Date of Graduation
12-2023
Document Type
Thesis
Degree Name
Master of Science in Statistics and Analytics (MS)
Degree Level
Graduate
Department
Statistics and Analytics
Advisor/Mentor
Chakraborty, Avishek A.
Committee Member
Kaman, Tulin
Second Committee Member
Robinson, Samantha E.
Keywords
Backtracking simulations; Lagrangian ocean analysis framework; Nurdle; Virtual Particles
Abstract
Over the last several decades, plastic waste has gradually accumulated while slowly degrading in terrestrial and oceanic environments. Recently, there has been an increased effort to identify the possible sources of plastic to understand how they affect vulnerable beaches. This issue is of particular concern in the Gulf of Mexico due to the presence of oil, natural gas, and plastic production. In this thesis, we expand upon existing Bayesian plastic attribution models and develop a rigorous statistical framework to map observed beached microplastics to their sources. Within this framework, we combine Lagrangian backtracking simulations of floating particles using nurdle beaching data with estimates of plastic input from coastlines, rivers, and fisheries. This allows us to build a spatiotemporal microplastic distribution in the Gulf of Mexico from source to sink. We infer that the primary sources of microplastics found on beaches throughout the region are centered around New Orleans, Galveston Bay, Corpus Christi, Mérida, the Grijalva, and Pearl Rivers, as well as from fishing activities around the Mississippi River Delta. We find strong seasonal effects of microplastic transport in the Gulf of Mexico caused by the time-varying ocean currents and tourism. We also study age’s effects on source attribution and provide time-scale estimates for debris transport across the region.
Citation
Pojunas, D. (2023). Bayesian Learning of Spatiotemporal Source Distribution for Beached Microplastic in the Gulf of Mexico. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5163
