SAR observation and model tracking of an oil spill event in coastal waters

GSCAT-UNET: Enhanced U-Net model with spatial-channel attention gate and three-level attention for oil spill detection using SAR data

Marine pollution due to oil spills presents major risks to coastal areas and aquatic life, leading to serious environmental health concerns. Oil Spill detection using SAR data has transitioned from traditional segmentation to a variety of machine learning & deep learning models like UNET proving its efficiency for the task. This research paper proposes a GSCAT-UNET model for efficient oil spill detection and discrimination from lookalikes. The GSCAT-UNET is an advanced UNET architecture comprising of Spatial-Channel Attention Gates(SCAG), Three Level Attention Module(TLM) and Global Feature Module(GFM) for global level oil spill feature enhancement leading to effective oil spill detection and discrimination from lookalikes. Sentinel-1 Dual-Pol SAR dataset of 1112 images and respective labeled images (5 classes) including confirmed oil spills and lookalikes is used to demonstrate the efficacy of the GSCAT-UNET model. The GSCAT-UNET model significantly enhances segmentation accuracy and robustness for oil spill detection with 5% higher accuracy and 29% higher IoU i.e. 93.7% compared to the UNET segmentation model, addressing the challenges of SAR data complexities and imbalanced datasets. The strong performance of the GSCAT-UNET model demonstrates its potential as a critical tool for disaster response and environmental monitoring.

In situ growth of octa-phenyl polyhedral oligomeric silsesquioxane nanocages over fluorinated graphene nanosheets: super-wetting coatings for oil and organic sorption

Superhydrophobic surfaces offer significant advantages through their hierarchical micro/nanostructures, which create optimal surface roughness and low surface energy, making the development of robust surfaces essential for enhancing their physical and chemical stability. Here, we introduce in situ growth of octa-phenyl polyhedral oligomeric silsesquioxane (O-Ph-POSS) nanocages over multi-layered fluorinated graphene (FG) nanosheets through hydrolysis/condensation of phenyl triethoxysilane in an alkaline medium to produce a robust POSS-FG superhydrophobic hybrid. The efficient in situ growth of O-Ph-POSS nanocages over FG nanosheets was confirmed by FT-IR spectroscopy, PXRD, SEM, TEM, TG analysis, 29Si NMR spectroscopy, N2 adsorption-desorption isotherms and XP spectroscopy. The as-synthesized O-Ph-POSS over FG becomes superhydrophobic with a water contact angle (WCA) of 152 ± 2° and a surface free energy (SFE) of 5.6 mJ m-2. As a result of the superhydrophobic property and robust nature of the POSS nanocage, O-Ph-POSS over FG nanosheets revealed the absorption capability for oils/organic solvents ranging from 200 to 500 wt% and were applied to coat onto the polyurethane (PU) sponge to effectively separate various oils and organic solvents from water mixtures, achieving separation efficiencies between 90% and 99%. Importantly, O-Ph-POSS-FG@Sponge still retained a separation efficiency of over 95% even after 25 separation cycles for hexane spill in water. The sponge efficiently separates toluene and chloroform using a vacuum pump, achieving flux rates of up to 20 880 and 12 184 L m-2 h-1, respectively. Weather resistance tests of O-Ph-POSS-FG@Sponge, prepared at intervals of 1 week and 1 year, showed that aged samples retained similar WCA values to freshly prepared sponges, confirming their long-term durability and performance. Mechanical stability assessments indicated that O-Ph-POSS-FG@Sponge maintained superhydrophobic properties, with WCA values of 151 ± 2° for tape peeling and emery paper treatments and 150 ± 2° for knife cutting, highlighting its excellent stability under physical deformation. Additionally, leveraging the exceptional resistance of O-Ph-POSS, the superhydrophobic O-Ph-POSS-FG@Sponge exhibited excellent stability and durability, even under supercooled and hot conditions during oil/water separation. Optical microscopy analysis of O/W and W/O emulsions, both stabilized by a surfactant, revealed complete droplet separation, further confirming the O-Ph-POSS-FG@Sponge's effectiveness for emulsion separation applications. The present work provides a straightforward method for the large-scale production of robust, superhydrophobic materials suitable for cleaning up oil spills on water surfaces.

