Modelling oil plumes from subsurface spills

A marine oil spill detection framework considering special disturbances using Sentinel-1 data in the Suez Canal

The Suez Canal is a crucial international waterway due to its strategic location. The significant traffic flow not only stimulates economic development along the coast but also leads to a high frequency of oil spill accidents, which negatively impact the ecosystem and natural resources. Synthetic aperture radar (SAR) is an important remote sensing technology for monitoring oil spills, offering all-day and all-weather capabilities. However, special disturbances (SD) caused by imaging conditions, sensor parameters, and other factors can affect image quality, reducing the accuracy and efficiency of oil spill detection. To mitigate the negative impact of SD, an original oil spill detection framework was developed, based on the analysis of these disturbances, to detect oil spills at the northern entrance of the Suez Canal from 2015 to 2019. The framework included an advantageous featureset with SD adaptability and designs a classifier, Boosting Random Support Vector Machine (BRSVM), which combines a boosting strategy with Support Vector Machine (SVM). The study found that the superiority of the featureset was pivotal in oil spill detection. The classification accuracy and F-1 score achieved by BRSVM were 94.72 % and 95.33 %, respectively, outperforming other algorithms in functionality. These results indicate that the proposed framework holds significant potential for applications requiring large-scale, automated oil spill detection.

Evaluation of factors causing lateral migration of light non-aqueous phase liquids (LNAPLs) in onshore oil spill accidents

Contamination of groundwater by harmful substances poses significant risks to both drinking water sources and aquatic ecosystems, making it a critical environmental concern. Most on-land spill events release organic molecules known as light non-aqueous phase liquids (LNAPLs), which then seep into the ground. Due to their low density and organic composition, they tend to float as they reach the water table. LNAPLs encompass a wide range of non-aqueous phase liquids, including various petroleum products, and can, over time, develop carcinogenic chemicals in water. However, due to frequent changes in hydraulic head, the confinement may fail to contain them, causing them to extend outward. When it contaminates water wells, people cannot reliably consume the water. The removal of dangerous contaminants from groundwater aquifers is made more challenging by LNAPLs. It is imperative to analyze the mechanisms governing LNAPL migration. As a response to this need and the associated dispersion of contaminants into adjacent aquifers, we have conducted a comprehensive qualitative literature review encompassing the years 2000-2022. Groundwater variability, soil structure, and precipitation have been identified as the three primary influential factors, ranked in the following order of significance. The rate of migration is shown to rise dramatically in response to changes in groundwater levels. Different saturation zones and confinement have a major effect on the lateral migration velocity. When the various saturation zones reach a balance, LNAPLs will stop moving. Although higher confinement slows the rate of lateral migration, it speeds up vertical migration. Beyond this, the lateral or vertical movement is also influenced by differences in the permeability of soil strata. Reduced mobility and tighter containment are the outcomes of migrating through fine-grained, low-porosity sand. The gaseous and liquid phases of LNAPLs move more quickly through coarse-grained soils. Due to the complexities and uncertainties associated with LNAPL behavior, accurately foreseeing the future spread of LNAPLs can be challenging. Although studies have utilized modeling techniques to simulate and predict LNAPL migration, the inherent complexities and uncertainties in the subsurface environment make it difficult to precisely predict the extent of LNAPL spread in the future. The granular soil structure considerably affects the porosity and pore pressure.

Formation and sedimentation of oil-mineral aggregates in the presence of chemical dispersant

The formation and sedimentation of oil-mineral aggregates (OMAs) is the major method to transport spilled oil to the seafloor. In this study, the formation and sedimentation experiments of OMA using montmorillonite and four crude oils were performed in a wave tank in the presence of chemical dispersant. Most of the formed OMAs were droplet OMAs, and single droplet OMA would aggregate into multiple ones under the action of the dispersant. The size of the oil droplets trapped in the OMA increased with time and was larger for the oil with higher viscosity. The sinking velocities of OMAs formed in this study were between 100-1200 μm s-1 and they were positively correlated with their diameter. The density of OMA was of the same order as that of the crude oil that formed them. An increase in the dispersant dosage could promote the formation of OMAs. The oil content in OMAs was higher for the denser oil in the presence of a dispersant. The maximum oil trapping efficiency of OMAs was 48.05%. This study provides fundamental data on the formation kinetics of OMAs.

