Search Results (62)

The present study has applied a probabilistic oil spill modeling framework to assess the potential risks associated with offshore oil spills in the Foz do Amazonas sedimentary basin, a region of exceptional ecological importance and increasing geopolitical and socio-environmental relevance. By integrating a large ensemble of simulations with validated hydrodynamic, atmospheric and wave-driven forcings, the analysis of said simulations has provided a robust and seasonally resolved assessment of oil drift and beaching patterns along the Guianas and the Brazilian Equatorial Margin. The model has presented a total of 47,500 simulations performed on 95 drilling sites located across the basin, using the Lagrangian Spill, Transport and Fate Model (STFM) and incorporating a six-year oceanographic and meteorological variability. The simulations have included ocean current fields provided by HYCOM, wind forcing provided by GFS and Stokes drift provided by ERA5. Model performance has been evaluated by comparisons with satellite-tracked surface drifters using normalized cumulative Lagrangian separation metrics and skill scores. Mean skill scores have reached 0.98 after 5 days and 0.95 after 10 days, remaining above 0.85 up to 20 days, indicating high reliability for short to intermediate forecasting horizons and suitability for probabilistic applications. Probabilistic simulations have revealed a pronounced seasonal effect, governed by the annual migration of the Intertropical Convergence Zone (ITCZ). During the JFMA period, shoreline impact probabilities have exceeded 40–50% along extensive portions of the French Guiana and Amapá state (Brazil) coastlines, with oil reaching the coast typically within 10–20 days. In contrast, during the JASO period, beaching probabilities have decreased to below 15%, accompanied by a substantial reduction in impact along the coastline and higher variability in arrival times. Although coastal exposure has been markedly reduced during JASO, a residual probability of approximately 2% of oil intrusion into the Amazonas river mouth has persisted. Full article

Frequent crude oil spills during offshore oil and gas production and transportation have inflicted irreversible detrimental effects on both human activities and marine ecosystems; with particular risks of secondary disasters such as combustion and explosions. To address these challenges; advanced oil sorption technologies have been developed to overcome the inherent limitations of conventional remediation methods. In this study, a flame-retardant protective coating was fabricated on melamine sponge (MS) through precipitation polymerization of octa-aminopropyl polyhedral oligomeric silsesquioxane (POSS) and hexachlorocyclotriphosphazene (HCCP), endowing the MS@PPOS-PDMS-Si composite with exceptional char-forming capability. Secondary functional layer: By coupling the complementary physicochemical properties of polydimethylsiloxane (PDMS) and SiO₂ nanofibers, we enabled them to function jointly, achieving superior performance in the material systems; this conferred enhanced hydrophobicity and structural stability to the MS matrix. Characterization results demonstrated a progressive reduction in peak heat release rate (PHRR) from 137.66 kW/m² to118.35 kW/m², 91.92 kW/m², and ultimately 46.23 kW/m², accompanied by a decrease in total smoke production (TSP) from 1.62 m² to 0.76 m², indicating significant smoke suppression. Furthermore, the water contact angle (WCA) exhibited substantial improvement from 0° (superhydrophilic) to 140.7° (highly hydrophobic). Cyclic sorption–desorption testing revealed maintained oil–water separation efficiency exceeding 95% after 10 operational cycles. These findings position the MS@PPOS-PDMS-Si composite as a promising candidate for emergency oil spill response and marine pollution remediation applications, demonstrating superior performance in fire safety, environmental durability, and operational reusability. Full article

The paper assesses brown seaweed diversity following the catastrophic events of the 2010 Deepwater Horizon (DWH) oil spill in offshore deep bank habitats at 45–90 m depth in the northwestern Gulf of Mexico, and their potential regeneration and recovery in the region. Innovative approaches to expeditionary and exploratory research resulted in the discovery, identification, and classification of brown seaweed diversity associated with rhodoliths (free-living carbonate nodules predominantly accreted by crustose coralline algae). Whereas the rhodoliths collected in situ at our research sites pre-DWH were teeming with brown algae growing on their surface, post-DWH they looked dead, bare, and bleached. These post-DWH impacts appear long-lasting, with little macroalgal growth recovery in the field. However, these apparent “dead” rhodoliths collected post-DWH at banks offshore Louisiana showed macroalgal regeneration starting within three weeks when placed in microcosms in the laboratory, with 19 brown algal species emerging from the bare rhodoliths’ surface. Some taxa corresponded to new records for the GMx (genus Cutleria and Dictyota cymatophila). Padina vickersiae is resurrected from synonymy with P. gymnospora. Reproductive sori evidence is presented for Lobophora declerckii. A detailed nomenclatural list, morphological plates, and phylogenetic/barcoding trees of brown seaweed that emerged from rhodoliths’ surfaces in laboratory microcosms are provided. These findings provide key molecular and morphological insights that reinforce species boundaries and highlight the significance of mesophotic rhodolith beds as previously overlooked reservoirs of cryptic brown algal diversity. Full article

