Search Results (296)

Marine oil spill management requires rapid and accurate forecasts to mitigate environmental and operational risks. Created in 2002 during the Prestige spill and formally established in 2006, the French Drift Committee (CODER) unites expertise from CEDRE, Météo-France, Ifremer and Shom to refine oil drift modeling and response strategies. CODER’s operational scope combines multi-model forecasting, including MOTHY and OILMAP, integrated with real-time observations from drifting buoys, satellite/aerial observations and high-resolution environmental data, such as Copernicus Marine Environment Monitoring Service (CMEMS) current forecasts and Météo-France’s ARPEGE and AROME wind models. Annual exercises, such as RAMOGEPOL 2025, provide a controlled environment to test model performance in diverse hydrodynamic conditions and recalibrate simulations using real-time buoy data. During this exercise, the French Navy’s Anti-Pollution Practical Expertise Center (CEPPOL) deployed buoys in the Mediterranean Sea, off the coast of Saint-Tropez, France, in both the Northern Current and wind-driven coastal waters, enabling CODER to assess model performance and refine simulations by comparing buoy trajectories with model outputs. This process enhances the accuracy of drift predictions, improves the understanding of local hydrodynamics, and identifies models’ strengths and limitations. By systematically evaluating and adjusting models using operational data, CODER strengthens the reliability of drift forecasts, ensuring more effective and adaptive responses to marine pollution incidents. Full article

Marine oil spills represent a critical environmental threat. Petroleum contamination systematically accumulates in the world’s oceans, driving severe and long-term damage to the biodiversity of vulnerable coastal ecosystems. As its primary objective, this study assesses the ocean’s intrinsic genetic capacity to degrade aliphatic and aromatic hydrocarbons. Using the Ocean Gene Atlas v2.0 (OGA2) database, bacterial metabolic pathways were profiled via a four-marker framework: PF00487 (AlkB) and PF03433 (LadA) for medium and long-chain alkanes, alongside PF00355 and PF00848 domains for aromatic ring activation. The analyses revealed that while salinity levels between 34–36 PSU sustain baseline abundances, temperature acts as a primary selective filter, segregating microbial communities into distinct thermal niches. Medium-chain aliphatic potential (PF00487) is ubiquitous, reaching maximum values in surface polar waters near 0 °C before declining with depth. Conversely, long-chain machinery (PF03433) is restricted to warm surface hotspots. Aromatic-degrading potential (PF00355/PF00848) displayed high thermal resilience, narrowing vertically except for a mesopelagic cluster in the Arabian Sea. Global taxonomic analysis confirmed the dominance of Pseudomonadota (59%), which was mainly represented by the class Gammaproteobacteria (15%), with Alcanivorax (10%) as the most abundant genus. On the other hand, aromatic degraders persist as a low-abundance seed bank. In conclusion, the mere presence of specific genes does not automatically imply metabolic expression; actual in situ biodegradation remains strictly governed by transcriptional triggers and environmental factors. Full article

Marine oil spills severely threaten marine ecosystems, the coastal economy, and marine engineering structures. Because it enables all-weather and all-time acquisition of rich target information, fully polarimetric synthetic aperture radar (FP SAR) is widely used for monitoring marine oil spills. However, the differences in the scattering characteristics among oil types can cause variability in the information contained in the features extracted using FP SAR. Herein, RADARSAT-2 images obtained from a rare oil-on-water experiment conducted in the Norwegian North Sea were used to compare the distribution differences in polarimetric features based on the oil slick type and incident angle. Results showed that the incident angle exerted some influence on polarimetric features and the detection performance for oil spills with a low oil–water contrast, particularly at large incident angles. The polarimetric features related to scattering mechanisms exhibited good robustness and effectiveness across various incident angles. The polarimetric feature that combines the scattering entropy H and modified anisotropy A₁₂ exhibited strong overall performance and high suitability for extracting information on oil spills at different incident angles. This study demonstrates that incorporating appropriate polarimetric features according to the incident angle enables the identification of different oil slick types and facilitates oil spill detection and monitoring. Full article

