Search Results (64)

Oil spills represent a significant environmental hazard, particularly in marine ecosystems, where their impacts extend to coastal infrastructure, biodiversity, and economic activities. This study utilizes GNOME v.47.2 (General NOAA Operational Modeling Environment) and ADIOS2 v.2.10.2 (Automated Data Inquiry for Oil Spills) to simulate and analyze oil spill dynamics in the Romanian sector of the Black Sea, focusing on trajectory prediction, hydrocarbon weathering, and shoreline contamination risk assessment. The research explores multiple spill scenarios involving different hydrocarbon types (light vs. heavy oils), vessel dynamics, and real-time environmental variables (wind, currents, temperature). The findings reveal that lighter hydrocarbons (e.g., gasoline, aviation fuel) tend to evaporate quickly, while heavier fractions (e.g., crude oil, fuel oil #6) persist in the marine environment and pose a higher risk of coastal pollution. In the first case study, a spill of 10,000 metric tons of medium oil (Arabian Medium EXXON) was simulated using GNOME v.47.2, showing that after 22 h, the slick reached the shoreline. Under forecasted hydro-meteorological conditions, 27% evaporated, 1% dispersed, and 72% remained for mechanical or chemical intervention. In the second simulation, 10,000 metric tons of gasoline were released, and within 6 h, 98% evaporated, with only minor residues reaching the shore. A real-world validation case was also conducted using the December 2024 Kerch Strait oil spill incident, where the model accurately predicted the early arrival of light fractions and delayed coastal contamination by fuel oil carried by subsurface currents. These results emphasize the need for future research focused on the vertical dispersion dynamics of heavier hydrocarbon fractions. Full article

This paper presents an original approach to predicting oil slick movement and dispersion at the water surface. Special emphasis is placed on the impact of evolving hydro-meteorological conditions and the thickness of the oil spill layer. The main gap addressed by this study lies in the need for a comprehensive understanding of how changing environmental conditions and oil thickness interact to influence the movement and dispersion of oil slicks. By focusing on this aspect, this study aims to provide valuable insights into the complex dynamics of oil spill behaviour, enhancing the ability to predict and mitigate the environmental impacts of such incidents. Self-designed software was applied to develop and modify previously established mathematical probabilistic models for predicting changes in the shape of the oil trajectory. First, a semi-Markov model of the process is constructed, and the oil thickness is analysed at the sea surface over time. Next, a stochastic-based procedure to forecast the horizontal movement and dispersion of an oil slick in diverse hydro-meteorological conditions considering a varying oil layer thickness is presented. This involves determining the trajectory and movement of a slick domain, which consists of an elliptical combination of domains undergoing temporal changes. By applying the procedure and program, a short-term forecast of the horizontal movement and dispersion of an oil slick provided its trajectory at the Bornholm Basin of the Baltic Sea within two days. The research results obtained are preliminary prediction results, although the approach considered in this paper can help responders understand the scope of the problem and mitigate the effects of environmental damage if the oil discharge reaches sensitive ecosystems. Finally, further perspectives of this research are given. Full article

Pollution caused by marine oil spills can lead to persistent ecological disasters and severe social and economic damages. Numerical simulations are useful and essential tools for accurate decision making during emergencies and planning response actions. In this study, we applied the Princeton Ocean Model (POM) to determine current data, including seawater velocity, salinity, and temperature, and we obtained the fate and trajectory of spilled oil using OpenOil. Several probable oil slicks around Taiwan were simulated over time (12 months) and space (four spill locations in the marine area of each coastal city or county) using the model. The percentage risk under the effect of an oil spill is estimated. The risk zone of the coastal waters of Taiwan was identified based on the frequency of simulated oil slicks hitting the coast and sensitive resources. This information not only helps authorities guide the preparation of effective plans to minimise the impacts of oil spill incidents but could also be used to improve regulations related to shipping and vessel navigation in regional seas. Full article

