Oil Spill Scenarios Research Articles

Enhancing Environmental Sensitivity and Vulnerability Assessments for Oil Spill Responses in the Caspian Sea

Oil spills pose significant threats to marine and coastal ecosystems, necessitating advanced methodologies for environmental sensitivity and vulnerability assessments. This study enhances existing frameworks to better manage oil spill risks in the Caspian Sea, a region characterized by its ecological sensitivity and economic dependence on oil extraction. Utilizing the Environmental Sensitivity Index (ESI), we adapted global standards to the unique conditions of the Caspian Sea and built a sensitivity map of the coastline, which later became one of the components of the integral sensitivity map for the entire Caspian Sea, which includes several biotic and abiotic components. We also developed a comprehensive geodatabase incorporating topographic, infrastructural, and hydrodynamic data. Through the sophisticated modeling of oil spill scenarios using the Oil Spill model of the MIKE 21 software (Release 2016) suite, we simulated spills of varying magnitudes to analyze their potential impacts on the marine and coastal environment. The results enabled the creation of vulnerability maps, pinpointing areas at highest risk and facilitating strategic response planning. Our study demonstrates the critical importance of integrating advanced geospatial analyses and dynamic modeling techniques to improve oil spill preparedness and response strategies. The findings of this study suggest that enhanced monitoring and adaptive management strategies are essential for protecting the Caspian Sea from environmental risks posed by its oil industry.

Hydrocarbonoclastic Biofilm-Based Microbial Fuel Cells: Exploiting Biofilms at Water-Oil Interface for Renewable Energy and Wastewater Remediation.

Microbial fuel cells (MFCs) represent a promising technology for sustainable energy generation, which leverages the metabolic activities of microorganisms to convert organic substrates into electrical energy. In oil spill scenarios, hydrocarbonoclastic biofilms naturally form at the water-oil interface, creating a distinct environment for microbial activity. In this work, we engineered a novel MFC that harnesses these biofilms by strategically positioning the positive electrode at this critical junction, integrating the biofilm's natural properties into the MFC design. These biofilms, composed of specialized hydrocarbon-degrading bacteria, are vital in supporting electron transfer, significantly enhancing the system's power generation. Next-generation sequencing and scanning electron microscopy were used to characterize the microbial community, revealing a significant enrichment of hydrocarbonoclastic Gammaproteobacteria within the biofilm. Notably, key genera such as Paenalcaligenes, Providencia, and Pseudomonas were identified as dominant members, each contributing to the degradation of complex hydrocarbons and supporting the electrogenic activity of the MFCs. An electrochemical analysis demonstrated that the MFC achieved a stable power output of 51.5 μW under static conditions, with an internal resistance of about 1.05 kΩ. The system showed remarkable long-term stability, which maintained consistent performance over a 5-day testing period, with an average daily energy storage of approximately 216 mJ. Additionally, the MFC effectively recovered after deep discharge cycles, sustaining power output for up to 7.5 h before requiring a recovery period. Overall, the study indicates that MFCs based on hydrocarbonoclastic biofilms provide a dual-functionality system, combining renewable energy generation with environmental remediation, particularly in wastewater treatment. Despite lower power output compared to other hydrocarbon-degrading MFCs, the results highlight the potential of this technology for autonomous sensor networks and other low-power applications, which required sustainable energy sources. Moreover, the hydrocarbonoclastic biofilm-based MFC presented here offer significant potential as a biosensor for real-time monitoring of hydrocarbons and other contaminants in water. The biofilm's electrogenic properties enable the detection of organic compound degradation, positioning this system as ideal for environmental biosensing applications.