Marine oil spills pose serious threats to coastal ecosystems and maritime activities, and synthetic aperture radar (SAR) has become an important tool for all-weather marine monitoring. However, SAR oil spill detection remains challenging because oil spills usually appear as weak dark anomalies with blurred boundaries, elongated or fragmented shapes, and strong interference from lookalike phenomena such as low-wind areas and internal waves. To address these issues, we propose RGCNet, a lightweight radiometric–geometric–contextual detection framework based on YOLOv11n. Firstly, the H_SPDRFF module is incorporated into the backbone to enhance weak radiometric responses through constrained feature amplification, thereby reducing missed detections caused by low-contrast oil slicks. Secondly, the C3k2_GSR module is designed in the neck to strengthen anisotropic geometric refinement and preserve the continuity of elongated and fragmented oil spill regions during multi-scale feature fusion. Finally, a SAR-adapted large selective kernel block (LSKBlock) is embedded in the high-level backbone to improve contextual discrimination between true oil spills and lookalike dark formations. Experiments on DeepSAR show that RGCNet increases mAP@0.5 and mAP@0.5:0.95 by 3.6 and 3.0 percentage points over the YOLOv11n baseline, respectively. Cross-dataset evaluation on SAR-Oil-Spill demonstrates a 3.9-point mAP@0.5 gain, indicating strong transferability. Furthermore, with a compact model size of 2.67 M parameters and 6.4 G FLOPs, RGCNet achieves an inference speed of 162.5 FPS on an RTX A4000 GPU, demonstrating its efficiency and potential for real-time maritime surveillance. Nevertheless, the current bounding-box formulation cannot precisely delineate irregular oil-spill boundaries. Future work will therefore investigate fine-grained segmentation and cross-sensor adaptation. Full article

In recent years, wars have gradually increased the risk of marine oil spill accidents. Marine oil spill monitoring becomes more and more important for preventing marine oil pollution. The Chinese Zhuhai-1 satellite can capture abundant spectral reflectance signals. It is a significant way of detecting marine oil spills. Most of the traditional oil spill detection methods only used a small amount of spectral information. It made it difficult identify oil spills accurately from the inhomogeneous marine environment. In order to mine the key differential spectral information of oil slicks, inspired by the encoding method of spectral DNA, an advanced spectral DNA encoding (ASDE) strategy was proposed to describe the spectral details in Zhuhai-1 images. On this basis, two kinds of key spectral information extraction methods were proposed to mine the spectral genes of oil slicks. Finally, the extracted spectral genes were used to detect the marine oil spills. Three Zhuhai-1 satellite images were used to validate the performance of the proposed method based on ASDE strategy. The experimental results indicated that the proposed method could precisely describe the spectral differences in oil slicks and seawater in Zhuhai-1 images. In addition, the extracted spectral genes could detect marine oil spills correctly. Full article

To tackle the environmental challenges associated with industrial oily wastewater discharges and recurrent marine oil spill incidents, developing high-efficiency oil–water separation technologies represents a pressing environmental challenge. This research presents a novel design approach comprising the deposition of a stable SiO₂ anchoring layer followed by the fabrication of a PDA/CS crosslinked coating, thereby achieving successful construction of a superhydrophilic/underwater superoleophobic (SH/UWSO) coating on stainless steel meshes (SSM). In the first step, SiO₂ microspheres were deposited via vapor deposition to create a micro-rough surface architecture. Subsequently, a dopamine/chitosan (DA/CS) reaction solution was introduced to form a Polydopamine/chitosan (PDA/CS) coating, yielding a SiO₂@PDA/CS-SSM separation membrane. The resulting membrane exhibited separation efficiencies surpassing 99% for various oil–water mixtures, achieving a flux of 1.24 × 10⁵ L·m⁻²·h⁻¹ in petroleum ether systems. Notably, the membrane maintained high efficiency and structural stability even after 25 separation cycles, immersion in strong acid and base solutions for 72 h, and 100 abrasion tests. The rational design of the anchoring and crosslinking layers endows SiO₂@PDA/CS-SSM with high efficiency and stability, making it an effective oil–water separation material. Full article

