Using Data Science to Protect Tap Water Quality

SPONSOR: Lucy Family Institute for Data and Society

Rob Nerenberg (PI) Michael Lemmon, Matt Sisk, Emily Clements, Yuying Duan, University of Notre Dame

Abstract: The proposed research uses data science to identify homes at risk for unhealthy tap water and develop data science-based strategies to mitigate these risks. While tap water is safe in the majority of homes in the US, there are notable exceptions, as seen recently in Flint, MI and Benton Harbor, MI. Unfortunately, water quality problems often impact low-income families, which are the least prepared to manage them. In most cases, water supplied to homes from the public distribution networks meets stringent EPA drinking water standards. The problems occur within homes. Pipes may leach toxic metals, such as lead and copper, or promote growth of pathogenic bacteria, such as Legionella pneumophila. In both cases, the decay in water quality correlates to the “water age” or stagnation within the plumbing system. Longer water ages provide more time for metals to leach and for bacteria to grow. Given the complex nature of plumbing systems that 1) may have been built over 100 years ago and lack documentation, 2) may have been modified over the years without documentation, 3) have pipes that are often hidden beneath the ground or behind walls, and 4) have highly variable water demands whose characteristics change depending on the type of fixtures and appliances, and the number and type of occupants, home plumbing systems often behave as black boxes. Data science can be used to identify which homes are at risk, and to develop strategies to mitigate the risks. For example, actively controlling water age in premise plumbing systems could help improve water quality and reduce health risks. The specific objectives of this project are to (1) develop a GIS-based model to predict homes with high water ages, and (2) develop machine-learning-based strategies to reduce water age. This project studies a data-based approach to identify homes with potential water quality concerns:, to identify homes likely to have high water ages.
Products:
  1. R. Nerenberg, M. Sisk, and M.D. Lemmon, "Using Data Science to Protect Residential Water Quality", original proposal, Lucy Family Institute for Data and Society Annual Fall Symposium, March 30, 2022.
  2. R. Nerenberg, M. Sisk, M.D. Lemmon, E. Clements, Y. Duan, ``Using Data Science to Protect Residential Water Quality’’, Lightning Talk and Poster, Lucy Family Institute for Data and Society Annual Fall Symposium, October 11, 2022.
  3. R. Nerenberg, M. Sisk, M.D. Lemmon, E. Clements, Y. Duan, ``Using Data Science to Protet Residential Water Quality’’ , Project Update presented to Katie Liu (Lucy Family Institute), March 30, 2023
  4. Yuying Duan, M.D. lemmon, "Fair Federated Learning For Deciding Community Improvement Grants", Final Report for Study 2, May 19, 2023.
  5. R. Nerenberg, M. Sisk, and M.D. Lemmon, "Using Data Science to Protect Residential Water Quality", Final Project Report, Lucy Family Institue ofr Data and Society, May 31, 2023.
Future Planned Deliverables:
  1. M.D. Lemmon, L. Montestruque, "SCC-IRG Track 2 - Project Overview - Transfer Learning for Fair and Scalable Flood Management in Smart Connected Cities", Preliminary NSF-SCC Project Overview, University of Notre Dame and HydroDigital LLC, May 1, 2023.