Expected direct costs of an oil spill in a UNESCO World Heritage area in New Zealand

AbstractFiordland National Park is a UNESCO site located in the remote, southwest of New Zealand's South Island, which attracts hundreds of thousands of visitors every year. One of its leading attractions is Milford Sound. An oil spill in this area could not only impact the industries that operate in Milford Sound but also impact all of the park. To improve the knowledge base about this area in regard to oil spills and policies regarding ship visitation, this study aimed to estimate the costs of clean‐up, impacts to fisheries, tourism, recreation and the wildlife/environment under three oil spill scenarios in Milford Sound. Minimum costs are estimated at NZD 140–154 million. These results demonstrate the need for proper planning and response measures, and appropriate domestic and international policies to help minimise the potential damages that may occur in the event of an oil spill in a remote and pristine area of New Zealand.

Water Ferns (Salvinia) as Oil Spill Sorbent

This research investigates the potential of water ferns (Salvinia) as a natural sorbent for oil spills. The experiment assessed the effectiveness of water ferns in absorbing used engine oil in a controlled environment. The findings indicate that water ferns can absorb oil spills, with the leaves changing color as they absorb the oil. Further research is recommended to evaluate their effectiveness in real-world oil spill scenarios.

Bioinspired melt-blown fiber felt with flame retardancy for efficient oil spill remediation by solar-driven oil evaporation and adsorption

Confronted with rapid-spread river oil leaks and marine oil spills containing toxic and light fractions, conventional emergency response measures often fall short. This study proposes a solar-driven oil evaporation and adsorption (SOEA) strategy that efficiently addresses the challenges posed by toxic and light oils in rivers and oceans. Inspired by the self-protection and anti-fouling effects of fish scales, a photothermal oil-adsorbing felt with a biomimetic scale structure for efficient SEOA is constructed by decorating functional components onto the melt-blown fiber felt using scalable spraying technology. The rational integration of a dual-layer scale structure and functional constituents imparts stable superhydrophobicity, self-cleaning, flame retardancy, enhanced mechanical strength, and a higher oil adsorption capacity. Benefiting from the superior photothermal conversion, the solar-vapor conversion efficiency of the resulting P-EG/MXene@PP reaches 85.6 % under 1 sunlight, with an evaporation rate for light oil of 13.17 kg/m2/h, 8 times higher than natural volatilization. For crude oil floating on water, it achieves a 98 % oil removal efficiency in 6 h, with 41 wt% of the light fraction evaporated. This research provides distinctive insights into solar-driven oil spill remediation behavior and pioneers the SOEA strategy, offering rapid, efficient, and safe solutions for intricate oil spill scenarios.

Deep attention transformer nets for accurate analysis of oil spilled images to minimize pollution in the marine environment

Oil spills in maritime areas pose a serious environmental risk, wreaking havoc on marine ecosystems, coastal habitats, and local residents. An accurate and timely evaluation of oil spill occurrences and extent is critical for effective pollution control and mitigation. In this study, we present a novel and cutting-edge approach for analyzing oil-spilled images using Deep Attention Transformer Nets (DATN) with Collective Intelligence (CI), with the goal of reducing pollution in the marine environment. This method takes advantage of deep learning capability, notably the incorporation of transformer-based attention processes, to improve the identification and measurement of oil spills in satellite and aerial images. The DATN model is intended to learn complicated features from images automatically, capturing complex patterns associated with oil spills and their surrounding context. The model chooses focus on key regions and add spatial links by using attention mechanisms, allowing for a more comprehensive understanding of the environmental influence. We thoroughly test DATN performance using a variety of datasets encompassing various oil spill scenarios and environmental circumstances. The results show that DATN surpasses standard approaches and other deep learning models in recognizing oil spill regions, with excellent accuracy, precision, and recall rates. Furthermore, the model has strong generalization capabilities across a wide range of image sources and situations.

