Evaluation and Comparison of Current Emulsification Algorithms and Their Uncertainty in Oil Spill Modeling Software

Abstract

Oil spill risk assessments (OSRAs) do not currently distinguish between potentially more toxic, fresh crude oil and less toxic, highly-weathered residues which limits the understanding of the highest risk areas to prioritize mitigation measures and allocate response resources. Fate and trajectory models, used commonly for OSRA, have advanced significantly over the last five years now with enhanced ability to model chemical and physical parameters at greater resolution. Using this enhanced resolution, modelers may be able to provide some indication of the weathered state of the oil as input to the OSRA. In order to evaluate the degree of certainty in such a prediction, it is necessary to better understand the uncertainty in the modeled weathering processes that influence the toxicity of the oil. Emulsification plays a significant role in modeling of oil thickness (and therefore photo-modification), evaporation, and dissolution which are important modulators of oil toxicity. In this project, the emulsification algorithms of three currently available fate and trajectory models, ADIOS, OILMAP/SIMAP, and OSCAR, were evaluated to gain a better understanding of the degree of certainty in the modeled weathered state of oil. In this work, the basis of emulsification algorithms implemented in the models referenced above were identified, and it was found that each of these models incorporates emulsification differently. ADIOS2 relies on emulsification data gathered from mixing oil and water in a food processor. An updated version of ADIOS2 (ADIOS3) is based on a new formulation that is dependent on measured SARA components of the oil, but is still under construction and is not yet implemented. OILMAP/SIMAP use the algorithm presented in Mackay and Zagorski (1982). OSCAR uses a water uptake algorithm that was calibrated to in-house laboratory experiments. Further investigation into the development of each of these emulsification algorithms provided insight into the degree of uncertainty in these models and their input parameters, and what oil types may not be appropriately characterized by the implemented emulsification model. Additionally, the impact of that uncertainty on oil fate was investigated by evaluating the changes in the amount of emulsification when modifying user input parameters within realistic assumption ranges. The findings and comparison of the implementation of these emulsification algorithms and the sensitivity of the results to different inputs is presented here.