Title: Developing a transport model for plastic distribution in the North Sea

Authors: Gergo Geza Dohi Trepszker, Dana Stuparu, Myra van der Meulen, Frank Kleissen, Dick Vethaak, Ghada El Serafy

Institute: Deltares

Abstract: This article serves as a first step in the assessing of the situation of plastic pollution in the North Sea. It studies the movement of microplastic particles, to comply with the requirements of European Marine Strategy Framework Directive, using a numerical model, which helps determine the accumulation areas, otherwise known as hotspots. The mapping, understanding and the recreation of the current situation regarding microplastic pollution in the North Sea is a necessary step in eliminating the pollution. Using Deltares software, a numerical model was created to simulate the transport and deposition of microplastic particles. In case of uncertainty regarding the data, namely particle size and quantity, a probabilistic approach was used for a better description of the reality, to be tested with different plastic types. The results, in the form of maps, represent residence time, concentration and quantity, and define affected areas, which serve as starting points for further research.

Keywords: microplastics, pollution, probabilistic, modeling, North Sea

1. Introduction

The presence of plastic litter within the marine environment is a relatively new issue. The first reports on this subject appeared in the early 1970s (Fowler, 1987, June) (Carpenter & Jr., 1972 March) (Coe & Rogers, 1997) (Colton, Knapp, & Bruce R., 1974, August 9) however they have had only a minimal impact on the scientific community. Over the years, many reports have been written focusing on the entanglement in plastic of marine mammals (Coe & B, 1997 January 1), cetaceans (Clapham, Young, & Bronwell, 1999) and other species (Erikson, 2003, September), called ghost fishing, caused by derelict gear. Another topic widely discussed was the ingestion of plastic by various species of birds (Mallory, 2008 August) (Cade, 2002, November) and turtles (Mascarenhas, 2004) (Bugoni, Krause, & M.V., 2001, December) (Toms, Guitart, Mateo, & Raga, 2002, March) with the conclusion that up to 44% of marine birds ingest plastics (Rios, Moore, & Jones, 2007 August) With the increasing observations of plastic in the marine environment,the European Unions new legislative framework has integrated an important directive related to the observation and study of the plastic pollution in the European seas. A particular type of plastic causing significant issues with the microbiology of the marine environment is the small sized plastic, not visible to the naked eye, referred to as micro-plastics (Matthew, Pennie, Claudia, & Tamara, 2011 December). The focus of the present paper is to present the ongoing developments of the Delft3D PART particle tracking model to simulate the evolution in time of the position of microplastic particles from their release (discharge points such as the Meuse and the Rhine) until the end of the simulation.

1.1 Plastic presence in the marine environment

The abundance of microplastics in the oceans has steadily increased over the last few decades with the rising plastic consumption worldwide (Ivar do Sul, 2013) .The origin of micro plastics in the marine environment can be traced from different possible sources. Generally, micro plastics can be divided in two groups: primary and secondary microplastics (Matthew, Pennie, Claudia, & Tamara, 2011 December). Primary microplastics are produced either for direct use, such as for industrial abrasives, cosmetics (Zitko, 1991), exfoliates (Derraik, 2002) (Fendall & Sewell, 2009, August), or for indirect use as precursors, resin pellets, for the production of manifold consumer products. Secondary micro plastics form in the environment as a consequence of the breakdown of larger plastic material (Ryan, 2009) (Thompson, et al., 2004 ), especially marine debris, into smaller and smaller fragments. The breakdown is caused by mechanical forces such as waves and photochemical processes triggered by sunlight (Browne, Galoway, & Thompson, 2007 October). Another source for secondary micro plastics is the shedding of synthetic fibres from textiles by domestic clothes washing. A comparison done between micro plastic particles in sewage effluent from clothes washing (urban areas) and micro plastics in the environment (marine) has concluded that much of the micro plastic pollution sized less than 1 mm may consist of shed synthetic textile fibres (Browne M. A., 2012) (Moore C. J., 2008). Scientifically, the integral impact of microplastics on wildlife and human health is not well established, however there are studies (Dr. Hideshige, 2013) which state that micro plastics have the potential to cause adverse effects in wildlife and humans (e.g. cancer, malformation, decrease in the immune response or impaired reproductive ability). Another study (Browne, Dissanayake, Galloway, Lowe, & Thompson, 2008) states that concentration of microplastics is the highest 12 days after ingestion, and it accumulates in the circulatory system of mussels, Mytilus edulis, where it stays for up to 48 days. It was shown that in the short term there are no significant biological effects, however it is unknown whether ahigher threat is posed in the long term.

1.2 Marine Strategy Framework Directive As an answer to the environmental concerns regarding micro plastics pollution, the Marine Strategy Framework Directive (MSFD) was adopted by the European Union in 2008, requiring that Member States put in place measures to achieve good environmental status (GES) in the seas of Europe by the year 2020 (European Comission, Legislation: the Marine Directive, 2008). The directive lists 11 good environmental status descriptors for the specific areas where this must be achieved. The properties and quantities of marine litter are highlighted in the attributes of the 10th descriptor, as follows:

- Amount, source and composition of litter washed ashore and/or deposited on coastlines. - Amount and composition of litter in the water column, including floating and suspended

litter, and accumulation on the sea floor. - Amount and composition of litter ingested by marine animals. - Amount, distribution and composition of micro-particles (mainly micro plastics).

