ORCID Profile
0000-0003-1047-0279
Current Organisation
Universiteit Utrecht
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Publisher: Copernicus GmbH
Date: 16-06-2022
DOI: 10.5194/EGUSPHERE-2022-426
Abstract: Abstract. The Galapagos Marine Reserve was established in 1986 to ensure protection of the islands' unique bio ersity. Unfortunately, the islands are polluted by marine plastic debris and the island authorities face the challenge to effectively remove plastic from its shorelines due to limited resources. To optimise efforts, we have identified 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 for plastic to travel from one location and beach at another. We have found four network centralities that provide the best coastline ranking to optimise the cleanup effort based on various impact metrics. In particular locations with a high retention rate are favourable 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.
Publisher: American Geophysical Union (AGU)
Date: 2016
DOI: 10.1002/2015JC011133
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-274
Abstract: & & The Galapagos Archipelago and the Galapagos Marine Reserve host one of the world& #8217 s most unique ecosystems. Although being a UNESCO world heritage site and being isolated from any dense population, over 8 tonnes of plastic are collected on the islands each year. To decrease the impact of plastic waste in the region, scientific evidence is needed on the sources and fate of the marine debris. Here, we will assess the skill of machine learning techniques to predict beaching events on these islands. In order to do so, we combine various hydrodynamic fields from ocean-, wave-, wind- and tide-models using the OceanParcels particle tracking framework to track virtual particles through the marine reserve. In addition, a beaching parameterization has been developed and implemented to quantify where and when virtual particles wash ashore. The results show that the particle pathways and beaching probabilities strongly depend on the dry and wet seasons characteristic for the Galapagos Islands.& & & & & Therefore, it is expected that the beaching events can to some extent be predicted from the forecasts of currents, tides and waves - without performing a Lagrangian simulation. To test this hypothesis, PCA analysis and random forests are applied to a set of over 100 variables and their skill to explain the beaching variability given by the particle model is determined. In addition, the results are compared to a timeseries of observed beached litter on one of the Island of San Cristobal to apply the models in a realistic case study. This work, in combination with a growing observational data set, will form the basis of a predictive model that will support the Galapagos National Park in their efforts to free the Galapagos Archipelago from marine debris.& &
Publisher: Copernicus GmbH
Date: 03-03-2022
Abstract: Abstract. Coastlines potentially harbor a large part of litter entering the oceans, such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014 and 2019. This data set is unique in the sense that data are gathered consistently over various years by many volunteers (a total of 14 000) on beaches that are quite similar in substrate (sandy). This makes the data set valuable to identify which environmental variables play an important role in the beaching process and to explore the variability of beach litter concentrations. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that tides play an especially important role, where an increasing tidal variability and tidal height leads to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding which processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment, such as organizing beach cleanups during low tides at exposed coastlines. We estimate that 16 500–31 200 kg (95 % confidence interval) of litter is located along the 365 km of Dutch North Sea coastline.
Publisher: American Geophysical Union (AGU)
Date: 2021
DOI: 10.1029/2020JC016654
Publisher: Elsevier BV
Date: 03-2019
Publisher: Copernicus GmbH
Date: 14-09-2021
DOI: 10.5194/OS-2021-83
Abstract: Abstract. Coastlines potentially harbor a large part of litter entering the oceans such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014–2019. This data set is unique in the sense that data is gathered consistently over various years by many volunteers (a total of 14,000), on beaches which are quite similar in substrate (sandy). This makes the data set valuable to identify what environmental variables might play an important role in the beaching process, and to explore the variability of beach litter. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that especially tides play an important role, where an increasing tidal variability and tidal height lead to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding what processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment. We estimate that 16,000–31,400 kilograms (95 % confidence interval) of litter are located on the 365 kilometers of Dutch North Sea coastline.
Publisher: Copernicus GmbH
Date: 20-10-2022
Abstract: Abstract. The Galapagos Marine Reserve was established in 1986 to ensure protection of the islands' unique bio ersity. 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.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-5288
Abstract: & & Over 8 tonnes of plastic are removed from the coastlines of the Galapagos Islands each year. Although the Galapagos Marine Reserve is expanding to ensure an even larger protection of its unique bio ersity, the island authorities face the challenge to effectively remove plastic from its shorelines due to limited resources. We are developing a clean-up efficacy model that will optimize for most cost-effective and least-invasive clean-up locations. Network (connectivity) theory is widely applied in ecology to study the interaction of species between spatially separated habitats. Here, we use a similar approach to discern the most effective removal hubs on the Galapagos Islands. A connectivity matrix 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 that plastic travels from one location and beaches at another. To measure the impact of removal, various centralities are determined, such as degree centrality, betweenness centrality (using the most likely path) and eigenvector centrality. Combining the results with other metrics such as the distance to the nearest port or tourist attractions, recommendations are made for& & & ul& & li& most effective & em& intervention& /em& removal hubs that would prevent further spread of plastic throughout the marine reserve& /li& & li& most effective & em& accumulation& /em& removal hubs that would negate the impact of plastic on wildlife& /li& & li& most suited regions for protection resulting from the existence of clusters (e.g. regions of limited connectivity)& /li& & /ul& & & Though we focus on the Galapagos Islands, the methods we present are directly applicable to archipelagos worldwide that face marine plastic pollution issues.& &
No related grants have been discovered for Stefanie Ypma.