RAPID: AI-Integrated Mechanistic Investigation of Multi-Phase Fecal Contamination Pulses in Groundwater Following Extreme Storm Events

Heavy rainstorms in Hawaii can quickly raise groundwater levels. This makes it easier for wastewater from cesspools to leak into groundwater. Since groundwater is Hawaii's main source of drinking water, it's very important to understand how storms can affect water safety. This project will examine how heavy storms move harmful bacteria and viruses from wastewater systems into underground water supplies. At a groundwater monitoring site in Honolulu, scientists will collect water samples over time to track how pollutants move after major storms. The results will help communities and agencies better understand and respond to contamination risks following heavy precipitation. This information will be important not only to Hawaii but also to rural regions across the United States that rely on onsite wastewater systems.This research examines the mechanisms controlling time-dependent transport of fecal contamination in basalt aquifers under episodic hydrologic forcing. The project will quantify contamination mobilization associated with storm-induced groundwater-table rise, identify distinct temporal transport pathways including dissolved transport, particle-associated transport, and delayed release from attached phases, and develop an integrated predictive framework combining process-based groundwater modeling with artificial intelligence-assisted forecasting. The numerical framework will simulate transient groundwater flow and multi-phase microbial transport under changing hydrologic conditions, while surrogate predictive models will enable rapid forecasting of contamination timing and persistence. The resulting mechanistic understanding and predictive tools will advance groundwater contamination science and support risk assessment and mitigation in vulnerable coastal and rural island groundwater 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.