Search Results (70)

This study investigates the spatiotemporal evolution of oil released during the Agia Zoni II shipwreck in the Saronic Gulf in 2017, employing the OpenOil module of the OpenDrift framework. The simulation integrates oceanographic and meteorological data to model the transport, weathering, and fate of spilled oil over a six-day period. Oil behavior is examined across key transformation processes, including dispersion, emulsification, evaporation, and biodegradation, using particle-based modeling and a comprehensive set of environmental inputs. The modeled results are validated against in situ observations and visual inspection data, focusing on four critical dates. The study demonstrates OpenOil’s potential for accurately simulating oil dispersion dynamics in semi-enclosed marine environments and highlights the significance of environmental forcing, vertical mixing, and shoreline interactions in determining oil fate. It concludes with recommendations for improving real-time response strategies in similar spill scenarios. Full article

In the North Atlantic and the Northern Ocean, from the second half of 2010 to 2014, satellite imagery data showed increased surface water temperatures (in the Icelandic Depression area in September–October 2010, it was 1.3 °C higher than in 2009). The peak of the annual sum of mean monthly ocean surface temperatures near the Icelandic Depression in 2010 (109.3 °C), as well as the negative values of the monthly averaged North Atlantic Oscillation (NAO) indices, estimated in the second half of 2010 and until March 2011, can be explained by the appearance of an additional film of oil origin on the water surface, formed after an oil spill accident at the Deepwater Horizon drilling rig in the Gulf of Mexico. Insufficient evaporative cooling of surface waters near the Icelandic Depression related to the formation of an additive film due to the influence of pollution of the North Sea by oil can explain the earlier peak in the annual sum of mean monthly ocean surface temperatures near the Icelandic Depression in 2003 (107.2 °C). Although global warming is usually ascribed to increased greenhouse gases in the atmosphere, ocean surface water pollution could increase the heat content of the ocean and explain the steady temperature stratification and desalination of these waters due to the melting of Greenland’s glaciers. Thus, when analyzing the concept of global warming, it is necessary to take into account the aspects of pollution of the ocean surface waters to assess the changes in their capacity to accumulate solar radiation, as well as the changes in the heat content of the ocean mixing zone (~200 m). 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

Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model. Full article

The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies. Full article

Oil spills have always been a threat to the marine ecological environment; thus, it is important to identify and divide oil spill areas on the ocean surface into segments after an oil spill accident occurs to protect the marine ecological environment. However, oil spill area segmentation using ordinary optical images is greatly interfered with by the absorption of light by the deep sea and the distribution of algal organisms on the ocean surface, and it is difficult to improve segmentation accuracy. To address the above problems, a hyperspectral ocean oil spill image segmentation model with multiscale feature fusion (MFFHOSS-Net) is proposed. Specifically, the oil spill segmentation dataset was created using hyperspectral image data from NASA for the Gulf of Mexico oil spill, small-size images after the waveband filtering of the hyperspectral images were generated and the oil spill images were annotated. The model makes full use of having different layers with different characteristics by fusing feature maps of different scales. In addition, an attention mechanism was used to effectively fuse these features to improve the oil spill region segmentation accuracy. A case study, ablation experiments and model evaluation were also carried out in this work. Compared with other models, our proposed method achieved good results according to various evaluation metrics. Full article

