Search Results (17)

The ubiquitous presence of marine litter has brought huge environmental pressure. A wide range of measures have been developed to address this problem. This paper focuses on the removal measure—Fishing for Litter (FEL). It aims to identify the potential factors affecting fishers’ participation in the FFL program. A two-step approach, including interviews and questionnaire surveys, was employed. A total of 10 fishers participated in the interviews, and 8 factors were initially identified using thematic analysis and utilized in the questionnaire design. A total of 412 valid samples were collected. Descriptive statistics and binary logit regression were used for data analysis. The results showed that rewards, the participation of other friends, and inconveniences or troubles incurred from handling trash feature most in fishers’ decision-making on the participation. Furthermore, fishers’ views toward marine environments also had a behavioral impact on their participation in the program. Potential management measures were proposed, including reducing inconveniences incurred from handling trash on board as well as at ports, providing rewards, encouraging environmental education for fishers, and distributing information regarding the program. It is hoped that fishers will eventually make it a normal onboard practice to collect trash found at sea and develop a sense of marine environmental stewardship. Full article

Environmental pollution from plastic debris is raising concerns not only for the vulnerability of marine species to ingestion but also for potential human health hazards posed by small particles, known as microplastics. In this context, marine areas suffer from a lack of constant shoreline cleanups to remove accumulated debris, preventing their degradation and fragmentation. To establish optimal strategies for streamlining plastic recovery and recycling operations, it is important to have a system for recognizing plastic debris on the beach and, more specifically, for identifying the type of polymer and mapping (e.g., topologically assessing) the distribution of plastic debris on shoreline sands. This study aims to provide an operative tool finalized to perform an in situ detection, analysis, and characterization of plastic debris present in the coastal environment (i.e., beaches), adopting a near-infrared (NIR)-based hyperspectral imaging (HSI) approach. In more detail, the possibility of identifying and classifying polymers of plastic debris by NIR-HSI in three different areas along the Pontine coastline of the Lazio region (Latina, Italy) was investigated. The study focused on three distinct beaches (i.e., Foce Verde, Capo Portiere, and Sabaudia), each characterized by a different type of sand. For each location, the adopted approach allowed for the systematic classification of the various types of plastic waste found. Three Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed using a cascade detection strategy. The first model was designed to distinguish plastics from other materials in sand samples, the second to detect plastic particles in the sand, and the third to classify the type of polymer composing each identified plastic particle. Obtained results showed that, on the one hand, plastics were correctly detected from sand and other materials (i.e., sensitivity = 0.892–1.000 and specificity = 0.909–0.996), and on the other, the recognition of polymer type was satisfactory, according to the performance statistical parameters (i.e., sensitivity = 1.000 and specificity = 0.991–1.000). This research highlights the potential of the NIR-HSI approach as a reliable, non-invasive method for plastic debris monitoring and polymer classification. Its scalability and adaptability suggest possible future integration into mobile systems, enabling large-scale monitoring and efficient debris management. Full article

Autonomous underwater vehicles equipped with target recognition algorithms are a primary means of removing marine debris. However, due to poor underwater visibility, light scattering by suspended particles, and the coexistence of organisms and debris, current methods have problems such as poor recognition and classification effects, slow recognition speed, and weak generalization ability. In response to these problems, this article proposes a marine debris identification and classification algorithm based on improved YOLOv8. The algorithm incorporates the CloFormer module, a context-aware local enhancement mechanism, into the backbone network, fully utilizing shared and context-aware weights. Consequently, it enhances high- and low-frequency feature extraction from underwater debris images. The proposed C2f-spatial and channel reconstruction (C2f-SCConv) module combines the SCConv module with the neck C2f module to reduce spatial and channel redundancy in standard convolutions and enhance feature representation. WIoU v3 is employed as the bounding box regression loss function, effectively managing low- and high-quality samples to improve overall model performance. The experimental results on the TrashCan-Instance dataset indicate that compared to the classical YOLOv8, the mAP@0.5 and F1 scores are increased by 5.7% and 6%, respectively. Meanwhile, on the TrashCan-Material dataset, the mAP@0.5 and F1 scores also improve, by 5.5% and 5%, respectively. Additionally, the model size has been reduced by 12.9%. These research results are conducive to maintaining marine life safety and ecosystem stability. 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

