Advertisement

Environmental Modeling & Assessment

, Volume 24, Issue 5, pp 479–494 | Cite as

Protecting Sensitive Coastal Areas with Exclusion Booms during Oil Spill Events

  • Tony GrubesicEmail author
  • Ran Wei
  • Jake Nelson
Article

Abstract

Oil spills at sea remain a serious threat to coastal settlements and sensitive ecosystems. Although the impacts of spills are contingent upon a variety of environmental factors and the chemical composition of the oil itself, spill effects can be long lasting in the pelagic zone with broad impacts on sensitive bacterial, microbial, plant, and animal communities. Efforts to contain, deflect, protect, and mitigate the effects of oil are increasingly important, given the massive social, economic, and environmental fallout connected to large spills. The purpose of this paper is to provide geographic perspective for protecting coastal areas with exclusion booms during oil spill events. Specifically, we introduce a generalized, extendable, spatial optimization model that simultaneously minimizes spill effects on vulnerable shorelines and the total costs associated with dispatching booms. The multiobjective model is solved with a weighting method to produce a Pareto optimal curve that reveals how the costs and protection operations change under different priorities. A simulated tanker spill near Mobile Bay, AL, USA, is used as an illustrative example.

Keywords

Oil spill response Optimization Planning Gulf of Mexico Spatial analysis 

Notes

Funding Information

This work was supported by the National Academies of Science Gulf Research Program (No. 2000007349).