This study provides, to the best of our knowledge, the first detailed analysis of how surface oil modifies air–sea interactions in a two-way coupled model, i.e., the coupled–ocean–atmosphere–wave–sediment–transport (COAWST) model, modified to account for oil-related changes in air–sea fluxes. This study investigates the effects of oil on surface roughness, surface wind, surface and near-surface temperature differences, and boundary-layer stability and how those conditions ultimately affect surface stress. We first conducted twin-coupled modeling simulations with and without the influence of oil over the Deepwater Horizon (DWH) oil spill period (20 April to 5 May 2010) in the Gulf of Mexico. Then, we compared the results by using a modularized flux model with parameterizations selected to match those selected in the coupled model adapted to either ignore or account for different atmospheric/oceanic processes in calculating surface stress. When non-oil inputs to the bulk formula were treated as being unchanged by oil, the surface stress changes were always negative because of oil-related dampening of the surface roughness alone. However, the oil-related changes to 10 m wind speeds and boundary-layer stability were found to play a dominant role in surface stress changes relative to those due to the oil-related surface roughness changes, highlighting that most of the changes in surface stress were due to oil-related changes in wind speed and boundary-layer stability. Finally, the oil-related changes in surface stress due to the combined oil-related changes in surface roughness, surface wind, and boundary-layer stability were not large enough to have a major impact on the surface current and surface oil transport, indicating that the feedback from the surface oil to the surface oil movement itself is insignificant in forecasting surface oil transport unless the fractional oil coverage is much larger than the value found in this study. Full article

Following the Deepwater Horizon (DWH) oil spill in 2010, poor pulmonary health and reproductive failure in bottlenose dolphins (Tursiops truncatus) in the northern Gulf of Mexico were well-documented. One postulated etiology for the increased fetal distress syndrome and pneumonia found in affected perinatal dolphins was maternal hypoxia caused by lung disease. The objective of this study was to evaluate the utility of blood gas analysis and capnography in determining oxygenation status in bottlenose dolphins with and without pulmonary disease. Blood and breath samples were collected from 59 free-ranging dolphins in Barataria Bay, Louisiana (BB), during a capture–release health assessment program, and from 30 managed dolphins from the U.S. Navy Marine Mammal Program in San Diego, CA. The former was the oil-exposed cohort and the latter served as a control cohort with known health histories. Capnography and select blood gas parameters were compared based on the following factors: cohort, sex, age/length class, reproductive status, and severity of pulmonary disease. Animals with moderate–severe lung disease had higher bicarbonate concentrations (p = 0.005), pH (p < 0.001), TCO₂ (p = 0.012), and more positive base excess (p = 0.001) than animals with normal–mild disease. Capnography (ETCO₂) was found to have a weak positive correlation with blood PCO₂ (p = 0.020), with a mean difference of 5.02 mmHg (p < 0.001). Based on these findings, indirect oxygenation measures, including TCO₂, bicarbonate, and pH, show promise in establishing the oxygenation status in dolphins with and without pulmonary disease. Full article

Extensive intertidal asphalt pavements and oiled sediment accumulations extend more than 20 km along the northern shoreline of Abu Ali Island, located north of Jubail on the Arabian (Persian) Gulf coast of Saudi Arabia. This shoreline oiling likely originated from two platforms in the Nowruz oil field, which spilled oil from 1983 to 1985; this was one of the largest marine spills in history, with shoreline impacts that were little known. In this study, we used a novel methodology that combined remote sensing analyses with hybrid machine learning–geostatistical modeling of field-collected data to quantify the distribution, extent, and volume of these contaminated sediments to investigate the mechanisms for their persistence and to support the development of remediation plans. After nearly 40 years, approximately 25,000 m³ of contaminated sediments remain, with nearly 50% of these buried underneath clean sediments. The presence of exposed or subsurface carbonate beach rock platforms or ramps clearly influences the ongoing persistence of these asphalt pavements by protecting them from physical energy and sediment mobilization. These rock platforms complicate potential remediation options, with more than 66% of the modeled volume of asphalt pavement estimated to be directly on top of and in contact with carbonate beach rock. The asphalt pavements present persistent ongoing PAH toxicity and continually shed smaller fragments when exposed to wave energy along with localized sheens and liquid oil, presenting a pathway for ongoing chronic exposure of biota. Full article

