This paper investigates the linkage between the acute impacts on apex marine mammals with polar cod responses to an oil spill. It proposes a Bayesian network-based model to link these direct and indirect effects on the apex marine mammals. The model predicts a recruitment collapse (for the scenarios considered), causing a higher risk of mortality of polar bears, beluga whales, and Narwhals in the Arctic region. Whales (adult and calves) were predicted to be at higher risk when the spill was under thick ice, while adult polar bears were at higher risk when the spill occurred on thin ice. A spill over the thick ice caused the least risk to whale and adult polar bears. The spill's timing and location have a significant impact on the animals in the Arctic region due to its unique sea ice dynamics, simple food web, and short periods of food abundance.

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The Arctic Ocean has drawn major attention in recent years due to its rich natural resources and shorter navigational routes. Arctic development and transportation involve significant risk caused by the unique features of this region, such as ice, severe operating conditions, unpredictable climatic changes, and remoteness. Considering the high degree of uncertainty in the performance of vessel operating systems and humans, robust risk analysis and management tools are required to provide decision-support to prevent accidents and ensure safety at sea. This paper proposes an Object-Oriented Bayesian Network model to dynamically predict ship-ice collision probability based on navigational and operational system states, weather and ice conditions, and human error. The model, when integrated with potential consequences, may help estimate risk. A case study related to oil tanker navigation on the Northern Sea Route (NSR) is used to show the application of the proposed model to predict oil tanker collision with sea ice.

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A model that can evaluate the ecological risk posed to the Arctic marine ecosystem is presented in this paper. The proposed model is aimed at evaluating the risk of an accidental oil release. The model incorporates a release and dispersion model, fate and transport model, and ecotoxicological modelling. Uncertainties in the proposed model and data are addressed through a probabilistic framework implemented using a fugacity model to estimate the exposure concentration in the different media that are in contact with oil. This is the focus of this paper. The 95th percentile of Predicted Exposure Concentration (PEC_95%) is compared with the 5th percentile of the Predicted No Effect Concentration (PNEC_5%) to produce a Risk Quotient (RQ) profile, which indicates the level of risk posed to the Arctic marine ecosystem. The application of the proposed model is illustrated through a case study. The RQ obtained is useful for making decisions on the management of safety for Arctic marine ecosystems, such as setting operational goals to prevent accidents and for designing emergency preparedness plans. The uniqueness of this work in comparison to ealier studies is that, the methodology takes into account all the significant component models needed to address a potential oil spill in a probabilistic way and demonstrated in an Arctic setting. This study also shows that the methodology is useful as a first step to decision making in the absence of data on accidental releases in the Arctic marine waters.

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The prospect of shipping in the Arctic poses potential oil spill risk. To minimize this risk, it requires detailed evaluation of the effectiveness of measures to be used to control and mitigate the spill. In this paper, a model is presented to study and evaluate strategies for reducing the effects of oil spills on the Arctic marine ecosystem. Risk is estimated by considering exposure concentration and no-effect reference concentration. The exposure concentration is modelled using a fugacity approach and the response is estimated based on ecotoxicology studies. Control, design, and response measures are studied to ensure that risks remain within an acceptable range. The proposed model helps to evaluate efficacy of specific measures and combinations of measures on anticipated spill scenarios. A case study is used to illustrate the application of the proposed model. In the case study, the model is applied to the Exxon Valdez oil spill to identify the most effective combination of the measures to employ before and after the accident. The case study illustrates how the proposed model can assist decision makers to better prepare for potential oil spill in an Arctic marine ecosystem.@en