The motivation of the current study is working towards the goal of reducing the amount of plastic debris and pollution in the North Sea. To achieve this, as a first step, a plastic tracking model has been developed with the aim of simulating the route of microplastic particles in the North Sea, from their origin (discharge points such as rivers) until the spot where particles end up when the

simulation ends. This approach allows for the identification of the main contributors and sources of the pollution, the hot-spots for the accumulation of the particles and the tracking of the particle movement.

1.3 Review of existing plastic measurements in the North Sea The availability of information and data regarding plastic quantity in the North Sea is scarce, however first assumptions can be deducted from general data on plastic presence. Since the middle of the last century (more than two hundred million tonnes annually) (Barnes, Francois, Barlaz, & Richard, 2009), (Thompson R. S., 2009 June) and (Andrady, 2011), several million tonnes of plastics have been produced. However, speculation exists over how much of this plastic will end up in the ocean, where it suffers degradation and fragmentation (Barnes, Francois, Barlaz, & Richard, 2009) (Andrady, 2011). The abundances observed in the present day, in the surface waters of the North Sea, (0-300 items per square km) are much higher than that of a previous report (Dixon, 1983) (0-3 items per square km). This is most likely due to the methodological differences of field sampling, but the possibility that marine litter has increased in the past 25 years is also valid.

Since the availability of information and data regarding the quantity of plastic present is not specific enough regarding the North Sea, modelling is a good approach simulate plastic transport and estimate the existing quantities of microplastics.

Different types (polyethylene, polystyrene, PET, PVC) and sizes (10 m, 330 m and 5 mm) of plastics were analysed. The added value of the probability approach in this study helps the definition of the variation of element sizes present in the marine environment of the North Sea. Since data regarding the exact sizes and quantity of the plastic particles is either not available or the current measurements do not show conclusive results, probability is one of the best ways to define the existing situation. By defining elements with the help of probability the part of fragmentation of plastics and uncertainty of element sizes is covered to some extent, providing more precise simulation results.

The proposal for the study was the combination of numerical modelling with a probabilistic approach. The size of the microplastics will be described using different probability distributions. In this way, the existing variation in microplastic size in the marine environment will be accounted for. The trajectories of different plastic types were simulated and different size distributions were tested. The probabilistic approach together with the observational data can help to build a numerical model of the transport and accumulation of micro plastics within the North Sea, with a high enough precision to be later used by decision makers.

1.4 Further developments of the Delft3D-PART module To gain more knowledge regarding the amount and distribution of micro plastics in the North Sea, the powerful tool of numerical modelling was used. To present results, an extended version of the Delft3D model, developed by Deltares, was used to simulate the movement of microplastic particles in the marine environment of the North Sea and gain more knowledge regarding the

amount and distribution of microplastics in the North Sea. Plastic modelling was performed using the Delft3D-PART module. This module is part of the Delft3D model suite, which consists of a unique, fully integrated computer software modules for a multi-disciplinary approach and 3D computations for coastal, river and estuarine areas. It can carry out simulations of flows, sediment transports, waves, water quality, morphological developments and ecology. The PART module of Delft3D simulates transport and simple water quality processes by means of a particle tracking method using the (2 or 3-dimensional) flow data from the Delf3D-FLOW module. The tracks are followed in three dimensions over time, whereby a dynamic concentration distribution is obtained by calculating the mass of particles in the model grid cells. The processes are assumed to be deterministic except for a random displacement of the particle at each time step. The particle tracking method is based on a random-walk method since the simulated behaviour is stochastic and the number of particles is limited (Rubinstein, 1981). (It is worth noting that D-Waq PART is the only stochastic model in the whole Delft3D suite.) The hydrodynamic conditions (velocities, water elevations, density, salinity, vertical eddy viscosity and vertical eddy diffusivity) are calculated in the Delft3D-FLOW modules and are used as input to Delft3D-PART.

In Delft3D-PART every particle is subject to a random displacement. The position of every individual particle can be influenced by advection (transport by water flow), diffusion/dispersion (a random component) and settling (including sedimentation/erosion characteristics).The distance of the displacement depends on the local dispersion and the time step in the model. The direction of the displacement is random. The method used is often referred to as the random walk method or the Monte Carlo method. Also, the mass of a particle represents the amount of a substance attached to it. The mass of a particle can be influenced by the decay rate (first order decay).

Also, the position of every individual particle (x, y and z co-ordinate) is known from its release until the end of the simulation. The position of a particle does not depend on the grid (size) used and so sub-grid concentration patterns can be calculated. The concentration of a substance is determined by the number of particles per unit volume. The volume used to calculate the concentrations is defined by using the hydrodynamic segments (from the Delft3D-FLOW computation) The model simply counts the number of particles present in each volume and calculates the corresponding concentration of each substance (Deltares, 2013).

