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.

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.

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.

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.

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.

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.