Plastics are produced, consumed, and disposed of worldwide, with more than eight million tons of plastic litter entering the ocean each year. Plastic litter accumulates in marine and terrestrial environments through a variety of pathways. Large plastic debris can be broken down into micro- and nano-plastic particles through physical/mechanical mechanisms and biologically or chemically mediated degradation. Their toxicity to aquatic organisms includes the scavenging of pollutant compounds and the production of reactive oxygen species (ROS). Higher levels of ROS cause oxidative damages to microalgae and bacteria; this triggers the release of large amounts of exopolymeric substances (EPSs) with distinct molecular characteristics. This review will address what is known about the molecular mechanisms phytoplankton and bacteria use to regulate the fate and transport of plastic particles and identify the knowledge gaps, which should be considered in future research. In particular, the microbial communities react to plastic pollution through the production of EPSs that can reduce the plastic impacts via marine plastic snow (MPS) formation, allowing plastics to settle into sediments and facilitating their removal from the water column to lessen the plastic burden to ecosystems. Full article

Given the propagation characteristics of sound waves and the complexity of the underwater environment, denoising forward-looking sonar image data presents a formidable challenge. Existing studies often add noise to sonar images and then explore methods for its removal. This approach neglects the inherent complex noise in sonar images, resulting in inaccurate evaluations of traditional denoising methods and poor learning of noise characteristics by deep learning models. To address the lack of high-quality data for FLS denoising model training, we propose a simulation algorithm for forward-looking sonar data based on RGBD data. By utilizing rendering techniques and noise simulation algorithms, high-quality noise-free and noisy sonar data can be rapidly generated from existing RGBD data. Based on these data, we optimize the loss function and training process of the FLS denoising model, achieving significant improvements in noise removal and feature preservation compared to other methods. Finally, this paper performs both qualitative and quantitative analyses of the algorithm’s performance using real and simulated sonar data. Compared to the latest FLS denoising models based on traditional methods and deep learning techniques, our method demonstrates significant advantages in denoising capability. All inference results for the Marine Debris Dataset (MDD) have been made open source, facilitating subsequent research and comparison. Full article

The threat of microplastics to marine animals and habitats is increasing, which may affect sea turtle nesting grounds. The Qilianyu Islands are the largest remaining green turtle (Chelonia mydas) nesting grounds in China. Despite being far from the mainland, microplastic pollution cannot be ignored. In this study, the level of microplastic pollution in surface sediments from three different zones, namely, the bottom, intertidal, and supratidal zone, was investigated on North Island, Qilianyu Islands. The results showed that the abundance of microplastics in the supratidal zone was significantly higher than that in the bottom zone and intertidal zone (r = 3.65, p = 0.011), with the highest average abundance of microplastics located on the southwest coast of North Island. In the bottom zone, only plastic blocks (88%) and fibers (12%) were found. The main types of microplastics in the intertidal and supratidal zones were plastic blocks (48%) and foam (42%), with polyethylene (PE) (40%) and polystyrene (PS) (34%) being the predominant components. These types and components of microplastics differed from those in the surrounding seawater, but corresponding types and components were found in the plastic debris on the beach. Meanwhile, it was also observed that there were multiple instances of fragmented plastic on the beach. Thus, we suggest that the microplastics on the beach in North Island were mainly derived from the fragmentation of microplastic debris, indicating secondary microplastics. It is recommended to further strengthen the regular cleaning of plastic debris on the beach, especially the removal of small plastic debris, in order to reduce the pollution from secondary microplastics generated by the fragmentation of beach plastic debris and to better protect China’s most important sea turtle nesting site in the South China Sea. Full article