Mapping the shoreline risk assessment of oil spill in the eastern region of Qeshm channel

The northern shores of the Strait of Hormuz constitute one of the most diverse shorelines in the Persian Gulf, characterized by both utility and environmental richness. Situated in the Qeshm channel, which hosts the largest mangrove habitat, major industries, and commercial port, these shores are subject to the occurrence of oil spills, posing potential substantial harm. This study employs General NOAA Operational Modeling Environment (GNOME) and numerical modeling to assess the potential risks to shorelines from hypothetical oil spills, considering the distinctive features of the shores and their environmental sensitivity. The results indicate that high-risk levels are not excessively prevalent overall and are confined to approximately 24 km. The shape of the channel and hydrodynamic conditions highlight the eastern sector of the Bandar Abbas urban area as particularly susceptible to oil spill entrapment. The findings indicate that high-risk areas are predominantly located away from industrial-oil shores and primarily consist of muddy shores. Therefore, internal strategies of Oil Spill Contingency Plan (OSCP) of companies are deemed insufficient and necessitate comprehensive planning initiatives.

Emergency response to ecological protection in maritime phenol spills: Emergency monitor, ecological risk assessment, and reduction

Recently, hundreds of maritime accidental spills of hazardous chemicals have raised public concerns, especially for phenol due to its potential of spills and highly toxicity. Therefore, for marine ecological protection, this article prepared specific strategies of emergency response to phenol spills. Through the identification for phenol behavior at sea, migration prediction, emergency monitor, as well as their new methods were reviewed. Further, ecological risk assessment and seawater quality criteria were conducted by using a species sensitivity distribution (SSD) approach, wherein, risk quotient (RQ) indicated phenol of simulated marine spills posed a high risk (RQ > 1) in 30 days. The method with eco-friendliness and high-efficiency for phenol reduction was constructed by combination of dredging equipment such as pneumatic dredgers (Airlift) and bioremediation, where marine microorganisms that degraded phenol were summarized, as well as future research needs. This study provided a guidance for emergency response and policy development of phenol spills.

Modeling the Agia Zoni II tanker oil spill in Saronic Gulf, Greece

We employed GNOME to simulate the oil spill due to the sinking of the tanker "Agia Zoni ΙΙ" in September 2017 in Saronic Gulf. We performed simulations using various combinations of wind and current input, and values of the GNOME parameters, and compared the simulated oil spill trajectories with coastal pollution and satellite data. The best scenario, i.e., the combination that showed the most satisfactory agreement with field data, uses wind data from one of the closest meteorological stations, calculated currents by a hydrodynamic model and default values of the parameters, except for the windage and the refloat half-life whose proposed values are 3-4 % and 6 h, respectively. Neglecting the effect of the wind in the best scenario worsened the agreement. Mass balance results depicted that approximately 47 % of the total 500 tons of the oil spill ended up on the coastline of Attica peninsula and Salamina Island.

Combined oil spill modelling and shoreline sensitivity analysis for contingency planning in the Irish Sea

Offshore oil spills often result in severe environmental and socio-economic consequences. This work focuses on a busy, yet poorly studied part of NW Europe, the Irish Sea, to assess the impact of future oil spills on the nearby coast. By integrating numerical models and shoreline sensitivity analyses for two confined areas, Liverpool Bay and Milford Haven, this work acknowledges wind direction and speed as principal controls on the movement of oil under winter/storm conditions and in shallow waters. Ocean currents play a secondary role, but are significant in deeper waters and in low-wind summer conditions. The temporal elements used in the modelling thus stress that when the spill occurs is just as important as where. As a corollary, the fate of spilled oil is determined in this work for distinct scenarios and types. Response strategies are recommended to minimise the impact of future spills on coastal populations.

