Analysis of factors affecting the effectiveness of oil spill clean-up: A bayesian entwork approach

Ship-related marine oil spills pose a significant threat to the environment, and while it may not be possible to prevent such incidents entirely, effective clean-up efforts can minimize their impact on the environment. The success of these clean-up efforts is influenced by various factors, including...

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Detalhes bibliográficos
Autores: Zhong, Liangxia, Wu, Jiaxin, Wen, Yiqing, Yang, Bingjie, Grifoll Colls, Manel|||0000-0003-4260-6732, Hu, Yunping, Zheng, Pengjun
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/385899
Acesso em linha:https://hdl.handle.net/2117/385899
https://dx.doi.org/10.3390/su15064965
Access Level:acceso abierto
Palavra-chave:Oil spills
Oil pollution of the sea
Marine pollution
Bayesian statistical decision theory
Oil spill clean-up ratio
Analysis of factors
Bayesian network
Vessaments de petroli
Mar--Contaminació per hidrocarburs
Mar--Contaminació
Estadística bayesiana
Àrees temàtiques de la UPC::Nàutica::Impacte ambiental
Àrees temàtiques de la UPC::Nàutica::Seguretat marítima::Contaminació marina
Descrição
Resumo:Ship-related marine oil spills pose a significant threat to the environment, and while it may not be possible to prevent such incidents entirely, effective clean-up efforts can minimize their impact on the environment. The success of these clean-up efforts is influenced by various factors, including accident-related factors such as the type of accident, location, and environmental weather conditions, as well as emergency response-related factors such as available resources and response actions. To improve targeted and effective responses to oil spills resulting from ship accidents and enhance oil spill emergency response methods, it is essential to understand the factors that affect their effectiveness. In this study, a data-driven Bayesian network (TAN) analysis approach was used with data from the U.S. Coast Guard (USCG) to identify the key accident-related factors that impact oil spill clean-up performance. The analysis found that the amount of discharge, severity, and the location of the accident are the most critical factors affecting the clean-up ratio. These findings are significant for emergency management and planning oil spill clean-up efforts.