Optimizing the recovery efficiency of Finnish oil combating vessels in the Gulf of Finland using Bayesian Networks

A multi-criteria decision analysis model for ship biofouling management in the Baltic Sea

Biofouling of ship hulls form a vector for the introduction of non-indigenous organisms worldwide. Through increasing friction, the organisms attached to ships' hulls increase the fuel consumption, leading to both higher fuel costs and air emissions. At the same time, ship biofouling management causes both ecological risks and monetary costs. All these aspects should be considered case-specifically in the search of sustainable management strategies. Applying Bayesian networks, we developed a multi-criteria decision analysis model to compare biofouling management strategies in the Baltic Sea, given the characteristics of a ship, its operating profile and operational environment, considering the comprehensive environmental impact and the monetary costs. The model is demonstrated for three scenarios (SC1-3) and sub-scenarios (A-C), comparing the alternative biofouling management strategies in relation to NIS (non-indigenous species) introduction risk, eco-toxicological risk due to biocidal coating, carbon dioxide emissions and costs related to fuel consumption, in-water cleaning and hull coating. The scenarios demonstrate that by the careful consideration of the hull fouling management strategy, both money and environment can be saved. We suggest biocidal-free coating with a regular in-water cleaning using a capture system is generally the lowest-risk option. The best biocidal-free coating type and the optimal in-water cleaning interval should be evaluated case-specifically, though. In some cases, however, biocidal coating remains a justifiable option.

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.

Prioritizing risk mitigation measures for binary heavy metal contamination emergencies at the watershed scale using bayesian decision networks

Water pollution accidents have the characteristics of high uncertainty, rapid evolution and are difficult to control, thus posing great threats to human health, ecological security, and social stability. During the last 10 years, China has faced the occurrence of six extraordinarily serious heavy metal contamination pollution events at the watershed scale. This has alerted governments and enterprises of the significance of emergency decision-making. To quantitatively prioritize risk mitigation strategies for heavy metal emergencies, a Bayesian Decision Network-based probabilistic model is proposed under the Drivers-Pressures-States-Impacts-Responses (DPSIR) framework. A Copula-based exposure risk model is embedded to simulate the fate of heavy metal ions for each risk reduction option, whose joint probability distributions can then be used as input parameters in the Bayesian Decision Network. This method was applied to the emergency response prioritization for acute Cr(VI)-Hg(II) contamination accidents in the Danshui River watershed. The results indicated that comprehensive measure (M5) was the best option for decreasing ecological and human health risks. As for a single risk mitigation strategy, risk source prevention (M1) was the best alternative compared to exposure pathway interruption (M2) and human/ecological receptor protection (M3-M4). This probabilistic method can not only address the uncertainties between certain risk sources and receptors in the BDN structure, but also realize the risk system optimization in a satisfactory/preferred mode under the DPSIR framework. Overall, it provides the probabilistic risk estimates for watershed-scale risk management and policy making for local risk managers and stakeholders.

Developing a conceptual influence diagram for socio-eco-technical systems analysis of biofouling management in shipping - A Baltic Sea case study

Ship hulls create a vector for the transportation of harmful non-indigenous species (NIS) all over the world. To sustainably prevent NIS introductions, the joint consideration of environmental, economic and social aspects in the search of optimal biofouling management strategies is needed. This article presents a multi-perspective soft systems analysis of the biofouling management problem, based on an extensive literature review and expert knowledge collected in the Baltic Sea area during 2018-2020. The resulting conceptual influence diagram (CID) reveals the multidimensionality of the problem by visualizing the causal relations between the key elements and demonstrating the entanglement of social, ecological and technical aspects. Seen as a boundary object, we suggest the CID can support open dialogue and better risk communication among stakeholders by providing an illustrative and directly applicable starting point for the discussions. It also provides a basis for quantitative management optimization in the future.

Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.

Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions

The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.

Incorporating stakeholders' values into environmental decision support: A Bayesian Belief Network approach

Participatory modelling increases the transparency of environmental planning and management processes and enhances the mutual understanding among different parties. We present a sequential probabilistic approach to involve stakeholders' views in the formal decision support process. A continuous Bayesian Belief Network (BBN) model is used to estimate population parameters for stakeholder groups, based on samples of individual value judgements. The approach allows quantification and visualization of the variability in views among and within stakeholder groups. Discrete BBN is populated with these parameters, to summarize and visualize the information and to link it to a larger decision analytic influence diagram (ID). As part of ID, the resulting discrete BBN element serves as a distribution-form decision criteria in probabilistic evaluation of alternative management strategies, to help find a solution that represents the optimal compromise in the presence of potentially conflicting objectives. We demonstrate our idea using example data from the field of marine spatial planning. However, this approach is applicable to many types of management cases. We suggest that by advancing the mutual understanding and concrete participation this approach can further facilitate the stakeholder involvement also during the various stages of the environmental management process.

