A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight. (1st February 2020)
- Record Type:
- Journal Article
- Title:
- A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight. (1st February 2020)
- Main Title:
- A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight
- Authors:
- Liu, Zengkai
Callies, Ulrich - Abstract:
- Abstract: Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis. Graphical abstract: Image 1 Highlights: A Bayesian network (BN) for decision making on using chemical dispersants to combat oil spills is proposed. The BN integrates keyAbstract: Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis. Graphical abstract: Image 1 Highlights: A Bayesian network (BN) for decision making on using chemical dispersants to combat oil spills is proposed. The BN integrates key criteria of decision making such as drift behavior, dispersion efficacy and ecological impacts. Based on expected satisfaction, the BN supports contingency planning regarding oil spills in the German Bight. … (more)
- Is Part Of:
- Water research. Volume 169(2020)
- Journal:
- Water research
- Issue:
- Volume 169(2020)
- Issue Display:
- Volume 169, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 169
- Issue:
- 2020
- Issue Sort Value:
- 2020-0169-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-02-01
- Subjects:
- Oil spill -- Chemical dispersants -- Decision making -- German bight -- Bayesian network
Water -- Pollution -- Research -- Periodicals
363.7394 - Journal URLs:
- http://catalog.hathitrust.org/api/volumes/oclc/1769499.html ↗
http://www.sciencedirect.com/science/journal/00431354 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.watres.2019.115196 ↗
- Languages:
- English
- ISSNs:
- 0043-1354
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9273.400000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12518.xml