Safe water for drinking, cooking, bathing, and cleaning is an essential cornerstone of public health. Without clean water, people get sick. The Centers for Disease Prevention and Control (CDC) works with other organizations such as the Pan American Health Organization (PAHO) to educate communities about safe water storage, treatment, cooking, and hygiene. Community water treatment plants are the primary element in providing clean and safe water for public consumption.
Blue-green algae, cyanobacteria, are a group of bacteria types that commonly threaten public water supplies. Blue-green algae proliferate rapidly once introduced to a water system, such as in a reservoir. Without rapid response and proper treatment, the algae can contaminate water resources and require that the system be shut down. Current testing methods for blue-green algae involve sending water samples to labs for analysis: a process that typically takes a day or longer.
Engineers at Canada’s University of Waterloo developed software that employs artificial intelligence to identify and quantify the various types of blue-green algae in just a few hours using standard microscopes. In a study published in Nature, the Waterloo researchers showed that their system accurately identifies and measures blue-algae concentrations. The system uses standard enumeration techniques with microscopic images of water samples. Machine learning-based AI speeds the bacteria differentiation and counting. A human analyst then confirms significant findings.
According to the University of Waterloo researchers, fully developed commercial water sample testing technology based on their work that is capable of continuous monitoring is three to four years distant. The potential for faster detection and response to threats to safe water supplies is an exciting technology to support community health.