The oil and gas resources in the deep Paleogene system of the South China Sea are abundant. However, due to its poor reservoir physical properties and strong heterogeneity, the deep Paleogene system needs to be commercially exploited by hydraulic fracturing technology. In view of the challenges of offshore low-permeability reservoirs, large-scale fracturing is not allowed because of the limited operation sites and complex string structure. Taking the H oilfield in the South China Sea as the target, based on the concept of the integration of geologic and engineering techniques, parameters such as the number of fracturing stages and the fracture length were optimized by a numerical simulation, and a study on the slurry rate and fracturing scale was carried out based on the type of fracturing and the pipe string structure. The results show that multistage fracturing technology is available in low-permeability offshore oil fields. It is suggested to adopt networking fracturing technology with a “slick water + high slurry rate” framework. A higher rate is recommended, and the fracturing scale of each stage should be 50 m³ of the sands and 700 m³ of the fluids. This research provides a new model for offshore low-permeability oilfield development. Full article

Air–sea interactions within the ocean’s near-surface layer play a pivotal role in climate regulation and are essential for understanding the dispersion of marine pollutants such as microplastics and oil slicks. Despite its significance, high-resolution data exploring the physical dynamics near the air–sea interface are noticeably sparse. To address this, we introduced a novel Lagrangian observational platform, outfitted with an upward-facing high-resolution ADCP, designed to measure current shear within the top 2 m of the surface water. Through two short field experiments, we identified enhanced currents and shear in the near-surface layer, and observed a negative vertical momentum flux aligned with the wind direction and a positive one orthogonal to it. The measurement suggest that Stokes drift contributes to 10% of horizontal mass transport and 20% of shear in the top surface layer, with the direct and local wind-driven current being the predominant influence. To accurately model the physical behavior of buoyant microplastics, this observation underscores the necessity of parameterizations that account for both the Stokes drift and the direct, local wind-driven current, a factor that is often overlooked in many models. Full article

The paper deals with an important issue related to the identification, modelling, and prediction of environmental pollution in aquatic ecosystems of the Baltic Sea caused by anthropopressure. Water ecosystems are in danger nowadays because of the negative influence of chemical releases in seas, oceans, or inland waters. The crucial issue is to prevent the oil spills and mitigate their consequences. Thus, there is a need for methods capable of reducing the water pollution and enhancing the effectiveness of port and marine environment preservation. The challenge in implementing actions to remove and prevent horizontal oil discharge lies in accurately determining its shape and direction of oil spreading. The author employed a self-designed software utilizing modified and developed mathematical probabilistic models to forecast the movement and dispersion of an oil spill in diverse hydrological and meteorological conditions. This involved determining the trajectory and movement of a spill domain, which consists of elliptical sub-domains undergoing temporal changes. The research results obtained are the initial results in the oil spill simulation problem. This approach represents an expanded and innovative method for determining the spill domain and tracking its movement, applicable to oceans and seas worldwide. It expands upon the methodologies firstly discussed, thereby broadening the range of available techniques in this field. A simple model of an oil spill trajectory simulation and a surface oil slick as an ellipse is illustrated using a time-series of selected hydro-meteorological factors that change at random times. The author proposes a Monte Carlo simulation method to determine the extent of an oil spill in an aquatic ecosystem, taking into account the influence of varying hydro-meteorological conditions. A semi-Markov model is defined to capture the dynamics of these conditions within the spill area and develop an enhanced algorithm for predicting changes in the shape and movement of the spill domain under changing these conditions. By applying the algorithm, a simulation is conducted to provide short-term prediction of the oil discharge trajectory in a selected Baltic Sea waterway. To enhance the accuracy of predicting the process of changing conditions, uniformly tested joint datasets from the open sea water area were incorporated. Finally, the potential future prospects and directions for further research in this field are discussed. Full article

