations: leaf litter (LL), red volcanic rock (RVR), corn cobs (CC), and a composite mixture of all three, for the rehabilitation of the “Paseo de Los Ahuehuetes” River in Veracruz, Mexico. Over a 182-day monitoring period, in situ water quality parameters and plant growth responses were systematically assessed. The results indicate that substrate selection is a decisive design factor governing the establishment and development of C. zizanioides in FTWs. Among the substrates tested, LL exhibited the most favorable performance, achieving the highest plant survival (82%), enhanced shoot elongation (71.5 ± 12.1 cm), greater root development (49.7 ± 10.0 cm), and the highest relative growth rate (0.028 g g⁻¹ d⁻¹), with statistically significant differences (p < 0.05) compared to CC. Additionally, localized improvements in water quality within the FTW zone were observed, including an increase in dissolved oxygen (2.07%) and a reduction in total dissolved solids (5.65%), likely associated with intensified rhizospheric processes. Overall, these findings identify leaf litter as a low-cost, locally available, and environmentally sustainable substrate that enhances vetiver establishment in FTWs. The study provides practical, evidence-based criteria for the design of nature-based phytoremediation systems aimed at the restoration of urban river ecosystems. 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

This study addresses the observation of floating plastic debris in freshwater environments using an Unmanned Aerial Vehicle (UAV) multi-sensor strategy. An experimental campaign is described where an heterogeneous plastic assemblage, namely a plastic target, and a naturally occurring leaf-litter mat are observed by a UAV platform in the Lake Calore (Avellino, Southern Italy) within the framework of the “multi-layEr approaCh to detect and analyze cOastal aggregation of MAcRo-plastic littEr” (ECOMARE) Italian Ministry of Research (MUR)-funded project. Three UAV platforms, equipped with optical, multispectral, and thermal sensors, are adopted, which overpass the two targets with the objective of analyzing the sensitivity of optical radiation to plastic and the possibility of discriminating the plastic target from the natural one. Georeferenced orthomosaics are generated across the visible, multispectral (Green, Red, Red Edge, Near-Infrared—NIR), and thermal bands. Two novel indices, the Plastic Detection Index (PDI) and the Heterogeneity Plastic Index (HPI), are proposed to discriminate between the detection of plastic litter and natural targets. The experimental results highlight that plastics exhibit heterogeneous spectral and thermal responses, whereas natural debris showed more homogeneous signatures. Green and Red bands outperform NIR for plastic detection under freshwater conditions, while thermal imagery reveals distinct emissivity variations among plastic items. This outcome is mainly explained by the strong NIR absorption of water, the wetting of plastic surfaces, and the lower sensitivity of the Mavic 3′s NIR sensor under high-irradiance conditions. The integration of optical, multispectral, and thermal data demonstrate the robustness of UAV-based approaches for distinguishing anthropogenic litter from natural materials. Overall, the findings underscore the potential of UAV-mounted remote sensing as a cost-effective and scalable tool for the high-resolution monitoring of plastic pollution over inland waters. 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