References

  1. 1.
    Elmgren, R., Hansson, S., Larsson, U., Sundelin, B., & Boehm, P. D. (1983). The “Tsesis” oil spill: Acute and long-term impact on the benthos. Marine Biology, 73(1), 51–65.Google Scholar
  2. 2.
    Gundlach, E. R., & Hayes, M. O. (1978). Vulnerability of coastal environments to oil spill impacts. Marine Technology Society Journal, 271, 164–165.  https://doi.org/10.1038/271164a0.CrossRefGoogle Scholar
  3. 3.
    Peterson, C. H., Rice, S. D., Short, J. W., Esler, D., Bodkin, J. L., Ballachey, B. E., & Irons, D. B. (2003). Long-term ecosystem response to the Exxon Valdez oil spill. Science, 302(5653), 2082–2086.Google Scholar
  4. 4.
    Kingston, P. F. (2002). Long-term environmental impact of oil spills. Spill Science & Technology Bulletin, 7(1), 53–61.Google Scholar
  5. 5.
    Allen, A. A., Schlueter, R. S., & Mikolaj, P. G. (1970). Natural oil seepage at coal oil point, Santa Barbara, California. Science, 170(3961), 974–977.Google Scholar
  6. 6.
    Jernelöv, A. (2010). The threats from oil spills: Now, then, and in the future. Ambio, 39(5–6), 353–366.  https://doi.org/10.1007/s13280-010-0085-5.CrossRefGoogle Scholar
  7. 7.
    O’Rourke, D., & Connolly, S. (2003). Just oil? The distribution of environmental and social impacts of oil production and consumption. Annual Review of Environment and Resources, 28(1), 587–617.Google Scholar
  8. 8.
    Reed, M., Aamo, O. M., & Daling, P. S. (1995). Quantitative analysis of alternate oil spill response strategies using OSCAR. Spill Science & Technology Bulletin, 2(1), 67–74.Google Scholar
  9. 9.
    Jensen, J. R., Narumalani, S., Weatherbee, O., Murday, M., Sexton, W. J., & Green, C. J. (1993). Coastal environmental sensitivity mapping for oil spills in the United Arab Emirates using remote sensing and GIS technology. Geocarto International, 8(2), 5–13.Google Scholar
  10. 10.
    Jensen, J. R., Halls, J. N., & Michel, J. (1998). A systems approach to environmental sensitivity index (ESI) mapping for oil spill contingency planning and response. Photogrammetric Engineering and Remote Sensing, 64, 1003–1014.Google Scholar
  11. 11.
    Kokkonen, T., Ihaksi, T., Jolma, A., & Kuikka, S. (2010). Dynamic mapping of nature values to support prioritization of coastal oil combating. Environmental Modelling & Software, 25(2), 248–257.Google Scholar
  12. 12.
    Nelson, J., Grubesic, T., Sim, L., Rose, K., & Graham, J. (2015). Approach for assessing coastal vulnerability to oil spills for prevention and readiness using GIS and the Blowout and Spill Occurrence Model. Ocean & Coastal Management, 112, 1–11.  https://doi.org/10.1016/j.ocecoaman.2015.04.014.Google Scholar
  13. 13.
    Kim, A. (2005). Experts discuss risk of future oil spills. The Seattle Times, p. 1. Seattle. Retrieved from http://tinyurl.com/knkq6ww
  14. 14.
    GAO. (1997). Coast guard: Challenges for addressing budget constraints. United States General Accounting Office. Retrieved from http://tinyurl.com/lhlafjo
  15. 15.
    Owens, E. H., & Robilliard, G. A. (1981). Shoreline sensitivity and oil spills-a re-evaluation for the 1980’s. Marine Pollution Bulletin, 12(3), 75–78.  https://doi.org/10.1016/0025-326X(81)90196-X.CrossRefGoogle Scholar
  16. 16.
    EIA. (2016). Oil production in federal Gulf of Mexico projected to reach record high in 2017. Today in Energy. Retrieved December 5, 2017, from https://www.eia.gov/todayinenergy/detail.php?id=25012
  17. 17.
    BOEM. (2017). Offshore statistics by water depth. Leasing Data Center. Retrieved December 5, 2017, from https://www.data.boem.gov/Leasing/OffshoreStatsbyWD/Default.aspx
  18. 18.
    Muehlenbachs, L., Cohen, M. A., & Gerarden, T. (2013). The impact of water depth on safety and environmental performance in offshore oil and gas production. Energy Policy, 55, 699–705.Google Scholar
  19. 19.
    AP. (2010). 27,000 abandoned oil and gas wells in Gulf of Mexico ignored by government, industry. Associated Press. Retrieved from http://tinyurl.com/3x8vdu7
  20. 20.
    Incardona, J. P., Gardner, L. D., Linbo, T. L., Brown, T. L., Esbaugh, A. J., Mager, E. M., et al. (2014). Deepwater Horizon crude oil impacts the developing hearts of large predatory pelagic fish. Proceedings of the National Academy of Sciences, 111(15), E1510–E1518.Google Scholar
  21. 21.
    Hansel, T. C., Osofsky, H. J., Osofsky, J. D., & Speier, A. (2015). Longer-term mental and behavioral health effects of the Deepwater Horizon Gulf oil spill. Journal of Marine Science and Engineering, 3(4), 1260–1271.Google Scholar
  22. 22.
    Beyer, J., Trannum, H. C., Bakke, T., Hodson, P. V., & Collier, T. K. (2016). Environmental effects of the Deepwater Horizon oil spill: A review. Marine Pollution Bulletin, 110(1), 28–51.Google Scholar
  23. 23.
