Potable water reuse was not initially on Emily Clements’ radar when she entered the University of Notre Dame at 15 years old, but both the school and the field proved a perfect fit. Clements first majored in chemical engineering, then returned to Notre Dame for both her master’s and Ph.D., culminating with a dissertation on modeling the impact of stochastic water demands in premise plumbing.
Following a two-year post-doc modeling water reuse in Lake Mead with the Southern Nevada Water Authority, Clements now serves as Reuse Innovation Manager at Carollo Engineers, Inc., where she works on advanced treatment performance. “I’ve always loved the environment, being outside, and asking questions, and I think my current role fits right in there,” Clements said. “Especially since my childhood dream job of scooping ice cream wouldn’t be as fun now that I’m dairy-free.”
A key component of that role is public health modeling, including evaluating pathogen and indicator removal, studying membrane fouling behavior, assessing treatment reliability, and quantitative microbial risk assessments. Water treatment systems can be highly complex, varying depending on the source water quality, operational conditions, and seasons. Understanding how these systems behave is an important but challenging task, especially given the low-probability, high-consequence nature of public health risks.
Clements leverages machine learning and statistical modeling techniques to address these challenges. “Many systems generate large datasets, but integrating those data into predictive tools, machine learning models, and risk assessment frameworks remains a developing area,” she said. Advancing those tools, she said, requires a “stronger integration” across disciplines, including environmental engineering, microbiology, computational analysis, and policymaking.
That integrated approach has already been featured in two NAWI projects: “Data-driven Fault Detection and Process Control for Potable Reuse with Reverse Osmosis” (5.17) and “A Convergent Monitoring Platform for Dynamic Characterization of Reverse Osmosis Membrane Fouling and Demonstration of Innovative Control Strategies” (3.13).
Risk mitigation is particularly important for potable reuse, where local environments often have limited alternatives. “Scarcity, population growth, and climate-related stress are increasing pressure on existing water supplies,” she said. Data analysis and predictive modeling can help utilities optimize system performance and anticipate treatment decline, improving reliability. “Potable reuse has the potential to provide sustainable and resilient water resources, but strong scientific understanding of treatment performance and risk is essential.”
Scientific findings must then be applied to a patchwork regulatory landscape, where risk tolerances and policies vary significantly across state and local boundaries. Communication with policymakers is now a critical two-way street. Improved understanding of treatment performance and risk allows for more informed water reuse regulations, in turn creating a better environment for utilities to operate in.
“Developing science-based, adaptable, and consistent regulatory approaches will be important for advancing safe and reliable reuse practices while maintaining public trust,” Clements said. Ultimately, the benefits are significant for resource-strained communities. “Improving confidence in potable reuse systems and strengthening treatment reliability can help support long-term water security while protecting public health.”
In addition to her professional work, Clements supports the Clean Water Help initiative, an organization that provides water disinfection and filtration services in countries including Cambodia, Ghana, and Guatemala. She is an avid Brandon Sanderson reader and writes creatively. She also enjoys hiking with her dog Toast, discovering new cafes, and a good thrift store find.