A Bayesian Network risk model for assessing oil spill recovery effectiveness in the ice-covered Northern Baltic Sea

The Northern Baltic Sea, as one of the few areas with busy ship traffic in ice-covered waters, is a typical sea area exposed to risk of ship accidents and oil spills in ice conditions. Therefore, oil spill capability for response and recovery in this area is required to reduce potential oil spill effects. Currently, there are no integrated, scenario-based models for oil spill response and recovery in ice conditions. This paper presents a Bayesian Network (BN) model for assessing oil spill recovery effectiveness, focusing on mechanical recovery. It aims to generate holistic understanding and insights about the oil spill-to-recovery phase, and to estimate oil recovery effectiveness in representative winter conditions. A number of test scenarios are shown and compared to get insight into the impact resulting from different oil types, spill sizes and winter conditions. The strength of evidence of the model is assessed in line with the adopted risk perspective.

An online platform for rapid oil outflow assessment from grounded tankers for pollution response

The risk of oil spills is an ongoing societal concern. Whereas several decision support systems exist for predicting the fate and drift of spilled oil, there is a lack of accurate models for assessing the amount of oil spilled and its temporal evolution. In order to close this gap, this paper presents an online platform for the fast assessment of tanker grounding accidents in terms of structural damage and time-dependent amount of spilled cargo oil. The simulation platform consists of the definition of accidental scenarios; the assessment of the grounding damage and the prediction of the time-dependent oil spill size. The performance of this integrated online simulation environment is exemplified through illustrative case studies representing two plausible accidental grounding scenarios in the Gulf of Finland: one resulting in oil spill of about 50 t, while in the other the inner hull remained intact and no spill occurred.

Risk management model of winter navigation operations

The wintertime maritime traffic operations in the Gulf of Finland are managed through the Finnish-Swedish Winter Navigation System. This establishes the requirements and limitations for the vessels navigating when ice covers this area. During winter navigation in the Gulf of Finland, the largest risk stems from accidental ship collisions which may also trigger oil spills. In this article, a model for managing the risk of winter navigation operations is presented. The model analyses the probability of oil spills derived from collisions involving oil tanker vessels and other vessel types. The model structure is based on the steps provided in the Formal Safety Assessment (FSA) by the International Maritime Organization (IMO) and adapted into a Bayesian Network model. The results indicate that ship independent navigation and convoys are the operations with higher probability of oil spills. Minor spills are most probable, while major oil spills found very unlikely but possible.

A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: A Bayesian network model for the Gulf of Finland

Large-scale oil accidents can inflict substantial costs to the society, as they typically result in expensive oil combating and waste treatment operations and have negative impacts on recreational and environmental values. Cost-benefit analysis (CBA) offers a way to assess the economic efficiency of management measures capable of mitigating the adverse effects. However, the irregular occurrence of spills combined with uncertainties related to the possible effects makes the analysis a challenging task. We develop a probabilistic modeling approach for a CBA of oil spill management and apply it in the Gulf of Finland, the Baltic Sea. The model has a causal structure, and it covers a large number of factors relevant to the realistic description of oil spills, as well as the costs of oil combating operations at open sea, shoreline clean-up, and waste treatment activities. Further, to describe the effects on environmental benefits, we use data from a contingent valuation survey. The results encourage seeking for cost-effective preventive measures, and emphasize the importance of the inclusion of the costs related to waste treatment and environmental values in the analysis. Although the model is developed for a specific area, the methodology is applicable also to other areas facing the risk of oil spills as well as to other fields that need to cope with the challenging combination of low probabilities, high losses and major uncertainties.

Identifying evaluation considerations for the recovery and restoration from the 2010 Gulf of Mexico oil spill: An initial appraisal of stakeholder concerns and values

Understanding what can be achieved and what should be avoided by environmental management decisions requires an understanding of values, or what is cared about in a decision. Decision analysis provides tools and processes for constructing objectives that transparently reflect the values being considered in environmental management decisions. The present study demonstrates parts of the initial decision analysis steps for identifying a decision context and constructing objectives for the recovery and long-term restoration of the Gulf of Mexico following the 2010 Deepwater Horizon oil spill. From a review of multiple reports, including those developed by policy makers and nongovernmental organizations, a preliminary structuring of concerns and considerations into objectives was derived to highlight features of importance in the recovery from the spill and long-term restoration. The fundamental objectives constructed for the long-term restoration context reflect broader concerns regarding well-being and quality of life. When developed through stakeholder engagement processes, clarifying objectives can potentially 1) lend insight into the values that can be affected, 2) meaningfully include stakeholders in the decision-making process, 3) enhance transparency and communication, and 4) develop high-impact management strategies reflecting broad public interests. This article is a US government work and is in the public domain in the United States of America.

