The Lead and Copper Rule Revisions (LCRR) deadline for lead service line inventories that indicate what a utility knows about the materials of their service lines is quickly approaching October 16, 2024.
Although this topic has been ongoing, a lot of unreported lead still exists in service lines because of the lack in accurate data. Whether there are missing, outdated or analog records outlining a utility’s responsibility versus a property owner’s, there is an urgent need to more efficiently identify lead. Ad hoc approaches taken to identify the culprit such as happenstance discoveries when replacing broken mains or repairing pipes, the accidental occurrence uncovered at a construction site, or even digging holes at random are no longer an option given the immediacy of LCRR. Furthermore, disadvantaged areas do not receive the same attention using these methods.
Machine learning that can produce a predictive model narrows the search and increases the likelihood of finding lead by concentrating efforts to be more cost efficient. The same solutions to this issue go part and parcel with reporting and compliance with the law and also aid clear public communication that is not only legally required but needed to restore faith between the utilities and those they serve. Utilities are also tasked with increased sampling for lead and copper in drinking water with a renewed emphasis on schools and daycares with additional requirements for public communications.
While some utilities are in good shape to hit the deadline, some companies lag, and a proactive approach is encouraged. And when those service line inventories are completed, following proper sampling and communications protocols will be vital. The solutions in this gallery were showcased at AWWA ACE23 in Toronto, Canada.
