Improve Oil Spill Research Articles

Understanding the impact of marine oil spill on corporate performance for improving oil spill claims system

Marine oil spill disasters occur frequently and have serious economic impacts on coastal activities. Taking the Penglai 19–3 oil spill (PL 19–3 oil spill) event as an example, this paper employs a difference-in-difference estimation method to examine the impact of marine oil spill on corporate performance. The main results show that the PL19–3 oil spill reduces the net revenue of marine enterprises relying on the Bohai Sea and coastal resources, as high as 102.8 %. In the aftermath of the PL19–3 oil spill, there was a fluctuating and increasing trend of the negative impacts on the net profits of marine companies, indicating the long-term persistence of negative impacts. Furthermore, heterogeneity analysis reveals that the marine secondary industry is more likely to be affected by the PL19–3 oil spill than the marine primary and tertiary industries. The marine tertiary industry is the least affected. Oil spill disasters are also found to reveal great influences on non-state-owned and young enterprises. This study provides a reference for improving the scope of oil spill claimants and is conducive to further improving the mechanism for claiming compensation for damage caused by marine oil spills.

Polycyclic aromatic hydrocarbon (PAH) source identification and a maternal transfer case study in threatened killer whales (Orcinus orca) of British Columbia, Canada

The northeastern Pacific (NEP) Ocean spans the coast of British Columbia (Canada) and is impacted by anthropogenic activities including oil pipeline developments, maritime fossil fuel tanker traffic, industrial chemical effluents, agricultural and urban emissions in tandem with stormwater and wastewater discharges, and forest wildfires. Such events may expose surrounding marine environments to toxic polycyclic aromatic hydrocarbons (PAHs) and impact critical habitats of threatened killer whales (Orcinus orca). We analyzed skeletal muscle and liver samples from stranded Bigg’s killer whales and endangered Southern Resident killer whales (SRKWs) for PAH contamination using LRMS. C3-phenanthrenes/anthracenes (mean: 632 ng/g lw), C4-dibenzothiophenes (mean: 334 ng/g lw), and C4-phenanthrenes/anthracenes (mean: 248 ng/g lw) presented the highest concentrations across all tissue samples. Diagnostic ratios indicated petrogenic-sourced contamination for SRKWs and pyrogenic-sourced burdens for Bigg’s killer whales; differences between ecotypes may be attributed to habitat range, prey selection, and metabolism. A mother-fetus skeletal muscle pair provided evidence of PAH maternal transfer; low molecular weight compounds C3-fluorenes, dibenzothiophene, and naphthalene showed efficient and preferential exposure to the fetus. This indicates in-utero exposure of PAH-contamination to the fetus. Our results show that hydrocarbon-related anthropogenic activities are negatively impacting these top predators; preliminary data found here can be used to improve oil spill and other PAH pollution management and regulation efforts, and inform policy to conserve killer whale habitats in the NEP.

A mixed integer programming approach to improve oil spill response resource allocation in the Canadian arctic

Determining proper locations to establish emergency response facilities is a critical strategic element of pollution preparedness and response planning for oil spills in remote areas. Many location-allocation models are available in the literature, but Arctic contexts such as remoteness and environmental sensitivities are still inadequately investigated while building optimization models. A Mixed Integer Programming (MIP) based optimization model is developed to devise a location-allocation problem: maximizing weighted spill coverage considering spill size, environmental sensitivity, and response time. Strategic decisions - e.g. allocation of stockpiling resources to resource stations and which response stations to open - are incorporated into the model as decision variables. Input parameters of the model are estimated using numerical and geospatial data of potential oil spills and response stations. The model is illustrated for hypothetical oil spill scenarios in the Canadian Arctic. The model provides optimal allocation of resources and recommends best-suited locations to build response facilities. Data visualization tools including Network Diagrams and sensitivity analysis on different model configurations, show the adequacy of the proposed mathematical modelling approach to solve the given problem. Multiple facility locations have been compared to cover all possible oil spills along Arctic shipping routes, further revealing a few better locations considering realistic constraints. Decision makers can use such optimization modelling information – e.g., how many stations to build in the Arctic to adequately cover potential oil spills – to aid strategic decision-making of maritime shipping.

Do oil droplets and chemical dispersants contribute to uptake of oil compounds and toxicity of crude oil dispersions in cold-water copepods?