The settling velocity of each particle at time t is calculated with the following formula:

= + sin

Equation 1 Settling velocity of particles

Where: Settling velocity [m/s] C Local concentration of particles [particles/m] N Exponent for adjusting concentration [] Non-cyclic component of the settling velocity[m/s]

Amplitude of periodic sinusoidal variation in time [m/s] T Period of sinusoidal variation [s] Phase lag for the sinusoidal variation[] Concentration dependent factor for settling velocity [particles/m]

In order to account for different plastic particle characteristics, the above formulation has been improved, such that the settling velocity of the particles is dependent on the difference in densities between the micro-plastics and the marine environment, and also on the particle sizes. This has resulted in a calculation of the settling velocity according to Stokes law:

= 29 !"# %"& '() *+

Equation 2 Settling velocity of particles according to Stokes Where: Settling velocity [m/s] !"# Density of the particles [kg/m ] %"& ' Density of the sea water [kg/m] ) Dynamic viscosity [kg/(s*m)] g Gravitational acceleration [m/s ] R Radius of particle [m]

By combining the initial formulation with the Stokes law, the following formula has been derived:

= - + sin .2/0 + 12 34 +29 !"# %"& '() *+

Equation 3 Combined settling velocity formula

2. Assumptions in the modeling framework

In case of mathematical modeling, assumptions are made, creating a functioning model which can correctly reproduce the modeled event. Modeling is a theoretical approach so it is imperative to know the process of simplification involved, and the correct way of interpretation of the results. The assumptions introduced as follows were involved in the modeling process.

2.1. General assumptions

Several assumptions were made regarding the domain and its driving forces. The model was run for the hydrodynamics of the year 2008, as a test case. It is worth mentioning that this is a relatively calm year. As previously stated the plastic introduced into the model domain was solely from the rivers, which discharge into the North Sea. Assumptions were made for the estimation of input percentage per river according to pollution.

Regarding the domain of the North Sea, the assumptions made are explained below. The model domain was restricted to the North Sea, where the density of water is calculated as a function of salinity and temperature. For a more realistic representation, the settling velocity is calculated with the Stokes formula. To make testing and comparison viable, the observation points were placed according to ones existing in reality. To be in conformity with previous studies and research, the quantity of plastic is assumed to be 0.1 g per cubic meter of incoming fresh water, according to the study done by (Andrs, et al., 2014). Several test cases done have shown that the plastic input coordinates from rivers Rhine and Scheldt are better to be inside the domain of the North Sea and not at the end of the water way, as is the case with the rest of the rivers. This was done to assure that the plastic particles end up inside the model domain and not get stuck in the waterway of the river. Since this is a basic model, the density of plastic is considered constant throughout the simulation period as fouling is not yet included in the model. Plastic incoming from rivers is divided between each of the rivers by percentages according to the following figure.

Figure 1 Plastic input according to rivers in the North Sea

Since the main source of plastic is directly tied to the human activity on land (Andrady, 2011), one assumption of this study was that the discharge of plastic occurs at the river estuaries and close to industrial areas, especially plants which produce packaging.

2.2. Plastic types and behaviour A study made by (Martin Thiel, 2011 April) on the floating objects in the Northern Sea states that anthropogenic debris is commonly found on the shorelines and beaches of the North Sea. Floating debris in the North Sea is dominated by plastics (Vauk & Schrey Eckhart, 1987 June). The high longevity of plastics combined with their disproportionally high supply is most likely responsible for this pattern. There is a contradiction between sources as some suggest that micro plastics come from fragmentation while others prove that they are transported into the North Sea from rivers. The high amount of large plastic fragments observed in the North Sea is probably also an indication of the long persistence of plastics at the sea surface accompanied by initial fragmentation. Sources of anthropogenic debris may be local and distant. A study made by (Galgani, et al., 2000, June) suggests fisheries to be an important source of anthropogenic pollution in the North Sea. The importance of rivers in transporting litter into coastal areas is emphasized in a study made by (Willams, 1997, Autumn). Floating debris will end up on nearby beaches or may accumulate in convergence zones, where it might finally sink to the seafloor (Acha, 2003). Highlighting the relationship between litter accumulation on the beaches (Vauk & Schrey Eckhart, 1987 June) suggested that the winds pushed anthropogenic debris from source regions (shipping lanes) onto local beaches. The study made by (Galgani, et al., 2000, June) also suggests the fact that the debris gets transported north and will eventually sediment on the western parts of the North Sea, probably due to the loss of buoyancy caused by a progressive accumulation of organisms (Harms, 1990) (Lobelle & Cunliffe, 2011 January). Plastic materials also end up in the marine environment when accidentally lost, carelessly handled (Wilber, 1987) or left behind by beachgoers (Pruter, 1987 June). They also reach the sea as litter carried by rivers and municipal drainage systems (Pruter, 1987 June), (Willams, 1997, Autumn). There are major inputs of plastic litter from land-based sources in densely

populated or industrialized areas (Pruter, 1987 June), (Gregory, 1991), most in the form of packaging.

