An Inter-Laboratory Comparison of Protocols for Evaluating Oil Emulsification: Recommendations for Reporting Water-in-Oil Emulsion Formation and Properties to Inform Oil Spill Model Development

When oil is spilled into brackish or marine waters, the formation of water-in-oil emulsions can rapidly occur depending on the oil type and in-situ conditions. Water-in-oil emulsions have very different physical properties from the original oil, sometimes becoming almost solid mousses that pose additional challenges to existing response options. To further evaluate the factors that affect this process, an inter-laboratory study was performed across eight laboratories. The goal of the project was to identify lab-based protocols that represent or result in the same outcomes as field conditions to the best extent possible so that the properties measured from emulsions made with these protocols can be used to improve the accuracy of numerical modeling. Each laboratory followed their specific protocols to emulsify up to five different oil types covering a wide range of properties. Prior to experimentation the source oils’ physical properties were measured, and throughout the emulsification process samples were taken to evaluate the degree to which emulsification changed the physical and chemical properties. The timing of emulsion formation, the factors driving emulsification, and the measured properties were evaluated to understand the importance of factors such as light intensity, evaporative weathering, and water uptake in influencing these processes and properties. As oil spill models require consistent quantitative measurements that are representative of field conditions to develop reliable algorithms to predict emulsification and its influence on oil fate and exposure, measurement techniques among the laboratories. Based on findings from this study, recommendations for reporting the measurement techniques, emulsion properties, and sampling frequency as it pertains to oil spill modeling needs and the application of laboratory data to inform algorithm development.