Assessing oil spill risks in the northern Baltic Sea with Bayesian network applications

Coastal and marine ecosystems across the globe are heavily impacted by various anthropogenic stressors, which has led to a significant loss of biodiversity and ecosystem services in recent decades. In order to find means to counteract this trend, there is a need to develop methods for assessing the environmental impacts of human activities and the effectiveness of management practices to mitigate the harmful effects. However, this is a challenging task due to the complex interactions within and between the ecosystems and human components, and various uncertainties related to them. Bayesian networks (BNs) are graphical models for reasoning under uncertainty. A BN consists of a set of probabilistic variables connected with links describing causalities within the system. As the states of the variables are described with probability distributions, uncertainty can be described in an explicit manner. BNs also enable integration of qualitative and quantitative knowledge from various sources such as observational data sets, models and expert knowledge. In this thesis I have developed BN models to study environmental risks related to anthropogenic stressors in the Gulf of Finland and the Finnish Archipelago Sea. The main aim is to quantify human impacts on the environment, and to assess the ability of different management measures to lessen these impacts. I focus especially on oil spills resulting from potential tanker accidents and I set out to fill various information gaps related to this recently emerged threat. The thesis includes five papers. In paper I, the main aim is to assess the spatial risk posed by oil spills in the Gulf of Finland and the Finnish Archipelago Sea, and identify species and habitat types with the highest risk. In paper II, I focus on the effectiveness of different oil combating methods to mitigate the negative impacts of oil spills on the ecosystem, and paper III widens the approach to a probabilistic cost-benefit analysis of preventive and post-spill measures. Paper IV deals with multiple risks as, in addition to oil spills, eutrophication and harvesting of species are studied. Paper V reviews and discusses various methods that can be applied to evaluate the uncertainty related to deterministic models, which could increase their usefulness in decision-making. The results suggest that risks related to tanker accidents are distributed unevenly between areas, habitats and species. Furthermore, the results support the current Finnish strategy to base oil combating primarily on offshore recovery vessels instead of chemical dispersants. However, as the efficiency of mechanical recovery is dependent on several factors, there is also a need to develop preventive measures. Although major oil accidents are estimated to be rare events, the costs can be very high, if a spill occurs. The work offers new insights to the oil spill risks in the study area and provides examples how Bayesian networks can be applied in environmental risk assessment. The thesis is a part of the work needed in order to develop comprehensive decision support tools related to environmental risk management in the northern Baltic Sea.Ihmistoiminta vaikuttaa monin haitallisin tavoin merien ja rannikkoalueiden ekosysteemeihin, mikä on johtanut luonnon monimuotoisuuden vähenemiseen ja monien ekosysteemipalveluiden heikkenemiseen. Mikäli tämä kehityskulku halutaan pysäyttää, tarvitaan työkaluja, joilla voidaan tarkastella ihmistoiminnan vahingollisia vaikutuksia ympäristöön ja eri toimenpidevaihtoehtojen kykyä vähentää haittoja. Tämä ei ole kuitenkaan yksinkertaista johtuen ekosysteemien ja ihmisvaikutusten monimutkaisista vuorovaikutussuhteista ja niihin liittyvistä suurista epävarmuuksista. Väitöskirjatyössä on kehitetty malleja Suomenlahden ja Saaristomeren ympäristöriskien arviointiin. Työssä sovelletaan Bayes-verkkoja eli graafisia malleja, joissa kuvataan tutkimusongelmalle keskeiset muuttujat ja niiden väliset syy-seuraussuhteet todennäköisyysjakaumien avulla. Todennäköisyyspohjaisten mallien avulla voidaan ilmentää myös epävarmuutta luontevasti. Bayes-verkot mahdollistavat niin määrällisen kuin laadullisen tiedon käytön samassa mallissa, ja tietoja voidaan yhdistellä monista eri lähteistä kuten tilastoista, muista malleista ja asiantuntija-arvioista. Väitöskirjassa keskitytään erityisesti alusöljyonnettomuuksien ympäristöriskeihin. Työssä arvioidaan öljyonnettomuuksien seurauksia tutkimusalueella esiintyvien lajien ja elinympäristöjen kannalta sekä eri toimenpiteiden tehokkuutta vähentää mahdollisia haittoja. Lisäksi työssä tarkastellaan todennäköisyyspohjaisen kustannus-hyötyanalyysin avulla onnettomuuksien ennaltaehkäisyä ja jälkitorjuntaa sekä esitellään malli, jossa riskitekijöinä ovat öljyonnettomuuksien lisäksi rehevöityminen ja kalastus. Tulosten mukaan öljyonnettomuusriski vaihtelee niin alueiden, lajien kuin elinympäristöjenkin välillä. Tietyt alueet rannikolla ovat suuremmassa vaarassa öljyyntyä kuin toiset, ja alueiden välillä on eroja myös öljylle alttiiden luontoarvojen suhteen. Tulokset myös tukevat Suomen nykyistä öljyntorjuntastrategiaa, joka perustuu mekaaniseen öljynkeruuseen kemiallisten torjunta-aineiden sijaan. Mekaanisen öljyntorjunnan tehokkuus riippuu kuitenkin monesta tekijästä, minkä takia olisi tärkeää kehittää edelleen myös onnettomuuksia ennaltaehkäiseviä toimenpiteitä. Suuret öljyonnettomuudet ovat harvinaisia, mutta tapahtuessaan onnettomuus voi aiheuttaa hyvinkin mittavat kustannukset. Työssä kehiteltyjä malleja ja tuloksia voidaan käyttää päätöksenteon tukena ja kehitettäessä kokonaisvaltaisia työkaluja ympäristöriskien arviointiin ja hallintaan

