Modeling and evaluation of the oil-spill emergency response capability based on linguistic variables

2016 ◽  
Vol 113 (1-2) ◽  
pp. 293-301 ◽  
Author(s):  
Jian Kang ◽  
Jixin Zhang ◽  
Yongqiang Bai
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Linguistic Variables ◽  
Response Capability

UNNECESSARILY NEGLECTED IN PLANNING: ILLUSTRATION OF A PRACTICAL APPROACH TO IDENTIFY HUMAN DIMENSION IMPACTS OF MARINE OIL SPILLS

2012 ◽  
Vol 14 (02) ◽  
pp. 1250012 ◽  
Author(s):  
FABIENNE LORD ◽  
SETH TULER ◽  
THOMAS WEBLER ◽  
KIRSTIN DOW
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Damage Assessment ◽  
Oil Spills ◽  
Social Systems ◽  
Human Dimensions ◽  
Contingency Planning ◽  
Human Dimension ◽  
Contingency Plans ◽  
Negative Impacts

Technological hazards research, including that on oil spills and their aftermath, is giving greater attention to human dimension impacts resulting from events and response. While oil spill contingency planners recognize the importance of human dimension impacts, little systematic attention is given to them in contingency plans. We introduce an approach to identifying human dimensions impacts using concepts from hazard and vulnerability assessment and apply it to the Bouchard-120 oil spill in Buzzards Bay, MA. Our assessment covers the spill, emergency response, clean-up, damage assessment, and mid-term recovery. This approach, while still exploratory, did demonstrate that the spill produced a range of positive and negative impacts on people and institutions and that these were mediated by vulnerabilities. We suggest ways in which the framework may help spill managers to learn from events and improve contingency planning by anticipating risks to social systems and identifying strategies to reduce impacts.

scholarly journals Optimization on Emergency Materials Dispatching Considering the Characteristics of Integrated Emergency Response for Large-Scale Marine Oil Spills

2019 ◽  
Vol 7 (7) ◽  
pp. 214 ◽  
Author(s):  
Song Li ◽  
Manel Grifoll ◽  
Miquel Estrada ◽  
Pengjun Zheng ◽  
Hongxiang Feng
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Large Scale ◽  
Oil Spills ◽  
Marine Oil ◽  
Emergency Managers ◽  
Distribution Point ◽  
Dispatching Algorithm ◽  
Marine Oil Spills ◽  
The Difference

Many governments have been strengthening the construction of hardware facilities and equipment to prevent and control marine oil spills. However, in order to deal with large-scale marine oil spills more efficiently, emergency materials dispatching algorithm still needs further optimization. The present study presents a methodology for emergency materials dispatching optimization based on four steps, combined with the construction of Chinese oil spill response capacity. First, the present emergency response procedure for large-scale marine oil spills should be analyzed. Second, in accordance with different grade accidents, the demands of all kinds of emergency materials are replaced by an equivalent volume that can unify the units. Third, constraint conditions of the emergency materials dispatching optimization model should be presented, and the objective function of the model should be postulated with the purpose of minimizing the largest sailing time of all oil spill emergency disposal vessels, and the difference in sailing time among vessels that belong to the same emergency materials collection and distribution point. Finally, the present study applies a toolbox and optimization solver to optimize the emergency materials dispatching problem. A calculation example is presented, highlighting the sensibility of the results at different grades of oil spills. The present research would be helpful for emergency managers in tackling an efficient materials dispatching scheme, while considering the integrated emergency response procedure.

Resource allocation, emergency response capability, and infrastructure concentration around vulnerable sites

2011 ◽  
Vol 14 (5) ◽  
pp. 597-613 ◽  
Author(s):  
Jeffrey S. Simonoff ◽  
Carlos E. Restrepo ◽  
Rae Zimmerman ◽  
Zvia Segal Naphtali ◽  
Henry H. Willis
Keyword(s):  
Resource Allocation ◽  
Emergency Response ◽  
Response Capability

scholarly journals Application of the SOSim v2 Model to Spills of Sunken Oil in Rivers

2020 ◽  
Vol 8 (9) ◽  
pp. 729
Author(s):  
Mary Jacketti ◽  
James D. Englehardt ◽  
C.J. Beegle-Krause
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Likelihood Function ◽  
Transport Processes ◽  
Longitudinal Distribution ◽  
Concentration Data ◽  
Deterministic Models ◽  
Oil Transport ◽  
Bathymetric Data ◽  
Sunken Oil

Sunken oil transport processes in rivers differ from those in oceans, and currently available models may not be generally applicable to sunken oil in river settings. The open-source Subsurface Oil Simulator (SOSim) model has been expanded to handle spills of sunken oil in navigable rivers, utilizing Bayesian inference to integrate field concentration data with bathymetric data to predict the location and movement of sunken oil. A novel prior likelihood function incorporates bathymetric input, with sampling grid and default parameters adapted appropriately for rivers. SOSim v2 was demonstrated versus field observations taken following the M/T (Motor Tanker) Athos I oil spill. The model was also modified to operate in 1-D, to assess the longitudinal distribution of sunken oil in a non-navigable river using available poling data collected following the Enbridge Kalamazoo River oil spill in 2010. Results of both case studies were consistent with observed data and local bathymetry in 2-D and 1-D, and the model is suggested as a complement to deterministic models for oil spill emergency response in rivers.

scholarly journals Innovative Approaches for Geometric Uncertainty Quantification in an Operational Oil Spill Modeling System

2019 ◽  
Vol 7 (8) ◽  
pp. 259 ◽  
Author(s):  
Dongyu Feng ◽  
Paola Passalacqua ◽  
Ben R. Hodges
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Learning Algorithm ◽  
Coarse Grid ◽  
Data Driven ◽  
Model Integration ◽  
Fine Grid ◽  
Oil Transport ◽  
Uncertainty Model ◽  
Geometric Uncertainty

Reliable and rapid real-time prediction of likely oil transport paths is critical for decision-making from emergency response managers and timely clean-up after a spill. As high-resolution hydrodynamic models are slow, operational oil spill systems generally rely on relatively coarse-grid models to provide quick estimates of the near-future surface-water velocities and oil transport paths. However, the coarse grid resolution introduces model structural errors, which have been called “geometric uncertainty”. Presently, emergency response managers do not have readily-available methods for estimating how geometric uncertainty might affect predictions. This research develops new methods to quantify geometric uncertainty using fine- and coarse-grid models within a lagoonal estuary along the coast of the northern Gulf of Mexico. Using measures of geometric uncertainty, we propose and test a new data-driven uncertainty model along with a multi-model integration approach to quantify this uncertainty in an operational context. The data-driven uncertainty model is developed from a machine learning algorithm that provides a priori assessment of the prediction’s confidence degree. The multi-model integration generates ensemble predictions through comparison with limited fine-grid predictions. The two approaches provide explicit information on the expected scale of modeling errors induced by geometric uncertainty in a manner suitable for operational modeling.

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