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.

Sustainability manifesting as a multi-material and -sited network effect: How boat-sourced sewage management facilities serve as governance artefacts advancing sustainability in nautical tourism.

Marinas are a part of coastal areas' touristic appeal, but also hotspots for boat-sourced pollution. Considering the manifestation of sustainability in marina operation, we utilize actor-network theory (ANT) in demonstrating a conceptual systems analysis on boat-sourced sewage management (BSSM) as one important socio-eco-technical sub-system of sustainable nautical tourism. We describe a multi-material collective of dynamically interacting human and non-human entities to understand how and under what conditions BSSM facilities advance the sustainability of marina operation. Our analysis insightfully uncovers BSSM facilities as both core marina services and governance artefacts and reveals that managing boat-sourced sewage successfully is an outcome of a multi-sited network of heterogeneous elements that together enable both sustainable boating practices and marina operation. We suggest the presented ANT-based systemic thinking has potential for providing novel perspectives to sustainability analyses in diverse tourism-related contexts.

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.

Bayesian Networks in Environmental Risk Assessment: A Review.

Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Spatial aspects of the dioxin risk formation in the Baltic Sea: A systematic review.

Dioxins have been an inconvenience to the Baltic Sea ecosystem for decades. Although the concentrations in the environment and biota have continuously decreased, dioxins still pose a risk to human health. The risk and its formation vary in different parts of the Baltic Sea, due to variability in the environmental and societal factors affecting it. This paper presents a systematic literature review and knowledge synthesis about the regional dioxin risk formation in four sub-areas of the Baltic Sea and evaluates, whether systemic approach changes our thinking about the risk and its effective management. We studied the dioxin flux from atmospheric deposition to the Baltic Sea food webs, accumulation to two commercially and culturally important fish species, Baltic herring (Clupea harengus membras) and Baltic salmon (Salmo salar), and further to risk group members of four Baltic countries. Based on 46 studies, we identified 20 quantifiable variables and indexed them for commensurable regional comparison. Spatial differences in dioxin pollution, environmental conditions, food web dynamics, and the following dioxin concentrations in herring and salmon, together with fishing and fish consumption, affect how the final health risk builds up. In the southern Baltic Sea, atmospheric pollution levels are relatively high and environmental processes to decrease bioavailability of dioxins unfavorable, but the growth is fast, which curb the bioaccumulation of dioxins in the biota. In the North, long-range atmospheric pollution is minor compared to South, but the local pollution and slower growth leads to higher bioaccumulation rates. However, based on our results, the most remarkable differences in the dioxin risk formation between the areas arise from the social sphere: the emissions, origin of national catches, and cultural differences in fish consumption. The article suggests that acknowledging spatial characteristics of socio-ecological systems that generate environmental risks may aid to direct local focus in risk management.

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 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.

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

Oil transport has greatly increased in the Gulf of Finland over the years, and risks of an oil accident occurring have risen. Thus, an effective oil combating strategy is needed. We developed a Bayesian Network (BN) to examine the recovery efficiency and optimal disposition of the Finnish oil combating vessels in the Gulf of Finland (GoF), Eastern Baltic Sea. Four alternative home harbors, five accident points, and ten oil combating vessels were included in the model to find the optimal disposition policy that would maximize the recovery efficiency. With this composition, the placement of the oil combating vessels seems not to have a significant effect on the recovery efficiency. The process seems to be strongly controlled by certain random factors independent of human action, e.g. wave height and stranding time of the oil. Therefore, the success of oil combating is rather uncertain, so it is also important to develop activities that aim for preventing accidents. We found that the model developed is suitable for this type of multidecision optimization. The methodology, results, and practices are further discussed.