Progress on new artificial intelligence (AI) technology could make monitoring at water treatment plants cheaper and easier and help safeguard public health.
Researchers at the University of Waterloo have developed AI software capable of identifying and quantifying different kinds of cyanobacteria, or blue-green algae, a threat that can shut down water systems when it suddenly proliferates. Cyanobacteria blooms have prompted a number of water quality advisories across Canada this summer.
In August 2014, the city of Toledo, Ohio issued a “do not drink” water advisory and declared a state of emergency. This was due to elevated levels of the dangerous toxin microcystin in treated drinking water. Microcystin is produced by cyanobacteria.
“We need to protect our water supplies,” said Monica Emelko, a professor of civil and environmental engineering and member of the Water Institute at Waterloo. “This tool will arm us with a sentinel system, a more rapid indication when they are threatened.
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The operational AI system uses software in combination with a microscope to inexpensively and automatically analyze water samples for algae cells in about one to two hours, including confirmation of results by a human analyst.
Current testing methods, which typically involve sending samples to labs for manual analysis by technicians, take one to two days. Some automated systems already exist as well, but they require extremely expensive equipment and supplies.
According to Emelko and collaborator Alexander Wong, a systems design engineering professor at Waterloo, the AI system would provide an early warning of problems since testing could be done much more quickly and frequently.
Moving forward, the goal is an AI system to continuously monitor water flowing through a microscope for a wide range of contaminants and microorganisms.
“This brings our research into a high-impact area,” said Wong. “Helping to ensure safe water through widespread deployment of this technology would be one of the great ways to really make AI count.”
The researchers estimate it may take two to three years to refine a fully commercial sample testing system for use in labs or in-house at treatment plants. The technology to provide continuous monitoring could be three to four years away.
Adjunct engineering professor Chao Jin, doctoral student Jason Deglint and research associate Maria Mesquita are also collaborators.
A study on the research, Quantification of cyanobacterial cells via a novel imaging-driven technique with an integrated fluorescence signature, was recently published in the journal Scientific Reports.
For more information, visit: www.uwaterloo.ca