CAREER: Unraveling Membrane Fouling Mechanisms in Wildfire-Impacted Water Treatments

Wildfires are becoming more frequent and severe across the United States. After a fire, rain washes burned organic matter and metals on the ground into streams and reservoirs. These events place growing stress on drinking water systems that serve millions of people. As a result, water treatment reliability declines and operational risks increase. This project will examine how water runoff from wildfire-affected land causes ongoing treatment problems, and how utilities can respond effectively. The research combines advanced chemical measurements with artificial intelligence (AI) tools to predict membrane fouling, an indicator of treatment efficiency deterioration. The team will identify indicators that signal high fouling risk before system performance declines. These indicators will support a decision-making framework that helps utilities adjust pretreatment strategies in near real time. The project outcome will strengthen water system resilience in fire-prone regions. The team will incorporate the research with education and workforce development through courses and K-12 learning modules for outreach. These efforts will prepare the next generation of scientists and engineers while supporting reliable and resilient drinking water systems. This project will elucidate the molecular mechanisms by which dissolved organic matter (DOM) in water from wildfire-impacted areas promotes irreversible membrane fouling. The research is motivated by preliminary study indicates that high-severity burns produce smaller, more hydrophilic DOM molecules enriched in polar functional groups, which strongly complex with metals such as Fe and Al. The project will characterize DOM in soil leachates and post-fire in-stream waters collected from areas across burn severities using Orbitrap high-resolution mass spectrometry, fluorescence spectroscopy, ultraviolet–visible absorbance, and inductively coupled plasma mass spectrometry to resolve molecular composition and metal complexation behavior. Water quality impact on membrane fouling propensity will be examined by controlled ultrafiltration experiments to determine flux decline, fouling reversibility, and cleaning efficiency across multiple membrane materials. Fouled membranes will be analyzed using scanning electron microscopy with energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, zeta potential measurements, contact angle analysis, and atomic force microscopy–based adhesion mapping to determine dominant foulants and adhesion energetics. Interfacial interactions will be quantified using an extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) framework that incorporates electrostatic, Lifshitz–van der Waals, and acid–base interactions while accounting for iron- and aluminum-mediated metal–ligand coordination and surface-bridging mechanisms. Fouling dynamics will be described using physics-informed hybrid models that couple resistance-in-series transport with pore blocking and cake growth kinetics. These mechanistic insights will be integrated with AI-enabled, physics-constrained machine learning approaches to develop interpretable predictive relationships between measurable water quality indicators and fouling propensity. The resulting framework will deliver chemistry-informed decision tools for coagulant selection and near real-time optimization, advance hybrid AI–mechanistic modeling for resilient membrane infrastructure, establish transferable design principles relevant to advanced materials manufacturing, and provide quantitative guidance for biotechnology-relevant water reuse 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. NSF Award ID: 2541682 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Xue Jin | Institution: Oregon State University, CORVALLIS, OR | Award Amount: $576,712 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541682 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541682.html