ABSTRACT Accidental crude oil spills to the marine environment cause dispersion of oil into the water column through the actions of breaking waves, a process that can be facilitated using chemical dispersants. Oil dispersions contain dispersed micron-sized oil droplets and dissolved oil components, and the toxicity of oil dispersions has been assumed to be associated primarily with the latter. However, most hydrophobic, bioaccumulative and toxic crude oil components are retained within the droplets which may interact with marine filter-feeders. We here summarize the findings of 15 years of research using a unique methodology to generate controlled concentrations and droplet size distributions of dispersed crude oil to study effects on the filter-feeding cold-water copepod Calanus finmarchicus. We focus primarily on the contribution of chemical dispersants and micron-sized oil droplets to uptake and toxicity of oil compounds. Oil dispersion exposures cause PAH uptake and oil droplet accumulation on copepod body surfaces and inside their gastrointestinal tract, and exposures to high exposure (mg/L range) reduce feeding activity, causes reproductive impairments and mortality. These effects were slightly higher in the presence of chemical dispersants, possibly due to higher filtration of chemically dispersed droplets. For C. finmarchicus, dispersions containing oil droplets caused more severe toxic effects than filtered dispersions, thus, oil droplets contribute to the observed toxicity. The methodology for generating crude oil dispersion is a valuable tool to isolate impacts of crude oil microdroplets and can facilitate future research on oil dispersion toxicity and produce data to improve oil spill models.

Advances to the CROSERF protocol to improve oil spill response decision making

The Chemical Response to Oil Spills: Ecological Effects Research Forum (CROSERF) created a standardized protocol for comparing the toxicity of physically dispersed oil versus chemically dispersed oil to address environmental concerns related to the proposed use of dispersants in the early 2000s. Since then, many revisions have been made to the original protocol to diversify the intended use of the data generated, incorporate emerging technologies, and to examine a wider range of oil types including non-conventional oils and fuels. Under the Multi-Partner Research Initiative (MPRI) for oil spill research under Canada's Oceans Protection Plan (OPP), a network of 45 participants from seven countries representing government, industry, non-profit, private, and academic sectors was established to identify the current state of the science and formulate a series of recommendations to modernize the oil toxicity testing framework. The participants formed a series of working groups, targeting specific aspects of oil toxicity testing, including: experimental conduct; media preparation; phototoxicity; analytical chemistry; reporting and communicating results; interpreting toxicity data; and appropriate integration of toxicity data to improve oil spill effects models. The network participants reached a consensus that a modernized protocol to assess the aquatic toxicity of oil should be sufficiently flexible to address a broad range of research questions in a ‘fit-for-purpose’ manner, where methods and approaches are driven by the need to generate scientifically-defensible data to address specific study objectives. Considering the many needs and varied objectives of aquatic toxicity tests currently being conducted to support and inform oil spill response decision making, it was also concluded that the development of a one size fits all approach would not be feasible.

A Principal Component Analysis Methodology of Oil Spill Detection and Monitoring Using Satellite Remote Sensing Sensors

Monitoring, assessing, and measuring oil spills is essential in protecting the marine environment and in efforts to clean oil spills. One of the most recent oil spills happened near Port Fourchon, Louisiana, caused by Hurricane Ida (Category 4), that had a wind speed of 240 km/h. In this regard, Earth Observation (EO) Satellite Remote Sensing (SRS) images can effectively highlight oil spills in marine areas as a “fast and no-cost” technique. However, clouds and the sea surface spectral signature complicate the interpretation of oil spill areas in the optical images. In this study, Principal Component Analysis (PCA) has been applied of Landsat-8 and Sentinel-2 SRS images to improve information from the optical sensor bands. The PCA produces an output unrelated to the main bands, making it easier to distinguish oil spills from clouds and seawater due to the spectral diversity between oil, clouds, and the seawater surface. Then, an additional step has been applied to highlight the oil spill area using PCAs with different band combinations. Furthermore, Sentinel-1 (SAR), Sentinel-2 (optical), and Landsat-8 (optical) SRS images have been analyzed with cross-sections to suppress the “look-alike” effect of marine oil spill areas. Finally, mean and high-pass filters were used for Land Surface Temperature (LST) SRS images estimated from the Landsat thermal band. The results show that the seawater value is about −17.5 db and the oil spill area shows a value between −22.5 db and −25 db; the Landsat 8 satellites thermal band 10, depicting contrast at some areas for oil spill, can be determined by the 3 × 3 and 5 × 5 Kernel High pass and the 3 × 3 Mean filter. The results demonstrate that the SRS images should be used together to improve oil spill detection studies results.