Plastic (P) and microplastic (MP) pollution in marine and freshwater environments is an increasingly urgent issue that needs to be addressed at many levels. The Seabin (an easily operated and cost-effective floating debris collection device) can help clean up buoyant plastic debris in calm waters while monitoring water pollution. A Seabin was used to conduct a comparative analysis of plastic and microplastic concentrations in northern Lake Garda (Italy) during peak and low tourist seasons. The composition of the litter was further investigated using Fourier-Transform Infrared (FTIR) spectroscopy. The analysis showed a decreased mean amount of plastic from summer (32.5 mg/m³) to autumn (17.6 mg/m³), with an average number of collected microplastics per day of 45 ± 15 and 15 ± 3, respectively. Packaging and foam accounted for 92.2% of the recognized plastic waste products. The material composition of the plastic mass (442 pieces, 103.0 g) was mainly identified as polypropylene (PP, 47.1%) and polyethylene (PE, 21.8%). Moreover, 313 microplastics (approximately 2.0 g) were counted with average weight in the range of 1–16 mg. A case study of selected plastic debris was also conducted. Spectroscopic, microscopic, and thermal analysis of specimens provided insights into how aging affects plastics in this specific environment. The purpose of this study was to establish a baseline for further research on the topic, to provide guidelines for similar analyses from a multidisciplinary perspective, to monitor plastic pollution in Lake Garda, and to inform policy makers, scientists, and the public. 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 presents an assessment of X-band radar’s detection capability to monitor Small Garbage Islands (SGIs), i.e., floating aggregations of marine litter consisting chiefly of plastic, under changing sea states. For this purpose, two radar measurement campaigns were carried out with controlled releases at sea of SGI modules assembled in the laboratory. One campaign was carried out with a calm sea and almost no wind in order to determine the X-band radar system’s detection capabilities in an ideal scenario, while the other campaign took place with rough seas and wind. An analysis of the data acquired during the campaigns confirmed that X-band radar can detect small aggregations of litter floating on the sea surface. To demonstrate the radar’s ability to detect SGIs, a statistical analysis was carried out to calculate the probability of false alarm and the probability of detection for two releases at two different distances from the radar. For greater readability of this work, all of the results obtained are presented both in terms of radar intensity and in terms of the radar cross-section relating to both the targets and the clutter. Another interesting study that is presented in this article concerns the measurement of the speed of movement (drift) of the SGIs compared with the measurement of the speed of the surface currents provided at the same time by the radar. The study also identified the radar detection limits depending on the sea state and the target distance from the antenna. Full article

Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption features of plastic surfaces. In this paper, we present developments of new and emerging remote-sensing technologies of marine plastic litter such as passive techniques: fluid lensing, multi-angle polarimetry, and thermal infrared sensing (TIS); and active techniques: light detection and ranging (LiDAR), multispectral imaging detection and active reflectance (MiDAR), and radio detection and ranging (RADAR). Our review of the detection capabilities and limitations of the different sensing technologies shows that each has their own weaknesses and strengths, and that there is not one single sensing technique that applies to all kinds of marine litter under every different condition in the aquatic environment. Rather, we should focus on the synergy between different technologies to detect marine plastic litter and potentially the use of proxies to estimate its presence. Therefore, in addition to further developing remote-sensing techniques, more research is needed in the composition of marine litter and the relationships between marine plastic litter and their proxies. In this paper, we propose a common vocabulary to help the community to translate concepts among different disciplines and techniques. Full article

This study presents results from an experimental campaign to explore how different riparian zone characteristics may facilitate the transport or capturing of plastics floating through the fluvial system. Specifically, following field observations for the transport of plastics through fluvial vectors, a substantial number of flume experiments has been designed to assess the effect of floating macro-plastics and riparian zone characteristics. The results from flume experiments were analyzed using particle tracking velocimetry techniques to derive transport metrics (such as transport velocities) of macro-plastics of different sizes and shapes, released at five locations across a wide channel with distinct distance from the vegetated riverbank. The findings are discussed while considering the trapping mechanisms along the vegetated riverbank, which include a range of vegetation densities and arrangements, aiming to identify and quantify the degree of impact of each of the control parameters on the transport of floating plastics. The flow velocimetry records obtained at locations near and within the riverbank correlate well with the transport velocities of the floating plastics. Macro-plastic litter carried downstream away from the riverbank can have up to nine times the transport velocity, compared to those found within the riverbank. The change from a low to a high average density can result in about three times decrease in the transport velocity of floating macro-plastic litter within the riparian zone. These outcomes can help inform better practices for the management of riparian vegetation to maximize the trapping efficiency of macro-plastics, adapted to different flow conditions and river morphologies. Full article

