A Bayesian Network risk model for assessing oil spill recovery effectiveness in the ice-covered Northern Baltic Sea. (February 2019)

• Record Type: Journal Article
• Authors: Lu, Liangliang; Goerlandt, Floris; Valdez Banda, Osiris A.; Kujala, Pentti; Höglund, Anders; Arneborg, Lars
• Is Part Of: Marine pollution bulletin. Volume 139(2019)
• Page Start: 440
• Page End: 458
• Publication Date: 2019-02
• Subjects: Risk analysis -- Oil spill -- Recovery effectiveness -- Sea ice -- Maritime safety -- Marine pollution; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2018.12.018
• Languages: English
• ISSNs: 0025-326X

Abstract

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. Highlights: Oil spills risks in ice-infested waters are of great concern to Arctic and sub-Arctic states. A Bayesian Network model is developed for oil recovery effectiveness in ice covered waters. Models, data, and expert judgment are used as evidence base in the model development. The model provides insights in the recovery effectiveness in various scenarios. Overall, the oil recovery effectiveness using mechanical means is relatively low.