Surf Scoters use deeper offshore waters during nocturnal resting periods in the Salish Sea of Washington and British Columbia

Abstract Diurnally biased monitoring and research dominate our ecological understanding and guide conservation and management decisions regarding important indicator sea duck species, such as Surf Scoters (Melanitta perspicillata). Advances in telemetry technology are improving our ability to track wildlife across the entire 24-hr day. Using location data derived from birds with surgically implanted satellite transmitters and habitat layers within a GIS (geographic information system), we analyzed Surf Scoter location data to (1) estimate movement distances between diurnal and nocturnal sites, (2) assess differences in environmental conditions at diurnal and nocturnal use locations, (3) evaluate the environmental conditions influencing nocturnal habitat selection, and (4) develop a predictive model to estimate likely nocturnal resting areas across the Salish Sea, which is composed of the inland marine waters of British Columbia, Canada, and Washington State. Surf Scoters in the Salish Sea traveled an average of 4,287 m between diurnal and nocturnal habitats, but distances varied regionally. We found that distance to shore, water depth, tidal current, and vessel traffic influenced nocturnal habitat use. Surf Scoter nocturnal use sites were characterized by greater distances from shore and deeper water than diurnal locations, although scoters avoided areas with strong tidal currents and heavy shipping traffic at night. The Salish Sea is experiencing increased ship traffic from multiple U.S. and Canadian ports. Oil tanker and shipping container vessel traffic pose a potential threat to nocturnal scoter flocks aggregating in deeper water adjacent to and sometimes overlapping with shipping lanes in narrow, glacial-carved channels of the Salish Sea. Current oil spill response plans in Washington State waters utilize diurnal distribution data, lacking important nocturnal use data. Our findings provide critical nocturnal habitat use information that can improve oil spill response policy and highlight the importance of elucidating nocturnal ecology for marine vertebrate species.

Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection

Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively.

Toward the Detection of Oil Spills in Newly Formed Sea Ice Using C-Band Multipolarization Radar

Oil spills in the Arctic are becoming more likely as shipping traffic increases in response to climate-related sea ice loss. To improve oil spill detection capability, we used a controlled mesocosm to analyze the multipolarized C-band backscatter response of oil in newly formed sea ice (NI). Artificial sea ice was grown in two cylindrical tubs at the Sea-ice Environmental Research Facility, University of Manitoba. The sea ice physical characteristics, including surface roughness, thickness, temperature, and salinity, were measured before and after oil injection below the ice sheet. Time-series C-band radar backscatter measurements detected the differences in the sea ice evolution and oil migration to the sea ice surface in the oil-contaminated tub, which was compared to uncontaminated ice in a control tub. Immediately prior to the presence of oil on the ice surface, the copolarized backscatter is increased by 13-dB local maximum, while the cross-polarized backscatter is decreased by 9-dB. Ice physical properties suggest that the local backscatter maximum and minimum, which occurred immediately before oil migrated onto the surface, were related to a combination of brine and oil upward migration. The findings of this work provide a baseline data interpretation for oil detection in the Arctic Ocean using current and future C-band multipolarization radar satellites.

Assessing the performance of wax-based microsorbents for oil remediation

Millions of tons of crude oil are drilled from onshore and offshore locations to satisfy the yearly global fuel demand, increasing the risk for spills and major ecological disasters. Remediation strategies involving chemical surfactants and oil burning are employed, but the toxicity and undesired environmental effects of these treatments raise more concerns and prompt the investigation for alternative approaches. Amongst these novel methods are organic sorbents. In addition to being cost-effective, these materials absorb oil molecules and facilitate oil recovery in an eco-friendly manner. Here, we explore the ability of organic microparticles to improve oil spill clean-up with a focus on interfacial properties and sorption capacities. We investigate the potential of soy, soy-bee and carnauba waxes as secondary oil remediation technology. The waxes are used to design hollow-core microcapsules, which confer them a superoleophilicity ideal for oil recovery. The wax-based microcapsules can remediate oils, including crude oil, mineral oil, and hexadecane, with a sorption capacity up to 5 times their dry mass. Due to low density and high hydrophobicity, the oil-laden microparticles float on top of the water, easing the collection process. Thus, this study lays the foundation for the development of the new class of wax-based microsorbents, exhibiting attractive physical properties in addition of being made of FDA approved plant and animal derivatives. Future studies involving microparticle-assisted bioremediation will provide new insights on how the design of the microparticles could be tailored to fit various ecosystems.

