Search Results (216)

Viruses are abundant and widespread in extreme marine environments, such as sea ice, hydrothermal vents, and ocean trenches. They occur at temperatures up to 122 °C and down to −30 °C and pressures exceeding 100 MPa. Their distribution in these environments is closely correlated with that of their extremophile hosts, which are mostly bacteria, archaea, and microeukaryotes. Viruses have been shown to be capable of long-term survival in conditions simulating interstellar conditions. However, for them to reproduce, they would still need a host. Many recent astro-biological investigations have focused on habitability, specifically the ability of a planet to support the activity of at least one lifeform. The most likely candidates for extraterrestrial habitability in our solar system are the sea ice moons of Jupiter and Saturn, namely Europa and Enceladus. These are both thought to contain subsurface oceans of liquid water and potentially access to the necessary elements for microbial growth. If microorganisms were to be detected in these extraterrestrial environments, viruses might also be found coexisting with their host cells. Full article

Microplastics (MPs) and microfibres (MFs) are widespread contaminants that are found in natural environments worldwide. Although their presence has been documented in Arctic snow, sea ice and marine systems, data on their occurrence in Greenland glacier surface ice remain limited. Because of their small size, persistence, and mobility, MPs and MFs pose significant risks to both habitats and species, reaching even the most remote areas. Monitoring these environments is crucial for assessing the extent of pollution, while dissemination activities are essential for transferring scientific knowledge to local communities and fostering active engagement in adopting sustainable behaviours. A preliminary survey was conducted on a glacier in Greenland, collecting samples along the routes travelled by the Extreme E staff during the electric off-road racing series expedition in the region. Preliminary results confirmed the presence of MPs and MFs in the study area with high abundances. Fibrous and small-sized microparticles were the most prevalent types detected. The most common synthetic material was polyethylene terephthalate (PET), while natural and regenerated MFs were predominantly cellulosic. A deeper understanding of MP and MF contamination in extreme environments was achieved, highlighting the importance of environmental education and public awareness as key tools in mitigating pollution and promoting sustainable strategies. The integration of different sectors can synergistically promote sustainability efforts and address the urgent challenges of climate change and environmental pollution. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

Very-high-resolution (VHR) satellite imagery has expanded the scale at which researchers can monitor marine mammals in remote regions and improved monitoring efforts in data-deficient areas. Relatively little is known about beluga whale (Delphinapterus leucas) distribution in their wintering grounds, due partly to the unpredictability of sea ice formation and limited accessibility. VHR satellite imagery has been used successfully to estimate the abundance of summering beluga whales; however, the feasibility of tasking VHR satellite imagery in the winter and determining the detectability of beluga whales amongst sea ice have not been formally assessed. Our objective was to assess the feasibility of acquiring VHR satellite imagery in the winter and whether beluga whales could be reliably distinguished from sea ice in the imagery. Our study focused on beluga whale populations that are winter residents within James Bay and Cumberland Sound, occupying nearshore open water and ice leads in the winter. Two images were collected in Cumberland Sound covering known beluga whale wintering grounds in February and March 2022 encompassing 745 km², with ice covering >75% of the image, and three images were acquired within James Bay from January to March 2024 spanning over 5700 km², with ice covering >86% of the survey area. We observed 0 certain and 294 uncertain detections, suggesting that current satellite imagery resolutions are too low for confidently detecting beluga whales amongst densely packed ice. High-definition sharpening to 15 cm reduced the number of uncertain detections, but we were still unable to identify any certain whales. Continued advancements in imagery resolution are required to distinguish beluga whales from sea ice and improve year-round beluga whale monitoring. Full article

Equipped with an Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 (Ice, Cloud and land Elevation Satellite-2) is a photon-counting laser altimetry mission with strong potential for nearshore bathymetry. In this study, a novel filtering and bathymetric method termed a segmented adaptive filtering bathymetry has been proposed. Sea-surface photons are identified from peaks in the elevation-density histogram, enabling separation of surface and seafloor photons. The seafloor photons are then partitioned into along-track segments, where seafloor signal photons are extracted using an adaptive elliptical kernel whose parameters and orientation are determined from local density patterns and seafloor slope. The seafloor profile is obtained by polynomial fitting, and nearshore depth is estimated from the elevations of the surface and seafloor signal photons. To ensure and improve the accuracy and reliability of the proposed method, ICESat-2 data from Qilianyu Islands at the South China Sea and West Island at the Florida Keys of the United States were adopted to perform experiments. Furthermore, the bathymetric results obtained by ICESat-2 datasets at different experimental areas were compared with the reference bathymetry obtained by the airborne light detection and ranging (LiDAR) bathymetry (ALB) system. Finally, the bathymetric accuracy validation and assessment were performed. The highest accuracy of root mean square error (RMSE) and coefficient of determination (R²) has reached 0.37 m and 98%, respectively. The accuracy validation of bathymetric results at different study areas demonstrated that the method proposed in this study can automatically and effectively achieve high-precision nearshore bathymetry and topographic surveys. Full article

