Grist published a post today highlighting the work of Meena on her startup working to improve the quality of water systems – water quality and leaks through digital sensors and monitoring. Our water infrastructure is probably (US) older than any other system and most is buried deeply without quality or leak sensing. Until relatively recently, who worried about having enough clean water in the US?
Here’s description of the problem, quote: “Meanwhile, aging pipes, industrial pollution, and agricultural runoff mean that lead, nitrate, arsenic, and PFAs (terrifyingly dubbed “forever chemicals”) are just a few of the substances that are showing up at dangerously high levels in tap water around the world, and right here in the United States. While Flint, Michigan continues to make headlines, contamination is much more widespread. Research found that in 2015, nearly 21 million U.S. residents relied on water systems that violate the standards set by the federal Safe Drinking Water Act. And it’s no surprise that low-income communities of color are often hardest hit.”
Here is her approach to solving said problem, quote: “Utilizing machine learning, KETOS applies an algorithm to that data that governments can use to forecast contamination, leaks, shortages and more — and begin taking preventative measures. “We can start to understand the rate at which aquifers and groundwater and surface water are being depleted, instead of waiting until after those resources go into the red zone,” says Sankaran. The State of California Division of Drinking Water, for example, relies on KETOS’ water quality reports as an official source for its operations. According to Hong, KETOS’s ability to turn data into something actionable is rare in the world of sensors. Among Sankaran’s goals is not only to help stop disease outbreaks and water shortages — she wants water digitization to flag injustice. If a zip code with predominantly Black and brown residents is experiencing disproportionately high levels of toxins, for example, KETOS’ data lake would capture the disparity.”
Image source – Grist