Development and testing of a 2D offshore oil spill modeling tool (OSMT) supported by an effective calibration method

An Oil Spill Modeling Tool (OSMT) has been developed in this study to predict the transport and fate of oil spills resulting from surface releases. Particularly, the Kullback-Leibler (KL) divergence method is adopted as a performance metric for the first time to formulate a calibration framework for spill trajectory prediction (STP) from the Lagrangian transport model (LTM). By finding the candidate with minimal KL divergences from modeling scenarios using designed parameter combinations, the prediction discrepancy between simulated trajectories of the LTM and oil slicks detected from satellite images is reduced. The developed approach has been evaluated through a comparison analysis between OSMT and Operational Oil Modeling Environment (GNOME) model. Subsequently, a real case study is conducted to examine the applicability and effectiveness of the OSMT. The study results indicate that OSMT can provide reliable spill trajectory simulations, and the KL divergence-based calibration method is effective in calibrating the oil spill LTM.

Advances in Asymmetric Wettable Janus Materials for Oil-Water Separation

The frequent occurrence of crude oil spills and the indiscriminate discharge of oily wastewater have caused serious environmental pollution. The existing separation methods have some defects and are not suitable for complex oil-water emulsions. Therefore, the efficient separation of complex oil-water emulsions has been of great interest to researchers. Asymmetric wettable Janus materials, which can efficiently separate complex oil-water emulsions, have attracted widespread attention. This comprehensive review systematically summarizes the research progress of asymmetric wettable Janus materials for oil-water separation in the last decade, and introduces, in detail, the preparation methods of them. Specifically, the latest research results of two-dimensional Janus materials, three-dimensional Janus materials, smart responsive Janus materials, and environmentally friendly Janus materials for oil-water separation are elaborated. Finally, ongoing challenges and outlook for the future research of asymmetric wettable Janus materials are presented.

Development of a novel method for estimating and planning automatic skimmer operation in response to offshore oil spills

Offshore oil production and transportation of oil by pipelines and tankers are associated with the risks of an oil spill, and accidents of various scales, from emissions of several liters to several thousand tons, occur regularly in different parts of the world. Currently, there are no automatic or automated systems for responding to such incidents, although special equipment exists that is able to collect oil from the surface of water. The oil collected by skimmers can be used for its intended purpose. The purpose of this research is to develop a novel method for estimating the number of skimmers required for automated oil recovery in the event of oil contamination in the open sea, taking into account errors in measured weather conditions and initial spill information. In this work, a program is developed to simulate the position and state of an oil slick on the water surface, based on realistic weather conditions, and the movement of a group of skimmers while performing the oil removal task. The results of the study demonstrate the robustness of the system with respect to errors in the initial data, weather condition, position and parameters of the oil spill. Two main emergencies are considered: an instantaneous release of oil from a tanker and continuous leakage from a damaged pipeline. The developed system detects and collects oil on the map in a limited time, even with a significant shift in the initial coordinates, and limits the spread of the oil slick where there is continuous leakage. In addition, the designed method has a short-term overestimation of the skimmer group size in case of time delay in the response to the spill. The developed method can be applied in real cases of oil spills to create and update the plan of movement and collection of oil for a group of skimmers.

Exploring the Potential of Optical Polarization Remote Sensing for Oil Spill Detection: A Case Study of Deepwater Horizon

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.

Marine Oil Pollution in an Area of High Economic Use: Statistical Analyses of SAR Data from the Western Java Sea

In this paper, we analyze more than 2000 Synthetic Aperture Radar (SAR) images of the Western Java Sea acquired by Sentinel-1 SAR-C and ENVISAT ASAR, with the aim to generate oil pollution statistics for a sea region of high economic use. The spatial distributions show that most oil pollution occurs along the major shipping routes and at oil production sites in that area. The majority of the spills have sizes of less than 1 km2 and an axial ratio smaller than 10. For two sets of SAR images, we compared the results obtained by different operators, who analyzed the same images. While more than 50% of the spills were not found by both operators, the overall spatial patterns derived from their results are the same. Our results indicate that the observed differences are mainly due to lookalikes, which can easily be confused with oil spills, but also due to small oil spills that were overseen by one of the operators. These assumptions are supported by the fact that the percentage of spills jointly found by both operators increased when only oil spills were considered that were found on SAR images acquired at higher mean wind speeds.

