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An explorative object-oriented Bayesian network model for oil spill response in the Arctic Ocean

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Abstract

This paper proposes an Object-Oriented Bayesian Network model to study oil spill mitigative measures using cost-effectiveness as a decision-making factor. The cost-effectiveness is defined as the ratio of dollar spent to the efficiency of oil removal in the spill area. The proposed model considers the complexity, lack of data, and uncertainties in an oil spill and response modelling scenario for Arctic shipping accident occurrence. The crude oil release, slick formation, weathering and transport, ecological impact, and response scenarios are modelled using Bayesian Network. Application of the model is demonstrated using a hypothetical scenario of an oil spill involving a ship in the Arctic region. The results show that a combination of in-situ burning, mechanical and manual recovery, and use of dispersants would cost the highest, while the use of in-situ burning, and dispersants gives the cheapest option. The study provides a deeper understanding of oil spill dynamics and effectiveness of the response techniques. The proposed model could serve as a useful tool for oil spill response decision-making.

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References

  • Afenyo M, Khan F, Veitch B, Yang M (2016) Modeling oil weathering and transport in sea ice. Mar Pollut Bull 107:206–215

    Article  Google Scholar 

  • Afenyo M, Khan F, Veitch B, Yang M (2017) A probabilistic ecological risk model for Arctic marine oil spills. J Environ Chem Eng 5:1494–1503

    Article  Google Scholar 

  • Afenyo M, Jiang C, Ng A (2019) Climate change and Arctic shipping: a method for assessing the impacts of oil spills in the Arctic. Transportation Research Part D: Transport and Environment, Transportation Research Part D: Transport and Environment 77:476-490. Assessed from https://www.sciencedirect.com/science/article/pii/S1361920919300884?via%3Dihub Assessed 30 Nov 2019

  • Afenyo M, Khan F, Ng AKY (2020). Assesing the risk of potential oil spills in the Arctic due to shipping,pp 179-193. In: Ng AKY, Monois J, Jiang C(Eds). Maritime Transport and Regional Sustainability. Elservier, 3-345. Assessed from https://www.sciencedirect.com/science/article/pii/B9780128191347000113

  • Al-Majed AA, Adebayo AR, Hossain ME (2012) A sustainable approach to controlling oil spills. J Environ Manag 113:213–227

    Article  Google Scholar 

  • Andersson K, Brynolf S, Lindgren F, Wilewska-Bien M (2016) Shipping and the environment: improving environmental performance in marine transportation. Springer, Berlin

    Book  Google Scholar 

  • Anon (1993) Agreement between the Inuit of Nunavut settlement area and her majesty the queen in right of Canada. Iqaluit, Northwest Territories. Assessed from https://gov.nu.ca/sites/default/files/files/013%20-%20Nunavut-Land-Claims-Agreement-English.pdf. On 11 Oct 2010

  • Anon (2017). Guidelines on implementing spill impact mitigation assessment (SIMA). A technical support document to accompany the IPIECA-IOGP guidance on net environmental benefit analysis (NEBA). IOGP Report 593, pp 1-46. Assessed from http://www.ipieca.org/resources/awareness-briefing/guidelines-on-implementing-spill-impact-mitigation-assessment-sima/

  • Arctic Oil Spill Response Technology-Joint Industrial Program (ART) (2015). Mechanical recovery in ice summary report. February 2015 pp 1-22. Assessed from http://www.arcticresponsetechnology.org/wp-content/uploads/2017/09/ACS-Mechanical-Recovery-of-Oil-in-Ice-Feasiblity-Report-Final-1208.pdf on April 2018

  • Benjamin-Fink N, Reilly BK (2017) A road map for developing and applying object-oriented Bayesian networks to “WICKED” problems. Ecol Model 360:27–44

    Article  Google Scholar 

  • Buist I, Potter S, Trudel B, Walker A, Scholz D, Brandvik P, Fritt-Rasmussen J, Allen A, Smith P (2013) In situ burning in ice affected waters: a technology summary and lessons learned from key experiments. Final report 7

  • Carriger JF, Barron MG (2011) Minimizing risks from spilled oil to ecosystem services using influence diagrams: the deepwater horizon spill response. Environ Sci Technol 45:7631–7639

    Article  Google Scholar 

  • Carriger JF, Barron MG, Newman MC (2016) Bayesian networks improve causal environmental assessments for evidence-based policy. Environ Sci Technol 50:13195–13205

