Search Results (13)

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

The estuaries of the Mississippi River Delta are among the most productive coastal ecosystems in the world and have attracted human fishing communities for centuries. Beginning in the early 20th century, the oil and gas industry also emerged as a powerful economic force in exploiting coastal fossil fuel deposits. This paper reviews the complex history of the oil and gas industry in Southeast Louisiana, including its relationships with political corruption, inequality, pollution, and environmental catastrophe; and also its role in supporting coastal fishing communities with complementary economic opportunities. In the 21st century, a series of disasters—above all Hurricane Katrina in 2005 and the B.P. oil spill in 2010—drew attention to the risks inherent to the region, as well as its crucial role in buffering the impacts of tropical storms for inland urban communities. This paper examines the evolution of fisheries regulations and their consequences of small-scale fishers, focusing especially on the banning of gill net use in 1990s. By combining historical information with ethnographic interviews and participant observation, this paper examines the complex political–economic forces involved in shifting regulatory frameworks and policies, and it shows their negative consequences for fishing communities facing an existentially threatening combination of coastal erosion, fisheries declines, and various macroeconomic headwinds. This paper argues that resilient coastal communities are crucial to combating the environmental problems facing coastal regions and that rethinking fisheries regulations may be a dynamic tool in enhancing community resilience. 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

Shipping activities continue to experience growth across a multitude of industrial sectors within the Arctic, hence there are risks in terms of severity and likelihood of accidents. The Arctic region is inherently dangerous to transportation and human existence due to its extreme climate and environmental conditions, and hence the complexities associated with emergency situations within the maritime domain are amplified when operating within the Arctic and North-Atlantic (ANA). The definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region are significant enablers in providing a set of critical and sustainable tools for Search and Rescue (SAR), Oil Spill Response (OSR), and emergency management practitioners. Therefore, in this paper we aim to identify and characterise high-priority potential seaborne disasters and catastrophic incidents in the ANA region such as cruise ship accidents, oil leaks, radiological leaks, and fishing boat groundings. These were compiled as an outcome of a set of workshops carried out as part of the ARCSAR, EU Horizon 2020 funded project, and from analysis of the literature. We also provide root cause analysis techniques, tools for strategic decision-making, and means of mitigation. We demonstrate how such tools can be used by applying some of them to a selective case study and drawing lessons learned from the application of root cause analysis, which can help emergency response organisations with preparedness work and hence more efficient response. In doing so, we provide a set of tools that can be used for strategic and operational learning. Such approaches can help standardise the definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region in both prospective and retrospective analysis. Full article

An oil spill at sea represents a catastrophic environmental event resulting from the release of oil into marine ecosystems. These incidents pose substantial risks to marine biodiversity, wildlife habitats, and coastal populations, often engendering enduring and widespread repercussions. Cleaning up oil spills is costly due to logistical challenges. The accurate measurement of spill characteristics like the volume, thickness, and area of the spill is crucial before deploying cleanup crews to optimize resource allocation and reduce expenses. The main objective of this research is to use computer vision to detect oil spills and estimate its thickness, helping in decision-making processes to clean up the spill area. The system architecture proposed in this study integrates a drone equipped with a camera module to inspect sea areas and capture images. These images are processed using a deployed computer vision segmentation model to detect oil spills and estimate oil thickness. Predicted results help in decision making via a dedicated application by applying predefined criteria to determine the thickness of the spill, which further help in taking actions for the removal of oil spills. The computer vision model developed in this research could detect and estimate oil thickness with an mAP of 91%. The proposed system in this study uses instance segmentation to detect and segment oil spills in drone footage. This computer vision-based approach accurately identifies and outlines oil spill areas, aiding in the selection of efficient cleanup strategies. Real-time monitoring and assessment capabilities enable quick decision making and effective response measures. Full article