For a better understanding of the movement of micro plastic elements in the marine environment a brief introduction about the different types of plastic is necessary. The plastic elements presented below consist of 90% of the worlds total plastic production (Andrady A.L., 2009 July 27), it can therefore be assumed that they are the ones which eventually arrive and remain present in the marine environment of the North Sea as well. These elements together with their physical properties are as follows: Polystyrene is a synthetic aromatic polymer made from the monomer styrene, a liquid petrochemical. Polystyrene can be rigid or foamed. The general properties of polystyrene are clear, hard and brittle. It is a very inexpensive resin per unit weight. It is a rather poor barrier to oxygen and water vapour and has relatively low melting point. Polystyrene is one of the most widely used plastics, the scale of its production being several billion kilograms per year. Polystyrene can be naturally transparent, but can be coloured with colorants. Uses include protective packaging (such as packing peanuts and CD or DVD cases), containers, lids, bottles, trays, tumblers, and disposable cutlery (Jrgen, Bruce G., Jeffrey R., Herbert, Karl-Heinz, & Christian, 2007) (American Chemistry Council, 2012). Polyethylene or polythene is the most common plastic. The annual global production is approximately 80 million tons. Its primary use is in packaging (plastic bags, films, geo-membranes, containers including bottles, etc.). Many kinds of polyethylene are known, with most having the chemical formula (C2H4)nH2. Thus Polyethylene is usually a mixture of similar organic compounds that differ in terms of the value of n (Piringer & Baner, 2008). Polyvinyl chloride, commonly abbreviated PVC, is the third-most widely produced polymer, after polyethylene and polypropylene. PVC is used in construction because it is more effective than traditional materials such as copper, iron or wood in pipe and profile applications. It can be made softer and more flexible by the addition of plasticizers, the most widely used being phthalates. In this form, it is also used in plumbing, electrical cable insulation, inflatable products and many applications in which it replaces rubber (Allsopp & Vianello, 2012) (Titow, Phil., & F.R.S.C., 1984). Polyethylene terephthalate (sometimes written poly(ethylene terephthalate)), commonly abbreviated PET is a thermoplastic polymer resin of the polyester family and is used in synthetic fibres; beverage food and other liquid containers; thermoforming applications; and engineering resins often in combination with glass fibre. The majority of the worlds PET production is for synthetic fibres (in excess of 60%), with bottle production accounting for around 30% of global demand. In the context of textile applications, PET is referred to by its common name polyester, whereas the acronym PET is generally used in relation to packaging (James G. & Lange, 2005). Polypropylene, also known as polypropene, is a thermoplastic polymer used in a wide variety of applications including packaging and labelling, textiles (e.g., ropes, thermal underwear and carpets), stationery, plastic parts and reusable containers of various types, laboratory equipment, loudspeakers, automotive components, and polymer banknotes. An addition polymer made from the monomer propylene, it is rugged and unusually resistant to many chemical solvents, bases and acids (Maier & Haber, 1998).

The following table represents the chemical and physical properties, integrated, of the materials presented above:

Nr. Name Chemical formula Density [*/678] 1 Polystyrene 9:9 1.05 2 Polyethylene :;: 0.88-0.94 3 Polyvinyl chloride (PVC) :8 0.946 Table 1 Plastic types

In this study, four types of plastics described previously have been modelled. This decision is based on the estimation that they account for 80% of the total plastic production of the globe (Andrady A.L., 2009 July 27). The goal is to create a model that produces results which can be explained by physics. Correct results were generated when talking about the difference between the density of the seawater and the plastic particles. The PET and PVC type particles have sunk to the lower layers of the model and most likely deposit, according to the hydrodynamics present in the model since their density is higher than that of the seawater. The Polyethylene has gathered on the superior layers and was floating, since its density is lower than that of the seawater. Interesting results were gained when talking about Polystyrene since the density of Polystyrene is 1050 kilograms per cubic meter which is close to the density of seawater (approximately 1024 kilograms per cubic meter). In the present state, the model does not include the biological process of fouling which, in time, influences the density of the plastic particles. Since the density of Polystyrene is close to that of the seawater (which evolves in time according to temperature and salinity), the results are a preview of what the model will produce once the fouling process is implemented.