A novel lightweight bilateral segmentation network for detecting oil spills on the sea surface

Accidental oil spills from pipelines or tankers have posed a big threat to marine life and natural resources. This paper presents a novel lightweight bilateral segmentation network for detecting oil spills on the sea surface. A novel deep-learning semantic-segmentation algorithm is firstly created for analyzing the characteristics of oil spill images. A Bilateral Segmentation Network (BiSeNetV2) is then selected as the basic network architecture and evaluated by using experimental comparison of the current mainstream networks on detection accuracy and real-time performances for oil samples. Furthermore, the Gather-and-Expansion (GE) layer of the semantic branch in the traditional network is redesigned and the parameter complexity is reduced. A dual attention mechanism is deployed in the two branches of the BiSeNetV2 to solve the problem of inter-class similarity. Finally, experimental results are given to show the good detection accuracy of the proposed network.

Fate of MV Wakashio oil spill off Mauritius coast through modelling and remote sensing observations

This study aims at assessing the fate of MV Wakashio oil spill, and the driving forces responsible for possible environmental consequences of polluted coastal region. GNOME simulations were performed, considering various meteo-oceanographic forcings such as (i) winds and currents, (ii) only winds, and (iii) only winds with different diffusion coefficients, and validated with the satellite images. The results revealed that the simulations performed with 'only winds' reasonably match with the satellite observations, indicating that winds are the primary driving forces. The conducive stokes drift is an added contribution to the predominant northwestward drift of the spill. The oil budget analysis suggests that beaching and evaporation together accounted for a significant portion of the spilled oil (1000 tons), in which ~60% of the oil was accounted only for beaching. Our results depict that the diffusion coefficient of 100,000 cm²/s and 3% windages are optimal for oil-spill simulations off the southeastern Mauritius coast.

Bayesian identification of oil spill source parameters from image contours

Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.

SAR Oil Spill Detection System through Random Forest Classifiers

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.

Oil spill forecast assessment using Fractions Skill Score

In the event of an oil spill, emergency responders must quickly deploy cleanup and protection equipment using guidance provided by a forecast trajectory. Forecasting the location of the surface oil over time is standard practice; however, current performance metrics used for assessing the quality of the spill forecast lack both an appropriate numerical model accuracy score and specification of the expected spatial resolution limit for useful forecast information. This paper adapts the Fractions Skill Score method, commonly used in weather forecasting, to oil forecasting. A subset of satellite images and trajectory forecasts from the Deepwater Horizon oil spill are used as an example of the method.

Oil spill trajectory modelling and environmental vulnerability mapping using GNOME model and GIS

This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m³ (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill.

K. R. D, Shanmugan S. A superhydrophobic covalent zeolitic imidazolate framework-polyhedral oligomeric silsesquioxane hybrid material as a highly efficient and reusable sorbent for organic solvents

A robust, fluorine-free, superhydrophobic ZIF-POSS hybrid material is prepared by a post-covalent reaction between ZIF-90 and POSS-NH₂ via imine bond formation. The ZIF-POSS material is highly effective and reusable sorbent for organic solvents.

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

Deep-learning-based information mining from ocean remote-sensing imagery

With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning-a powerful technology recently emerging in the machine-learning field-has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.

A Self-Cleaning Heterostructured Membrane for Efficient Oil-in-Water Emulsion Separation with Stable Flux

Lack of clean water is a major global challenge. Membrane separation technology is an ideal choice for the treatment of industrial, domestic sewage owing to its low energy consumption and cost. However, membranes are highly susceptible to contamination, particularly during wastewater treatment, which has limited their practical applications in this field. Similarly, the flux of the membrane decreases with prolonged use due to its reduced interlayer spacing. Preparation of membranes with anticontamination properties and stable flux is the key to addressing this problem. In this study, a 2D heterostructure membrane with visible-light-driven self-cleaning performance is prepared via a self-assembly process. Notably, the addition of palygorskite increases the interlayer spacing of the graphene and heterojunction structures, which increases the flux of the membrane and avoids a decrease of the interlayer spacing of the membrane under pressure. The presence of a heterojunction with visible light catalytic properties effectively avoids membrane fouling and avoids a sharp decrease of the permeation flux. Importantly, the prepared 2D membrane has excellent separation performance for oil-water emulsions with both high flux and efficiency. These features suggest great potential for the prepared 2D membrane in wastewater treatment applications.