Oil spills threaten marine ecosystems and hinder progress toward Sustainable Development Goal (SDG) 14 on ocean conservation and sustainable marine resource use. Coastal ecosystems in Myanmar face growing risks from expanding maritime infrastructure, including ports, special economic zones, and offshore projects. This study aims to develop a spatial Environmental Sensitivity Index (ESI) map for the Tanintharyi region by integrating biological, socio-economic, and physical factors. Using the Analytical Hierarchy Process (AHP), weighting values were derived from local conservation and livelihood experts to ensure regional relevance. The inclusion of chlorophyll-a as a biological indicator improves the assessment of marine productivity and ecosystem health, linking ESI mapping to ocean acidification. The results showed that 8% of the area was very highly sensitive, 25% was highly sensitive, and 23% was moderately sensitive. The most sensitive zones were concentrated along the southern coastline, particularly in Thayetchaung Township, due to dense mangroves, critical habitats, and resource-dependent fisheries. This study presents the first spatial ESI assessment for Tanintharyi, providing a practical framework for oil spill preparedness and ecosystem protection, with potential for future enhancement through integration with oil spill simulation modeling. Full article

Offshore drilling platforms leak hundreds of thousands of tons of oil every year causing immeasurable damage to the marine environment, therefore it is important to be able to monitor for oil leakage. A hyperspectral camera, as an advanced device integrating spectral technology and imaging technology, can keenly capture the differences in spectral reflectance of different types of oil and seawater. This study presents the design of a hyperspectral camera covering the 400 nm–900 nm spectral band (90 bands total) and establishes a monitoring system comprising a high-precision inertial navigation system, a stabilization system, and a data acquisition system. Furthermore, this study conducted a field flight experiment using a Cessna aircraft, acquiring hyperspectral data with a one m spatial resolution of a drilling platform around the South China sea at 3000 m altitude, which effectively delineated the spectral characteristics of the oil spill area. The detection system developed in this study provides a robust means for oil spill monitoring on drilling platforms in remote sensing of the marine environment. Full article

With the increasingly frequent exploitation and transportation of offshore oil, the threat of oil spill accidents to the marine ecological environment has become increasingly serious. It is urgent to develop efficient and reliable oil film monitoring technology. Based on the marine radar oil spill data, an innovative OAT-NGN hybrid strategy segmentation algorithm was proposed. By integrating the local feature learning ability of a Neural Gas Network (NGN) and the global search strategy of the Oat optimization algorithm (OAT), the proposed method effectively meets the challenges of traditional oil film segmentation methods in complex sea conditions. Firstly, the raw data of marine radar were preprocessed by using co-frequency interference and speckle noise suppression. Then, the OAT algorithm guided the updating of neural weights in the NGN on a global scale for the exploration of a more optimal solution space during the optimization process. Finally, the oil spill segmentation results were projected to the polar coordinate system through post-processing technology. The experimental results showed that this method effectively balanced the problem of false detection and missing detection. Compared with existing methods, OAT-NGN shown stronger adaptability in complex scenarios. In order to improve the segmentation performance, its innovative dynamic weight adjustment mechanism and spatial constraint design provide a new technical path. Full article

In the wake of the continuous development of the international strategic petroleum reserve system, the tonnage and quantity of oil tankers have been increasing. This trend has driven the expansion of offshore oil exploration and transportation, resulting in frequent incidents of ship oil spills. Catastrophic impacts have been exerted on the marine environment by these accidents, posing a serious threat to economic development and ecological security. Therefore, there is an urgent need for efficient and reliable methods to detect oil spills in a timely manner and minimize potential losses as much as possible. In response to this challenge, a marine radar oil film segmentation method based on feature fusion and the artificial bee colony (ABC) algorithm is proposed in this study. Initially, the raw experimental data are preprocessed to obtain denoised radar images. Subsequently, grayscale adjustment and local contrast enhancement operations are carried out on the denoised images. Next, the gray level co-occurrence matrix (GLCM) features and Tamura features are extracted from the locally contrast-enhanced images. Then, the generalized least squares (GLS) method is employed to fuse the extracted texture features, yielding a new feature fusion map. Afterwards, the optimal processing threshold is determined to obtain effective wave regions by using the bimodal graph direct method. Finally, the ABC algorithm is utilized to segment the oil films. This method can provide data support for oil spill detection in marine radar images. Full article

A rapid identification of oil would facilitate a prompt response and efficient removal in the event of an oil spill. Traditional chemical methods in oil fingerprinting have limitations in terms of both time and cost. This study considers machine learning models that can be applied immediately upon measurement of oil density and viscosity. The main objective was to compare models generated from various combinations of features and data. Under five different algorithms, the resulting models were evaluated in terms of their feasibility, advantages, and limitations (FAL). The extra tree (ET) and histogram-based gradient boosting (HGB) models, which incorporated physical features, their rates of change, and environmental features, were found to be the most accurate, achieving 88.55% and 88.41% accuracy, respectively. The accuracy of the models was further enhanced by adjusting the features. In particular, incorporating the rate of change in oil properties led to an enhancement in the accuracy of ET to 92.83%. However, further inclusion of secondary features led to a reduction in accuracy. The effect of input imprecision was analyzed. A 10% of inherent error reduced the accuracy of the HGB model to 60%. Comparing these FAL, machine learning can be a simple, rapid, and cost-effective auxiliary for forensic analysis in diverse spill environments. Full article