2.3. Probabilistic modeling

To successfully simulate the movement of microplastic particles in the marine environment of the North Sea, a distribution of size is necessary for the definition of the particles. Since the approach used was a probabilistic one, this was one of the elements to which probability was attached. To generate the element sizes and to make sure that the proper probability and randomness is introduced in this step, the usage of sampling techniques is necessary. In statistics, quality assurance and survey methodology, sampling is concerned with the selection of a subset of elements from within a statistical group to estimate characteristics of the whole group. Each observation measures one or more properties (such as weight, location or colour, in this case size) of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling. Results from probability theory and statistical theory are employed to guide practice. In probability sampling every unit in the group has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. The combination of these traits makes it possible to produce unbiased estimates of group totals, by weighting sampled units according to their probability of selection. Probability sampling includes: simple random sampling, systematic sampling and stratified sampling. These various ways of probability sampling have two things in common: every element has a known non-zero probability of being sampled and they involve a random selection at some point. The sampling techniques used for testing purposes were Systematic sampling, Stratified sampling and Latin Hypercube sampling. The principle used was a simple one: generate a vector which has 100 times more values than required and sample 1% of it to create a vector with the required number of values. The Monte Carlo method was used to test the accuracy of the sampling methods. The Systematic and Stratified sampling methods presented equally good results regarding precision, and the systematic sampling method was selected since it took less computational effort to run. After testing various number of particles it has been concluded that for a good precision and an acceptable computational time the optimal number for running the simulation is 5 000 000 of particles. The standard deviation for each distribution of particle size was 25% of the mean value and the sizes for particles have been sampled with the systematic sampling technique. Studies regarding the North Sea climate show that the occurrence of storms will impact the shallow regions by increasing the resuspension and resurfacing of sunken material (Fettweis,

Monbaliu, Baeye, Nechad, & Van den Eynde, 2012). Prevailing winds will, impact the residual transport of suspended particles in the North Sea. The more protected Southern Bight exhibits, relatively stronger influences transport, whereas in the central North Sea and the German Bight, resuspension is more pronounced. This pattern results in an alternation of relatively high suspended matter concentration in the Southern Bight and in the rest of the southern North Sea during certain weather conditions. A series of processes may induce variations in the concentration of suspended particulate matter on temporal and spatial scales. On short time scales, the predominant forcing is related to tides, waves and atmospheric circulation (Baeye, Fettweis, Voulgaris, & Van Lancker, 2011), and locally to stratification by fresh water input (Pietrzak, Boe, & Eleveld, 2011 April 15). On longer time scales neapspring cycles and meteorological and climatological variations become the more significant influencing factors. Annual variations are caused by seasonal changes in wind pattern and strength and result in higher suspended matter concentration during winter and lower concentrations during summer. Climatological effects are linked to the frequency of occurrence of certain weather patterns, e.g. the North Atlantic Oscillation is responsible for much of the observed weather and climate variability in the North Sea, especially during winter months (Hurrell, 1995, August 4) (Schwierz, 2006 November) and this has thus a pronounced effect on suspended matter concentration. The present research focuses therefore on large scale geographical variability of high turbidity zones in the North Sea and the English Channel induced by meteorological and climatological variations using remote sensing data. The southern North Sea, with stronger tidal currents and shallower water, has higher SPM concentrations than the northern North Sea. The most important high turbidity areas are the Belgian Dutch Coastal zone (Flemish Banks); the Thames plume extending eastward into the East Anglian plume; the Humber coast; the Wadden Sea and the coastline along the West Frisian (The Netherlands), East Frisian (Germany) and North Frisian (Germany and Denmark) Islands. The following figure represents a broad overview of the currents present in the North Sea (SeaOnScreen, 2014). Following the directions of the currents and the presence of plastic results within the following models it can be stated that the model is a correct one from a hydrodynamic point of view.

Figure 2 Currents in the North Sea

The purpose of the study however, was to see the differences and/or similarities between cases which have the same type of plastic (density) but different element sizes and different distributions and how they affect the movement and accumulation of the plastic material within the studied domain. Since there are so many different types of plastic present in the marine

environment, it is good to look at the differences to see more clearly which of the zones are polluted by a specific type of plastic. The difference of density causes different types of plastic to generate hot-spots in different places in the model. Viewing the differences between different types helps in the identification process, while differences between sizes of the same plastic can expand the domain of where the hot-spots might appear.

3. Steps towards model validation

To create a reliable and trustworthy model, its results must be compared and adjusted to represent values close to those taken from fieldwork. The measurements used were taken as part of the MWTL monitoring program of Rijkswaterstaat within the framework of the Interreg Iva MICRO project. Sampling took place on board the vessel Zirfea, in a two week period in June, 2014 (MWTL sampling round 16). Sediment samples were collected using a Van Veen grab. On each location, 3 sequential sediment grabs were taken from which the upper 5 cm of sediment were collected and pooled into one sample. Samples were collected in 1 Litre glass jars and cooled (4 degrees centigrade). In necessary cases, visible biota was removed. Upon arrival on shore, samples were further cooled to -20 degrees centigrade for further analysis.