Throughout the decades, the production, transport, and use of fossil fuels have led to numerous environmental concerns. Crude oil has caused catastrophic accidents after its spillage into the aqueous environment and accumulation on coastal sediments. To tackle this problem in a sustainable manner, researchers have used alternative remediation agents to extract these crude oil spills from the sediments. In this study, the biodiesels fatty acid methyl, ethyl, and butyl esters (FAME, FAEE, and FABE, respectively) were synthesized via transesterification reaction from waste cooking oil and corresponding alcohol in the presence of a catalyst, potassium hydroxide, and used as remediation agents for crude oil extraction. The influence of different experimental conditions on the crude-oil removal efficiency was studied (time of 1, 2, or 4 h; mass ratio of biodiesel to crude oil of 0.5:1, 1:1, or 2:1), with a simulation of coastal effects using a shaker. UV/Vis spectrophotometry was used to determine crude-oil separation efficiency based on the correlation of the residual crude-oil mass fraction and corresponding absorbance. The results show that FAME and FAEE were most effective in the removal of crude oil from sand (removing 88–89%), while FAEE and FABE extracted the most crude oil from gravel (removing 74–77%). Full article

Maritime transport, responsible for delivering over eighty percent of the world’s goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to human activities, highlighting the urgent need for continuous and effective surveillance. To address this pressing issue, this paper introduces a novel technique named OS-BREEZE. This method employs an Unmanned Surface Vehicle (USV) for assessing the extent of oil pollution on the sea surface. The OS-BREEZE algorithm directs the USV along the spill edge, facilitating rapid and accurate assessment of the contaminated area. The key contribution of this paper is the development of this novel approach for monitoring and managing marine pollution, which significantly reduces the path length required for mapping and estimating the size of the contaminated area. Furthermore, this paper presents a scale model experiment executed at the Coastal and Marine Engineering Research Institute (CAMERI). This experiment demonstrated the method’s enhanced speed and efficiency compared to traditional monitoring techniques. The experiment was methodically conducted across four distinct scenarios: the initial and advanced stages of an oil spill at the outer anchoring, as well as scenarios at the inner docking on both the stern and port sides. Full article

Catastrophic oil spill accidents, oily industrial wastewater, and other types of uncontrolled release of oils into the environment are major global issues since they threaten marine ecosystems and lead to a big economic impact. It can also affect the public health of communities near the polluted area. This review addresses the different types of oil collecting methods. The focus of this work will be on the different approaches to materials and technologies for oil/water separation, with a special focus on water/oil emulsion separation. Emulsified oil/water mixtures are extremely stable dispersions being, therefore, more difficult to separate as the size of the droplets in the emulsion decreases. Oil-absorbent materials, such as sponges, foams, nanoparticles, and aerogels, can be adjusted to have both hydrophobic and oleophilic wettability while displaying a porous structure. This can be advantageous for targeting oil spills in large-scale environmental and catastrophic sets since these materials can easily absorb oil. Oil adsorbent materials, for example, meshes, textiles, membranes, and clays, involve the capture of the oily material to the surface of the adsorbent material, additionally attracting more attention than other technologies by being low-cost and easy to manufacture. Full article

Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used as a powerful means to conduct oil spill detection. Optical polarization remote sensing, thriving in recent years, shows a novel potential for oil spill detection. This paper provides a demonstration of the use of open-source POLDER/PARASOL polarization time-series data to detect oil spill. The Deepwater Horizon oil spill, one of the largest oil spill disasters, is utilized to explore the potential of optical polarization remote sensing for oil spill detection. A total of 24 feature combinations are organized to quantitatively study the positive effect of adding polarization information and the appropriate way to describe polarization characteristics. Random forest classifier models are trained with different combinations, and the results are assessed by 10-fold cross-validation. The improvement from adding polarization characteristics is remarkable ((average) accuracy: +0.51%; recall: +2.83%; precision: +3.49%; F₁ score: +3.01%, (maximum) accuracy: +0.80%; recall: +5.09%; precision: +6.92%; F₁ score: +4.72%), and coupling between the degree of polarization and the phase angle of polarization provides the best description of polarization information. This study confirms the potential of optical polarization remote sensing for oil spill detection, and some detailed problems related to model establishment and polarization feature characterization are discussed for the further application of polarization information. Full article

Fire accidents on ships and offshore structures lead to complex non-linear material and geometric behavior, which can cause structural collapse. This not only results in significant casualties, but also environmental catastrophes such as oil spills. Thus, for the fire safety design of structures, precise prediction of the structural response to fire using numerical and/or experimental methods is essential. This study aimed to validate the two-way fluid-structure interaction (FSI) method for predicting the non-linear structural response of H-beams to a propane burner fire by comparison with experimental results. To determine the interaction between a fire simulation and structural analysis, the Fire-Thermomechanical Interface model was introduced. The Fire Dynamics Simulator and ANSYS Parametric Design Language were used for computational fluid dynamics and the finite element method, respectively. This study validated the two-way FSI method for precisely predicting the non-linear structural response of H-beams to a propane burner fire and proposed the proper time increment for two-way FSI analysis. Full article