Dose-response modeling is one of the most important steps of ecological risk assessment. It requires concentration-effects relationships for the species under consideration. There are very limited studies and experimental data available for the Arctic aquatic species. Lack of toxicity data hinders obtaining dose-response relationships for lethal (LC50 values), sub-lethal and carcinogenic effects. Gaps in toxicity data could be filled using a variety of in-silico ecotoxicological methods. This paper reviews the suitability of such methods for the Arctic scenario. Mechanistic approaches like toxicokinetic and toxicodynamic analysis are found to be better suited for interspecies extrapolation than statistical methods, such as Quantitative Structure-Activity Relationships/Quantitative Structure Activity-Activity Relationship, ICE, and other empirical models, such as Haber's law and Ostwald's equation. A novel approach is proposed where the effects of the toxicant exposure are quantified based on the probability of cellular damage and metabolites interactions. This approach recommends modeling cellular damage using a toxicodynamic model and physiology or metabolites interactions using a toxicokinetic model. Together, these models provide more reliable estimates of toxicity in the Arctic aquatic species, which will assist in conducting ecological risk assessment of Arctic environment.@en

This paper presents a methodology for the analysis of Arctic shipping accident scenarios using Bayesian Networks (BN). The proposed methodology is applied to a scenario involving a collision between a vessel and an iceberg. The study aims to identify the most significant causative factors to the potential accident scenarios. It is achieved by undertaking a sensitivity analysis study. The results inform the development of measures to avoid and control accidents during Arctic shipping.

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This paper presents a model of oil weathering and transport in sea ice. It contains a model formulation and scenario simulation to test the proposed model. The model formulation is based on state-of-the-art models for individual weathering and transport processes. The approach incorporates the dependency of weathering and transport processes on each other, as well as their simultaneous occurrence after an oil spill in sea ice. The model is calibrated with available experimental data. The experimental data and model prediction show close agreement. A sensitivity analysis is conducted to determine the most sensitive parameters in the model. The model is useful for contingency planning of a potential oil spill in sea ice. It is suitable for coupling with a level IV fugacity model, to estimate the concentration and persistence of hydrocarbons in air, ice, water and sediments for risk assessment purposes.

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Improved understanding of ecological risk associated with Arctic shipping would help advance effective oil spill prevention, control, and mitigation strategies. Ecological risk assessment involves analysis of a release (oil), its fate, and dispersion, and the exposure and intake of the contaminant to different receptors. Exposure analysis is a key step of the detailed ecological risk assessment, which involves the evaluation of the concentration and persistence of released pollutants in the media of contact. In the present study, a multimedia fate and transport model is presented, which is developed using a fugacity-based approach. This model considers four media: air, water, sediment, and ice. The output of the model is the concentration of oil (surrogate hydrocarbons-naphthalene) in these four media, which constitutes the potential exposure to receptors. The concentration profiles can subsequently be used to estimate ecological risk thereby providing guidance to policies for Arctic shipping operations, ship design, and ecological response measures.

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This paper describes the essence of establishing a dynamic fugacity model, mainly in the framework of assessing the potential risk posed by oil spills during Arctic shipping. Key to evaluating this risk is the fate and transport processes as they occur in ice-covered waters. A review of relevant weathering and transport processes is therefore presented. The study shows that more understanding is needed of the behavior of oil in ice for assessing the risk posed during Arctic shipping.@en

Arctic waters have historically been relatively inaccessible for marine transport. Lately, climate change has made more of this region ice-free in the summer season. This has reduced the difficulty of marine transport in Arctic waters. Further, exploration and development of natural resources is increasing in Arctic regions, as is destinational shipping. The unique risk factors of this region, such as extremely low temperature, ice conditions and drifting icebergs, continue to pose threats to transportation. Potential impacts associated with marine transportation accidents warrant contingency plans that recognize that preventative measures may fail. To plan effectively, a transportation accident risk assessment model for Arctic waters is helpful. There is limited work on the development of such models. A new cause-consequences based risk assessment model is proposed here. The model estimates the probability of a transportation accident and also the related consequences during navigation in Arctic waters. To illustrate the application of the methodology, it is applied to a case of an oil-tanker collision on the Northern Sea Route.@en