Newswise — Public utility managers will be able to predict drinking water quality more accurately thanks to a team of scientists with the Global Change Center at Virginia Tech.
The team, which includes ecologists, social scientists, geologists, and engineers, was awarded a $1 million National Science Foundation Smart and Connected Communities grant to develop a system that can create a real-time water forecast similar to a weather forecast for Falling Creek Reservoir near Roanoke, Virginia. Falling Creek is one of five drinking water reservoirs utilized by the Western Virginia Water Authority.
The system will collect multiple real-time environmental datasets, such as levels of metals in the reservoir water, presence of aquatic life, oxygenation levels from current treatment, and use these data, along with local weather predictions and a state-of-the-art reservoir model, to forecast future water quality.
Warmer temperatures brought on by climate change can spur algal blooms and unlock metals stored in the sediment of the reservoir, both of which can cause taste, staining, and odor issues in the drinking water.
“This smart system will allow us to predict times at which water may require additional treatment due to environmental factors,” said Cayelan Carey, an assistant professor of biological sciences in the College of Science. “We can then use adaptive management practices to create the best water possible at all times for consumers.”
The system is based on a similar technique that forecasts the growth of loblolly pine forests in order to better inform land management in light of global change. Quinn Thomas, an assistant professor of ecosystem science in the College of Natural Resources and Environment, designed the technique and will implement it in this project as well. The system will transmit data to a cloud-based network available to scientists and water utility managers.
“Our goal is to create probabilistic forecasts of water quality, similar to a ’20 percent chance of rain’ weather forecast,” said Thomas.
Another component of the project involves researching the best way to ensure the forecasts will be integrated into management decisions. Social scientist Michael Sorice, an associate professor in the College of Natural Resources and Environment, is an expert in the study of human dimensions of natural resource management. He will engage with water managers to understand current practices and how the new scientific data and technology could best be implemented into daily tasks. In addition, he will examine public perceptions of this new technology and its effect on public trust in the water authority.
The Appalachian region’s geology results in high levels of iron and manganese in sediment that lines the bottom of the reservoir. However, in the past, the team found that pumping additional oxygen into the bottom waters of the reservoirs can keep these metals safely locked up in the sediment, even in warmer temperatures. Madeline Schreiber, a professor of geosciences in the College of Science, and John Little, the Charles E. Via Jr. Professor of Civil and Environmental Engineering in the College of Engineering, were key to these findings.
“The Falling Creek Reservoir is a unique ecosystem, and we are excited to expand our partnership with the Global Change Center to benefit the drinking water consumers of the Western Virginia Water Authority and limnologists around the world,” said Jamie Morris, water production manager for the Western Virginia Water Authority.
The research team also includes partners at North Carolina State University and the University of Florida. Francois Birgand, an assistant professor of biological and agricultural engineering at North Carolina State University, developed the next-generation sensors that can be used to monitor water chemistry in approximately 10-30 minutes, taking much less time than the weeks needed for traditional lab work.
Renato Figueiredo, a professor of electrical and computer engineering at the University of Florida, will use cyberinfrastructure that he developed to securely transmit the sensor data and run the models creating the water quality forecasts.
The team was initially supported with a seed grant from the Global Change Center, an arm of the Fralin Life Science Institute at Virginia Tech.