With the continuous and rapid development of global industries, issues such as offshore oil spills, leakage of organic chemicals, and the direct discharge of industrial oily sewage have caused serious damage to the ecological environment and water resources. Efficient oil–water separation is widely recognized as the solution. However, there is an urgent need to address the difficulties in treating thin oil films on the water surface and the low separation efficiency of existing oil–water separation materials. In view of this, this study aims to investigate high-efficiency oil–water separation materials for thin oil films. Four types of oil–water separation materials with different materials are designed to treat thin oil films on the water surface. The effects of factors such as oil film thickness, pressure, and temperature on the oil–water separation performance of these materials are studied. The viscosities of kerosene and diesel oil are tested, and the adsorption and separation effects of the oil–water separation materials on different oil products and oily organic solvents are examined. In addition, the long-term stability of the movable and portable oil–water separation components is verified. The results show that the oil-absorbing sponge-based oil–water separation membrane has an excellent microporous structure and surface roughness, endowing the membrane surface with excellent hydrophobicity and lipophilicity, and exhibiting good oil–water separation performance. The filtration flux of oil increases with the increase in pressure and temperature. It has good adsorption and separation performance for different oil products and oily organic solvents. Moreover, it maintains stable operation performance during the 12-month long-term oil–water separation process for kerosene and diesel oil. Full article

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

Here, we review the functioning and importance of deltaic coastal systems in the northern and southern Gulf of Mexico and how petroleum activities have impacted these two important systems. The Mississippi and Usumacinta-Grijalva Deltas are areas of high biological productivity and biodiversity that support the two largest fisheries in the Gulf. The north central Gulf receives inflow from the Mississippi river, the largest discharge in North America. The Mississippi Delta covers about 10,000 km². The Usumacinta-Grijalva River is the second highest freshwater input to the Gulf and discharges to the Usumacinta-Grijalva/Laguna de Terminos deltaic complex. These two areas are the largest petroleum producing regions in the Gulf, involving both inshore and offshore production. Petroleum activities impact coastal ecosystems in two important ways. In inshore areas dominated by coastal wetlands, there has been enormous physical disruption of the natural environment that affected hydrology and system functioning. In both inshore and offshore areas, spilled oil and release of high salinity produced water has led to widespread toxic pollution. Documentation of petroleum activity impacts on coastal marine ecosystems is much more advanced in the Mississippi Delta. Here, we describe how petroleum production impacts coastal ecosystems and discuss how restoration and management can restore the functioning of impacted coastal ecosystems. Full article

The development of offshore wind farms is an important step toward increasing the share of green energy in Poland’s energy mix, offering promising prospects for the energy industry. However, in addition to numerous benefits, such investments also carry potential risks for the marine environment, including the risk of spills of hazardous substances such as gear oils, hydraulic oils, and lubricants. This paper analyses the potential impact of oil spills from offshore wind farms on the marine ecosystems of the Baltic Sea, taking into account hydrometeorological factors, particularly protected areas (such as Natura 2000 sites) and the intensity of ship traffic in the area of the planned farms. Simulations of spill scenarios are also presented to assess the potential extent of pollution and its impact on the environment. This paper emphasises the importance of advanced monitoring and safety systems in minimising the risk of accidents and responding quickly to possible incidents. The development of offshore wind farms in Poland presents itself as a key element in a sustainable energy development strategy, combining advanced technology with environmental concerns. Full article

In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts. Full article

Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models—DRSNet, CNN-Visual Transformer, and GCN—conducting a comprehensive analysis to evaluate the advantages and limitations of each model. Full article

The coastal zone is a fascinating place that comprises the interface between sea and land. This interface, which is both very dynamic and sensitive, has been affected by strong urban and industrial pressures, and an increase in both traffic and recreational uses, leading to the deterioration of natural habitats and the growing instability of residential areas. Added to this disruption is ongoing climate change, which will lead to rising sea levels and increased wave action. Another problem we are increasingly concerned about is ocean pollution, which has been one of the main causes of threats to deep-water coral reef areas. The main sources of pollution include oil spills and offshore oil drilling. The effects of pollution caused by oil spills can not only seriously affect the global environmental balance of our planet but can also, on a different scale, seriously affect the economy of countries whose main resources depend heavily on the sea. Wave energy has the potential to alleviate the world's dependence on depleting fossil energy resources. With regard to coastal protection, the development of ecological solutions to preserve ecosystems and address coastal processes as an alternative to traditional coastal protection structures (seawalls, groins and breakwaters) is becoming increasingly important. These structures, generally referred to as passive measures, are usually built to alter the effects of sea waves, currents and the movement of sand along the coastline, with the aim of protecting beaches, ports and harbors. The concerns outlined are critically addressed throughout this review article. All of them are highly relevant today and, as demonstrated throughout this article, are expected to grow even more and with much more pronounced consequences starting from the middle of the current century. Full article