OIL SPILL SIMULATION IN THE BALI STRAIT USING THE GNOME AND FVCOM MODELS ON EASTERLY SEASON

Among several forms of marine pollution, oil damages coastal ecosystems. Repeated reports of oil contamination in the marine environment can partly load with major shipping lines. The Ketapang-Gilimanuk crossing over the Bali Strait is Indonesia's second busiest ferry port after Merak-Bakauheni. The most congested shipping routes can carry a significant risk of environmental damage from an oil spill. Oil spill trajectory modelling is carried out to reduce the impact of this possible disaster. Therefore, the use of modelling to ascertain the route of the oil spill was considered. The oil leak path is simulated using the GNOME model. Two oil spill scenarios were used as input models. The chosen location is around the Ketapang Gilimanuk crossing, as well as the time of the easterly season. An ocean HF radar stationed in the Bali Strait verifies the accuracy of the current marine data generated by FVCOM. To see the pattern of the oil spill trajectory based on a ten days simulation, we combined the latest data from FVCOM with GNOME. To determine the ability of the model to predict the flow and trajectory of oil spills in the Bali Strait, this study try to analyze and interpret the oil spill trajectory from model and then validate the model results with satellite imagery.
 Keywords: Bali strait; oil spill; trajectory; GNOME; FVCOM; HF Radar

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.

Mind the gap – Relevant design for laboratory oil exposure of fish as informed by a numerical impact assessment model

Laboratory experiments provide knowledge of species-specific effects thresholds that are used to parameterize impact assessment models of oil contamination on marine ecosystems. Such experiments typically place individuals of species and life stages in tanks with different contaminant concentrations. Exposure concentrations are usually fixed, and the individuals experience a shock treatment being moved from clean water directly into contaminated water and then back to clean water. In this study, we use a coupled numerical model that simulates ocean currents and state, oil dispersal and fate, and early life stages of fish to quantify oil exposure histories, specifically addressing oil spill scenarios of high rates and long durations. By including uptake modelling we also investigate the potential of buffering transient high peaks in exposure. Our simulation results are the basis for a recommendation on the design of laboratory experiments to improve impact assessment model development and parameterization. We recommend an exposure profile with three main phases: i) a gradual increase in concentration, ii) a transient peak that is well above the subsequent level, and iii) a plateau of fixed concentration lasting ∼3 days. In addition, a fourth phase with a slow decrease may be added.

Recreational impacts of an oil spill in a world heritage area: a combined travel cost and contingent behaviour method approach

ABSTRACT Fiordland National Park is a UNESCO world heritage site that draws hundreds of thousands of visitors every year. An oil spill in this area could impact the visitation to not only Milford Sound, but to all of Fiordland National Park and the lower South Island. This study examined the impacts on the recreational value of Fiordland National Park under three oil spill scenarios in Milford Sound. Using a combined approach of travel cost and contingent behaviour methods revealed a consumer surplus of a visit to Fiordland National Park of NZD 534.76 and NZD 1,449.28 per person, with and without accommodation respectively. This leads to an aggregate consumer surplus estimate ranging from NZD 427.8–1,159.4 million for Fiordland National Park based on pre-COVID visitor numbers. The three oil spill scenarios represented small, medium, and large-scale oil spills, limiting various water-based activities at each step. This showed that for each increase in oil level, there was a decrease of NZD 164.10 and NZD 923.73 per person with and without accommodation respectively. This leads to an estimated minimum decrease of the welfare of visitors by 30.7–63.7 percent, or a decrease of NZD 131.3–734.0 million in the annual recreational value. Key Policy Highlights Aggregate annual recreational value estimates ranging from NZD 427.8 million – NZD 1.6 billion pre-COVID19. An oil spill occurring in Milford Sound demonstrated a minimum annual recreational value decrease of NZD 131.3 million over five years. These results demonstrate the need for appropriately funded prevention and preparedness policies to minimize possible damages.

Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. As a consequence, scientific literature contains various up-to-date, advanced oil spill predictive models, capable of simulating the trajectory and evolution of an oil slick generated by the accidental release from ships, hydrocarbon production, or other activities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. The present study reviews the available different met-ocean forcings that have been used in oil-spill modeling, simulating hypothetical or real oil spill scenarios, worldwide. Seven state-of-the-art oil-spill models are critically examined in terms of the met-ocean data used as forcing inputs in the simulation of twenty-three case studies. The results illustrate that most oil spill models are coupled to different resolution, forecasting meteorological and hydrodynamic models, posing, however, limited consideration in the forecasted wave field (expressed as the significant wave height, the wave period, and the Stokes drift) that may affect oil transport, especially at the coastal areas. Moreover, the majority of oil spill models lack any linkage to the background biogeochemical conditions; hence, limited consideration is given to processes such as oil biodegradation, photo-oxidation, and sedimentation. Future advancements in oil-spill modeling should be directed towards the full operational coupling with high-resolution atmospheric, hydrodynamic, wave, and biogeochemical models, improving our understanding of the relative impact of each physical and oil weathering process.

Semi-quantitative risk assessment of marine mammal oil exposure: A case study in the western Gulf of Mexico

Marine mammals are highly vulnerable to oil spills, although the effects at both individual and population levels are not fully understood. A first approximation to evaluate the possible consequences of oil spills on marine life is using ecological risk assessments, which are analytical tools used to assess the likelihood of adverse environmental effects due to exposure to stressors derived from human activities. We developed a semi-quantitative framework to evaluate the risk of oil spill exposure on marine mammals that combines the likelihood of exposure based on species-specific biological and ecological traits, and the feasibility of encounter, which considers not only the overlap between the distribution of the species and the total affected area by a spill but also considers the distribution of spilled oil within this area, thus reducing the uncertainty in the estimate. We applied our framework to assess the risk of exposure of eight cetaceans to scenarios of large heavy oil (API gravity<22) spills originating from three hypothetical deep-water wells in the western Gulf of Mexico. High habitat suitability areas obtained using the maximum entropy (MaxEnt) modeling approach were used as a proxy for the geographic regions where each species is likely to be distributed, and oil spill scenarios were generated using numerical models incorporating transport, dispersion, and oil degradation. The analysis allowed identifying those species for which there is a significant risk of exposure in each spill scenario. However, our results suggest that the risk does not appear to be high for any species under any scenario. The information generated by our risk assessment is key to developing management plans in those areas of the Gulf of Mexico where deep-water activities of the hydrocarbon industry are currently being developed or planned.

Toward the Discrimination of Oil Spills in Newly Formed Sea Ice Using C-Band Radar Polarimetric Parameters

Climate-driven sea ice loss has exposed the Arctic to increased human activity, which comes along with a higher risk of oil spills. As a result, we investigated the ability of C-band polarimetric parameters in a controlled mesocosm to accurately identify and discriminate between oil-contaminated and uncontaminated newly formed sea ice (NI). Parameters, such as total power, copolarization ratio, copolarization correlation coefficient, and others, were derived from the normalized radar cross section and covariance matrix to characterize the temporal evolution of NI before and after oil spill events. For separation purposes, entropy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> ) and mean-alpha ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha$ </tex-math></inline-formula> ) were extracted from eigen decomposition of the coherency matrix. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> versus <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> scatterplot revealed that a threshold classifier of 0.3- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> and 18°- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> could distinguish oil-contaminated NI from its oil-free surroundings. From the temporal evolution of the polarimetric parameters, the results demonstrate that the copolarization correlation coefficient is the most reliable polarimetric parameter for oil spill detection, as it provides information on a variety of oil spill scenarios, including oil encapsulated within ice and oil spreading on top of ice. Overall, these findings will be used to support existing and future C-band polarimetric radar satellites for resolving ambiguities associated with Arctic oil spill events, particularly during freeze-up seasons.