Dispersants have been used in several oil spill accidents, but little information is available on their effectiveness in Baltic Sea conditions with low salinity and cold seawater. This study investigated the effects of dispersant use on petroleum hydrocarbon biodegradation rates and bacterial community structures. Microcosm experiments were conducted at 5 °C for 12 days with North Sea crude oil and dispersant Finasol 51 with open sea Gulf of Bothnia and coastal Gulf of Finland and Norwegian Sea seawater. Petroleum hydrocarbon concentrations were analysed with GC-FID. Bacterial community structures were studied using 16S rDNA gene amplicon sequencing, and the abundance of genes involved in hydrocarbon degradation with quantitative PCR. The highest oil degradation gene abundances and oil removal were observed in microcosms with coastal seawater from the Gulf of Bothnia and Gulf of Finland, respectively, and the lowest in the seawater from the Norwegian Sea. Dispersant usage caused apparent effects on bacterial communities in all treatments; however, the dispersant’s effect on the biodegradation rate was unclear due to uncertainties with chemical analysis and variation in oil concentrations used in the experiments. Full article

The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) as natural or anthropic assuming that the samples employed to train and test the models in the source domain (D_S) follow the same statistical distribution of unknown samples to be predicted in the target domain (D_T). When such assumptions are not held, Transfer Learning (TL) allows the extraction of knowledge from validated models and the prediction of new samples, thus improving performances even in scenarios never seen before. A database with 26 geometric features extracted from 6279 validated oil slicks was used to develop predictive models in the Gulf of Mexico (GoM) and its Mexican portion (GMex). Innovatively, these well-trained models were applied to predict the OSS of unknown events in the GoM, the American (GAm) portion of the GoM, and in the Brazilian continental margin (BR). When the D_S and D_T domains are similar, the TL and generalization are null, being equivalent to the usual ML. However, when domains are different but statically related, TL outdoes ML (58.91%), attaining 87% of global accuracy when using compatible SAR sensors in the D_S and D_T domains. Conversely, incompatible SAR sensors produce domains statistically divergent, causing negative transfers and generalizations. From an operational standpoint, the evidenced generalization capacity of these models to recognize geometric patterns across different geographic regions using TL may allow saving time and budget, avoiding the collection of validated and annotated new training samples, as well as the models re-training from scratch. When looking for new exploratory frontiers, automatic prediction is a value-added product that strengthens the knowledge-driven classifications and the decision-making processes. Moreover, the prompt identification of an oil spill can speed up the response actions to clean up and protect sensitive areas against oil pollution. Full article

Extensive asphalt pavements have persisted along >25 km (km) of shoreline on Abu Ali Island, on the Arabian (Persian) Gulf coast of Saudi Arabia, reportedly stranding as a result of the 1983–1985 Nowruz oil spills. A study was conducted in October 2020 to support development of a remediation plan. Cross-shore transects were surveyed at 100 m intervals and 1434 shovel test pits were dug to determine oil type, thickness, and depth of burial. Oiling of any description was observed at 76% of the pits. Using 15 diagnostic biomarker ratios, only 5 of the 94 oiled samples from Abu Ali Island in 2020 likely contain other oils. Data on historical spills were identified from the literature. Based on chemical biomarker data for potential source oils in the northern Arabian (Persian) Gulf, the diagnostic ratio for the biomarkers 18a-22,29,30-Trisnorneohopane (Ts) and 17a(H)-22,29,30-Trisnorhopane (Tm) for the 94 samples only matched one Iraq crude oil. No large individual spills of Iraq crude oil were identified in the literature or spill databases, although releases of both Kuwait and Iraq crudes were reported for the 1991 Gulf War oil spills. However, oil residues from Abu Ali did not match most prior samples of Saudi shoreline oiling from the Gulf War oil spills, which largely consisted of spilled Kuwait crude. Though we cannot definitely conclude that the majority of the residual oil on Abu Ali Island delineated during the 2020 survey is oil from the Nowruz oil spills, because there is no source oil from these spills, we use a weight of evidence approach to say that it is highly likely that the majority of the residual oiling is from the Nowruz spills. Full article

