Detecting the most effective cleanup locations using network theory to reduce marine plastic debris: a case study in the Galapagos Marine Reserve

Abstract

Abstract. The Galapagos Marine Reserve was established in 1986 to ensure protection of the islands' unique biodiversity. Unfortunately, the islands are polluted by marine plastic debris and the island authorities face the challenge to effectively remove plastic from its shorelines owing to limited resources. To optimize efforts, we have developed a methodology to identify the most effective cleanup locations on the Galapagos Islands using network theory. A network is constructed from a Lagrangian simulation describing the flow of macroplastic between the various islands within the Galapagos Marine Reserve, where the nodes represent locations along the coastline and the edges the likelihood of plastic leaving one location and beaching at another. We have found four network centralities that provide the best coastline ranking to optimize the cleanup effort based on various impact metrics. Locations with a high retention rate are particularly favorable for cleanup. The results indicate that using the most effective centrality for finding cleanup locations is a good strategy for heavily polluted regions if the distribution of marine plastic debris on the coastlines is unknown and limited cleanup resources are available.

A novel filtering method for geodetically determined ocean surface currents using deep learning

“…Ocean current transports are also of critical importance to marine life, carrying essential nutrients and food to marine ecosystems while distributing larvae and reproductive cells (Merino and Monreal-Gómez, 2009). Ocean currents also act as a global dispersal mechanism for pollutants such as mircoplastics (van Duinen et al, 2022; Ypma et al, 2022), and those arising from power generation, industry, and other human activities (Doglioli et al, 2004; Buesseler et al, 2011; Goni et al, 2015). Finally, ocean currents affect shipping and fishing industries, with consequences for safe and efficient navigation (Singh et al, 2018).…”