Project Overview

Summary:

In January 2016, Dr. Adrian Burd at the University of Georgia was awarded an RFP-V grant of $893,187 to lead the GoMRI project entitled Oil-Marine Snow-Mineral Aggregate Interactions and Sedimentation during the 2010 Deepwater Horizon Oil Spill consisted of 2 collaborative institution and approximately 8 research team members (including students)

The goal of this project will be to use coagulation theory to develop a predictive, mechanistic model for how oil coagulates with particulate material in the marine environment. There is strong observational evidence that oil interacts with particles in the marine environment forming heterogeneous aggregates comprised of oil droplets, mineral particles such as clay and silica, and biological particles such as phytoplankton cells, zooplankton fecal pellets, and marine snow (large heterogeneous aggregates). Such oil-aggregates have been observed in surface waters and in sediment traps, indicating that oil contained in these aggregates can be transported vertically from the surface to the deep ocean, ultimately providing a flux of oil to the seafloor. Estimates from the Deepwater Horizon oil spill suggest that oil released into the environment accumulated at the seafloor as a result of such interactions, potentially affecting an area of at least 3,200 km2. However, sinking of oil associated with aggregates is rarely incorporated into models of the transport of oil, which focus on the distribution of oil by currents.

The PIs propose to build on their combined expertise to develop a depth-dependent model of the coagulation of oil with marine particles to determine the dominant factors governing oil-particle interactions and to predict rates and timing of sedimentation of oil associated with aggregates to the sea floor. They will use detailed particle observations, including size distributions and abundances, from the NE Gulf of Mexico, and observations from sediment traps and laboratory experiments, to develop and validate the model. The model will also incorporate environmental variables such as water temperature and salinity profiles. Multiple simulations will be run, varying factors that affect coagulation rates; e.g. oil-droplet size distributions, phytoplankton cell sizes, salinity, and mineral particle concentrations. From the results of these simulations the investigators will develop parameterizations that can be incorporated into predictive models of oil transport and deposition. The goal will be to build a generic framework that can be used in different locations with appropriate parameterization as part of an oil spill emergency response tool.

This proposal directly addresses the GOMRI Research Theme 1, "Physical distribution, dispersion, and dilution of petroleum (oil and gas), its constituents, and associated contaminants (e.g., dispersants) under the action of physical oceanographic processes, air-sea interactions, and tropical storms" and also directly addresses knowledge gaps identified in the GOMRI-funded MOSSFA Workshop report. Coagulation is a physical process, mediated by such physical oceanographic processes such as fluid shear and turbulence. However, these coagulation processes are not generally represented in models, thereby making predictions of oil deposition less certain. The development of accurate parameterizations will directly improve the community's ability to quantitatively model the distribution of petroleum and its constituents under a wide variety of oceanic conditions. This will have major societal implications by improving predictions of oil transport and areas that will be affected after a spill, and assisting emergency responders in understanding the physical conditions that will promote or hinder oil interactions with marine particles and the subsequent sedimentation to the deep ocean. This model will also help improve predictions of the impacts of oil spills on deep-sea biological communities by improving predictions of the footprint of oil deposition.

Research Highlights

As of December 31, 2019, this project’s research resulted in, 5 peer-reviewed articles, 2 book chapters, 12 scientific presentations and 6 datasets being submitted to the GoMRI Information and Data Cooperative (GRIIDC), which are/will be made available to the public. The project also engaged 1 Masters and 1 PhD student over its award period and 1 undergraduate. Significant outcomes of this project’s research according to GoMRI Research Theme are highlighted below.

Theme One:

In brief, this project has shown that the formation of marine-oil-snow and the occurrence of Marine Oil Snow Sedimentation and Flocculent Accumulation (MOSSFA) events seen during and after the Deepwater Horizon oil spill can be successfully modeled and used to predict the flux of oil carried to the sediments.

The main component of this project was the development of predictive computer models of the formation and fate of marine-oil-snow and Marine Oil Snow Sedimentation and Flocculent Accumulation (MOSSFA) events. In the course of this project, two models were developed that showed that such models could be developed. Dissanayake et al. (2018) developed a one-dimensional (i.e. depth only) time-dependent model that was able to track the formation and fate of individual components of marine-oil-snow. This model relied heavily on the analysis of image data of particles for the physical characteristics of the marine-oil-snow (e.g. the size range of particles and their fractal dimension). The model included multiple processes and simulated the changes in the marine-oil-snow size spectrum as MOS were formed and sank. The model was successful in reproducing the major changes in particle size spectra from the surface to depths of about 350 m (where the last measurements were made) and was able to predict the flux of oil delivered to the seafloor (at 1500 m depth) via sinking marine snow. The predicted oil fluxes compared well with those inferred from measurements of sediment cores. The model was used successfully at 5 locations in the vicinity of the Deepwater Horizon well where detailed size spectra were available, and performed well in four cases — for one site, this was attributed to the higher concentration of particles at that location. The model predicted bulk settling velocities of aggregates of approximately 60 m d^-1, in line with laboratory estimates (though sinking rates of individual particles can be much higher).

