Estimating the acute impacts of Arctic marine oil spills using expert elicitation

An overview of scholarly literature on navigation hazards in Arctic shipping routes

Maritime transport plays a crucial role in international trade. As the number and tonnage of ships continue to increase, traditional shipping routes are becoming progressively congested. The development of Arctic shipping routes has the potential to significantly improve trade efficiency and decrease reliance on traditional shipping routes. At the same time, the harsh navigation conditions in the Arctic pose a huge challenge to ships crossing the Arctic shipping routes. To address the above issues, this paper reviews the natural, navigational environment and unique navigational modes of ships in the Arctic shipping routes. Furthermore, the navigational risks caused by factors including low temperature, sea ice, poor visibility, communication, lack of infrastructure, lack of navigational experience, lack of historical data, high collision risk, and complex navigational environment are summarized and analyzed, providing a reference for researchers and policymakers to conduct research related to Arctic shipping routes.

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.

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.

Dynamic ecological risk modelling of hydrocarbon release scenarios in Arctic waters

The Arctic is an ecologically diverse area that is increasingly vulnerable to damages from oil spills associated with commercial vessels traversing newly open shipping lanes. The significance of such accidents on Arctic marine habitats and the potential for recovery can be examined using ecological risk assessment (ERA) coupled with a dynamic object-oriented Bayesian network (DOOBN). A DOOBN approach is useful to represent the probabilistic relationships inherent in the interactions between key events associated with an oil spill, including oil dispersion from the source, ice-oil slick interactions, seawater-oil slick formation, sedimentation, and exposures to different aquatic life. From such analysis, a probabilistic cost analysis can be performed to examine the theoretical cost of habitat services lost and restored. The application of an ERA-DOOBN model to assess oil spills in the Arctic is demonstrated using a case study. The utility of the model output for determining habitat restoration costs and developing policy guidelines for ecological response measures in the Arctic is also discussed.

Impacts of Oil Spills on Arctic Marine Ecosystems: A Quantitative and Probabilistic Risk Assessment Perspective

Oil spills resulting from maritime accidents pose a poorly understood risk to the Arctic environment. We propose a novel probabilistic method to quantitatively assess these risks. Our method accounts for spatiotemporally varying population distributions, the spreading of oil, and seasonally varying species-specific exposure potential and sensitivity to oil. It quantifies risk with explicit uncertainty estimates, enables one to compare risks over large geographic areas, and produces information on a meaningful scale for decision-making. We demonstrate the method by assessing the short-term risks oil spills pose to polar bears, ringed seals, and walrus in the Kara Sea, the western part of the Northern Sea Route. The risks differ considerably between species, spatial locations, and seasons. Our results support current aspirations to ban heavy fuel oil in the Arctic but show that we should not underestimate the risks of lighter oils either, as these oils can pollute larger areas than heavier ones. Our results also highlight the importance of spatially explicit season-specific oil spill risk assessment in the Arctic and that environmental variability and the lack of data are a major source of uncertainty related to the oil spill impacts.

A state-of-the-art model for spatial and stochastic oil spill risk assessment: A case study of oil spill from a shipwreck

Oil spills are serious environmental issues that potentially can cause adverse effects on marine ecosystems. In some marine areas, like the Baltic Sea, there is a large number of wrecks from the first half of the 20th century, and recent monitoring and field work have revealed release of oil from some of these wrecks. The risk posed by a wreck is governed by its condition, hazardous substances contained in the wreck and the state of the surrounding environment. Therefore, there is a need for a common standard method for estimating the risks associated with different wrecks. In this work a state-of-the-art model is presented for spatial and stochastic risk assessment of oil spills from wrecks, enabling a structured approach to include the complex factors affecting the risk values. A unique feature of this model is its specific focus on uncertainty, facilitating probabilistic calculation of the total risk as the integral expected sum of many possible consequences. A case study is performed in Kattegat at the entrance region to the Baltic Sea to map the risk from a wreck near Sweden. The developed model can be used for oil spill risk assessment in the marine environment all over the world.