Bayesian Learning of Spatiotemporal Source Distribution for Beached Microplastic in US Gulf Coast

Music City Center 

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 study specifically focuses on microplastic beached in US Gulf Coast. 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 Coast from source to sink. We infer that the main sources of microplastics found on the Gulf beaches in the US are centered around New Orleans, Galveston Bay, Corpus Christi, M\'erida, the Grijalva and Pearl Rivers, as well as from fishing activities around the Mississippi River Delta. We also find strong seasonal effects of microplastic transport in the Gulf caused by the time-varying ocean currents and tourism.

Backtracking simulations

Lagrangian ocean analysis framework

Nurdles

Virtual particles 

Section on Statistics and the Environment