Synthetic Aperture Radar (SAR) is widely used for marine oil spill surveillance due to its all-weather capabilities and sensitivity to sea surface roughness. However, oil slicks often appear as dark formations that can be confounded with visually similar “look-alikes”, making automated detection and boundary delineation challenging. This study proposes a two-stage deep learning framework for oil spill mapping in Sentinel-1 SAR imagery. First, a ConvNeXt-T classifier screens image patches for likely slick presence, reducing the search space for dense prediction. Second, spill boundaries are extracted with a domain-adapted Segment Anything Model (SAM) configured for prompt-free, single-shot segmentation. The input representation is enhanced by combining preprocessed Sentinel-1 VV backscatter with Gray-Level Co-occurrence Matrix (GLCM) texture measures (homogeneity and variance) to better separate oil from heterogeneous background sea at the segmentation level. Quantitative evaluation against established segmentation baselines demonstrates that our adapted SAM achieves the highest overall accuracy, reaching an F1-score of 0.86. This outperforms traditional models such as UNet and CBDNet (0.83), as well as DeepLabV3, SegNeXt, and OFCNet (all at 0.82). Furthermore, an analysis of the wind speed on the test set shows that wind speed affects detectability but does not by itself determine segmentation quality. The results indicate that combining transformer-based screening with efficient foundation-model adaptation can provide accurate and scalable oil spill mapping for operational SAR monitoring. Full article

Marine oil spill accidents pose a serious threat to the marine ecological environment. Therefore, efficient and accurate oil spill detection is of great significance for emergency response. To address the issues of blurred oil-slick boundaries, prominent co-frequency interference and severe speckle noise in shipborne radar images, this study proposed an oil spill detection method based on radar data collected from a real oil spill event at a terminal in Dalian Bay. The proposed method integrates an autoencoder, feature dimensionality reduction, pseudo-labeling, reinforcement learning and an improved intelligent optimization algorithm. First, an autoencoder was adopted to extract compact nonlinear local features from the radar images, and principal component analysis (PCA) was employed for feature dimensionality reduction. Subsequently, K-Means clustering was used to construct pseudo-labels, and the reduced features were discretized to build the state space for reinforcement learning. Based on this, the Q-learning algorithm was introduced to automatically extract the region of interest (ROI). Finally, for the ROI, an improved bat algorithm incorporating a dynamic weighting factor and a multi-constraint fitness function was designed to achieve fine segmentation of the oil-slick target. The experimental results showed that the proposed method outperformed classic intelligent optimization algorithms and the conventional bat optimization algorithm in oil-slick segmentation performance. Ablation experiments further verified the effectiveness of autoencoder-based feature learning, K-Means pseudo-labeling, and Q-learning-based ROI localization. This method may provide a new technical approach for timely offshore oil spill monitoring and emergency analysis. Full article

Plastic pellet spills are a growing environmental concern, yet response strategies remain limited and poorly adapted. This study evaluates whether existing oil spill recovery tools, including booms, skimmers, and specialized vessels, can be repurposed to respond to acute releases of plastic pellets at sea. Plastic pellets, although small (typically 1–5 mm in diameter), exhibit variation in physical properties, including polymer type, size, shape, color, and density. These features strongly influence dispersion dynamics and the feasibility of cleanup. Our analysis reveals critical limitations in current response technologies, primarily due to their oil-centric design and lack of consideration for the unique behavior of plastic pellets. By bridging expertise in oil spills and emerging plastic threats, we outline opportunities for adaptive, cross-sector response strategies tailored to the realities of plastic-pellet spills. This study includes a field demonstration in the Northern Adriatic Sea, where oil-spill skimmers and booms were successfully tested for plastic pellet recovery under real-world marine conditions. Full article

