Harmful algal blooms are caused by the growth of algae that produce a toxin known as microcystin. Microcystin is linked to liver cancer and is difficult to treat using conventional drinking water treatment practices. This project investigates biological filtration ("biofiltration") to remove microcystin during drinking water treatment. Biofiltration relies on natural communities of bacteria in sand filters to degrade microcystin. This research focuses on establishing a stable biofiltration treatment system under the highly variable environment of a drinking water treatment plant. The project will develop models to predict biofiltration treatment behavior with the goal of making biofiltration an effective treatment process to protect the Nation's drinking water supply. The educational program will train students in cross-disciplinary science and engineering to improve the scientific literature and the technological skills of the Nation.
The goal of this project is to develop a predictive understanding of the environmental and operational factors governing microbial community dynamics and microcystin biodegradation in biofiltration systems. The specific objectives are to: 1) quantify and compare the steady-state growth characteristics of suspended and immobilized microcystin-degrading bacterial consortia (MC-DBC) in engineered reactors; 2) define environmental and hydraulic conditions that enhance the activity and stability of the aqueous and biofilm MC-DBC in these reactor systems; and 3) investigate and compare the microbial community structure and function within each MC-DBC to gain a holistic understanding of the microbial community interactions within MC-DBC. This research will combine culture based and genetic microbiological investigations with numerical simulations of fate and transport to optimize management of algal toxin removal in biofiltration systems. The effects of various nutrient conditions on the growth and degradation kinetics of MC-DBC will directly inform biostimulation practices to improve biodegradation kinetics during harmful algal bloom periods. Integration of experimental observations with the development of a mechanistic numerical model will highlight the relative importance of enzyme induction kinetics, enzyme dilution, or enzyme regulation to biofiltration efficiency. The numerical model simulations will advance our ability to design biofiltration systems by assessing the feasibility and treatment capability of algal toxin removal over a wide range of operating conditions.
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.