Samples were analysed by the Institute of Environmental Studies (Amsterdam, the Netherlands). Sediment samples were homogenized and subsamples were taken for microplastic analysis and determination of dry weight. The method of (Thompson, et al., 2004 ) was applied, in a slightly modified form to adapt to the needs of the extraction of microplastics from the sediment samples. The sediment (25 g) was added to an Erlenmeyer with MilliQ water and NaCl (saturated solution, 1.2 kg NaCl/L). For a minimum of 2 minutes, after which the suspension was stirred using a teflon stirring bar, at the bottom of the flask. This allowed the sample material to suspend and enabled density separation of the sediment and particle material. After stirring, the sediment particles were allowed to sink to the bottom, whereas the particles lighter than the saturated salt solution, were allowed to float (minimum 1 hour). The top water layer was retrieved and filtered over a 0.7 m Whatman GF/C glass filter, followed by a rinsing step with hydrogen peroxide (30%) to remove any residual organic material. For each series of samples, two blank and two duplicate analyses were performed. Filters were analysed by hand using light microscopy and the length of the particles was measured with MicroCamLab by Microsoft. The microplastic particles were counted and corrected for background levels determined by the blank samples (control chart mean 2 microplastic particles per analysis). The dry weight of the sediments was determined gravimetrically after freeze-drying a 5g subsample of the homogenized sample, to a constant weight. Microplastic concentrations were expressed as a number of particles per kg of dry sediment. The following figure represents a map of the locations from which the samples were extracted.

Figure 3 Observation points for field measurements

Following is a table containing comparison of results from the model to the field measurements, calculated with relative error.

Observation Point Measured (nr. of part.) Model (nr. of part

Relative Error (%)

BE MIC 1 252 15 94,04 BE MIC 1 110 15 86,36 BE MIC 3 54 30 44,44 BE MIC 1 59 15 74,57 BE 830 146 23 84,24 BE Ooh 3146 5 99,84 UK CSEMP 475 0 180 100 UK CSEMP 536 348 200 42,52 UK CSEMP 484 643 45 93,01 UK CSEMP 466 233 20 91,41 NL Noordwijk 70 96 2 97,91 NL - Noordwijk 20 418 5 98,81 NL - Noordwijk 10 301 20 93,35 NL - Noordwijk 2 109 7 93,57 NL Goere 2 0 20 100 NL Walcrn 70 225 8 96,44 NL Walcrn 20 0 10 100 NL Walcrn 2 62 35 43,54

Table 2 Differences in model results and field measurement

The table above clearly shows that there is a large difference between the field measurements and the model results, in favour of the field measurements. Before stating any conclusions a recap of the model assumptions is necessary in order to understand and explain the differences. The model runs for one year and only includes plastic pollution incoming from rivers, even from those just the heavy ones which surely settle on the bottom to be compared to the measurements taken from the sediments. Taking into account that the field measurements include plastic which has been gathering for more than 60 years, including all sources (recreation, dumping, neglect, etc.), and all types of plastic (heavy, medium and light with biota), the difference can be explained by looking at the model assumptions. It is expected for the model to produce results which are greatly underestimating the amount of plastic pollution present within

the studied domain, since the assumptions of the model scale down the results. Further research will imply the running of the model for more consecutive years and with an input from different sources than rivers for a better representation of the plastic pollution. 4. Model results

The following section will present a series of results, in the form of figures, generated by the model. The results represent the hydrodynamics of the North Sea in the time periods of the years 2006, 2007 and 2008. The simulated time period had been 365 days for each of the different test cases. The following figures present the mean values and the presence time for plastic for the whole year (365 days). In all of the figures with mean values, the scales is between 0 and 0.1 milligrams (10^-4grams). Regarding the presence time, all figures have a scale between 0 and 365 days which represents how many days of the whole year was plastic present in the respective layer. Along the Dutch coastal area the model used has a much more dense mesh than the rest of the areas. Since residence time is calculated by days in which plastic was present in a specific cell, areas with smaller cell size will always represent lower values than areas with large cells. This is the reason why in some of the figures the Dutch coastal zone shows lower values than the rest of the model domain.

Figure 4 Difference (right) in mean values for light (left) and heavy (middle) plastic in mg/l Layer 1

Figure 5 Difference (right) in mean values for light (left) and heavy (middle) plastic in mg/l Layer 12 It can be clearly seen that in the case of heavy plastic there is a high mean value close to the coastal area in the deeper part (represented by layer 12 in the model) of the North Sea, while in case of the light plastic the mean value is very low in the deep parts, while the case being exactly the opposite for the surface waters (layer 1). In both cases the element sizes and distribution types are the same, 330 microns (mean value for size) and normal distribution.

Figure 6 Difference (right) in residence time for light (left) and heavy (middle) plastic in days Layer 1

Figure 7 Difference (right) in residence time for light (left) and heavy (middle) plastic in days Layer 12 Regarding the residence time (presence time within a specific grid cell), the simulation results reveal that light plastic spreads out a lot more and as such covers larger areas creating medium to high residence times in the areas it reaches (surface waters). In case of heavy plastic since settling velocities are higher, spreading is not so obvious (exception around the coastal area), however in the areas where plastic is present it shows high or very high residence times. If analysed further, the difference between medium (Polystyrene) and light (Polyethylene) plastic present similar finds such as the differences between heavy and light. The medium plastic has high or very high residence times in the coastal zones in the deeper layers and almost nothing in the superior ones, compared to the relatively low residence time of the light plastic within the lower layers and incredibly high ones in the surface layers.