Real-time predictions of seabird distribution improve oil spill risk assessments

Current knowledge of the distribution of sensitive seabirds is inadequate to safeguard seabird populations from impacts of oil spills in the Arctic. This gap is mainly driven by the fact that statistical models applied to survey data are coarse-scale and static with limited documentation of the distributional dynamics and patchiness of seabirds relevant to risk assessments related to oil spills. This paper describes a dynamic modelling framework solution for prediction of fine-scale densities and movements of seabirds in close-to-real time using fully integrated 3-D hydrodynamic models, dynamic habitat suitability models and agent-based models. The modelling framework has been developed and validated for the swimming migration of Brünnich's Guillemot Uria lomvia in the Barents Sea. The results document that the distributional dynamics of Brünnich's Guillemot and other seabird species to a large degree can be simulated with in-situ state variables and patterns reflecting the physical meteorology and oceanography and habitat suitability.

A Multifaceted Approach to Advance Oil Spill Modeling and Physical Oceanographic Research at the United States Bureau of Ocean Energy Management

The Environmental Studies Program (ESP) at the United States Bureau of Ocean Energy Management (BOEM) is funded by the United States Congress to support BOEM’s mission, which is to use the best available science to responsibly manage the development of the Nation’s offshore energy and mineral resources. Since its inception in 1973, the ESP has funded over $1 billion of multidisciplinary research across four main regions of the United States Outer Continental Shelf: Gulf of Mexico, Atlantic, Alaska, and Pacific. Understanding the dynamics of oil spills and their potential effects on the environment has been one of the primary goals of BOEM’s funding efforts. To this end, BOEM’s ESP continues to support research that improves oil spill modeling by advancing our understanding and the application of meteorological and oceanographic processes to improve oil spill modeling. Following the Deepwater Horizon oil spill in 2010, BOEM has invested approximately $28 million on relevant projects resulting in 73 peer-reviewed journal articles and 42 technical reports. This study describes the findings of these projects, along with the lessons learned and research information needs identified. Additionally, this paper presents a path forward for BOEM’s oil spill modeling and physical oceanographic research.

A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Marine oil spills are among the most significant sources of marine pollution. Synthetic aperture radar (SAR) has been used to improve oil spill observations because of its advantages in oil spill detection and identification. However, speckle noise, weak boundaries, and intensity inhomogeneity often exist in the oil spill regions of SAR imagery, which will seriously affect the accurate identification of oil spills. To enhance marine oil spill segmentation of SAR images, a fast, edge-preserving framework based on the distance-regularized level set evolution (DRLSE) model was proposed. Specifically, a bilateral filter penalty term is designed and incorporated into the DRLSE energy function (BF-DRLSE) to preserve the edges of oil spills, and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity. Two sets of RadarSat-2 SAR data were used to test the proposed method. The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution. Compared with other methods, the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy (97.83%). In addition, using an appropriate initial box boundary for the DRLSE method accelerated the global search process, improved the accuracy of oil spill segmentation, and reduced computational time. Therefore, the results suggest that the proposed framework is effective and applicable for marine oil spill segmentation.

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

Effects of Exposure to Low Concentrations of Oil on the Expression of Cytochrome P4501a and Routine Swimming Speed of Atlantic Haddock (Melanogrammus aeglefinus) Larvae In Situ.

Exposure to environmentally relevant concentrations of oil could impact survival of fish larvae in situ through subtle effects on larval behavior. During the larval period, Atlantic haddock (Melanogrammus aeglefinus) are transported toward nursery grounds by ocean currents and active swimming, which can modify their drift route. Haddock larvae are sensitive to dispersed oil; however, whether exposure to oil during development impacts the ability of haddock larvae to swim in situ is unknown. Here, we exposed Atlantic haddock embryos to 10 and 80 μg oil/L (0.1 and 0.8 μg ∑PAH/L) of crude oil for 8 days and used a novel approach to measure its effect on the larval swimming behavior in situ. We assessed the swimming behavior of 138 haddock larvae in situ, in the North Sea, using a transparent drifting chamber. Expression of cytochrome P4501a (cyp1a) was also measured. Exposure to 10 and 80 μg oil/L significantly reduced the average in situ routine swimming speed by 30–40% compared to the controls. Expression of cyp1a was significantly higher in both exposed groups. This study reports key information for improving oil spill risk assessment models and presents a novel approach to study sublethal effects of pollutants on fish larvae in situ.

