Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

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

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

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

On the interplay between downwelling, deep convection and mesoscale eddies in the Labrador Sea

In this study, an idealized eddy-resolving model is employed to examine the interplay between the downwelling, ocean convection and mesoscale eddies in the Labrador Sea and the spreading of dense water masses. The model output demonstrates a good agreement with observations with regard to the eddy field and convection characteristics. It also displays a basin mean net downwelling of 3.0 Sv. Our analysis confirms that the downwelling occurs near the west Greenland coast and that the eddies spawned from the boundary current play a major role in controlling the dynamics of the downwelling. The magnitude of the downwelling is positively correlated to the magnitude of the applied surface heat loss. However, we argue that this connection is indirect: the heat fluxes affect the convection properties as well as the eddy field, while the latter governs the Eulerian downwelling. With a passive tracer analysis we show that dense water is transported from the interior towards the boundary, predominantly towards the Labrador coast in shallow layers and towards the Greenland coast in deeper layers. The latter transport is steered by the presence of the eddy field. The outcome that the characteristics of the downwelling in a marginal sea like the Labrador Sea depend crucially on the properties of the eddy field emphasizes that it is essential to resolve the eddies to properly represent the downwelling and overturning in the North Atlantic Ocean, and its response to changing environmental conditions

On the interplay between downwelling, deep convection and mesoscale eddies in the Labrador Sea

In this study, an idealized eddy-resolving model is employed to examine the interplay between the downwelling, ocean convection and mesoscale eddies in the Labrador Sea and the spreading of dense water masses. The model output demonstrates a good agreement with observations with regard to the eddy field and convection characteristics. It also displays a basin mean net downwelling of 3.0 Sv. Our analysis confirms that the downwelling occurs near the west Greenland coast and that the eddies spawned from the boundary current play a major role in controlling the dynamics of the downwelling. The magnitude of the downwelling is positively correlated to the magnitude of the applied surface heat loss. However, we argue that this connection is indirect: the heat fluxes affect the convection properties as well as the eddy field, while the latter governs the Eulerian downwelling. With a passive tracer analysis we show that dense water is transported from the interior towards the boundary, predominantly towards the Labrador coast in shallow layers and towards the Greenland coast in deeper layers. The latter transport is steered by the presence of the eddy field. The outcome that the characteristics of the downwelling in a marginal sea like the Labrador Sea depend crucially on the properties of the eddy field emphasizes that it is essential to resolve the eddies to properly represent the downwelling and overturning in the North Atlantic Ocean, and its response to changing environmental conditions

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

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

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

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

On the interplay between downwelling, deep convection and mesoscale eddies in the Labrador Sea

In this study, an idealized eddy-resolving model is employed to examine the interplay between the downwelling, ocean convection and mesoscale eddies in the Labrador Sea and the spreading of dense water masses. The model output demonstrates a good agreement with observations with regard to the eddy field and convection characteristics. It also displays a basin mean net downwelling of 3.0 Sv. Our analysis confirms that the downwelling occurs near the west Greenland coast and that the eddies spawned from the boundary current play a major role in controlling the dynamics of the downwelling. The magnitude of the downwelling is positively correlated to the magnitude of the applied surface heat loss. However, we argue that this connection is indirect: the heat fluxes affect the convection properties as well as the eddy field, while the latter governs the Eulerian downwelling. With a passive tracer analysis we show that dense water is transported from the interior towards the boundary, predominantly towards the Labrador coast in shallow layers and towards the Greenland coast in deeper layers. The latter transport is steered by the presence of the eddy field. The outcome that the characteristics of the downwelling in a marginal sea like the Labrador Sea depend crucially on the properties of the eddy field emphasizes that it is essential to resolve the eddies to properly represent the downwelling and overturning in the North Atlantic Ocean, and its response to changing environmental conditions