Use of chemical concentration changes in coastal sediments to compute oil exposure dates

Oil spills can result in changes in chemical contaminant concentrations along coastlines. When concentrations are measured along the Gulf of Mexico over time, this information can be used to evaluate oil spill shoreline exposure dates. The objective of this research was to identify more accurate oil exposure dates based on oil spill chemical concentrations changes (CCC) within sediments in coastal zones after oil spills. The results could be used to help improve oil transport models and to improve estimates of oil landings within the nearshore. The CCC method was based on separating the target coastal zone into segments and then documenting the timing of large increases in concentration for specific oil spill chemicals (OSCs) within each segment. The dataset from the Deepwater Horizon (DWH) oil spill was used to illustrate the application of the method. Some differences in exposure dates were observed between the CCC method and between oil spill trajectories. Differences may have been caused by mixing at the freshwater and sea water interface, nearshore circulation features, and the possible influence of submerged oil that is unaccounted for by oil spill trajectories. Overall, this research highlights the benefit of using an integrated approach to confirm the timing of shoreline exposure.

Self-Healing Graphene-Reinforced Composite for Highly Efficient Oil/Water Separation

One of the most pervasive environmental problems is oily sewage; emerging materials are needed that could effectively solve this global challenge. Special wetting materials typically combine micro/nanoscale hierarchical structures with a low surface energy, which could produce superhydrophobic performance and these superhydrophobic materials are very important for a wide variety of applications, including self-cleaning and antiadhesives. However, the majority of these manmade materials still suffer from poor durability, which seriously hinders their practical applications. A better choice is that use of supramolecular materials with self-healing ability, which could provide an efficient method to solve materials poor durability problem. However, lightweight materials with special wettbility and self-healing still remain a challenge. In this work, we confine polyborosiloxane (PBS) in an ultralight graphene network to form a robust, special function graphene foam that has the ability to self-repair. Hydroxyl terminated poly(dimethylsiloxane) and boric acid as the as raw material were used to synthesis PBS at room temperature. The as-prepared composite network could be compressed and their properties fully restored without an external stimulus after being subjected to repeated damage. In addition, the prepared composite foam retains the porosity of the original graphene foam. The present work suggests encouraging applications of the self-healing graphene/PBS foam in water/organic solvent separations.

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.

Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.

Forecasting of oil-spill trajectories by using SCHISM and X-band radar

In this study, we propose a two-step strategy for tracking oil-spill trajectories. First, an X-band radar is established to monitor oil spills. Accordingly, we propose a radar image-processing technique for identifying the oil slicks from the nautical radar images. Second, we apply the SCHISM to determine the water surface elevations and currents at the event site and obtain the trajectories of the oil slicks using a Lagrangian particle-tracking method incorporated in the SCHISM. An oil-spill event caused by the container ship T. S. Taipei is used as a case study for testing the capability of the proposed oil-tracking strategy. The SCHISM simulation results for the fouled coastline obtained using the wind data from a nearby data buoy agree quite well with those obtained from field observations. However, the predicted fouled coastline based on the forecasted wind data is unsatisfactory. The reasons for the unsatisfactory prediction are discussed and revealed.

