Lead Alert is a data-driven approach to infrastructure system management. By combining several data sources and applying machine learning, Lead Alert aims to predict water based lead contamination in our communities.
Lead has been known to be harmful to humans even in small doses for over 50 years. Steps have been taken to limit its use in construction materials, particularly water pipes, in the 1970s before it was outright banned by the EPA in 1984. However, children are still experiencing lead poisoning at alarming rates. While the Flint water crisis has brought attention to this issue, reports have shown many communities across the country with rates of lead poisoning in children exceeding that of Flint. The persistence of this major public health problem and the creation of new relevant datasets create an opportunity to apply new thinking and techniques to solve it.
Testing of water systems is typically only performed at the treatment plant, or the upstream end of a system. By the time water has been delivered to a residence, the distribution system may have added contamination to the water, as is typical with lead contamination. Utilities, especially those in older communities, often don’t know the details of their distribution systems including pipe material, pipe size, or even the location of pipe. Early systems were built without meticulous records, and construction in the field often deviates from plan. Digging things up to find out what is in the field is prohibitively expensive and intrusive. Prioritizing where to spend municipal dollars is a common approach, and new datasets give us a chance to redefine the prioritization method using machine learning to predict areas where the population is at highest risk.