From the late 1900s onward, hydrocarbon exploitation has led to severe environmental footprints in the Khuzestan province, Iran. However, no comprehensive study has been conducted to evaluate such issues. In this research, an inclusive analysis was performed to investigate these environmental impacts. To do this, first, two datasets related to a 15-year period (2006–2021) were collated: the satellite data from the Sentinel-1 mission and the seismic data recorded by the National Iranian Geophysics Institute as well as the catalog of the global Centroid Moment Tensor project (CMT). These datasets were processed using generic mapping tools (GMT), differential synthetic aperture radar (D-InSAR) techniques, and multiple processing algorithms using a specific toolbox for oil spill application in the sentinel application platform (SNAP) programming, respectively. The results revealed three critical footprints, including regional earthquakes, land subsidence, and oil spill issues in the area. The most frequent earthquakes originated from depths less than 15 km, indicating the disturbance of the crustal tectonics by the regional hydrocarbons. Furthermore, an annual rate of land subsidence equal to 10–15 cm was observed in the coastal areas of the Khuzestan province. Moreover, two regions located in the north and west of the Persian Gulf were detected as the permanently oil-spilled areas. The applied methodology and results are quite applicable to restrict the harmful consequences of hydrocarbon production in the study area. This research will benefit not only government officials and policymakers, but also those looking to understand the environmental challenges related to oil and gas production, especially in terms of sustainable goals for the management of natural resources. Full article

Oil spill incidents threaten the marine ecological environment. Detecting sea surface oil slicks by remote sensing images provides support for the efficient treatment of oil spills. This is important for sustainable marine development. However, traditional methods based on field analysis are time-consuming. Spectral indices lack applicability. In addition, traditional machine learning methods strictly rely on training and testing samples which are in short supply in oil spill images. Inspired by the spectral DNA encoding method, a spectral gene extraction (SGE) method was proposed to detect oil spills in hyperspectral images (HSI) and multispectral images (MSI). The SGE method contained a parameter and two strategies. The parameter of elimination was designed based on the population genetic frequency. It was used to control the number of spectral genes. The spectral gene extraction strategies, named largest in-class similarity (LIS) strategy and largest inter-class difference (LID) strategy, were proposed to mine the spectral genes by oil spill samples. The oil spills would be determined by calculating the similarity of the extracted spectral genes to the DNA encoded images. In this research, the SGE method was validated by two AVIRIS images of the Gulf of Mexico oil spill, one MODIS image of the Gulf of Mexico oil spill, and one Landsat 8 image of a Persian Gulf oil spill. The oil spills in different remote sensing images could be detected accurately by the proposed method in a small set of samples. Experimental results indicated that the proposed method was suitable for detecting marine oil spills in AVIRIS, MODIS, and Landsat 8 images. In addition, the SGE method with the LIS strategy was more suitable for detecting oil spills in HSI. Its proper elimination rates were 0.8~1.0. The SGE method with the LID strategy was more suitable for detecting oil spills in MSI. Its proper elimination rates were 0.5~0.7. Full article

The aftermath of the 2010 Deepwater Horizon oil spill highlighted the lack of baseline spatial, behavioral, and abundance data for many species, including imperiled marine turtles, across the Gulf of Mexico. The ecology of marine turtles is closely tied to their vertical movements within the water column and is therefore critical knowledge for resource management in a changing ocean. A more comprehensive understanding of diving behavior, specifically surface intervals, can improve the accuracy of density and abundance estimates by mitigating availability bias. Here, we focus on the proportion of time marine turtles spend at the top 2 m of the water column to coincide with depths where turtles are assumed visible to observers during aerial surveys. To better understand what environmental and oceanographic conditions influence time at surface, we analyzed dive and spatial data from 136 satellite tags attached to three species of threatened or endangered marine turtles across 10 years. We fit generalized additive models with 11 remotely sensed covariates, including sea surface temperature (SST), bathymetry, and salinity, to examine dive patterns. Additionally, the developed model is the first to explicitly examine the potential connection between turtle dive patterns and ocean frontal zones in the Gulf of Mexico. Our results show species-specific associations of environmental covariates related to increased time at surface, particularly for depth, salinity, and frontal features. We define seasonal and spatial variation in time-at-surface patterns in an effort to contribute to marine turtle density and abundance estimates. These estimates could then be utilized to generate correction factors for turtle detection availability during aerial surveys. Full article