    Article  Google Scholar 

  • Contreras LF, Brown ET, Ruest M (2018) Bayesian data analysis to quantify the uncertainty of intact rock strength. J Rock Mech Geotech Eng 10:11–31

    Article  Google Scholar 

  • Davidson WF, Lee K, Cogswell A (2008) Oil spill response: a global perspective. Springer Science & Business Media, Dordrecht

    Book  Google Scholar 

  • Davies AJ, Hope MJ (2015) Bayesian inference-based environmental decision support systems for oil spill response strategy selection. Mar Pollut Bull 96:87–102

    Article  Google Scholar 

  • Etkins DS (1999). Estimating Cleanup Costs for Oil Spills. International Oil Spill Conference Proceedings 1999 1999:1, 35-39. Assessed from https://ioscproceedings.org/doi/abs/10.7901/2169-3358-1999-1-35 on Apr 2018

  • Etkin DS (2000) Worldwide analysis of marine oil spill cleanup cost factors. Arctic and marine oilspill program technical seminar. Citeseer, pp 161–174

  • Fingas M (2015) Handbook of oil spill science and technology. Wiley, Hoboken

    Google Scholar 

  • Gill DA, Picou JS, Ritchie LA (2012) The Exxon Valdez and BP oil spills: a comparison of initial social and psychological impacts. Am Behav Sci 56:3–23

    Article  Google Scholar 

  • Government of Canada (GC) (2019) Canada’s Arctic and northern policy. Assessed from https://www.rcaanc-cirnac.gc.ca/eng/1560523306861/1560523330587. On 12 Oct 2019

  • Hassler B (2011) Accidental versus operational oil spills from shipping in the Baltic Sea: risk governance and management strategies. Ambio 40:170–178

    Article  Google Scholar 

  • Helle I, Ahtiainen H, Luoma E, Hänninen M, Kuikka S (2015) A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: a Bayesian network model for the Gulf of Finland. J Environ Manag 158:122–132

    Article  Google Scholar 

  • Herath D, Khan F, Yang M (2016) Risk-based winterization to prevent hydrate formation in northern harsh environment. Ocean Eng 119:208–216

    Article  Google Scholar 

  • Hoang AT, Pham V, Nguyen D (2018) A report of oil spill recovery technologies. Int J Appl Eng Res 13:4915–4928

    Google Scholar 

  • Juntunen T, Rosqvist T, Rytkönen J, Kuikka S (2005) How to model the oil combating technologies and their impacts on ecosystem: a Bayesian networks application in the Baltic Sea. ICES CM. Assessed from http://www.ices.dk/sites/pub/CM%20Doccuments/2005/S/S0205.pdf in Apr 2018

  • Khakzad N, Khan F, Amyotte P (2013) Quantitative risk analysis of offshore drilling operations: a Bayesian approach. Saf Sci 57:108–117

    Article  Google Scholar 

  • Khan B, Khan F, Veitch B, Yang M (2018) An operational risk analysis tool to analyze marine transportation in Arctic waters. Reliab Eng Syst Saf 169:485–502

    Article  Google Scholar 

  • Lee K, Boufadel M, Chen B, Foght J, Hodson P, Swanson S, Venosa A (2015) The behaviour and environmental impacts of crude oil released into aqueous environments. The Royal Society of Canada, Ottawa

    Google Scholar 

  • Lehikoinen A, Hänninen M, Storgård J, Luoma E, Mäntyniemi S, Kuikka S (2015) A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland. Environ Sci Technol 49:5301–5309

    Article  Google Scholar 

  • Li P, Cai Q, Lin W, Chen B, Zhang B (2016) Offshore oil spill response practices and emerging challenges. Mar Pollut Bull 110:6–27

    Article  Google Scholar 

  • Liu Q, Pérès F, Tchangani A (2016) Object oriented Bayesian network for complex system risk assessment. IFAC-PapersOnLine 49:31–36

    Article  Google Scholar 

  • Montewka J, Weckstrom M, Kujala P (2013) A probabilistic model estimating oil spill clean-up costs–a case study for the Gulf of Finland. Mar Pollut Bull 76(1–2):61–71

    Article  Google Scholar 

  • National Energy Board (NEB) (2011) Review of offshore drilling in the Canadian Arctic. Calgary. https://www.cer-rec.gc.ca/nrth/rctcffshrdrllngrvw/2011fnlrprt/index-eng.html. Assessed 11 Oct 2019

  • Nielsen TD, Jensen FV (2009). Bayesian networks and decision graphs. Springer Science & Business Media. Assessed from https://www.springer.com/gp/book/9780387682815.