Recognizing the critical role of polar Sea Ice Concentration (SIC) in climate feedback mechanisms, this study presents the first comprehensive investigation of China’s Fengyun-3E(FY-3E) GNOS-II Global Navigation Satellite System Reflectometry (GNSS-R) for bipolar SIC retrieval. Specifically, reflected signals from multiple Global Navigation Satellite Systems (GNSS) are utilized to extract characteristic parameters from Delay Doppler Maps (DDMs). By integrating regional partitioning and dynamic thresholding for sea ice detection, a Random Forest Regression (RFR) model incorporating a rolling-window training strategy is developed to estimate SIC. The retrieved SIC products are generated at the native GNSS-R observation resolution of approximately 1 × 6 km, with each SIC estimate corresponding to an individual GNSS-R observation time. Owing to the limited daily spatial coverage of GNSS-R measurements, the retrieved SIC results are further aggregated into monthly composites for spatial distribution analysis. The model is trained and validated across both polar regions, including targeted ice–water boundary zones. Retrieved SIC estimates are compared with reference data from the OSI SAF Special Sensor Microwave Imager Sounder (SSMIS), demonstrating strong agreement. Based on an extensive dataset, the average correlation coefficient (R) reaches 0.9450 in the Arctic and 0.9602 in the Antarctic for the testing set, with corresponding Root Mean Squared Error (RMSE) of 0.1262 and 0.0818, respectively. Even in the more challenging ice–water transition zones, RMSE values remain within acceptable ranges, reaching 0.1486 in the Arctic and 0.1404 in the Antarctic. This study demonstrates the feasibility and accuracy of GNSS-R-based SIC retrieval, offering a robust and effective approach for cryospheric monitoring at high latitudes in both polar regions. Full article

Antarctic ice shelves regulate ice-sheet discharge and global sea-level rise, yet their rapid retreat underscores the need for new, low-cost monitoring tools. We analyze ambient seismic noise recorded by seismometers on the Pine Island Glacier ice shelf to characterize wind-induced signals and detect persistent structural resonances. Power spectral analysis shows that wind sensitivity is strongly damped compared with bedrock sites: noise increases only 5–7 dB from 0 to 25 m s⁻¹ winds, versus a 42 dB increase at an inland bedrock station, reflecting the contrasted coupling environments of floating and grounded substrates. The horizontal-to-vertical spectral ratio (HVSR) spectrograms reveal two temporally stable peaks at ~2.2 Hz and ~4.3 Hz that persist across stations and remain independent of environmental forcing. Forward modeling indicates that these peaks correspond to S-wave resonances within the ice shelf. The inferred ice-water interface depth (~440 m) agrees with the Bedmap2 thickness estimate (466 m). This work demonstrates that HVSR provides an effective passive, single-station method for measuring ice shelf thickness. Full article

Diabetes mellitus represents one of the main health challenges worldwide, characterized by hyperglycemia and long-term serious microvascular and macrovascular complications. Marine organisms are a promising reservoir of bioactive metabolites for developing effective antidiabetic therapies with fewer side effects. The sea urchin Tripneustes gratilla (T. gratilla) is widely distributed in the Red Sea, with limited reports of its pharmacological activities and chemical characterization. In this study, a nanosuspension formulation of T. gratilla extract (T. gratilla-NS) was developed to enhance the bioavailability of its bioactive constituents. This study investigated the antidiabetic potential of T. gratilla extract through an integrated approach encompassing chemical profiling of the extract, assessment of its alcoholic extract for in vitro inhibitory effects on α-amylase and α-glucosidase, and in vivo evaluation of T. gratilla-NS in an alloxan-induced diabetic rat model. We found that the alcoholic extract showed potent inhibitory action toward α-amylase with IC₅₀ 5.31 ± 0.05 µg/mL and moderate inhibitory activity toward α-glucosidase with IC₅₀ 21.36 ± 0.06 µg/mL. T. gratilla-NS significantly increased insulin levels, reduced blood glucose levels, and restored pancreatic damage. Furthermore, it enhanced the levels of superoxide dismutase and total antioxidant capacity with a concomitant decrease in malondialdehyde concentration in pancreatic tissue. The observed activities could be attributed to a wide array of diverse compounds, terpenes, mainly sesquiterpenes, diterpenes, steroids, and polyunsaturated fatty acids detected by GC-MS, compounds with a phenolic nucleus equal to 54.26 ± 1.27 mg. GAE/g of extract. This research highlights the dual role of T. gratilla-NS in combating diabetes and subsequently attenuating its associated complications. Full article

