Development of an Algorithm for Chemically Dispersed Oil Spills

Abstract

An algorithm utilizing four basic processes was described for chemical oil spill dispersion. Initial dispersion was calculated using a modified Delvigne equation adjusted to chemical dispersion, then the dispersion was distributed over the mixing depth, as predicted by the wave height. Then the droplets rise to the surface according to Stokes’ law. Oil on the surface, from the rising oil and that undispersed, is re-dispersed. The droplets in the water column are subject to coalescence as governed by the Smoluchowski equation. A loss is invoked to account for the production of small droplets that rise slowly and are not re-integrated with the main surface slick. The droplets become less dispersible as time proceeds because of increased viscosity through weathering, and by increased droplet size by coalescence. These droplets rise faster as time progresses because of the increased size. Closed form solutions were provided to allow practical limits of dispersibility given inputs of oil viscosity and wind speed. Discrete solutions were given to calculate the amount of oil in the water column at specified points of time. Regression equations were provided to estimate oil in the water column at a given time with the wind speed and oil viscosity. The models indicated that the most important factor related to the amount of dispersion, was the mixing depth of the sea as predicted from wind speed. The second most important factor was the viscosity of the starting oil. The algorithm predicted the maximum viscosity that would be dispersed given wind conditions. Simplified prediction equations were created using regression.

Transport of Microplastic and Dispersed Oil Co-contaminants in the Marine Environment

“…The influence of MP size on MODA transport was elucidated by MP density and SSA variations. Studies found that physical properties such as density and SSA would affect the vertical transport of agglomerates in oceans. ,, Since the density of 7 μm MPs (1070.00 kg/m 3 ) was larger than seawater (1022.60 kg/m 3 at a salinity of 34 psu), the formed MODA-L7 would have a high proportion in the seawater column due to their negative buoyancy. The density of 40 μm MPs (990.00 kg/m 3 ) and 90 μm MPs (990.00 kg/m 3 ) was less than seawater, thus the proportion of MODA-L40 and MODA-L90 in the seawater column was lower than that of MODA-L7.…”

Remote Sensing of Dispersed Oil Pollution in the Ocean—The Role of Chlorophyll Concentration

“…Despite the widely researched spilled oil including in situ, ship-borne, airplane and space-borne techniques [ 20 , 21 ], the fates of dispersed oil in seawater are much less known and are not monitored on a regular basis. Recently dispersed oil gained attention in marine research; however, most studies focus on its biological and ecological impact [ 22 , 23 ], chemical and microbiological consequences [ 24 , 25 , 26 ] or on oil spill modeling [ 27 , 28 , 29 ]. Few studies focus on optical properties of dispersed oil demonstrating the significance and potential utility of optical sensing of oil droplets using both active and passive remote techniques [ 30 , 31 , 32 , 33 ].…”

Influence of Dispersed Oil on the Remote Sensing Reflectance—Field Experiment in the Baltic Sea

“…Advanced oil spill models help predict the dynamics and evolution of an oil slick [14], assisting the scientists and the authorities in forecasting their trajectories in order to develop the best clean-up plans. Such models calculate the probable amount of oil in the water column at specified points of time [37]; however, the results highly depend on the model input data, the assumptions about the character of oil weathering processes, and a multitude of other factors [14]. The challenge of model execution in real time points toward the need for fast remote verification methods.…”