    Lin, Q., Mendelssohn, I. A., Graham, S. A., Hou, A., Fleeger, J. W., & Deis, D. R. (2016). Response of salt marshes to oiling from the Deepwater Horizon spill: Implications for plant growth, soil surface-erosion, and shoreline stability. Science of the Total Environment, 557, 369–377.Google Scholar
  24. 24.
    Prouty, N. G., Fisher, C. R., Demopoulos, A. W. J., & Druffel, E. R. M. (2016). Growth rates and ages of deep-sea corals impacted by the Deepwater Horizon oil spill. Deep Sea Research Part II: Topical Studies in Oceanography, 129, 196–212.Google Scholar
  25. 25.
    Galt, J. A., & Payton, D. L. (1999). Development of quantitative methods for spill response planning: A trajectory analysis planner. Spill Science & Technology Bulletin, 5(1), 17–28.Google Scholar
  26. 26.
    Violeau, D., Buvat, C., Abed-Meraïm, K., & De Nanteuil, E. (2007). Numerical modelling of boom and oil spill with SPH. Coastal Engineering, 54(12), 895–913.Google Scholar
  27. 27.
    Fingas, M. (2012). The basics of oil spill cleanup. CRC press.Google Scholar
  28. 28.
    ITOPF. (2014). Use of booms in oil pollution response. International Tanker Owners Pollution Federation. Retrieved February 10, 2015, from http://tinyurl.com/kbwoefb
  29. 29.
    Castanedo, S., Medina, R., Losada, I. J., Vidal, C., Méndez, F. J., Osorio, A., Juanes, J. A., & Puente, A. (2006). The Prestige oil spill in Cantabria (Bay of Biscay). Part I: Operational forecasting system for quick response, risk assessment, and protection of natural resources. Journal of Coastal Research, 226, 1474–1489.Google Scholar
  30. 30.
    ADSPR. (2017). Prevention preparedness and response. Alaska Division of Spill Prevention and Response. Retrieved from http://dec.alaska.gov/spar/ppr/star/docs.htm
  31. 31.
    Goodman, R. H., Brown, H. M., An, C.-F., & Rowe, R. D. (1996). Dynamic modelling of oil boom failure using computational fluid dynamics. Spill Science & Technology Bulletin, 3(4), 213–216.Google Scholar
  32. 32.
    Fang, F., & Johnston, A. J. (2001). Oil containment by boom in waves and wind. I: Numerical model. Journal of Waterway, Port, Coastal, and Ocean Engineering, 127(4), 222–227.Google Scholar
  33. 33.
    Muttin, F. (2008). Structural analysis of oil-spill containment booms in coastal and estuary waters. Applied Ocean research, 30(2), 107–112.Google Scholar
  34. 34.
    Zhu, S.-P., & Strunin, D. (2001). Modelling the confinement of spilled oil with floating booms. Applied Mathematical Modelling, 25(9), 713–729.Google Scholar
  35. 35.
    Zhu, S.-P., & Strunin, D. (2002). A numerical model for the confinement of oil spill with floating booms. Spill Science & Technology Bulletin, 7(5), 249–255.Google Scholar
  36. 36.
    French-McCay, D. P. (2004). Oil spill impact modeling: Development and validation. Environmental Toxicology and Chemistry, 23(10), 2441–2456.Google Scholar
  37. 37.
    De Dominicis, M., Pinardi, N., Zodiatis, G., & Archetti, R. (2013). MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting—part 2: Numerical simulations and validations. Geoscientific Model Development, 6(6), 1871–1888.Google Scholar
  38. 38.
    Belardo, S., Karwan, K. R., & Wallace, W. A. (1984). Managing the response to disasters using microcomputers. Interfaces, 14(2), 29–39.Google Scholar
  39. 39.
    Psaraftis, H. N., & Ziogas, B. O. (1985). A tactical decision algorithm for the optimal dispatching of oil spill cleanup equipment. Management Science, 31(12), 1475–1491.  https://doi.org/10.1287/mnsc.31.12.1475.CrossRefGoogle Scholar
  40. 40.
    Psaraftis, H. N., Tharakan, G. G., & Ceder, A. (1986). Optimal response to oil spills: The strategic decision case. Operations Research, 34(2), 203–217.Google Scholar
  41. 41.
    Iakovou, E., Ip, C. M., & Koulamas, C. (1996). Optimal solutions for the machining economics problem with stochastically distributed tool lives. European Journal of Operational Research, 92(1), 63–68.Google Scholar
  42. 42.
    Srinivasa, A. V., & Wilhelm, W. E. (1997). A procedure for optimizing tactical response in oil spill clean up operations. European Journal of Operational Research, 102(3), 554–574.Google Scholar
  43. 43.
    Grigalunas, T. A., Opaluch, J. J., French, D., Reed, M., & Knauss, D. (1988). A natural resource damage assessment model for coastal and marine environments. GeoJournal, 16(3), 315–321.Google Scholar
  44. 44.
    You, F., & Leyffer, S. (2011). Mixed-integer dynamic optimization for oil-spill response planning with integration of a dynamic oil weathering model. AICHE Journal, 57(12), 3555–3564.Google Scholar
  45. 45.
    Zhong, Z., & You, F. (2011). Oil spill response planning with consideration of physicochemical evolution of the oil slick: A multiobjective optimization approach. Computers & Chemical Engineering, 35(8), 1614–1630.Google Scholar