Omega-3 polyunsaturated fatty acids (PUFAs) play a vital role in human health, well-being, and the management of inflammatory diseases. Insufficient intake of omega-3 is linked to disease development. Specialized pro-resolving mediators (SPMs) are derived from omega-3 PUFAs and expedite the resolution of inflammation. They fall into categories known as resolvins, maresins, protectins, and lipoxins. The actions of SPMs in the resolution of inflammation involve restricting neutrophil infiltration, facilitating the removal of apoptotic cells and cellular debris, promoting efferocytosis and phagocytosis, counteracting the production of pro-inflammatory molecules like chemokines and cytokines, and encouraging a pro-resolving macrophage phenotype. This is an experimental pilot study in which ten healthy subjects were enrolled and received a single dose of 6 g of an oral SPM-enriched marine oil emulsion. Peripheral blood was collected at baseline, 3, 6, 9, 12, and 24 h post-administration. Temporal increases in plasma and serum SPM levels were found by using LC-MS/MS lipid profiling. Additionally, we characterized the temporal increases in omega-3 levels and established fundamental pharmacokinetics in both aforementioned matrices. These findings provide substantial evidence of the time-dependent elevation of SPMs, reinforcing the notion that oral supplementation with SPM-enriched products represents a valuable source of essential bioactive SPMs. Full article

The majority of marine debris is found in shallow waters; however, submerged debris accumulated at the sea bottom is affected by this kind of pollution. To mitigate the harmful effect of marine debris, we have to recognize its characteristics. However, it is hard to estimate the quantity of submerged marine debris because the monitoring of submerged marine debris requires greater cost and time compared to the monitoring of beach or coastal debris. In this study, we used the data for submerged marine debris surveyed in the sea near the Korean Peninsula from 2017 to 2020 and the data of fishing vessels passing through the areas from 2018 to 2020. In addition, the correlation of major factors affecting the amount of submerged marine debris was analyzed based on the fishing vessel data and the removal project data for submerged marine debris. Moreover, we estimated the amount of submerged marine debris based on the fishing vessels at the collection sites surveyed two or more times using a stepwise regression model. The average amount of submerged marine debris estimated by the model was 6.0 tonnes more than that by the removal project, for which the error was ~26.5% compared to the amount collected by the removal project. The estimation method for submerged marine debris developed in this study can provide crucial information for an effective collection project by suggesting areas that require a collection project for submerged marine debris based on the information of fishing vessels. Full article

Bottle marine debris (BMD) remains one of the most pressing global issues. This study proposes a detection method for BMD using unmanned aerial vehicles (UAV) and machine learning techniques to enhance the efficiency of marine debris studies. The UAVs were operated at three designed sites and at one testing site at twelve fly heights corresponding to 0.12 to 1.54 cm/pixel resolutions. The You Only Look Once version 2 (YOLO v2) object detection algorithm was trained to identify BMD. We added data augmentation and image processing of background removal to optimize BMD detection. The augmentation helped the mean intersection over the union in the training process reach 0.81. Background removal reduced processing time and noise, resulting in greater precision at the testing site. According to the results at all study sites, we found that approximately 0.5 cm/pixel resolution should be a considerable selection for aerial surveys on BMD. At 0.5 cm/pixel, the mean precision, recall rate, and F1-score are 0.94, 0.97, and 0.95, respectively, at the designed sites, and 0.61, 0.86, and 0.72, respectively, at the testing site. Our work contributes to beach debris surveys and optimizes detection, especially with the augmentation step in training data and background removal procedures. Full article

Small vessels operating in coastal waters are susceptible to propeller failure because of the entanglement of marine debris. Secondary accidents such as the injury of divers may also occur when removing entangling material. Rope cutters are devices used to prevent marine litter from entangling the propeller of small ships. However, installing rope cutters on propeller shafts might affect the working of the propeller. In this study, three-dimensional simulations were performed to investigate the effect of a rope cutter on flow characteristics behind the propeller. The Computational fluid dynamics (CFD) models were validated by particle image velocimetry (PIV) experiments performed in a rope cutter performance testing tank. The study results showed that the installation of a rope cutter on the propeller shaft led to an insignificant reduction in water flow velocity magnitude behind the propeller. Additionally, the effects of the rope cutter on the reductions of thrust (0.87%) and torque (0.76%) of the propeller were also negligible. However, it is very interesting to note that rope cutter installation resulted in a lower vortex formation, leading to a significant reduction in the turbulence intensity behind the propeller by 27.12%, 37.50%, and 47.29% at 100, 150, and 200 rpm propeller speed, respectively. Based on the study results, it can be concluded that rope cutters help to reduce propeller entanglements without significantly affecting the propeller’s working. Full article