Every year, harbor and sailing festivals attract close to 20 million visitors in the Baltic Sea region, but their consequences on marine litter pollution are still unknown. We combine field studies with model simulations and literature reviews to quantify the annual emissions of floating macro-litter and to assess its retention in estuaries and role in Baltic Sea pollution. Results focusing on Hanse Sail in Rostock and Kiel Week are extrapolated to the entire Baltic Sea region. After the Hanse Sail 2018, the harbor pollution amounted to about 950 floating macro-litter particles/km²; 85–90% were plastics. We calculated an emission between 0.24 and 3 particles per 1000 visitors, depending on the year and the waste management system. About 0.02% of all waste generated during a festival ends up in the harbor water. The Hanse Sails contributes less than 1% to the total annual macro-litter emissions in the Warnow estuary. Model simulations indicate that over 99% of the emitted litter is trapped in the estuary. Therefore, Hanse Sails are not relevant to Baltic Sea pollution. The extrapolated Baltic-Sea-wide annual emissions are between 4466 and (more likely) 55,830 macro-litter particles. The over-30 harbor and sailing festivals contribute an estimated <0.05% to the total annual macro-litter emissions in the Baltic Sea region. Full article

Mismanaged plastic waste threatens the sustainable development goals of the United Nations in social, economic, and ecological dimensions. In the pollution process, fluvial systems are critical transport paths for mismanaged plastic waste, connecting land areas with oceans and acting as plastic reservoirs and accumulation zones. The complex fluid–plastic particle interaction leads to a strong distribution of transported particles over the entire river width and flow depth. Therefore, a holistic plastic removal approach must consider lateral and vertical river dimensions. This study investigates the conceptual design of a comprehensive river-cleaning system that enables the removal of both floating and suspended litter particles from watercourses withstanding flow variations. The innovative technical cleaning infrastructure is based on a self-cleaning system using rotating screen drum units. In 42 prototype-scale experiments using ten representative plastic particle types (both 3D items and fragments) of five different polymer types, we prove the self-cleaning concept of the infrastructure and define its parameters for the best cleaning performance. Its cleaning efficiency is strongly dependent on the polymer type and shape. The overall cleaning efficiency for 3D items amounts to 82%, whereas plastic fragments are removed less efficiently depending on hydraulic conditions. Adaptions to the prototype can enhance its efficiency. Full article

Riparian areas offer many ecosystem services, especially in urban settings. Their conservation can be complex because of the many urban anthropogenic pressures they face. Adopting new technological approaches can provide insights on the most cost-effective and sustainable management for riparian areas. In this study, different new technological approaches were implemented to assess and map environmental variables and find the optimal location of nature-based solutions (e.g., litter traps). The study area was Agia Varvara Park in Drama, Greece, a unique natural urban riparian area. The approaches utilized were categorized as aerial, terrestrial, and surface/underwater. Specifically, these approaches included unmanned aerial vehicles that incorporated high-resolution regular and thermal cameras to capture the surface environmental conditions and unmanned underwater vehicles to capture the underwater environmental conditions. The produced orthomosaics and digital surface models enabled us to estimate the boundaries of the water surface in Agia Varvara Park. A GPS tracker was also used to record the potential movement route of litter. Finally, a sonar device was utilized to estimate the water depth of potential cross-sections of Agia Varvara’s stream where the litter trap could be installed. The above datasets were used to develop spatial datasets and accompanying maps that were utilized to find the optimal opportunity sites for the litter trap. A litter trap is a floating device that gathers and maintains litter, vegetation, and other debris. Two specific locations were proposed based on water presence, water depth, channel’s width, limited vegetation for accessibility, wildlife existence, litter’s water route, and stopping location time. Such traps enable the collection of anthropogenic litter. In one location, a litter trap has been installed and is being tested. Overall, the above approaches could be used to suggest other nature-based solutions and/or their optimal location, thus enhancing the sustainable management of urban riparian areas. Full article