Collaborative Research: Unraveling Microscale Spatiotemporal Interactions Between Water Additives, Biofilms, and Metals in Drinking Water Distribution Systems

Reliable and safe drinking water supplies are important to public health. Water utilities that use chlorine to kill harmful microbes must decide how much chlorine to add and how long it must stay in their systems. Pipe corrosion and microbial biofilms on pipe walls reduce chlorine levels, which can compromise water quality. Biofilms weaken the effects of chlorine, which may enable microorganisms and pathogens to survive. In addition, corrosion can release harmful metals such as lead and copper. Most utilities check chlorine loss, corrosion, and biofilm growth by measuring properties of the bulk water. These tests cannot show the chemical reactions that happen directly on the pipe wall, where chlorine loss begins. This project will study how disinfectants, corrosion inhibitors, and microorganisms interact at the pipe-water surface on a microscopic level. The project will create high-resolution data and combine them with artificial intelligence (AI) tools. This will help water utilities estimate chlorine demand more accurately, improve contact time, and prevent water quality problems. The results will also support better corrosion control and help with regulations such as the Lead and Copper Rule Improvements. The project will also train students in environmental engineering, biotechnology, electrochemistry, and AI, and it will strengthen collaboration between researchers and water utilities. This project will develop a mechanistic and predictive framework linking microscale biofilm-pipe interface processes to disinfectant decay rates, orthophosphate behavior, and localized corrosion in the drinking water distribution system. Controlled biofilm reactors containing ductile iron, copper, galvanic joints, and inert control materials will be operated under free chlorine and monochloramine regimes with defined orthophosphate dosing. Microelectrodes will provide in situ, depth-resolved measurements of pH, dissolved oxygen, oxidation–reduction potential, and disinfectant concentration gradients within biofilms and near metal surfaces. These chemical profiles will be coupled with corrosion metrics (metal release and surface characterization) and microscale multi-omics, including metagenomics and metatranscriptomics, to resolve microbial functional pathways associated with biofilm formation and microbiologically influenced corrosion. The resulting multimodal dataset will be integrated using interpretable machine learning approaches, including random forest, gradient boosting, and neural network models, with SHapley Additive exPlanations (SHAP) analysis to identify dominant predictors of chlorine decay and biofilm resistance. Microscale-derived wall reaction constants will be compared with traditional EPANET formulations to improve predictions of disinfectant decay and chlorine concentration-time requirements. By integrating advanced electrochemical sensing, spatially resolved multi-omics, and AI-driven modeling, the project will establish a mechanistically grounded predictive framework for managing disinfectant stability and corrosion control in water infrastructure. 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. NSF Award ID: 2550355 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Chamteut Oh | Institution: University of Florida, GAINESVILLE, FL | Award Amount: $200,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2550355 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2550355.html