A sediment bypassing plant based on innovative jet pump, ejectors, has been tested in the first-of-a-kind demo application at the harbor of Cervia (Italy, Northern Adriatic Sea). The ejector is a jet pump aimed to reduce sediment accumulation in navigation channels and coastal areas. Herein we present results of the first study assessing the potential ecological effects of the ejectors plant. Sediment characteristics, benthic, and fish assemblages before and after the plant activation have been analyzed in the putatively impacted (the sediment removal and discharge) areas and four control locations, one time before and two times after plant activation. Ejectors plant operation resulted in a reduction of the mud and organic matter content in the sediment, as well as in changes in shell debris amount in the impacted areas. Abundance and species richness of benthic macroinvertebrates, initially reduced in the impacted areas, probably due to the previous repeated dredging, returned to higher values during demo plant continuous operation. Higher diversity of fish fauna was observed in the study area during plant operation period. Observed dynamics of the ecological status of the marine habitat suggest that an ejectors plant could represent an eco-friendly solution alternative to dredging operations to solve harbor siltation problems. Full article

Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV imaging represents a quick method for acquiring pictures with a ground spatial resolution of a few centimeters. In this work, we investigate strategies for AMD mapping on beaches with different ground resolutions and with elevation and multispectral data in support of RGB orthomosaics. Operators with varying levels of expertise and knowledge of the coastal environment map the AMD on four to five transects manually, using a range of photogrammetric tools. The initial survey was repeated after one year; in both surveys, beach litter was collected and further analyzed in the laboratory. Operators assign three levels of confidence when recognizing and describing AMD. Preliminary validation of results shows that items identified with high confidence were almost always classified properly. Approaching the detected items in terms of surface instead of a simple count increased the percentage of mapped litter significantly when compared to those collected. Multispectral data in near-infrared (NIR) wavelengths and digital surface models (DSMs) did not significantly improve the efficiency of manual mapping, even if vegetation features were removed using NDVI maps. In conclusion, this research shows that a good solution for performing beach AMD mapping can be represented by using RGB imagery with a spatial resolution of about 200 pix/m for detecting macroplastics and, in particular, focusing on the largest items. From the point of view of assessing and monitoring potential sources of MP, this approach is not only feasible but also quick, practical, and sustainable. Full article

Observations show that after stormy events, anthropogenic litter is washed ashore for short periods of time, providing the opportunity to collect and remove it from the environment. However, water dynamics in sea coastal zones during and after storms are very complicated, and the transport properties of litter items are very diverse; thus, predicting litter wash-outs using classical numerical models is challenging. We analyze meteorological and hydrophysical conditions in the Baltic Sea coastal zone to further use the obtained data as a training sequence for an artificial neural network (ANN). Analysis of the physical processes behind large litter wash-outs links open-source meteorological (wind speed and direction) and hydrodynamic reanalysis (surface wave parameters) data to the time and location of these wash-outs. A detailed analysis of 25 cases of wash-outs observed at the shore of the Sambian Peninsula was performed. The importance of the duration of the storm and its subsiding phase was revealed. An ANN structure is proposed for forecasting marine debris wash-outs as the first step in the creation of a neural network-based tool for managers and beach cleaners, helping to plan effective measures to remove plastics and other anthropogenic contaminants from the marine environment. Full article

The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, utilized as feature extractors according to three different extraction schemes for the identification and classification of underwater marine debris. We compare the performance of a neural network (NN) classifier trained on top of deep CNN feature extractors when the feature extractor is (1) fixed; (2) fine-tuned on the given task; (3) fixed during the first phase of training and fine-tuned afterward. In general, fine-tuning resulted in better-performing models but is much more computationally expensive. The overall best NN performance showed the fine-tuned Inception-ResNetV2 feature extractor with an accuracy of $91.40\%$ and F1-score $92.08\%$, followed by fine-tuned InceptionV3 extractor. Furthermore, we analyze conventional ML classifiers’ performance when trained on features extracted with deep CNNs. Finally, we show that replacing NN with a conventional ML classifier, such as support vector machine (SVM) or logistic regression (LR), can further enhance the classification performance on new data. Full article