The model not only shows that predictions of marine-oil-snow formation and MOSSFA events can be done with reasonable predictive skill, it also allows us to examine areas where further research is needed. A sensitivity analysis showed that the model results were particularly sensitive to the assumed fractal dimension of the marine-oil-snow particles, and to the formulation used for determining the likelihood that particles will adhere once they have collided (the stickiness of the particles). These areas are also at the forefront of research on non-oil related marine snow.

This model has since been used to examine the range of conditions under which marine-oil-snow and MOSSFA events occur (Jokulsdottir and Burd, 2020). Marine-oil-snow formation and MOSSFA events depend on the co-occurrence of oil in the water with significant biological particles (e.g. phytoplankton) and lithogenic particles (e.g. rivering sediments). The model was further validated against sediment trap data, and showed that it could successfully reproduce the non-oil fluxes into sediment traps in the region, and was able to simulate reasonably well the oil trapped in the sediment traps. Simulating the Deepwater Horizon oil spill, the model showed the presence of three significant MOSSFA pulses, a phenomenon suspected from analysis of sediment trap data. We were also able to predict how the amount of oil being transported to the seafloor varied with the concentration of phytoplankton and lithogenic particles in the water column. This will be useful for helping to assess the likelihood of MOSSFA events occurring during future spills.

A simpler coagulation model was used to examine the interaction of oil in the water column with diatom aggregates. This model simulated the diatom bloom and resulting formation of marine snow and the subsequent scavenging of that oil, the degradation of the particles as they sank through the water column (Francis and Passow, 2020). This model shows that oil and diatoms caught in a sediment traps shortly after the Deepwater Horizon well was capped. This event is accurately represented by the model which also suggests that the trap was deployed after the peak in sedimentation, showing the power of such combined aggregation/oil transport models. The success of this model strongly suggests that the combined use of satellite data to estimate surface phytoplankton concentrations and aggregation modeling can be used to predict the likelihood of occurrence of marine-oil-snow formation and MOSSFA events following an oil spill.  

We have used a combination of image analysis and laboratory experiments to determine some of the physical parameters required for accurate aggregation modeling of marine-oil-snow and MOSSFA. First, we were able to determine the geometrical nature of the marine-oil-snow aggregates by analyzing images from the Gulf of Mexico taken during the Deepwater Horizon oil spill. This shows that the fractal dimension of the marine-oil-snow varied between 0.925 and 1.94 with a mean value of 1.44. This is lower than the standard value for non-marine-oil-snow. This was the first determination of this number that we are aware of. In addition, most particles were elongated, not spherical. In addition, we were able to measure settling velocities of marine-oil-snow as a function of their size in the laboratory (Passow, et al., 2019). These showed that for individual aggregates formed from diatoms and oil, sinking velocities varied from approximately 150 m d^-1 to over 1300 m d^-1 over a size range of equivalent spherical diameter of 300 µm to 30 mm.

References

Dissanayake, A.L., Burd, A.B., Daly, K.L., Francis, S., Passow, U., 2018. Numerical modeling of the interactions of oil, marine snow, and riverine sediments.  J. Geophys, Res: Oceans, 123, doi:10.1029/2018JC013790.

Francis, S., Passow, U., 2020. Transport of dispersed oil compounds to the seafloor by sinking phytoplankton aggregates: A modeling study. Deep-Sea Research I, 156, 103192, doi:10.106/j.dsr.2019.103192.

Jokulsdottir, T., Burd, A.B., 2020. Modeling marine-oil-snow formation and fate. In preparation for submission to Progress in Oceanography.

Passow, U., Sweet., J., Francis, S., Xu, C., Dissanayake, A.L., Lin, Y.-Y., Santschi, P.H., Quigg, A., 2019. Incorporation of oil into diatom aggregates. Mar. Ecol. Prog. Ser., 612:65–86.

  Proposal Abstract - RFP-V PI Adrian Burd