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Estimating the Impact of Oil on Flocculation Processes with a New Parameterized Sediment Model
When oil spills occur in marine environments, the oil droplets, marine snow, and mineral grains can combine to form Oil Mineral Aggregates (OMAs), which have a wide range of settling velocities and densities. As a result, their properties can strongly influence the eventual fate of the oil. As part of the Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO), we evaluated the role of turbidity in partitioning oil into OMAs by incorporating flocculation and aggregation processes into the Community Sediment Transport Modeling System (CSTMS) within the Coupled Ocean-Atmosphere-Wave-and-Sediment Transport (COAWST) modeling framework. Specifically, an existing size-class based aggregation and fragmentation model (FLOCMOD) was adopted to examine the impact of oil on the vertical transport of sediment. FLOCMOD acts as a population balance flocculation model and allows particle exchanges through aggregation, shear breakup and collision breakup. Our one-dimensional SED_FLOC_TOY model represented a muddy 50-m deep site on the northern Gulf of Mexico continental shelf. It was driven by horizontally uniform, steady currents, salinity and temperature, extracted from a three-dimensional hydrodynamic model. The initial sediment distribution was split among 11 floc size classes (ranging from 1 to 1024 micron diameter). Sediment was input at the top of the water column to represent fall out from a freshwater plume. Flocculation processes removed mass from the smaller and larger classes through aggregation and breakup, which resulted in a net increase in sediment mass of the middle sizes. FLOCMOD’s collision and breakup efficiencies were parameterized to represent the presence or absence of oil. Sensitivity tests of collision and breakup efficiencies indicated that total suspended sediment mass was decreased by 40% by increasing/decreasing the collision/breakup efficiency. FLOCMOD was computationally expensive, in this test case, computation was slowed down by 1.5 times after incorporating the aggregation and fragmentation processes.