  • O’Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH, Jenkinson DJ, Oakley JE, Rakow T (2006) Uncertain judgements: eliciting experts’ probabilities. Wiley, London

    Book  Google Scholar 

  • Office of Minister of Justice (OMJ) (2019) Northern Canada Vessel Traffic Services Zone Regulations. https://laws-lois.justice.gc.ca/eng/Regulations/SOR-2010-127/index.html. Assessed 12 Oct 2019

  • Robinson H, Gardiner W, Wenning RJ, Rempel-Hester MA (2017) Spill impact mitigation assessment framework for oil SPill response planning in the Arctic environment. In: International oil spill conference proceedings 2017, pp 1325–1344

  • Rogowska J, Namieśnik J (2010) Environmental implications of oil spills from shipping accidents. In: Reviews of environmental contamination and toxicology volume 206. Springer, New York, pp 95–114

    Chapter  Google Scholar 

  • Ryder HF, McDonough CM, Tosteson AN, Lurie JD (2009) Decision analysis and cost-effectiveness analysis. Semin Spine Surg. 2009 Dec; 21(4): 216–222. https://doi.org/10.1053/j.semss.2009.08.003 on April 2018

  • Sulistiyono H, Khan F, Lye L, Yang M (2015) A risk-based approach to developing design temperatures for vessels operating in low temperature environments. Ocean Eng 108:813–819

    Article  Google Scholar 

  • Sylves RT, Comfort LK (2012) The Exxon Valdez and BP Deepwater horizon oil spills: reducing risk in socio-technical systems. Am Behav Sci 56:76–103

    Article  Google Scholar 

  • Wang H, Xu J, Zhao W, Zhang J (2014) Effects and risk evaluation of oil spillage in the sea areas of Changxing Island. Int J Environ Res Public Health 11:8491–8507

    Article  Google Scholar 

  • Wilkinson J, Beegle-Krause CJ, Evers K-U, Hughes N, Lewis A, Reed M, Wadhams P (2017) Oil spill response capabilities and technologies for ice-covered Arctic marine waters: a review of recent developments and established practices. Ambio 46:423–441

    Article  Google Scholar 

  • Yang M, Khan FI, Lye L, Sulistiyono H, Dolny J, Oldford D (2013) Risk-based winterization for vessels operations in Arctic environments. J Ship Prod Des 29:199–210

    Article  Google Scholar 

Download references

Acknowledgements

The financial support of the Lloyd’s Register Foundation is acknowledged with gratitude. The Lloyd’s Register Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research. Mawuli Afenyo and Adolf K.Y. Ng are supported by the ‘Genomics Research in Oil Spill Preparedness and Emergency Response in an Arctic Marine Environment’ (GENICE) project funded by Genome Canada. Author Faisal Khan thankfully acknowledges the financial support provided by the Natural Sciences and Engineering Council of Canada and the Canada Research Chair (CRC) Tier I Program.

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Authors and Affiliations

  1. Transport Institute, University of Manitoba, Drake Centre, 181 Freedman Crescent, Winnipeg, MB, R3T 5V4, Canada

    Mawuli Afenyo

  2. Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, A1B 3X5, St. John’s, NL, Canada

    Faisal Khan, Brian Veitch, Zaman Sajid & Faisal Fahd

  3. Department of Supply Chain Management, Asper School of Business, St. John’s College, University of Manitoba, Winnipeg, MB, R3T 5V4, Canada

    Adolf K. Y. Ng

Authors
  1. Mawuli Afenyo
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  2. Faisal Khan
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  3. Brian Veitch
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  4. Adolf K. Y. Ng
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  5. Zaman Sajid
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  6. Faisal Fahd
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Corresponding author

Correspondence to Zaman Sajid.

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The authors declare that they have no conflict of interest.

Competing interests

Prof. Faisal Kahn is Editor-in-Chief of Safety in Extreme Environments. He was not involved in the peer-review or handling of the manuscript.

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Afenyo, M., Khan, F., Veitch, B. et al. An explorative object-oriented Bayesian network model for oil spill response in the Arctic Ocean. Saf. Extreme Environ. 2, 3–14 (2020). https://doi.org/10.1007/s42797-019-00012-7

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  • DOI: https://doi.org/10.1007/s42797-019-00012-7

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