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

Liangliang Lu; Floris Goerlandt; Osiris A Valdez Banda; Pentti Kujala; Anders Höglund; Lars Arneborg

doi:10.1016/j.marpolbul.2018.12.018

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

Mar Pollut Bull. 2019 Feb:139:440-458. doi: 10.1016/j.marpolbul.2018.12.018. Epub 2019 Jan 22.

Authors

Liangliang Lu¹, Floris Goerlandt², Osiris A Valdez Banda³, Pentti Kujala³, Anders Höglund⁴, Lars Arneborg⁴

Affiliations

¹ Aalto University, School of Engineering, Department of Mechanical Engineering, Marine Technology, Research Group on Maritime Risk and Safety, P.O. Box 15300, 00076 Aalto, Finland. Electronic address: liangliang.lu@aalto.fi.
² Aalto University, School of Engineering, Department of Mechanical Engineering, Marine Technology, Research Group on Maritime Risk and Safety, P.O. Box 15300, 00076 Aalto, Finland; Dalhousie University, Department of Industrial Engineering, Halifax, Nova Scotia B3H 4R2, Canada.
³ Aalto University, School of Engineering, Department of Mechanical Engineering, Marine Technology, Research Group on Maritime Risk and Safety, P.O. Box 15300, 00076 Aalto, Finland.
⁴ Swedish Meteorological and Hydrological Institute, Research Department, Sweden.

PMID: 30686447
DOI: 10.1016/j.marpolbul.2018.12.018

Abstract

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.

Keywords: Marine pollution; Maritime safety; Oil spill; Recovery effectiveness; Risk analysis; Sea ice.

MeSH terms

Bayes Theorem
Environmental Restoration and Remediation / methods*
Ice Cover
Models, Theoretical*
Petroleum Pollution*
Seasons
Ships