Improved oil spill dispersant monitoring in seawater using dual tracers: Dioctyl and monoctyl sulfosuccinates sourced from corexit EC9500A

A high resolution mass spectrometry method was developed for the environmental impact monitoring of oil spill dispersants. Previously reported instability of dioctyl sulfosuccinate (DOSS) dispersant tracer was addressed by the new procedure. The method monitors both DOSS and its degradation product, monooctyl sulfosuccinate (MOSS), by liquid chromatography time-of-flight mass spectrometry. The related isomer, 4-(2-ethylhexyl) 2-sulfobutanedioate, was chromatographically resolved from MOSS but was not a product of DOSS degradation. Using this direct injection method (10 μL), the practical lower limit of quantitation was 0.5 nM for each analyte, a concentration equivalent to 0.22 ng mL−1, or 0.30 ng mL−1 including initial dilution factor with acetonitrile. The method was shown applicable to analysis of the dispersants Corexit® EC9500 A, Finasol OSR 52, Slickgone NS, and Slickgone EW for which DOSS is an active ingredient. A marine microcosm study of Corexit EC9500A, together with diluted bitumen (dilbit), at 15 ± 1 °C, provided evidence of the stoichiometric conversion of DOSS to MOSS under conditions reflecting a western Canadian marine environment. The advantage of the developed method is in its ability to extend environmental seawater sample collection time from 4 days for DOSS alone, to 14 days when both DOSS and MOSS are simultaneously analysed and results combined. The collection time is likely extended beyond the 14 day period with cooler temperatures. Preservation of collected seawater samples using sodium hydroxide, converting DOSS into MOSS in situ, was rejected due to stability issues. Addition of disodium ethylenediaminetetraacetic acid did not improve hold times, thus eliminating the theory of cation induced micelle effects causing DOSS loss.

Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.

Tidal eddies at a narrow channel inlet in operational oil spill models

In operational oil spill modeling, hydrodynamic models often employ a coarse-resolution grid for computational efficiency. However, this practical grid resolution poorly resolves small-scale flow features, such as starting jet vortices (tidal eddies) that are common at the inlet of bar-built estuaries with narrow inlet channels, particularly where channel dredging and jetties have been employed to aid ship traffic. These eddies influence Lagrangian transport paths and hence the fate of an oil spill potentially entering or leaving an estuary. This research quantifies the effect of tidal eddies on the mixing process and effects at model scales relevant to the operational prediction of oil spills, using the Galveston Bay entrance channel as a study site. Model grid sensitivity was analyzed, yielding an adequate eddy solution at the horizontal grid size of ∼140 m. It is demonstrated that the SUNTANS model at a practical operational grid resolution (∼400 m) captures neither the eddies nor their effects on particle movement, despite showing a satisfactory prediction of net transport through the inlet. The need for subgrid eddy modeling is discussed, and an empirical approach is proposed that can improve oil spill predictions at operational grid resolution scales when results from a high-resolution model are available.

Improving oil spill trajectory modelling in the Arctic

As petroleum development and other activities move further north, the potential for oil spills in ice-covered waters is of great concern. As a tool for contingency planning and forecasting during response, oil spill models play a key role. With the development of new, high-resolution coupled ice-ocean models, better predictions of sea ice are becoming available. We have updated the OSCAR oil spill model to use sea-ice velocity and coverage fields from coupled ice-ocean models to improve simulation of oil fate and transport in ice-covered waters.We describe the implementation of oil transport in the presence of ice, and demonstrate the improvement by considering three case studies. We find clear improvement when taking ice velocity from a coupled ice-ocean model into account, compared to a heuristic model that uses surface current and wind velocity. The difference is found to be especially important in a response situation near the marginal ice zone.

Using a genetic algorithm to improve oil spill prediction

The performance of oil spill models is strongly influenced by multiple parameters. In this study, we explored the ability of a genetic algorithm (GA) to determine optimal parameters without the need for time-consuming manual attempts. An evaluation function integrating the percentage of coincidence between the predicted polluted area and the observed spill area was proposed for measuring the performance of a Lagrangian oil particle model. To maximise the objective function, the oil spill was run numerous times with continuously optimised parameters. After many generations, the GA effectively reduced discrepancies between model results and observations of a real oil spill. Subsequent validation indicated that the oil spill model predicted oil slick patterns with reasonable accuracy when equipped with optimal parameters. Furthermore, multiple objective optimisation for observations at different times contributed to better model performance.