Literature DB >> 28028707

Synergistic use of an oil drift model and remote sensing observations for oil spill monitoring.

Diana De Padova1, Michele Mossa2, Maria Adamo3, Giacomo De Carolis4, Guido Pasquariello3.   

Abstract

In case of oil spills due to disasters, one of the environmental concerns is the oil trajectories and spatial distribution. To meet these new challenges, spill response plans need to be upgraded. An important component of such a plan would be models able to simulate the behaviour of oil in terms of trajectories and spatial distribution, if accidentally released, in deep water. All these models need to be calibrated with independent observations. The aim of the present paper is to demonstrate that significant support to oil slick monitoring can be obtained by the synergistic use of oil drift models and remote sensing observations. Based on transport properties and weathering processes, oil drift models can indeed predict the fate of spilled oil under the action of water current velocity and wind in terms of oil position, concentration and thickness distribution. The oil spill event that occurred on 31 May 2003 in the Baltic Sea offshore the Swedish and Danish coasts is considered a case study with the aim of producing three-dimensional models of sea circulation and oil contaminant transport. The High-Resolution Limited Area Model (HIRLAM) is used for atmospheric forcing. The results of the numerical modelling of current speed and water surface elevation data are validated by measurements carried out in Kalmarsund, Simrishamn and Kungsholmsfort stations over a period of 18 days and 17 h. The oil spill model uses the current field obtained from a circulation model. Near-infrared (NIR) satellite images were compared with numerical simulations. The simulation was able to predict both the oil spill trajectories of the observed slick and thickness distribution. Therefore, this work shows how oil drift modelling and remotely sensed data can provide the right synergy to reproduce the timing and transport of the oil and to get reliable estimates of thicknesses of spilled oil to prepare an emergency plan and to assess the magnitude of risk involved in case of oil spills due to disaster.

Entities:  

Keywords:  Fun Shan Hai; Numerical model; Oil spill modelling; Oil thickness; Oil weathering; Remote sensing

Mesh:

Substances:

Year:  2016        PMID: 28028707     DOI: 10.1007/s11356-016-8214-8

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  7 in total

1.  Oil film thickness measurement using airborne laser-induced water Raman backscatter.

Authors:  F E Hoge; R N Swift
Journal:  Appl Opt       Date:  1980-10-01       Impact factor: 1.980

2.  Hindcast of oil-spill pollution during the Lebanon crisis in the Eastern Mediterranean, July-August 2006.

Authors:  Giovanni Coppini; Michela De Dominicis; George Zodiatis; Robin Lardner; Nadia Pinardi; Rosalia Santoleri; Simone Colella; Francesco Bignami; Daniel R Hayes; Dmitry Soloviev; Georgios Georgiou; George Kallos
Journal:  Mar Pollut Bull       Date:  2010-09-28       Impact factor: 5.553

3.  Trajectory of an oil spill off Goa, eastern Arabian Sea: field observations and simulations.

Authors:  P Vethamony; K Sudheesh; M T Babu; S Jayakumar; R Manimurali; A K Saran; L H Sharma; B Rajan; M Srivastava
Journal:  Environ Pollut       Date:  2007-02-07       Impact factor: 8.071

4.  Simulation of impact of oil spill in the ocean--a case study of Arabian Gulf.

Authors:  Parikshit Verma; Satish R Wate; Sukumar Devotta
Journal:  Environ Monit Assess       Date:  2007-12-20       Impact factor: 2.513

5.  Oil spills: measurements of their distributions and volumes by multifrequency microwave radiometry.

Authors:  J P Hollinger; R A Mennella
Journal:  Science       Date:  1973-07-06       Impact factor: 47.728

Review 6.  Review of oil spill remote sensing.

Authors:  Merv Fingas; Carl Brown
Journal:  Mar Pollut Bull       Date:  2014-04-20       Impact factor: 5.553

7.  An optical remote sensing model for estimating oil slick thickness based on two-beam interference theory.

Authors:  Yingcheng Lu; Xiang Li; Qingjiu Tian; Wenchao Han
Journal:  Opt Express       Date:  2012-10-22       Impact factor: 3.894

  7 in total
  1 in total

Review 1.  A Review of Oil Spill Remote Sensing.

Authors:  Merv Fingas; Carl E Brown
Journal:  Sensors (Basel)       Date:  2017-12-30       Impact factor: 3.576

  1 in total

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