Computational Multiphase Flow Modelling of Oil Spill Through a Hydrophobic Mesh

The current technologies for handling oil spill clean-up vary in expense and effectiveness and are largely ineffective. Oil spills occur due to accidents from well-heads and damaged facilities in the creeks of the Nigerian Delta and along the coastline of waters where hydrocarbons are prospected all over the world. They are unexpected and known to cause irreparable damage to aquatic environments and marine life. The development of a hydrophobic mesh is proposed to prevent oil from spreading into larger areas and from reaching sensitive coastlines. This will help engineers and clean-up crews in their quest to find an appropriate response to a given oil spill scenario as they race against the clock to prevent further damage and improve the oil recovery process. The overall goal of this project is to create a Numerical Simulation of meshes that repel water and attract oil using ANSYS, a Finite Element Analysis software. The mesh was modelled as a porous medium that acts like a filter that retains water on one side while allowing the passage of oil through it. In the course of this work, appropriate materials selection in fluid flow analysis was carried out. Also, the flow domain geometry was developed in such a way as to simulate a system containing an oil-water interface. Next, domain discretization (meshing) was carried out appropriately. After which appropriate boundary conditions and operating conditions were implanted in the model. Fluent was then set to initialize and run calculations. After calculations were run, results were gotten. These results were then interpreted pictorially. It was seen that the velocity streamlines for the oil phase passed through the hydrophobic mesh, while the velocity streamlines for the water phase were repelled from the hydrophobic mesh wall.

Bayesian inference modeling to rank response technologies in arctic marine oil spills.

Marine oil spills have a detrimental effect on aquatic systems. Yet, it is challenging to select appropriate technologies in the Arctic because of limited logistics support, inclement weather conditions, and remoteness, and limited research has been conducted in this direction. This article suggests a method to rank the oil response technologies, including mechanical recovery, chemical dispersant, and in-situ burning, for use in Arctic oil spill risk assessment and preparedness planning. The proposed Preference Learning based Bayesian Inference Modeling offers data-driven ranking of systems by learning a label function and considers factors such as ice covered sea areas, cold weather, and spill volume. A data generation system is developed to produce numerous oil spill scenarios, using a state-of-the-art engineering tool. Results demonstrate that the model, while simple, can efficiently and accurately select the best available technique, making it suitable primarily for marine pollution preparedness and response planning in strategic risk assessments.

Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles

Monitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system for contamination peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to information gathering, the vehicle must plan routes that are free of obstacles on non-convex static and dynamics maps. This work proposes a novel framework to obtain a collision-free policy using deterministic knowledge of the environment by means of a censoring operator and noisy networks that addresses the informative path planning with emphasis in temporal patrolling. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance. The obtained results demonstrate the effectiveness of the proposed algorithm for both cases in the Ypacaraí monitorization task. Simulations showed that the use of noisy-networks are a good choice for enhanced exploration, with 3 times less redundancy in the paths with respect to — greedy policy. Previous coverage strategies are also outperformed both in the accuracy of the obtained contamination model by a 13% on average and by a 37% in the detection of dangerous contamination peaks. Finally, the achieved results indicate the appropriateness of the proposed framework for monitoring scenarios with autonomous vehicles.

Potential changes in the recreational use value for Coastal Bay of Plenty, New Zealand due to oil spills: A combined approach of the travel cost and contingent behaviour methods

New Zealand is an island nation with an extensive coastline. Subsequently, marine water-based recreational activities are important to New Zealanders and bring income to coastal communities. Pollution events, such as marine oil spills, are incidents that can negatively impact the water quality and the recreational use of the New Zealand coastline. To improve the information base for informing maritime policies, and estimate the likely impacts of an oil spill to coastal areas of New Zealand, this study examined the potential impacts on recreational value with three oil spill scenarios at a stretch of beach in the Bay of Plenty, New Zealand. Using a combined approach of travel cost and contingent behaviour methods revealed that the consumer surplus of a visit to the beach by domestic visitors was approximately NZD 121 per person. The approach allowed assessment of the likely use of the beach under differing potential oil spill impacts restricting various marine-based recreational activities. A small oil spill with occasional traces of oil but no restrictions on recreational activities decreased the value to NZD 29.74. A moderate oil spill restricting only fishing, surfing, and swimming decreased the value to NZD 9.83. A severe oil spill stopping all recreational use decreased the value to NZD 4.30. When these recreational losses are combined with known clean-up costs and direct use losses in fisheries and tourism, the total direct use loss to a coastal area like this stretch of beach in the Bay of Plenty, where the MV Rena spill occurred in 2011, could be over NZD (2011) 111 million, of which NZD (2011) 66.14 million is recreational visitor use alone. This estimate allows consideration for the entire direct use values lost that an oil spill could have to a coastal community and to New Zealand as a whole.