Oil spill detection on X-band marine radar images based on sea clutter fitting model

Oil spills could cause great harm to the natural environment. The ability to identify them accurately is critical for prompt response and treatment. We proposed a sea clutter fitting model of marine radar images for oil spill detection. The model is derived from the geometric structure of the marine radar, the expression of marine radar received power, and the rough surface scattering model of the sea surface. In the denoised marine radar image, the sea clutter fitting model is used to detect coarse oil spills. Then the fine measurement is carried out by mean filter, the Otsu method, and noise reduction. The proposed oil spill detection method was used on radar images sampled after an oil spill accident happened in a coastal region in Dalian, China, on July 21, 2010. The proposed method can detect oil spills without human intervention, and the extracted oil spills are accurate and consistent with visual interpretation.

Combined effects of chemical dispersant and suspended minerals on the dispersion process of spilled oil

The dispersion process of spilled oil is an important concern for the effective disposal of oil spills. The dispersed oil concentration and oil droplets size distribution were studied through a wave tank test under the application of chemical dispersant and suspended minerals. The results indicated that dispersant and minerals increased the dispersed oil concentration and the effect of dispersant was more significant, and they had a synergistic effect on oil dispersion. When dispersant and minerals were applied together, the volume mean diameter of oil droplets decreased in the first 30 min, then increased and reached a maximum value at 90-120 min, and decreased again. Moreover, suspended minerals could inhibit the coalescence of oil droplets. This study can afford data support for oil spill emergency response that occurs in inshore or estuaries.

Catalyst development for tar reduction in biomass gasification: Recent progress and the way forward

Biomass valorization via catalytic gasification is a potential technology for commercizalization to industrial scale. However, the generated tar during biomass valorization posing numerous problems to the overall reaction process. Thus, catalytic tar removal via reforming, cracking and allied processes was among the priority areas to researchers in the recent decades. This paper reports new updates on the areas of catalyst development for tar reduction. The catalyst survey include metallic and metal-promoted materials, nano-structured systems, mesoporous supports like zeolites and oxides, group IA and IIA compounds and natural catalysts based on dolomite, palygorskite, olivine, ilmenite, goethite and their modified derivatives. The influence of catalyst properties and parameters such as reaction conditions, catalyst preparation procedures and feedstock nature on the overall activity/selectivity/stability properties were simultaneously discussed. This paper not only cover to model compounds, but also explore to real biomass-derived tar for consistency. The area that require further investigation was identified in the last part of this review.

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources. These models may range from simple parametric calculations to advanced, new-generation, operational, three-dimensional numerical models, coupled to meteorological, hydrodynamic, and wave models, forecasting in high-resolution and with high precision the transport and fate of oil. This study presents a review of the transport and oil weathering processes and their parameterization and critically examines eighteen state-of-the-art oil spill models in terms of their capacity (a) to simulate these processes, (b) to consider oil released from surface or submerged sources, (c) to assimilate real-time field data for model initiation and forcing, and (d) to assess uncertainty in the produced predictions. Based on our review, the most common oil weathering processes involved are spreading, advection, diffusion, evaporation, emulsification, and dispersion. The majority of existing oil spill models do not consider significant physical processes, such as oil dissolution, photo-oxidation, biodegradation, and vertical mixing. Moreover, timely response to oil spills is lacking in the new generation of oil spill models. Further improvements in oil spill modeling should emphasize more comprehensive parametrization of oil dissolution, biodegradation, entrainment, and prediction of oil particles size distribution following wave action and well blow outs.

Modelling oil trajectories and potentially contaminated areas from the Sanchi oil spill

Oil spills are major threats to marine ecosystems. Here, we establish a three-dimensional oil spill model to simulate and project the short- and long-term trajectories of oil slicks and oil-contaminated water that leaked from the Sanchi wreckage. The pollution probability in surrounding areas for the period up to 180 days after the Sanchi sank is statistically analysed. The short-term simulations are consistent with synchronous SAR images and observational reports. The potentially polluted areas depend on the properties of the released oil. The coastal areas most likely to be affected by the bunker oil are located in the Ryukyu Island Chain, Tsushima Strait, on the south and east coasts of Japan. Approximately 50% to 70% of oil particles remain in the ocean and mainly expand along the Ryukyu Island Chain and the region southeast of the Sanchi wreck. Subsurface oil-contaminated water is likely to enter the Sea of Japan along the Tsushima Strait. Due to the rapid evaporation rate of condensate oil, the potentially polluted area is confined to regions within a 100 × 100 km area around the location of the shipwreck, and the contaminated region is closely associated with the surface wind.

