The Centers for Disease Control and Prevention estimates that there are at least 20 million individuals sickened each year from pathogens in drinking water, most commonly with gastrointestinal or respiratory infections. Respiratory infections from two common pathogens alone in drinking water are responsible for 29,000 hospitalizations annually at a cost exceeding $800 million. A primary reason for pathogens in drinking water is deterioration of water quality in the distribution network, as water flows from treatment plants to homes and businesses. This happens, for example, when chlorine added to drinking water to inactivate pathogens is consumed because of long travel times in pipe networks, metal pipe corrosion, and reactions with organic matter. The goal of this Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project is to develop new science and engineering tools for mitigating public health risks associated with drinking water by deploying sensors in the water distribution network, and using data from these sensors and periodic sampling to develop computer models that forecast lapses in water quality and quantify public health risks. The research team will collaborate with public utilities and stakeholders to advance decision making for the issuance of drinking water advisory notices, implementing changes in water treatment, and prioritizing future infrastructure investments. This research will require input from the disciplines of hydrology, chemistry, microbiology, systems engineering, big data, risk assessment, and public health, and will help broaden participation of underrepresented groups and enhance engineering education.

The primary challenge for improving water quality in the distribution system is the timely and accurate collection of data for the development of accurate and computationally efficient forecasting tools to support decision making. The research objectives of this project are to: 1) advance scientific methods for integrating smart sensors into the water distribution network; 2) discover new fundamental relationships for coupled processes and their integration into a process-based model; 3) research new algorithms for data-driven modeling that link sensor data and process-based model results for forecasting public health risks; 4) explore investment strategy impacts on water quality; and 5) communicate the new knowledge to stakeholders. The research approach includes three test beds of different scales to deploy and test the smart sensors, incorporates complex reactions into an existing process-based model, and uses these tools to train and validate the data driven-model. The outcome of the modeling framework will be a multi-parameter, multi-process decision space to provide guidance for implementation actions and infrastructure investment for the management of water quality in water distribution systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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University of Texas Austin
United States
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