Without a doubt, deep-water oil spills are messy and dangerous. Cleanup efforts can take anywhere from days to years, with huge environmental impacts. What if we could lessen the cleanup time and increase efficiency? What if there were accurate models to predict where oil will spread so cleanup efforts can be directed to specific areas?
That’s exactly what one Faculty of Agriculture researcher is working on.
Haibo Niu of the Department of Engineering has always had an interest in the environment. He began his career with a Bachelor of Engineering from Xi’an University of Architecture and Technology in China, specializing in water and wastewater engineering. After that, he completed his masters in environmental engineering and a PhD in Environmental Engineering at Memorial University in Newfoundland. It was his research that led him to ±«Óătv in 2012.
Now, Dr. Niu is looking at ways to improve oil spill models that are used for predicting the spread of oil in the ocean after a deep-water oil spill.
“Oil is a big industry in Canada and with that comes the risk of oil spills,” Dr. Niu explains. “We need better models to better predict how far the oil will spread, the thickness of the spill and how much oil will surface if it is spilled. We have models like this to simulate the spread of oil on the surface, however our existing deep-water models are still very limited, due to our limited knowledge on droplet sizes.”
To further his research, Dr. Niu recently received funding from the Marine Environmental Observation, Prediction and Response (MEOPAR) network. MEOPAR, hosted at Dal, is funded by the Government of Canada’s Networks of Centres of Excellence Program and is dedicated to improving Canada’s ability to manage and respond to risk in the marine environment. Dr. Niu was awarded $84,000 through MEOPAR’s Early Career Faculty Development Program. The program supports 12 projects led by researchers across Canada whose research is aimed at improving Canada’s marine environment.
Improving the model
Dr. Niu is conducting experiments that will help him to develop computer models to predict the trajectory of deep-water oil spills. The 30-metre tank with simulated subsurface release allows him to carefully analyze the effects of oil mixing with water from below the surface. A high-tech camera and other instruments are used to measure the size of the oil droplets. By knowing the size of the droplets, Dr. Niu is able to predict the spread of the oil.
“When oil is released from the pipe at the bottom of the ocean it becomes droplets,” Dr. Niu says. “How fast the oil rises to the surface of the water depends on the size of the oil droplets. Bigger droplets have a higher rise velocity and will travel to the surface much faster than smaller droplets. Smaller droplets travel very slowly and currents can carry them farther downstream.”
After studying the oil droplets, Dr. Niu combines his findings with ocean circulation models that show him how the water flows. The results will help Dr. Niu to simulate the spread of the oil in certain areas of the ocean. This will allow cleanup crews to direct their cleanup efforts to certain areas of the ocean.
An even closer look
Dr. Niu is taking his oil spill model a step further. He is looking at predicting the behaviors of deep-water spills with and without the use of chemical dispersant application.
“The way chemical dispersant works on deep-water oil is similar to dishwasher liquid,” says Dr. Niu. “It breaks up the oil into small droplets that will stay in the water column.”
Dr. Niu explains that when using chemical dispersants, the droplet size distributions and the ultimate fate of oil could be changed significantly depending on the type of oil and amount of dispersant applied. The resulting smaller oil droplets can be carried away by currents to distances far from the release site, get diluted over a larger body of water, and have more time to be biodegraded. On the other hand, oil from the cases without dispersant application will rise to the surface rapidly, and have more chances to reach shoreline. With the capability of predicting the trajectory of oil with and without the use of chemical dispersant application, the model can be used to assist with net environmental benefit analysis and to help to make decisions on dispersant use.
“To use or not to use chemical dispersants is not a simple decision,” Dr. Niu explains. “You have to decide based on the case you are dealing with. There is no magic solution for all cases. Without the proper computer models though, it is harder to make the proper decisions.”
Oil spills don't just affect the natural environment; they affect the public as well, in many ways.
“I want to understand what happens after an oil spill,” he explains. “I want to know the potential environmental impact and the impact on marine life. I want everyone else to understand this as well. If the public doesn’t have enough information about it, they will worry about it. And we don’t want them to worry about it.”
Through his extensive research, Dr. Niu will help improve oil spill models so that cleanup is fast and efficient, lessening the impact on wildlife and the environment and hopefully giving everyone a little less to worry about.