  46. 46.
    Grubesic, T. H., Wei, R., & Nelson, J. (2017). Optimizing oil spill cleanup efforts: A tactical approach and evaluation framework. Marine Pollution Bulletin, 125(1–2), 318–329.  https://doi.org/10.1016/j.marpolbul.2017.09.012.CrossRefGoogle Scholar
  47. 47.
    Beegle-Krause, J. (2001). General NOAA Oil Modeling Environment (GNOME): A new spill trajectory model. International Oil Spill Conference Proceedings, 2001(2), 865–871.  https://doi.org/10.7901/2169-3358-2001-2-865.CrossRefGoogle Scholar
  48. 48.
    Cheng, Y., Li, X., Xu, Q., Garcia-Pineda, O., Andersen, O. B., & Pichel, W. G. (2011). SAR observation and model tracking of an oil spill event in coastal waters. Marine Pollution Bulletin, 62(2), 350–363.Google Scholar
  49. 49.
    Liu, Y., Weisberg, R. H., Hu, C., & Zheng, L. (2011). Tracking the Deepwater Horizon oil spill: A modeling perspective. Eos, Transactions …, 92(6), 2010–2012.  https://doi.org/10.1029/2011EO060001.CrossRefGoogle Scholar
  50. 50.
    Farzingohar, M., Z Ibrahim, Z., & Yasemi, M. (2011). Oil spill modeling of diesel and gasoline with GNOME around Rajaee Port of Bandar Abbas, Iran. Iranian Journal of Fisheries Sciences, 10(1), 35–46.Google Scholar
  51. 51.
    Zelenke, B., O’Connor, C., Barker, C., Beegle-Krause, C. J., & Eclipse, L. (2012). General NOAA Operational Modeling Environment (GNOME) technical documentation. US Dept. of Commerce. NOAA Technical Memorandum NOS OR&R, 40, 105.Google Scholar
  52. 52.
    FWC. (2017). Oil spill response equipment storage locations. Florida Fish and Wildlife Conservation Commission. Retrieved from https://tinyurl.com/ya75fas4
  53. 53.
    FWC. (2017). Geographic response plan (GRP) staging areas. Florida Fish and Wildlife Conservation Commission. Retrieved from https://tinyurl.com/yctvlq6z
  54. 54.
    Etkin, D. S. (2004). Modeling oil spill response and damage costs. In Proceedings of the Fifth Biennial Freshwater Spills Symposium.Google Scholar
  55. 55.
    Schmidt Etkin, D. (2009). Effectiveness of larger-area exclusion booming to protect sensitive sites in San Francisco Bay. Cortlandt Manor. Retrieved from http://tinyurl.com/y7p5hb59
  56. 56.
    NOAA. (2017). Environmental sensitivity index (ESI) maps. Office of Response and Restoration.Google Scholar
  57. 57.
    Al Shami, A., Harik, G., Alameddine, I., Bruschi, D., Garcia, D. A., & El-Fadel, M. (2017). Risk assessment of oil spills along the Mediterranean coast: A sensitivity analysis of the choice of hazard quantification. Science of the Total Environment, 574, 234–245.  https://doi.org/10.1016/j.scitotenv.2016.09.064.Google Scholar
  58. 58.
    Sepp Neves, A. A., Pinardi, N., Martins, F., Janeiro, J., Samaras, A., Zodiatis, G., & De Dominicis, M. (2015). Towards a common oil spill risk assessment framework – Adapting ISO 31000 and addressing uncertainties. Journal of Environmental Management, 159, 158–168.  https://doi.org/10.1016/j.jenvman.2015.04.044.Google Scholar
  59. 59.
    Cohon, J. L. (2013). Multiobjective programming and planning. Courier Corporation.Google Scholar
  60. 60.
    Miettinen, K. (1998). A posteriori methods. In Nonlinear multiobjective optimization (pp. 77–113). Springer.Google Scholar
  61. 61.
    Fingas, M. (2016). Oil spill science and technology. Gulf professional publishing.Google Scholar
  62. 62.
    Nelson, J. R., Bauer, J. R., & Rose, K. (2014). Assessment of geographic setting on oil spill impact severity in the United States—insights from two key spill events in support of risk assessment for science-based decision making. Journal of Sustainable Energy Engineering, 2(2), 152–165.Google Scholar
  63. 63.
    Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.Google Scholar
  64. 64.
    Davis, J. R., Paramygin, V. A., Figueiredo, R. J., Sheng, Y. P., Vogiatzis, C., & Pardalos, P. M. (2013). The coastal science educational virtual appliance (CSEVA). In Estuarine and coastal modeling (Vol. 2011, pp. 359–377).Google Scholar
  65. 65.
    Davis, J. R., Paramygin, V. A., Vogiatzis, C., Sheng, Y. P., Pardalos, P. M., & Figueiredo, R. J. (2014). Strengthening the resiliency of a coastal transportation system through integrated simulation of storm surge, inundation, and nonrecurrent congestion in northeast Florida. Journal of Marine Science and Engineering, 2(2), 287–305.Google Scholar
  66. 66.
    Malik, A., Maciejewski, R., Jang, Y., Oliveros, S., Yang, Y., Maule, B., White, M., & Ebert, D. S. (2014). A visual analytics process for maritime response, resource allocation and risk assessment. Information Visualization, 13(2), 93–110.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Center for Spatial Reasoning & Policy AnalyticsArizona State UniversityPhoenixUSA
  2. 2.Center for Geospatial SciencesRiversideUSA

Personalised recommendations