Figure 8 Difference (right) in residence time for medium (left) and light (middle) plastic in days Layer 1 Regarding the difference between the mean values of plastic concentration in case of Polystyrene and PVC it can be seen that in coastal regions in both cases high mean values of

plastic can be found. The values are clearly higher in case of the heavy plastic compared to that of medium plastic.

Figure 9 Difference (right) in mean value for medium (left) and heavy (middle) plastic in mg/l Layer 12 In all cases made for comparison the expected results are represented by the model, meaning that heavy plastic settles on the lower layers and lighter plastic gathers and floats on the surface layers. An interesting find is the one which shows that in case of plastic which settles, the regions with the highest concentration and residence time are the zones near the coast, and along one major current which goes through the middle of the North Sea.

4.1. Comparison regarding particle size Different sizes for plastic particles were tested. The simulation results show that micro-plastics with a medium mean value (330 microns) spreads out more than plastic with small mean value (10 microns). Computing the presence of the plastic also shows that medium sized particles have a higher presence time than the small sized particles.

Figure 10 Difference (right) in mean value for medium (left) and small (middle) sized plastic in mg/l Layer 1

Figure 11 Difference (right) in mean value for medium (left) and large (middle) sized light plastic in mg/l Layer 1 When comparing small sized plastic particles with large sized plastic particles similar results to the previous observation can be seen, meaning that plastic with higher particle sizes shows higher mean values and higher residence time in case of a comparison with small particle sized plastic.

Figure 12 Difference (right) in mean value for small (left) and large (middle) sized light plastic in mg/l Layer 1

Figure 13 Difference (right) in residence time for small (left) and large (middle) sized light plastic in days Layer 1

The results can be clearly explained by physics, since the formula used for settling velocity (in the present case of light particles with negative results) has the particle size on the second power it clearly contributes and highly influences the velocities for the particles. The model provides logical and physically self-explanatory (expected) results.

4.2. Differences regarding size distribution: In case of the analysis done regarding the differences between distribution types, two comparisons were made, one in case of medium density plastic and one in case of heavy density plastic. Both cases represent the same size distribution and plastic type as to only magnify the differences caused by the distribution types. Looking at the first comparison when talking about medium density plastic, the differences can only be seen in case of the mean values and only in the bottom layer (12) of the model at specific locations which represent river mouths (plastic introduction points). These differences are caused by the random factor introduced in the model and the time and distance it takes for the plastic to travel from the superior layer (1) to the inferior layer (12). In case of the residence time the differences are so small that they can be considered insignificant.

Figure 14 Difference (right) in mean value for normally (left) and lognormally (middle) distributed medium plastic in mg/l Layer 12

Figure 15 Difference (right) in residence time for normally (left) and lognormally (middle) distributed medium plastic in days Layer 12 In case of the second comparison when talking about heavy plastic the observations are the same as the ones for the medium density plastic.

Figure 16 Difference (right) in mean value for normally (left) and lognormally (middle) distributed heavy plastic in mg/l Layer 12

Figure 17 Difference (right) in residence time for normally (left) and lognormally (middle) distributed heavy plastic in days Layer 12 As a final word regarding the differences between normal and log-normal distributions, in this case they do not have a high effect on the model.

4.3. Conclusions regarding the size-density coupling The table below represents the results found regarding the relationship between the size and density of the studied particles, all summed up for a clearer view, divided by layers (depth). The green and red colours represent low and high plastic concentrations while the arrows pointing up and down represent an increase and a decrease in plastic quantity between the layers. The direction is always from shallow to deep waters.

Presence 10 microns 330 microns 5000 microns

Polystyrene 1050 kg/m3

1 2-4 5-11 12

1 2-4 5-9 10-11 12

1-11 12

Polyethylene 910 kg/ m3

1 2-4 5-9 10-11 12

1 2-11 12

1 2-12

Polyvinylchloride (PVC) 1275 kg/ m3

1 2-4 5-9 10-11 12

1-11 12

1-11 12

Polyethylene- terephthalate (PET) 1400 kg/ m3

1 2-4 5-9 10-11 12

1-11 12

1-11 12

Table 3 Summarised size-density results, Presence time Mean 10 microns 330 microns 5000 microns

Polystyrene 1050 kg/ m3

1 2-4 5-11 12

1 2-11 12

1-11 12

Polyethylene 910 kg/ m3

1 2-11 12

1 2-11 12

1 2-12

Polyvinylchloride (PVC) 1275 kg/ m3

1 2-11 12

1-11 12

1-11 12

Polyethylene- terephthalate (PET) 1400 kg/ m3

1 2-11 12

1-11 12

1-11 12

Table 4 Summarised size-density results, Mean value

To summarize the results of the research in a few key concepts: 1. Particles with a higher radius (size) will always move (settle or surface) faster than

particles with a lower radius. 2. Particles with a high density (heavy) will always settle on the lower layers, while particles

with a low density (light) will always gather on the surface layers of the model. 3. The difference between the tested distribution types (normal and log-normal) is not

significant enough to affect the model and show conclusive results.