Unmanned Aerial Vehicle (UAV) remote sensing images provide high-resolution and flexible monitoring data for oil spill detection. To address the high computational cost and low accuracy of traditional models, this study proposes an improved model, YOLOv8m-CGSE. The model replaces standard convolution with Group Shuffle Convolution (GSConv), substitutes the C2f module with SENetV2, and introduces a light-weight Cross-scale Context Fusion Module (CCFM) to enhance multi-scale feature representation while maintaining a lightweight structure. Mosaic augmentation was applied to the marine oil spill dataset, improving mAP50 and mAP50–95 to 85.4% and 62.0%, respectively. Based on YOLOv8m, the proposed YOLOv8m-CGSE achieved mAP50 and mAP50–95 of 91.2% and 73.3%, respectively, improving accuracy while reducing parameters by 16.1% and computational cost by 12.6%. Furthermore, a supplementary vulnerability test on highly deceptive oil-free sea surfaces demonstrated that the proposed model actively suppresses complex background clutter (e.g., ship wakes and wave anomalies), effectively reducing false positive detections from 21 (baseline) to 15. The results demonstrate that the proposed model effectively balances high precision, robustness against visual lookalikes and computational efficiency for real-time marine oil spill monitoring. Full article

The continuous expansion of global maritime trade and shipping networks has increased the risk of marine oil spills. The increasing frequency of oil spill incidents has seriously threatened nearshore ecosystems, marine biological resources, and the sustainable development of coastal regions. An improved Monarch Butterfly Optimization (MBO) algorithm was proposed to achieve accurate identification and segmentation of oil spill regions in radar images. The original radar images underwent preprocessing, including grayscale conversion and background pixel removal, to preserve the informative pixels of oil films and improve the contrast between oil films and the seawater background. Subsequently, a genetic algorithm was employed to optimize the radial basis function (RBF) neural network, and a three-dimensional pixel feature space was constructed for oil spill Region of Interest (ROI) extraction. Finally, the improved MBO algorithm was applied to design a multi-objective fitness function integrating within-class variance, between-class differences, and class proportion constraints. Global optimization of the segmentation threshold was achieved via dynamic parameter adjustment, reverse learning, and elite reproduction, enabling accurate oil spill extraction. The precision, recall, F1 score, and IoU of the algorithm were 92.4%, 93.3%, 92.8%, and 86.6%, respectively. The proposed method achieves a well-balanced performance in both detection accuracy and region overlap, exhibits clear advantages over the compared methods in overall segmentation quality. The results demonstrated that the improved MBO algorithm mitigated segmentation challenges induced by low contrast and strong clutter, achieving superior classification accuracy and region completeness for offshore oil spill monitoring. Full article

Background: Oil spills have dramatically increased, causing significant damage and pollution to marine ecosystems. The entry of petroleum hydrocarbons into the ocean may lead to the occurrence of harmful algal blooms (HABs). The amino acid changes in harmful algae after oil spills remain unclear. Methods: In order to study the effect of oil spills on the amino acid mechanism of typical causative species, the composition and relative abundance of amino acids in Heterosigma akashiwo were investigated under different water accommodated fractions (WAFs) of 180# fuel oil. Results: Random forest prediction of polycyclic aromatic hydrocarbon toxicity to microalgae identified pyrene, benzo[k]fluoranthene, and fluoranthene as significant contributors. A total of 16 species of amino acids were detected in Heterosigma akashiwo, among which alanine, proline, aspartic acid, cysteine, lysine, and histidine were the predominant ones. As the concentration of the WAF increased, alanine abundance decreased significantly, indicating that the WAF disrupted the metabolic balance of alanine, with the degree of interference being positively correlated with exposure concentration. With the increase in culture time, the abundance of cysteine increased at 1%, 3%, and 5% WAFs, whereas the cysteine increased and then decreased at 7% and 10% WAFs. The abundance of aspartic acid and lysine showed no obvious pattern with culture time under WAF stress. Significant increases in the abundance of proline and histidine were observed in the WAF treatments. Conclusions: This study investigated the impact of oil spill pressure on the amino acid content of harmful algae, providing a scientific basis for understanding the potential impact of oil spills on the occurrence of HABs. Full article