A probabilistic framework for risk management and emergency decision-making of marine oil spill accidents

Offshore oil spills may pose a severe threat to marine ecological environment. In this paper, a new methodology based on Bayesian network (BN) and Influence Diagram (ID) is developed for risk management and emergency decision-making of marine oil spill accidents. The methodology integrates risk management before accident and emergency response after accident, which can balance risk and cost, and render an optimal decision-making. Marine oil spill scenarios including root causations, intermediate and consequent events are identified and modeled using BN considering the dependencies and multi-state of incident process nodes. The probabilities of offshore oil spill incident and the resulting ecological disasters are estimated. The prevention and mitigation measures marine oil spill incidents are identified and added to BN for developing Bayesian ID model, in which the cost and utility of implementing each safety measure are considered. Bayesian ID model can estimate the cost and utility of all safety strategies, and the optimal risk management and emergency strategy are determined by balancing the cost and utility. A case study of marine oil spill accident due to subsea oil pipeline leak is used to illustrate the methodology. It is observed that the methodology can efficiently support the decision-making of oil and gas sector in risk management and emergency response of offshore oil spill accidents.

Resilience of larval wood frogs (Rana sylvatica) to hydrocarbons and other compounds released from naturally weathered diluted bitumen in a boreal lake.

The risks to aquatic wildlife from spills of diluted bitumen (dilbit) into inland waters are poorly understood. In this paper, we describe the response of larval wood frogs (Rana sylvatica) to hydrocarbons and other compounds released from experimental spills of dilbit in a temperate boreal lake. To simulate a wide range of environmentally relevant oil spill scenarios, different volumes of Cold Lake Winter Blend dilbit (0, 1.5, 2.9, 5.5, 18, 42, 82, and 180L) were added to 10m diameter in-lake limnocorrals. Larvae (n=360) were reared (from Gosner Stage (GS) 25 to ∼42) in land-based aquatic microcosms, where they were first exposed to clean water during a 2-week baseline phase, and then (at GS ∼30), to contaminated water withdrawn from the limnocorrals for 3 weeks. We observed no statistically significant trends in survival, growth, or development of larvae as a consequence of exposure to the chemical compounds released from naturally weathered dilbit. Likewise, neither cytochrome P450 1A biomarkers nor levels of thyroid hormones in wood frogs near metamorphic climax were significantly related to volume of the oil spills. However, there was a modest statistically significant decrease in larval activity (up to 8.7% relative to the control), but no change in other behavioral metrics (i.e., sociality or space use). Our work adds to the limited body of literature on the effects of unconventional oils on aquatic wildlife and helps to inform risk assessments regarding pipeline projects.

Assessing the exposure risk of large pelagic fish to oil spills scenarios in the deep waters of the Gulf of Mexico

Exposure risk is assessed based on modeling suitable habitat of large pelagic fish and oil spill scenarios originating at three wells located in the western GM's deep waters. Since the fate of the oil depends on the oceanographic conditions present during the accident, as well as the magnitude and duration of the spill, which are not known a priori, the scenarios used are a statistical representation of the area in which oil spilled from the well could be found, given all possible outcomes. The ecological vulnerability assessment identified a subset of bony fish with low-medium vulnerability and elasmobranchs with medium-high vulnerability. The oiling probability and exposure risk of both bony fish and elasmobranchs hotspots vary by well analyzed. Thus, these results provide essential information for a risk management plan for the assessed species and others with economic or conservation importance distributed in the GM and worldwide.