NSF Award
2409165
This project addresses alarming concerns raised by growing plastic pollution observed in rivers around the world. Microplastic particles, with sizes typically below 100 microns, account for over 80% of the plastic waste in rivers. A large amount of microplastic particles ends up within the pores of the bottom riverbed. This leads to the contamination of aquatic habitats and the food chain, as the small size of these particles (about the same size as plankton) makes them easily ingested by fish, oysters, and other animals. Despite these concerns, the processes controlling the trapping of microplastic particles in riverbeds are not fully understood. While effects related to turbulence at the interface between river stream and sediment bed, particle inertia, and biofouling-induced sticking are expected to have a large impact on the retention of microplastic particles within riverbed pores, there are limited studies that investigate these effects systematically. These knowledge gaps will be addressed in this research. Using high-fidelity numerical simulations and theoretical modeling, this project will reveal the physical processes involved in the trapping of microplastic particles in riverbeds and build a reduced-order model to predict the trapping rates efficiently and accurately. This research will enable a more accurate assessment of the impact of microplastic pollution on ecosystems and inform potential remediation strategies, such as new filtration methods implemented near sources of pollution (e.g., in wastewater treatment plants). Further, by involving undergraduate researchers hired from the diverse pool of students at Arizona State University (a Hispanic-serving institution) and Iowa State University, this project will support diversity in engineering and promote teaching, training, and learning.<br/><br/>The goal of this project is to reveal processes that control the trapping of microplastic particles (MP) in riverbeds and model the trapping rates. The research leverages synergistic Pore-Resolving Direct Numerical Simulations (PR-DNS) and theoretical modeling. PR-DNS of MP-laden turbulent flow over a model riverbed will be carried out to reveal the microscopic effects at the pore-level. Scaling laws for the MP effective diffusivity that account for stream turbulence, MP inertia, and sticking will be extracted from simulations. Predictive models for the MP retention rate will be derived using Population Balance Modeling and simulation data. These models will incorporate turbulence and inertial effects from first principles. The combined numerical and modeling work will make it possible to predict the retention rates of MPs for a wide variety of flow, bed, and particle configurations.<br/><br/>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.