Iceberg detection in Arctic sea-ice environments is essential for navigation safety and climate monitoring, yet remains challenging due to observational and environmental constraints. The scarcity of labelled data, limited optical coverage caused by cloud and polar night conditions, and the small, irregular signatures of icebergs in synthetic aperture radar (SAR) imagery make automated detection difficult. This study evaluates the environmental feasibility of applying a modern deep learning model for iceberg detection within land-fast sea ice. We adapt a YOLOv8 convolutional neural network within the Dual Polarisation Intensity Ratio Anomaly Detector (iDPolRAD) framework using dual-polarised Sentinel-1 SAR imagery from the Franz Josef Land region, validated against Sentinel-2 optical data. A total of 2344 icebergs were manually labelled to generate the training dataset. Results demonstrate that the network is capable of detecting icebergs embedded in fast ice with promising precision under highly constrained data conditions (precision = 0.81; recall = 0.68; F1 = 0.74; mAP = 0.78). These findings indicate that deep learning can function effectively within the physical and observational limitations of current Arctic monitoring, establishing a foundation for future large-scale applications once broader datasets become available. Full article

As global climate warming enhances the navigability of Arctic routes, their navigation value has become prominent, yet ships operating in ice-covered waters face severe threats from sea ice and icebergs. Existing manual observation and radar monitoring remain limited, highlighting an urgent need for efficient target recognition technology. This study focuses on polar environmental target detection by constructing a polar dataset with 1342 JPG images covering four classes, including sea ice, icebergs, ice channels, and ships, obtained via web collection and video frame extraction. The “Grounding DINO pre-annotation + LabelImg manual fine-tuning” strategy is employed to improve annotation efficiency and accuracy, with data augmentation further enhancing dataset diversity. After comparing YOLOv5n, YOLOv8n, and YOLOv11n, YOLOv8n is selected as the baseline model and improved by introducing the CBAM/SE attention mechanism, SCConv/AKConv convolutions, and BiFPN network. Among these models, the improved YOLOv8n + SCConv achieves the best in polar target detection, with a mean average precision (mAP) of 0.844–1.4% higher than the original model. It effectively reduces missed detections of sea ice and icebergs, thereby enhancing adaptability to complex polar environments. The experimental results demonstrate that the improved model exhibits good robustness in images of varying resolutions, scenes with water surface reflections, and AI-generated images. In addition, a visual GUI with image/video detection functions was developed to support real-time monitoring and result visualization. This research provides essential technical support for safe navigation in ice-covered waters, polar resource exploration, and scientific activities. Full article

Sea ice dirtiness is an important characteristic that is a marker of many processes occurring in sea ice cover throughout the period of ice formation. Data on dirty ice in the Arctic are scarce; the observations are spatially limited as they usually obtained during ship-based expeditions. There are also automated methods for dirty ice detection from satellite data. The paper presents, for the first time, maps of ice dirtiness in the East Siberian Sea based on four-class classification, drawn manually using satellite images in the visible range for the entire available period of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2025. The spatial and temporal variability of dirty ice, as well as the conditions and causes of its formation, are studied. The study reveals that there are sea areas where the ice is always heavily dirty. At the same time, the area and location of dirty ice in the sea varies greatly from year to year. Our analysis of the interannual variability of dirty ice in the East Siberian Sea reveals an increase in dirty ice area, which is associated with the intensification of dynamic processes leading to ice contamination during its formation. The study finds that vast areas of dirty ice are formed immediately after strong wind-wave activity, which induces resuspension of sediments in the shallow water. The influx of ice from the Chukchi Sea also makes a significant contribution to the amount of dirty ice in the East Siberian Sea. Full article