Oil spills are one of the most hazardous pollutants in marine environments with potentially devastating impacts on ecosystems, human health, and socio-economic sectors. Therefore, it is of the utmost importance to establish a prompt and efficient system for forecasting and monitoring such spills, in order to minimize their impacts. The present work focuses on the numerical simulation of the drift and spread of oil slicks in marine environments. The specific area of interest is the Azemmour estuary, located on Morocco’s Atlantic Coast. According to the environmental sensitivity index (ESI), given its geographical location at the intersection of the World’s Shipping Lines of oil transport, this area, as with many other sites in Morocco, has been classified as a high-risk area for oil spill accidents. By taking into account a range of factors, including the ocean currents, the weather conditions, and the oil properties, detailed numerical simulations were conducted, using the hydrodynamic TELEMAC-2D model, to predict the behavior and spread of an oil spill event in the aforementioned coastal region. The simulation results help to understand the spatial–temporal evolution of the spilled oil, the effect of wind on the spreading process, as well as the coastal areas that are most likely to be affected in the event of an oil spill accident. The simulations were performed with and without wind effects. The results showed that three days after the oil spill only 31% of the spilled oil remained on the sea surface. The wind was found to be the main factor responsible for oil drifting offshore. The results indicated that rapid action is needed to address the oil spill before it causes significant environmental damage and makes the oil cleanup process more challenging and expensive. The results of the present study are highly valuable for the management and prevention of environmental disasters in the Azemmour estuary area. The findings can be used to assess the efficacy of various response strategies, such as containment and cleanup measures, and to develop more effective emergency response plans. 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

In 1978, the SEASAT satellite was launched, carrying the first civilian synthetic aperture radar (SAR). The mission was the monitoring of ocean: application to land was also studied. Despite its short operational time of 105 days, SEASAT-SAR provided a wealth of information on land and sea, and initiated many spaceborne SAR programs using not only the image intensity data, but also new technologies of interferometric SAR (InSAR) and polarimetric SAR (PolSAR). In recent years, artificial intelligence (AI), such as deep learning, has also attracted much attention. In the present article, a review is given on the imaging processes and analyses of oceanic data using SAR, InSAR, PolSAR data and AI. The selected oceanic phenomena described here include ocean waves, internal waves, oil slicks, currents, bathymetry, ship detection and classification, wind, aquaculture, and sea ice. Full article

Satellite altimetric data routinely map sea surface topography by measuring the ocean return signal. One source of altimeter measurement contamination occurs when the radar ocean backscatter becomes unusually large, a situation termed a Sigma-0 bloom. Past research suggests Sigma-0 blooms are associated with weak wind and natural surface slick conditions where capillary waves at the air–sea interface are suppressed. To date, no explicit connection between these conditions and Sigma-0 bloom presence has been provided. Using a series of simplified equations, our reduced model determines capillary wave heights from estimates of planktonic carbon concentrations and regional wind speed. Our results suggest that the radar signal reflection increases as capillary wave height decreases. This relationship depends on surfactant concentration, surfactant composition, and wind speed. Model sensitivity analysis indicates that the interface reflectivity depends on biological activity and wind speed. Our proposed simplified model provides a method to identify potential Sigma-0 bloom regions. We conclude that because of the demonstrated impact of biological surfactants on ocean roughness, it is necessary to consider the biological activity, i.e., phytoplankton bloom events, when interpreting signals from radar altimetry and when developing ocean hydrology models. Full article

Synthetic Aperture Radar (SAR) is the primary equipment used to detect oil slicks on the ocean’s surface. On SAR images, oil spill regions, as well as other places impacted by atmospheric and oceanic phenomena such as rain cells, upwellings, and internal waves, appear as dark spots. Dark spot detection is typically the initial stage in the identification of oil spills. Because the identified dark spots are oil slick candidates, the quality of dark spot segmentation will eventually impact the accuracy of oil slick identification. Although certain sophisticated deep learning approaches employing pixels as primary processing units work well in remote sensing image semantic segmentation, finding some dark patches with weak boundaries and small regions from noisy SAR images remains a significant difficulty. In light of the foregoing, this paper proposes a dark spot detection method based on superpixels and deeper graph convolutional networks (SGDCNs), with superpixels serving as processing units. The contours of dark spots can be better detected after superpixel segmentation, and the noise in the SAR image can also be smoothed. Furthermore, features derived from superpixel regions are more robust than those derived from fixed pixel neighborhoods. Using the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm, we obtain an excellent subset of superpixel features for segmentation to reduce the learning task difficulty. After that, the SAR images are transformed into graphs with superpixels as nodes, which are fed into the deeper graph convolutional neural network for node classification. SGDCN leverages a differentiable aggregation function to aggregate the node and neighbor features to form more advanced features. To validate our method, we manually annotated six typical large-scale SAR images covering the Baltic Sea and constructed a dark spot detection dataset. The experimental results demonstrate that our proposed SGDCN is robust and effective compared with several competitive baselines. This dataset has been made publicly available along with this paper. Full article