A risk analysis of winter navigation in Finnish sea areas

Winter navigation is a complex but common operation in north-European sea areas. In Finnish waters, the smooth flow of maritime traffic and safety of vessel navigation during the winter period are managed through the Finnish-Swedish winter navigation system (FSWNS). This article focuses on accident risks in winter navigation operations, beginning with a brief outline of the FSWNS. The study analyses a hazard identification model of winter navigation and reviews accident data extracted from four winter periods. These are adopted as a basis for visualizing the risks in winter navigation operations. The results reveal that experts consider ship independent navigation in ice conditions the most complex navigational operation, which is confirmed by accident data analysis showing that the operation constitutes the type of navigation with the highest number of accidents reported. The severity of the accidents during winter navigation is mainly categorized as less serious. Collision is the most typical accident in ice navigation and general cargo the type of vessel most frequently involved in these accidents. Consolidated ice, ice ridges and ice thickness between 15 and 40cm represent the most common ice conditions in which accidents occur. Thus, the analysis presented in this article establishes the key elements for identifying the operation types which would benefit most from further safety engineering and safety or risk management development.

A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland

The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea, increases environmental risks by increasing the probability of oil accidents. By integrating the work of a multidisciplinary research team and information from several sources, we have developed a probabilistic risk assessment application that considers the likely future development of maritime traffic and oil transportation in the area and the resulting risk of environmental pollution. This metamodel is used to compare the effects of two preventative management actions on the tanker collision probabilities and the consequent risk. The resulting risk is evaluated from four different perspectives. Bayesian networks enable large amounts of information about causalities to be integrated and utilized in probabilistic inference. Compared with the baseline period of 2007-2008, the worst-case scenario is that the risk level increases 4-fold by the year 2015. The management measures are evaluated and found to decrease the risk by 4-13%, but the utility gained by their joint implementation would be less than the sum of their independent effects. In addition to the results concerning the varying risk levels, the application provides interesting information about the relationships between the different elements of the system.

Bayesian networks for maritime traffic accident prevention: benefits and challenges

Bayesian networks are quantitative modeling tools whose applications to the maritime traffic safety context are becoming more popular. This paper discusses the utilization of Bayesian networks in maritime safety modeling. Based on literature and the author's own experiences, the paper studies what Bayesian networks can offer to maritime accident prevention and safety modeling and discusses a few challenges in their application to this context. It is argued that the capability of representing rather complex, not necessarily causal but uncertain relationships makes Bayesian networks an attractive modeling tool for the maritime safety and accidents. Furthermore, as the maritime accident and safety data is still rather scarce and has some quality problems, the possibility to combine data with expert knowledge and the easy way of updating the model after acquiring more evidence further enhance their feasibility. However, eliciting the probabilities from the maritime experts might be challenging and the model validation can be tricky. It is concluded that with the utilization of several data sources, Bayesian updating, dynamic modeling, and hidden nodes for latent variables, Bayesian networks are rather well-suited tools for the maritime safety management and decision-making.

A probabilistic model for accidental cargo oil outflow from product tankers in a ship-ship collision

In risk assessment of maritime transportation, estimation of accidental oil outflow from tankers is important for assessing environmental impacts. However, there typically is limited data concerning the specific structural design and tank arrangement of ships operating in a given area. Moreover, there is uncertainty about the accident scenarios potentially emerging from ship encounters. This paper proposes a Bayesian network (BN) model for reasoning under uncertainty for the assessment of accidental cargo oil outflow in a ship-ship collision where a product tanker is struck. The BN combines a model linking impact scenarios to damage extent with a model for estimating the tank layouts based on limited information regarding the ship. The methodology for constructing the model is presented and output for two accident scenarios is shown. The discussion elaborates on the issue of model validation, both in terms of the BN and in light of the adopted uncertainty/bias-based risk perspective.

Toward integrative management advice of water quality, oil spills, and fishery in the Gulf of Finland: a Bayesian approach

Understanding and managing ecosystems affected by several anthropogenic stressors require methods that enable analyzing the joint effects of different factors in one framework. Further, as scientific knowledge about natural systems is loaded with uncertainty, it is essential that analyses are based on a probabilistic approach. We describe in this article about building a Bayesian decision model, which includes three stressors present in the Gulf of Finland. The outcome of the integrative model is a set of probability distributions for future nutrient concentrations, herring stock biomass, and achieving the water quality targets set by HELCOM Baltic Sea Action Plan. These distributions can then be used to derive the probability of reaching the management targets for each alternative combination of management actions.