Labeling ice cracks in Antarctic near-shore sea ice aerial orthophotos is critical for sea ice cargo route development; rapid, accurate identification and labeling of cracks in UAV imagery aids safe goods transfer between icebreakers and expedition stations, and studying ice crack distribution provides a key basis for assessing sea ice route reliability. Ice cracks have complex morphologies that traditional recognition methods struggle to handle, so this study proposes the ICI-YOLOv8 algorithm to improve sea ice crack detection near Antarctica’s Zhongshan Station, using crack density and fractal dimension to characterize spatial distribution and a Monte Carlo-based numerical model to quantify distribution probability. The algorithm achieves 0.628 accuracy and 0.662 mAP@0.5 (outperforming comparable methods in speed and accuracy) and reaches 0.933 accuracy and 0.657 mAP@0.5 with better generalization than similar models when tested on general remote sensing water datasets; a positive correlation exists between fractal dimension and ice crack density, and Monte Carlo simulation and probability distribution models verify their distribution properties. The proposed algorithm is suitable for rapid summer Antarctic near-shore sea ice crack identification, the numerical model effectively quantifies crack distribution to aid route development, and this study is important for understanding polar ice stability and sea ice route development. Full article

The Black Sea is a semi-enclosed basin subject to intense anthropogenic pressures and transboundary pollution, making reliable and comparable monitoring data essential for large-scale environmental assessments. However, national practices differ considerably, hindering data integration and coordinated reporting under international frameworks. This study, conducted within the Horizon 2020 project “Advancing Black Sea Research and Innovation to Co-develop Blue Growth within Resilient Ecosystems” (BRIDGE-BS), evaluated pollutant surveillance methodologies with a focus on heavy metals and priority organic contaminants (polycyclic aromatic hydrocarbons, polychlorinated biphenyls, organochlorine pesticides). Standard Operating Procedures (SOPs) were collected from institutions across Black Sea countries and systematically compared for water, sediment, and biota matrices. The analysis revealed shared reliance on internationally recognized techniques but also heterogeneity in sediment fraction selection, digestion and extraction conditions, instrumental approaches, and quality assurance/quality control (QA/QC) documentation. To complement this assessment, an intercalibration (IC) exercise was organized through the QUASIMEME proficiency testing scheme, accompanied by a follow-up structured questionnaire sent to participant institutions. While individual results remain confidential, collective feedback highlighted common challenges in calibration, blank correction, certified reference materials (CRMs) availability, digestion variability, instrument maintenance, and the reporting of uncertainty and detection limits. Together, these findings confirm that harmonization in the Black Sea requires not only improved comparability of laboratory methods but also the future alignment of assessment methodologies, including indicators and thresholds, to support coherent, basin-wide environmental evaluations under regional conventions and EU directives. Full article

In this study, the presence of seven polychlorinated biphenyl (PCB) congeners proposed by ICES-7 (International Council for the Exploration of the Sea) (PCBs 28, 52, 101, 118, 138, 153, and 180) in milk, dairy products, and beverages was investigated, and potential risks to consumer health were assessed. A total of 130 samples were analyzed using gas chromatography–mass spectrometry (GC–MS/MS). Most PCBs levels were below the limits of detection and quantification, but trace amounts, particularly of PCB 153 and PCB 180, were detected. Overall, 35% of milk and dairy products and 20% of beverage samples exceeded the reference limits for ICES-7, with higher accumulation observed in high-fat dairy products. Packaging type also appeared to influence contamination levels. The study findings indicate that PCBs contamination levels may vary depending on product type, content, production method, and packaging structure. Three consumption scenarios were modeled for children and adults, and the estimated daily intake (EDI) was calculated. All hazard index (HI) values found to be below 1. This result suggests no significant non-carcinogenic health concern across the examined products and packaging types. Nevertheless, given the persistence and bioaccumulation potential of PCBs, continuous monitoring remains essential. Full article

Collisions between ships and sea ice pose a significant threat to maritime safety, making it essential to detect sea ice and perform safety-oriented path planning for polar navigation. This paper utilizes an optimized You Only Look Once version 5 (YOLOv5) model, designated as YOLOv5-ICE, for the detection of sea ice in satellite imagery, with the resultant detection data being employed to input obstacle coordinates into a ship path planning system. The enhancements include the Squeeze-and-Excitation (SE) attention mechanism, improved spatial pyramid pooling, and the Flexible ReLU (FReLU) activation function. The improved YOLOv5-ICE shows enhanced performance, with its mAP increasing by 3.5% compared to the baseline YOLOv5 and also by 1.3% compared to YOLOv8. YOLOv5-ICE demonstrates robust performance in detecting small sea ice targets within large-scale satellite images and excels in high ice concentration regions. For path planning, the Any-Angle Path Planning on Grids algorithm is applied to simulate routes based on detected sea ice floes. The objective function incorporates the path length, number of ship turns, and sea ice risk value, enabling path planning under varying ice concentrations. By integrating detection and path planning, this work proposes a novel method to enhance navigational safety in polar regions. Full article