Abstract
Marine litter can enter the open ocean directly, or via coastal waters and then through numerous hydrological processes is transported to the open ocean, and potentially dispersed vast distances. The spatial and temporal variability of plastic marine litter is complex due to the interaction between differential characteristics, hydrological processes, and coastal morphology. Various field methods have been used globally to understand and quantify plastic pollution. However, the sparse population of the Atlantic coast of Scotland, combined with the complex coastline of numerous islands, sea lochs and headlands, has resulted in limited field data from this region. Hydrodynamic modelling offers a mechanism to explore such areas, and the interaction of marine litter with physical forces arising from ocean currents, and windage, and coupled with particle tracking models, can predict the trajectories and fate of simulated particles over space and time. The aim of this PhD was to understand the spatial distribution and origins of beached plastic litter to provide information that is of use to management organisations.First, a simulation of modelled particles from a principal land-based source within the study region, the Clyde Sea is presented. The particle tracking model is forced by currents, windage, and horizontal diffusion. It was refined to include three coastal boundary conditions; sticky coast, resuspending-unclassified coast, and a resuspending-classified coast, in which there is a distinction between rocky, reflective coasts, and retentive beaches. A mean of 6.6% of particles exited the Clyde Sea, crossed a northern boundary, and beached on the remote northwest coast primarily on windward facing coastlines (i.e., facing the prevailing south-westerly winds). These findings identified that although the Clyde Sea could be considered a source of pollution to the remote northwest coast, it was at low densities. The next stage was to validate the predictions of the particle tracking model through a series of beach-clean campaigns within the study region. Land-soured litter was found to dominate in industrialised and densely populated areas, while marine-sourced litter dominated on remote, less-populated coastlines. Overall, land-sourced plastic litter represented 8% of the dataset by mass and marine-sourced plastic litter represented 62%. Items that were unable to be identified represented 30%. It was also reported that windward coasts had a beached loading 58 times greater than leeward coasts by count, and 20 times greater by mass. These results demonstrate that the observational data from the beach-clean campaigns were consistent with model predictions. To understand the distribution of fishing-related litter, a simulation of modelled particles from a fishing-based source was conducted using the particle tracking model. Modelled masses were tuned to the observational data to understand the impact of fishing plastic litter as a major source within the study region. It was found that fishing-related litter was dominant along the northwest coastline, which is consistent with the results of the observational dataset. An input mass budget of between 234 and 614 tonnes of fishing-related plastic litter was estimated to be input to the ocean per year on the west coast of Scotland, which is believed to be the first estimate of this kind within the region.
Overall, this project highlights the influence that input source locations have on the spatial distribution and composition of plastic litter and identified fishing-related plastics as a major source of beached litter within the region. It also demonstrates that particle tracking models can be successful in identifying beached litter ‘hotspots’ and ‘coldspots’. The information presented in this project will be helpful to beach-clean monitoring organisations to maximise mitigation efforts.
| Date of Award | 21 Mar 2024 |
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| Original language | English |
| Awarding Institution |
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| Sponsors | Super DTP & Natural Environment Research Council (NERC) |
| Supervisor | Bhavani Narayanaswamy (Supervisor) & Andrew Dale (Supervisor) |