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

Environmental Decision Support Systems for Monitoring Small Scale Oil Spills: Existing Solutions, Best Practices and Current Challenges

In recent years, large oil spills have received widespread media attention, while small and micro oil spills are usually only acknowledged by the authorities and local citizens who are directly or indirectly affected by these pollution events. However, small oil spills represent the vast majority of oil pollution events. In this paper, multiple oil spill typologies are introduced, and existing frameworks and methods used as best practices for facing them are reviewed and discussed. Specific tools based on information and communication technologies are then presented, considering in particular those which can be used as integrated frameworks for the specific challenges of the environmental monitoring of smaller oil spills. Finally, a prototype case study actually designed and implemented for the management of existing monitoring resources is reported. This case study helps improve the discussion over the actual challenges of early detection and support to the responsible parties and stakeholders in charge of intervention and remediation operations.

Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: an assessment of the environmental impacts

Because of the environmental sensitivity of the Black Sea, as a semi-enclosed sea, any subsea oil spill can cause destructive impacts on the marine environment and beaches. Employing numerical modeling as a prediction tool is one of the most efficient methods to understand oil spill behavior under various environmental forces. In this regard, a coupled circulation/deepsea oil spill model has been applied to the Black Sea to address the behavior of the oil plume resulting from a representative hypothetical deepwater blowout. With climatological forcing, the hydrodynamic module based on DieCAST ocean circulation model realistically reproduces seasonally-varying circulation from basin-scale dominant structures to meso- and sub-mesoscale elements. The oil spill model utilizes pre-calculated DieCAST thermo-hydrodynamic fields and uses a Lagrangian tracking algorithm for predicting the displacement of a large number of seeded oil droplets, the sum of which forms the rising oil plume resulting from a deepwater blowout. Basic processes affecting the transport, dispersal of oil and its fate in the water column are included in the coupled model. A hypothetical oil source was set at the bottom, at the northwestern edge of the Shatsky Ridge in the area east of the Crimea Peninsula where the oil exploration/development is likely to be planned. Goals of the study are to elucidate the behavior of the subsea oil plume and assess scales of contamination of marine environment and coastlines resulting from potential blowouts. The two 20-day scenarios with the oil released by a hypothetical blowout were examined to reveal combined effects of the basin-scale current, near-shore eddies, and winds on the behavior of the rising oil plume and its spreading on the surface. Special attention is paid to the Caucasian near-shore anticyclonic eddy which is able to trap surfacing oil, detain it and deliver it to shores. The length of contaminated coastlines of vulnerable Crimean and Caucasian coasts are assessed along with amounts of oil beached and deposited.

Design and Implementation of a Coastal-Mounted Sensor for Oil Film Detection on Seawater

The routine surveillance of oil spills in major ports is important. However, existing techniques and sensors are unable to trace oil and micron-thin oil films on the surface of seawater. Therefore, we designed and studied a coastal-mounted sensor, using ultraviolet-induced fluorescence and fluorescence-filter systems (FFSs), to monitor oil spills and overcome the disadvantages of traditional surveillance systems. Using seawater from the port of Lingshui (Yellow Sea, China) and six oil samples of different types, we found that diesel oil’s relative fluorescence intensity (RFI) was significantly higher than those of heavy fuel and crude oils in the 180–300 nm range—in the 300–400 nm range, the RFI value of diesel is far lower. The heavy fuel and crude oils exhibited an opposite trend in their fluorescence spectra. A photomultiplier tube, employed as the fluorescence detection unit, efficiently monitored different oils on seawater in field experiments. On-site tests indicated that this sensor system could be used as a coastal-mounted early-warning detection system for oil spills.

Adaptive Enhancement of X-Band Marine Radar Imagery to Detect Oil Spill Segments

Oil spills generate a large cost in environmental and economic terms. Their identification plays an important role in oil-spill response. We propose an oil spill detection method with improved adaptive enhancement on X-band marine radar systems. The radar images used in this paper were acquired on 21 July 2010, from the teaching-training ship “YUKUN” of the Dalian Maritime University. According to the shape characteristic of co-channel interference, two convolutional filters are used to detect the location of the interference, followed by a mean filter to erase the interference. Small objects, such as bright speckles, are taken as a mask in the radar image and improved by the Fields-of-Experts model. The region marked by strong reflected signals from the sea’s surface is selected to identify oil spills. The selected region is subject to improved adaptive enhancement designed based on features of radar images. With the proposed adaptive enhancement technique, calculated oil spill detection is comparable to visual interpretation in accuracy.