Simulating Fine-Scale Marine Pollution Plumes for Autonomous Robotic Environmental Monitoring

Marine plumes exhibit characteristics such as intermittency, sinuous structure, shape and flow field coherency, and a time varying concentration profile. Due to the lack of experimental quantification of these characteristics for marine plumes, existing work often assumes marine plumes exhibit behavior similar to aerial plumes and are commonly modeled by filament based Lagrangian models. Our previous field experiments with Rhodamine dye plumes at Makai Research Pier at Oahu, Hawaii revealed that marine plumes show similar characteristics to aerial plumes qualitatively, but quantitatively they are disparate. Based on the field data collected, this paper presents a calibrated Eulerian plume model that exhibits the qualitative and quantitative characteristics exhibited by experimentally generated marine plumes. We propose a modified model with an intermittent source, and implement it in a Robot Operating System (ROS) based simulator. Concentration time series of stationary sampling points and dynamic sampling points across cross-sections and plume fronts are collected and analyzed for statistical parameters of the simulated plume. These parameters are then compared with statistical parameters from experimentally generated plumes. The comparison validates that the simulated plumes exhibit fine-scale qualitative and quantitative characteristics similar to experimental plumes. The ROS plume simulator facilitates future evaluations of environmental monitoring strategies by marine robots, and is made available for community use.

A new method to calibrate Lagrangian model with ASAR images for oil slick trajectory

Since Lagrangian model coefficients vary with different conditions, it is necessary to calibrate the model to obtain optimal coefficient combination for special oil spill accident. This paper focuses on proposing a new method to calibrate Lagrangian model with time series of Envisat ASAR images. Oil slicks extracted from time series images form a detected trajectory of special oil slick. Lagrangian model is calibrated by minimizing the difference between simulated trajectory and detected trajectory. mean center position distance difference (MCPD) and rotation difference (RD) of Oil slicks' or particles' standard deviational ellipses (SDEs) are calculated as two evaluations. The two parameters are taken to evaluate the performance of Lagrangian transport model with different coefficient combinations. This method is applied to Penglai 19-3 oil spill accident. The simulation result with calibrated model agrees well with related satellite observations. It is suggested the new method is effective to calibrate Lagrangian model.

A dynamical systems perspective for a real-time response to a marine oil spill

This paper discusses the combined use of tools from dynamical systems theory and remote sensing techniques and shows how they are effective instruments which may greatly contribute to the decision making protocols of the emergency services for the real-time management of oil spills. This work presents the successful interplay of these techniques for a recent situation, the sinking of the Oleg Naydenov fishing ship that took place in Spain, close to the Canary Islands, in April 2015.

Temporal variability of marine debris deposition at Tern Island in the Northwestern Hawaiian Islands

A twenty-two year record of marine debris collected on Tern Island is used to characterize the temporal variability of debris deposition at a coral atoll in the Northwestern Hawaiian Islands. Debris deposition tends to be episodic, without a significant relationship to local forcing processes associated with winds, sea level, waves, and proximity to the Subtropical Convergence Zone. The General NOAA Operational Modeling Environment is used to estimate likely debris pathways for Tern Island. The majority of modeled arrivals come from the northeast following prevailing trade winds and surface currents, with trajectories indicating the importance of the convergence zone, or garbage patch, in the North Pacific High region. Although debris deposition does not generally exhibit a significant seasonal cycle, some debris types contain considerable 3 cycle/yr variability that is coherent with wind and surface pressure over a broad region north of Tern.

Mussel-inspired one-step copolymerization to engineer hierarchically structured surface with superhydrophobic properties for removing oil from water

In the present study, a superhydrophobic polyurethane (PU) sponge with hierarchically structured surface, which exhibits excellent performance in absorbing oils/organic solvents, was fabricated for the first time through mussel-inspired one-step copolymerization approach. Specifically, dopamine (a small molecular bioadhesive) and n-dodecylthiol were copolymerized in an alkaline aqueous solution to generate polydopamine (PDA) nanoaggregates with n-dodecylthiol motifs on the surface of the PU sponge skeletons. Then, the superhydrophobic sponge that comprised a hierarchical structured surface similar to the chemical/topological structures of lotus leaf was fabricated. The topological structures, surface wettability, and mechanical property of the sponge were characterized by scanning electron microscopy, contact angle experiments, and compression test. Just as a result of the highly porous structure, superhydrophobic property and strong mechanical stability, this sponge exhibited desirable absorption capability of oils/organic solvents (weight gains ranging from 2494% to 8670%), suggesting a promising sorbents for the removal of oily pollutants from water. Furthermore, thanks to the nonutilization of the complicated processes or sophisticated equipment, the fabrication of the superhydrophobic sponge seemed to be quite easy to scale up. All these merits make the sponge a competitive candidate when compared to the conventional absorbents, for example, nonwoven polypropylene fabric.