Marine oil spills pose a persistent threat to marine ecosystems and coastal economies, and their rapid and unpredictable spread requires timely and reliable monitoring. In X-band marine radar images, oil slicks usually appear as low-contrast dark targets embedded in heterogeneous sea clutter, making accurate segmentation particularly challenging. To address this problem, this study proposes a training-free two-stage oil slick detection framework that combines an improved Slick Boundary Ratio (SBR) feature with an improved Whale Optimization Algorithm (WOA). First, the improved SBR feature is used to extract the oil slick region of interest (ROI). Then, the improved WOA is employed to determine the global threshold for oil slick segmentation. Experimental results show that the proposed method achieves accurate and spatially coherent oil slick segmentation in complex radar backgrounds, with an Accuracy of 99.36%, a Precision of 85.73%, a Recall of 84.42%, an F1-score of 85.07%, and an Intersection over Union (IoU) of 74.01%. These results indicate that the proposed framework can effectively suppress false positives while maintaining strong detection sensitivity, thereby improving segmentation robustness in low-contrast marine radar scenes. Owing to its training-free design, the proposed method shows potential for shipborne and coastal oil spill monitoring applications. Full article

X-band marine radar offers unique advantages for monitoring nearshore oil spills. However, oil films and sea clutter exhibit high pixel intensity overlap in radar images. Traditional threshold segmentation and machine learning methods have certain limitations in terms of feature extraction, Region of Interest (ROI) guidance, threshold optimization adaptability, and unsupervised capabilities. To address these issues, a method of oil film detection for ship radar based on multi-dimensional feature-guided extraction and hybrid optimization search is proposed. By combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering with multidimensional features, this method automatically extracts ROIs under unlabeled conditions, effectively suppressing sea clutter interference. Subsequently, an improved Beaver Behavior Optimizer (BBO) and simulated annealing (SA) hybrid algorithm (BBO-SA) is introduced within the ROIs, along with a designed adaptive temperature update strategy, to achieve coordinated optimization of global and local searches. The experimental results demonstrate that the method described in this paper performs exceptionally well across all evaluation metrics, confirming its accuracy and robustness in oil film detection. It provides a viable technical approach for emergency monitoring of nearshore oil spills. Full article

Marine oil spills pose critical challenges to environmental sustainability and socioeconomic stability. Taking four pivotal cases as the entry point, this study uses comparative case analysis, semi-structured stakeholder interviews, policy analysis and international gap comparison to systematically analyze the evolution of marine oil spill governance in the Taiwan region of China over two decades, aiming to identify systemic gaps and propose actionable reforms. By integrating and explicitly detailing these multiple methodologies, this research not only identifies but also systematically examines the Taiwan region of China’s unique challenges as a non-UN-member entity navigating international conventions like the international convention for the prevention of pollution from ships, 1973, as modified by the protocol of 1978 relating thereto (MARPOL 73/78). Key findings reveal persistent issues in decision-support tools, fragmented inter-agency coordination, and legal inadequacies in compensation mechanisms. The study’s novelty lies in its rigorous synthesis of localized case-driven insights compared with global best practices, proposing a concrete, phased model for a unified task force and context-aware, data-driven contingency plans to enhance real-time response efficiency. It further advocates for pragmatic steps to align the Taiwan region of China’s Marine Pollution Control Act with international standards while critically addressing the transboundary collaboration barriers imposed by its political status, exploring potential pathways through sub-national and regional partnerships. Notably, the 2023 Angel Container case underscores the urgency of modernizing enforcement capacities and integrating advanced technologies. By bridging gaps in governance, legal accountability, and practical international engagement, this research not only advances the Taiwan region of China’s preparedness but also offers a nuanced and adaptable blueprint for coastal regions facing similar geopolitical and environmental constraints. Its recommendations hold significant implications for global marine pollution management, emphasizing the interplay of policy innovation, technological adoption, and pragmatic cross-jurisdictional cooperation. Full article