Large volumes of crude oil accidentally released into the sea may cause irreversible adverse impacts on marine and coastal environments. Large swath optical imagery, acquired using platforms such as the moderate-resolution imaging spectroradiometer (MODIS), is frequently used for massive oil spill detection, attributing to its large coverage and short global revisit, providing rich data for oil spill monitoring. The aim of this study was to develop a suitable approach for massive oil spill detection in sun glint optical imagery. Specifically, preprocessing procedures were conducted to mitigate the inhomogeneous light field over the spilled area caused by sun glint, enhance the target boundary contrast, and maintain the internal homogeneity within the target. The image was then segmented into super-pixels based on a simple linear clustering method with similar characteristics of color, brightness, and texture. The neighborhood super-pixels were merged into target objects through the region adjacency graph method based on the Euclidean distance of their colors with an adaptive termination threshold. Oil slicks from the generated bright/dark objects were discriminated through a decision tree with parameters based on spectral and spatial characteristics. The proposed approach was applied to oil spill detection in MODIS images acquired during the Montara oil spill in 2009, with an overall extraction precision of 0.8, recall of 0.838, and F1-score of 0.818. Such an approach is expected to provide timely and accurate oil spill detection for disaster emergency response and ecological impact assessment. 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

In recent years, marine oil spills have adversely affected the marine economy and ecosystem, and the detection of marine oil slicks has attracted great attention. Combining different polarimetric features for better oil spill detection is a topic that needs to be studied in depth. Previous studies have shown that the compact polarimetric (CP) synthetic aperture radar (SAR) can be effectively applied to the detection of sea surface oil spill due to its own ability, which is conducive to the extraction of sea surface oil slick. In this paper, we apply the power–entropy (PE) decomposition theory, which decomposes the total scattered power according to the entropy contribution of each cell in the response, to CP SAR data for oil spill detection. The purpose of this study is to enhance the oil slick and the separability of the sea. As a result, an oil spill detection method based on the low-entropy radiation amplitude parameter $l_{esa}$ is proposed. We compare $l_{esa}$ with the other five popular polarimetric features and validate by quantitative evaluation that $l_{esa}$ is superior to other types of polarization feature parameters under different band data. Moreover, the random forest classification is performed on the feature map and achieves the visualization results of oil spill detection. The experimental results show that the $l_{esa}$ can combine the information of the two polarimetric characteristic parameters of entropy and total scattering power, and can clearly indicate the oil slick information under different scenarios. Full article

Suppression of radar backscattering from the sea surface has been studied in field experiments with surfactant films carried out from an Oceanographic Platform on the Black Sea and from onboard a research vessel on the Gorky Water Reservoir using an X-C-S-band two co-polarized radar instrument. Bragg and non-polarized (non-Bragg) radar backscatter components, BC and NBC, respectively, were retrieved when measuring the radar backscatter at vertical (VV-) and horizontal (HH-) polarizations. New features of microwave backscattering from the sea surface have been revealed, including a non-monotonic dependence of radar backscatter suppression (contrasts) in slicks on azimuth angle and particularities of BC contrasts on radar wave number. Namely, it is demonstrated that the backscatter contrasts achieve maximum values at azimuth angles in between the upwind and crosswind radar look directions, and BC contrasts increase with radar wave number along the wind and decrease in the crosswind directions. The suppression of BC is discussed in the frame of Bragg’s theory of microwave scattering and of a simple model of the wind wave spectrum, while the suppression of NBC is considered associated with the micro-breaking of wind waves. The obtained new features of radar contrasts can be used for the identification and characterization of marine films. Full article