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James Englehardt, UM professor of environmental engineering in the College of Engineering and team leader for the project, said: "Sunken oil is difficult to 'see' because sensing techniques show only a small space at a point in time. Moreover, the oil may re-suspend and sink, with changes in salinity, sediment load, and temperature, making fate and transport models difficult to deploy and adjust.
"For these reasons, we have developed a unique approach to the problem, bridging sampling plan techniques with pollutant transport modelling, to create models of sunken oil."
Development of a Predictive Bayesian Data-Derived Multi-Modal Gaussian Maximum-Likelihood Model of Sunken Oil Mass.
The problem addressed in this research is the need for cost-effective location and tracking of sunken oil following a spill. The term sunken oil is used to refer to oil on the bottom, though in the future the method may be extended to address oil suspended in the water column. As described in the Coastal Response Research Center (CRRC) Request for Proposals, "In the past few years, spills of non-floating oil and oils that become submerged as a function of sediment entrainment have presented significant response challenges and have resulted in enormous dollar-per-barrel recovery costs. Currently, the ability to forecast submerged oil movement, estimate water column concentrations of large droplets, and efficiently recover sunken masses in an operationally expedient way is quite limited. Additionally, as this category of oil is difficult to locate, track and retrieve, managers have difficulty maintaining public confidence with regard to response termination." Problems in locating and tracking sunken oil are further exacerbated by the expense of developing and deploying new remote sensing techniques, and because the oil may re-suspend and sink with changes in salinity, sediment load, and temperature. For more information about the CRRC visit www.crrc.unh.edu...