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

Tuuli Parviainen; Floris Goerlandt; Inari Helle; Päivi Haapasaari; Sakari Kuikka

doi:10.1016/j.jenvman.2020.111520

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

J Environ Manage. 2021 Jan 15;278(Pt 1):111520. doi: 10.1016/j.jenvman.2020.111520. Epub 2020 Nov 7.

Authors

Tuuli Parviainen¹, Floris Goerlandt², Inari Helle³, Päivi Haapasaari⁴, Sakari Kuikka⁵

Affiliations

¹ University of Helsinki, Marine Risk Governance Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; University of Helsinki, Fisheries and Environmental Management Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Helsinki Institute of Sustainability Science (HELSUS), Porthania (2nd Floor), Yliopistonkatu 3, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland. Electronic address: tuuli.parviainen@helsinki.fi.
² Aalto University, Department of Mechanical Engineering, Marine Technology, P.O. Box 15300, FI-00076, Aalto, Finland; Dalhousie University, Department of Industrial Engineering, Halifax, Nova Scotia, B3H 4R2, Canada.
³ Helsinki Institute of Sustainability Science (HELSUS), Porthania (2nd Floor), Yliopistonkatu 3, FI-00014, University of Helsinki, Finland; University of Helsinki, Environmental and Ecological Statistics Group, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland. Electronic address: inari.helle@luke.fi.
⁴ University of Helsinki, Marine Risk Governance Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland.
⁵ University of Helsinki, Fisheries and Environmental Management Group, Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014, University of Helsinki, Finland; Kotka Maritime Research Centre, Keskuskatu 7, FI-48100, Kotka, Finland.

PMID: 33166738
DOI: 10.1016/j.jenvman.2020.111520

Abstract

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.

Keywords: Bayesian networks; ISO 31000:2018; Oil spills; Pollution preparedness and response; Risk management; Uncertainty.

MeSH terms

Bayes Theorem*
Petroleum Pollution*
Research
Risk Assessment
Uncertainty