The Bureau of Ocean Energy Management (BOEM) is responsible for managing the development of US Outer Continental Shelf (OCS) energy and mineral resources. Because oil spills may occur from offshore oil and gas activities, BOEM conducts oil spill risk analysis (OSRA) prior to oil and gas lease sales. Since the 1970s, BOEM has developed and applied the OSRA model to evaluate the risk of potential oil spills to environmental resources. This paper summarizes some of the OSRA model progress and applications in the past decade: (1) calculation of the risk of catastrophic oil spills (with a volume over one million barrels), which concludes that the return period of a catastrophic oil spill in OCS areas is estimated to be 165 years; (2) a more efficient way to estimate the probability of oil spill contact to environmental resources in the Gulf of Mexico; (3) weathering calculations in OSRA, which enhances the accuracy of the OSRA model results; and (4) application of OSRA to the Ixtoc I oil spill as an example of how the OSRA model simulates large oil spills for oil spill preparedness and response. Full article

Recent catastrophic events in our oceans, including the spill of toxic oil from the explosion of the Deepwater Horizon drilling rig and the rapid dispersion of radioactive particulates from the meltdown of the Fukushima Daiichi nuclear plant, underscore the need for new tools and technologies to rapidly respond to hazardous agents. Our understanding of the movement and aerosolization of hazardous agents from natural aquatic systems can be expanded upon and used in prevention and tracking. New technologies with coordinated unmanned robotic systems could lead to faster identification and mitigation of hazardous agents in lakes, rivers, and oceans. In this study, we released a fluorescent dye (fluorescein) into a freshwater lake from an anchored floating platform. A fluorometer (fluorescence sensor) was mounted underneath an unmanned surface vehicle (USV, unmanned boat) and was used to detect and track the released dye in situ in real-time. An unmanned aircraft system (UAS) was used to visualize the dye and direct the USV to sample different areas of the dye plume. Image processing tools were used to map concentration profiles of the dye plume from aerial images acquired from the UAS, and these were associated with concentration measurements collected from the sensors onboard the USV. The results of this project have the potential to transform monitoring strategies for hazardous agents, enabling timely and accurate exposure assessment and response in affected areas. Fast response is essential in reacting to the introduction of hazardous agents, in order to quickly predict and contain their spread. Full article

Background: Although there is considerable public concern about the environmental impacts of oil pollution in the Niger Delta of Nigeria, actual evidence on the pathological and psychological effects in the health of local communities is minimally known. We sought to associate the perspective measures of exposure to oil pollution with health outcomes (inventory of health symptoms and functional capacity limitations) and determine how emotional reactions to environmental risks moderate these health outcomes. Method: The study was conducted with 600 participants selected from five local government areas in Akwa Ibom State where oil pollution is rampant. A structured questionnaire was used to collect the data on the respondents’ exposure to oil pollution, self-rated health and disease symptoms, perception of risk of exposure and emotional reactions to local oil pollution. Results: Most of the participants lived in areas with visible oil pollution and/or near gas flaring facilities and regularly suffered direct exposure to oil in their environment. High level of emotional distress was a part of everyone's life for the study population. Risk perception in the study area was mediated, to a large extent, by dreaded hazards (catastrophic fears of pipeline explosions and oil spill fire), visual cues (gas flares and smoke stacks) and chemosensory cues (off-flavor in drinking water). The exposure metrics were found to be significant predictors of the health effects and influencing factors (emotional reactions). Multi-levels models suggest that at the individual level, the demographic variables and direct contact with oil pollution were important mediators of functional capacity limitation. At the community level, emotional distress from fear of the sources of exposure was an important mediator of the health symptoms. Conclusions: This study documents high levels of disease symptoms and environmental distress (worry, annoyance and intolerance) associated with oil pollution in the Niger Delta areas of Nigeria. It highlights the need for some intervention to ameliorate the psychological distress associated with living under such environmental adversity. Full article