Preparing for the unprecedented - Towards quantitative oil risk assessment in the Arctic marine areas

The probability of major oil accidents in Arctic seas is increasing alongside with increasing maritime traffic. Hence, there is a growing need to understand the risks posed by oil spills to these unique and sensitive areas. So far these risks have mainly been acknowledged in terms of qualitative descriptions. We introduce a probabilistic framework, based on a general food web approach, to analyze ecological impacts of oil spills. We argue that the food web approach based on key functional groups is more appropriate for providing holistic view of the involved risks than assessments based on single species. We discuss the issues characteristic to the Arctic that need a special attention in risk assessment, and provide examples how to proceed towards quantitative risk estimates. The conceptual model presented in the paper helps to identify the most important risk factors and can be used as a template for more detailed risk assessments.Peer reviewe

An overview of methods to evaluate uncertainty of deterministic models in decision support

There is an increasing need for environmental management advice that is wide-scoped, covering various interlinked policies, and realistic about the uncertainties related to the possible management actions. To achieve this, efficient decision support integrates the results of pre-existing models. Many environmental models are deterministic, but the uncertainty of their outcomes needs to be estimated when they are utilized for decision support. We review various methods that have been or could be applied to evaluate the uncertainty related to deterministic models' outputs. We cover expert judgement, model emulation, sensitivity analysis, temporal and spatial variability in the model outputs, the use of multiple models, and statistical approaches, and evaluate when these methods are appropriate and what must be taken into account when utilizing them. The best way to evaluate the uncertainty depends on the definitions of the source models and the amount and quality of information available to the modeller.Peer reviewe

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.Peer reviewe

Assessment of the residential Finnish wolf population combines DNA captures, citizen observations and mortality data using a Bayesian state-space model

Assessment of the Finnish wolf population relies on multiple sources of information. This paper describes how Bayesian inference is used to pool the information contained in different data sets (point observations, non-invasive genetics, known mortalities) for the estimation of the number of territories occupied by family packs and pairs. The output of the assessment model is a joint probability distribution, which describes current knowledge about the number of wolves within each territory. The joint distribution can be used to derive probability distributions for the total number of wolves in all territories and for the pack status within each territory. Most of the data set comprises of both voluntary-provided point observations and DNA samples provided by volunteers and research personnel. The new method reduces the role of expert judgement in the assessment process, providing increased transparency and repeatability

Black Boxes and the Role of Modeling in Environmental Policy Making

Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence has improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. The lack of transparency in black box models, artificial intelligence based ones among them, can potentially affect the trust in science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretationsPeer reviewe

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.Peer reviewe

A proactive approach for maritime safety policy making for the Gulf of Finland : seeking best practices

A rapid increase in maritime traffic together with challenging navigation conditions and a vulnerable ecosystem has evoked calls for improving maritime safety in the Gulf of Finland, (the Baltic Sea). It is suggested that these improvements will be the result of adopting a regionally effective proactive approach to safety policy formulation and management. A proactive approach is grounded on a formal process of identifying, assessing and evaluating accident risks, and adjusting policies or management practices before accidents happen. Currently, maritime safety is globally regulated by internationally agreed prescriptive rules, which are usually revised in reaction to accidents. The proactive Formal Safety Assessment (FSA) is applied to risks common to a ship type or to a particular hazard, when deemed necessary, whereas regional FSA applications are rare. An extensive literature review was conducted in order to examine the opportunities for developing a framework for the GoF for handling regional risks at regional level. Best practices were sought from nuclear safety management and fisheries management, and from a particular case related to maritime risk management. A regional approach that sees maritime safety as a holistic system, and manages it by combining a scientific risk assessment with stakeholder input to identify risks and risk control options, and to evaluate risks is proposed. A regional risk governance framework can improve safety by focusing on actual regional risks, designing tailor-made safety measures to control them, enhancing a positive safety culture in the shipping industry, and by increasing trust among all involved.Peer reviewe

Best practices to improve maritime safety in the Gulf of Finland : a risk governance approach

The Gulf of Finland of the Baltic Sea is a vulnerable sea area with high volumes of maritime traffic and difficult navigation conditions. The reactive international rules are not anymore regarded adequate in ensuring safety in this sea area. In this paper, a regional proactive risk governance approach is suggested for improving the effectiveness of safety policy formulation and management in the Gulf of Finland, based on the risk governance framework developed by the International Risk Governance Council (IRGC), the Formal Safety Assessment approach adopted by the International Maritime Safety Organisation (IMO), and best practices sought from other sectors and sea areas. The approach is based on a formal process of identifying, assessing and evaluating accident risks at the regional level, and adjusting policies or management practices before accidents occur. The proposed approach sees maritime safety as a holistic system, and manages it by combining a scientific risk assessment with stakeholder input to identify risks and risk control options, and to evaluate risks. A regional proactive approach can improve safety by focusing on actual risks, by designing tailor-made safety measures to control them, by enhancing a positive safety culture in the shipping industry, and by increasing trust among all involved.Non peer reviewe