Ultrafast oleophobic-hydrophilic switching surfaces for antifogging, self-cleaning, and oil-water separation

Smooth copolymer-fluorosurfactant complex film surfaces are found to exhibit fast oleophobic-hydrophilic switching behavior. Equilibration of the high oil contact angle (hexadecane = 80°) and low water contact angle (<10°) values occurs within 10 s of droplet impact. These optically transparent surfaces display excellent antifogging and self-cleaning properties. The magnitude of oleophobic-hydrophilic switching can be further enhanced by the incorporation of surface roughness to an extent that it reaches a sufficiently high level (water contact angle <10° and hexadecane contact angle >110°), which, when combined with the inherent ultrafast switching speed, yields oil-water mixture separation efficiencies exceeding 98%.

Satellite observations and modeling of oil spill trajectories in the Bohai Sea

On June 4 and 17, 2011, separate oil spill accidents occurred at two oil platforms in the Bohai Sea, China. The oil spills were subsequently observed on different types of satellite images including SAR (Synthetic Aperture Radar), Chinese HJ-1-B CCD and NASA MODIS. To illustrate the fate of the oil spills, we performed two numerical simulations to simulate the trajectories of the oil spills with the GNOME (General NOAA Operational Modeling Environment) model. For the first time, we drive the GNOME with currents obtained from an operational ocean model (NCOM, Navy Coastal Ocean Model) and surface winds from operational scatterometer measurements (ASCAT, the Advanced Scatterometer). Both data sets are freely and openly available. The initial oil spill location inputs to the model are based on the detected oil spill locations from the SAR images acquired on June 11 and 14. Three oil slicks are tracked simultaneously and our results show good agreement between model simulations and subsequent satellite observations in the semi-enclosed shallow sea. Moreover, GNOME simulation shows that the number of 'splots', which denotes the extent of spilled oil, is a vital factor for GNOME running stability when the number is less than 500. Therefore, oil spill area information obtained from satellite sensors, especially SAR, is an important factor for setting up the initial model conditions.

Efficient tools for marine operational forecast and oil spill tracking

Ocean forecasting and oil spill modelling and tracking are complex activities requiring specialised institutions. In this work we present a lighter solution based on the Operational Ocean Forecast Python Engine (OOFε) and the oil spill model General NOAA Operational Modelling Environment (GNOME). These two are robust relocatable and simple to implement and maintain. Implementations of the operational engine in three different regions with distinct oceanic systems, using the ocean model Regional Ocean Modelling System (ROMS), are described, namely the Galician region, the southeastern Brazilian waters and the Texas-Louisiana shelf. GNOME was able to simulate the fate of the Prestige oil spill (Galicia) and compared well with observations of the Krimsk accident (Texas). Scenarios of hypothetical spills in Campos Basin (Brazil) are illustrated, evidencing the sensitiveness to the dynamical system. OOFε and GNOME are proved to be valuable, efficient and low cost tools and can be seen as an intermediate stage towards more complex operational implementations of ocean forecasting and oil spill modelling strategies.

The oil spill model OILTRANS and its application to the Celtic Sea

This paper describes details of an oil spill model, OILTRANS, developed by the authors. The model is an off-line particle-transport model coupled to the most up to date operational met-ocean model forecasts. Formulations for the dominant oil fate processes of spreading, advection, diffusion, evaporation, emulsification and dispersion have been encoded, providing the model with the ability to accurately predict the horizontal movement of surface oil slick, the vertical entrainment of oil into the water column and the mass balance of spilled oil. The application of the OILTRANS model to an accidental release during a ship-to-ship fuel transfer in the Celtic Sea in February 2009 is presented to validate the system. Comparisons with aerial observations of the oil slick at the time of the incident, and subsequent model simulations, indicate that the OILTRANS model is capable of accurately predicting the transport and fate of the oil slick.

Oil spill detection with fully polarimetric UAVSAR data

In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle - synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case.