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Development of Quantitative Methods For Spill Response Planning: A Trajectory Analysis Planner
Institution:1. Waterborne Environmental, Inc., Leesburg, VA, USA;2. Cardno, Inc, Greenback, TN, USA;3. Syngenta Crop Protection, LLC, Greensboro, NC, USA;1. Unit of Toxicology, Department of Zoology, School of Life Sciences, Bharathiar University, Coimbatore, 641046, Tamil Nadu, India;2. Department of Zoology, Annamalai University, Annamalainagar, Chidambaram, 608002, Tamil Nadu, India;1. Dpto. Ingeniería Energética, Escuela Técnica Superior de Ingenieros, Universidad de Sevilla, Sevilla, Spain;2. Dpto. Física de la Materia Condensada, Instituto de Ciencia de Materiales de Sevilla, Universidad de Sevilla, Consejo Superior de Investigaciones Científicas, Sevilla, Spain;1. Department of Geology and Petroleum Studies, Makerere University, Kampala, Uganda;2. Department of Earth Sciences, University of Bergen, Norway
Abstract:In planning for response to oil spills, a great deal of information must be assimilated. Typically, geophysical flow patterns, ocean turbulence, complex chemical processes, ecological setting, fisheries activities, economics of land use, and engineering constraints on response equipment all need to be considered. This presents a formidable analysis problem. It can be shown, however, that if an appropriate set of evaluation data is available, an objective function and appropriate constraints can be formulated. From these equations, the response problem can be cast in terms of game theory or decision analysis and an optimal solution can be obtained using common scarce-resource allocation methods. The optimal solution obtained by this procedure maximizes the expected return over all possible implementations of a given set of response options. While considering the development of an optimal spill response, it is useful to consider whether (in the absence of complete data) implementing some subset of these methods is possible to provide relevant and useful information for the spill planning process, even though it may fall short of a statistically optimal solution. In this work we introduce a trajectory analysis planning (TAP) methodology that can provide a cohesive framework for integrating physical transport processes, environmental sensitivity of regional sites, and potential response options. This trajectory analysis planning methodology can be shown to implement a significant part of the game theory analysis and provide `minimum regret' strategy advice, without actually carrying out the optimization procedures.
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