Identifying Marine Sources of Beached Plastics Through a Bayesian Framework: Application to Southwest Netherlands

Abstract

Most buoyant marine plastics are either beached or afloat in coastal waters (Morales-Caselles et al., 2021;Onink et al., 2021). Beached macro-plastic can more easily degrade into smaller micro-plastics than floating or submerged plastics, for example, due to a higher exposure to solar UV radiation (Andrady, 2011) and mechanical fragmentation (Chubarenko et al., 2020). The importance of cleaning up our beaches is therefore evident (Kataoka & Hinata, 2015). However, it is difficult to do this efficiently when much is unknown about the source and fate of the plastic (Cózar et al., 2014; van Sebille, 2015). By locating and then mitigating at upstream sources (Robinson et al., 2017; van Gennip et al., 2019), it may be possible to prevent this pollution and its consequences. Moreover, knowing the sources of plastic pollution enables "naming and blaming" of the polluters.Source attribution of beached plastics has been done before by using Lagrangian simulations (van Sebille et al., 2018) to compute virtual particle trajectories back in time. For example, Neumann et al. (2014) analyzed the sources of plastic beaching on the German Wadden Island of Sylt and Juist to find that there was a seasonal cycle in possible source regions. Gutow et al. (2018) analyzed the difference in source regions in the North Sea to find that these are sensitive to offshore locations and wind drag coefficient. Strand et al. (2021) analyzed the sources of plastic in the Arctic and northeast Atlantic, focusing particularly on fisheries sources. However, none of these studies took into account information on the spatial patterns of plastic input. There are studies that have combined source input data and Lagrangian simulations

Typical and anomalous pathways of surface-floating material in the northern North Atlantic and Arctic Ocean

“…where t denotes time, ∆t is the integration time step (positive or negative for forward or backward trajectories, respectively), R is a random number drawn from a [−1, 1] interval, and K h denotes the horizontal diffusion coefficient 3,42 .…”

“…The second source of uncertainties is related to the (generally unknown) value of K h in equation (5). In this study, it is assumed constant and is estimated from 3,11 :…”

“…Rapidly increasing levels of plastic pollution in the world oceans, extending to even the most remote locations, have motivated increasing research efforts towards better understanding of the associated processes and resulted in a boom of studies on their physical, biological, ecological, as well as economic and industrial aspects [1][2][3][4][5][6] . At a more general level, plastic parts -from micrometer-scale particles up to meter-sized objects -are just one of many types of material transported by ocean currents.…”

“…In this study, as in several earlier ones, 3,5,[11][12][13][14][15] Lagrangian (i.e., particle-tracking based) modelling is used. We concentrate on only one type of particles -passive floaters (as defined above) -and on their transport pathways, without going into details of release and deposition processes.…”

Attribution of Plastic Sources Using Bayesian Inference: Application to River-Sourced Floating Plastic in the South Atlantic Ocean

“…The main difference between these search & rescue applications of Bayesian inference and our application in the source attribution of floating plastic is that the sources of plastic are spatially very heterogeneous, and so is their distribution at sea. With regards to plastic source attribution, van Duinen et al (2021) took a similar approach in which they implemented a Bayesian framework to estimate the most likely sources that contribute to the plastic litter found on a beach in the North Sea. They performed a backtracking Lagrangian simulation from the beach towards the possible sources and combined this with estimates of plastic emitted at river mouths, fishing grounds, and coastlines.…”