A different overview of the results can be seen in the following figures where 3 different models are compared with each one representing a different scenario. The figures prove that the model used is good enough to simulate plastic propagation within the North Sea domain for dependable results. The first four of the figures will represent mean values for the amount of plastic while the results section will conclude with four other figures showing it by quantity (number of particles). The year defining the hydrodynamics of the North Sea was 2008. The first figure shows the sensitivity analysis of the model in in relation to the plastic concentration of the input. The three figures represent three different values of plastic concentration; however they have the same type of heavy plastic, polystyrene.

Figure 18 Sensitivity analysis of plastic concentration values in case of heavy plastic in mg/l Layer 12

The next figure shows the same sensitivity analysis with the difference of the plastic being a light one, Polyethylene.

Figure 19 Sensitivity analysis of plastic concentration values in case of light plastic, residence time in days Layer 1 To better understand and make the results more tangible the, the last two figures can be seen below with the difference of showing plastic quantity (number of particles) instead of concentration.

Figure 20 Sensitivity analysis of plastic concentration values in case of heavy plastic in mg/l Layer 12

Figure 21 Sensitivity analysis of plastic concentration values in case of light plastic, residence time, Layer 1 Analysing the results from the previous figures which included comparisons of 3 models, it can be clearly stated that model has a very high sensitivity to the input quantity of plastic regardless of it being heavy or light, however the effects are more visible in the case of light plastic.

5. Discussion

Since the modelling process is based on assumptions which simply the modelled event to be able to represent it, they can represent topics for discussion and further research. A good example for this is the fact that the model currently assumes that there is no plastic present in the North Sea domain when the simulation starts. As stated previously, plastic has been accumulating in the marine environment for over 50 years, with more field data, the model could be improved. However this assumption leads to the conclusion that actual amount of plastic present in the North Sea is much higher than the amount the model results present. The other assumption which is a major simplification is the fact that in the model, all particles are spherical shaped, however field measurements prove that there various other shapes and forms which plastic particles have. In the case of applying the different shapes to the model, the formula for settling velocity would be different, thus causing different spreading and settling zones (hot spots). Regarding the hot spots presented by the results, field measurements are necessary to provide data regarding plastic types and sources to be able to identify main polluters. Once this is done steps can be taken to reduce pollution. The values tested for plastic quantity input for the model have a large effect on the model. In case of light plastics, the increased concentration caused much larger areas to be impacted by high presenting high plastic pollution. In case of heavy plastics the concentration increase only increased the concentration in the settling zones. Quantifying the actual concentration of incoming plastic pollution would be an important step for further research. The comparison of the results with field sampling data raises new challenges and possibilities for further improvement and research. It is clear that the model needs to be scaled to be in a comparable form with the reality, the challenge consisting in discovering the correct way of doing it. Further steps must be taken, in order to conduct the validation process in the correct way, which will adjust the model to the required situation. The challenge consists in making the correct assumptions and not oversimplifying the model while recreating the existing situation.

6. Conclusions and recommendations for future research

The differences between the plastic particle densities and sizes prove that the model is now in a state which is stable enough to run basic and correct simulations. During the modelling process assumptions were made such as the fact, that there are processes in nature which are not present in the model, for example fouling and fragmentation. These need to be included in the model to better simulate the behaviour of micro plastics within the North Sea domain. A study made by (Song & Andrady, 1991 December) contains experiments which refer to the effects of fouling on plastic material when exposed to marine environment. This study states that the floating debris is more dangerous as it attracts different species like seals (Laist, 1987 June). Fouling of plastic surfaces in sea water is well known (Andrady A. L., 1989) and is likely to lead to marked changes in the density of the plastic item. It is conceivable that at least with some items, extensive fouling might cause an initially positively buoyant piece of plastic to partially or completely sink in sea water (Andrady A. L., June 10, 1987). Some assumptions within the model are also not completely representative of reality, such as the fact that the river Rhine introduces 30% of the total plastic within the model, however are necessary to start the modelling process. For such assumptions, measured data and more indicative values are necessary. To exemplify a few, values for the quantity of plastic per cubic meter of water or the number of particles released, the distribution of quantity of particles

amongst the rivers should all be measured and validated before a precise and representative model can be created. The introduction of particles in the model only occurs at river mouths however there might be other points where plastic is introduced into the North Sea. These need to be measured, tested and implemented in the further research process. The model used was mainly restricted to the hydrodynamics of the year 2008, other years and other periods (longer or shorter than a year) should also be considered. An interesting find, extracted from results is the fact that the most impacted areas are the coastal zones, and in some cases the south western floor of the North Sea. The highest concentrations of plastic, according to the model, were registered at the inflow of the river Elbe and close to the Dutch coast. Heavily affected zones, such as the coast can present plastic concentrations higher than 10 milligrams per cubic meter of water, while zones where plastic pollutants are present can go as high as 2 milligrams of plastic per cubic meter of water. There are studies which presents plastic as high as 0.1 milligrams per cubic meter in the open ocean (Andrs, et al., 2014), however it is not specific to a typical region and was used as a guideline to get the current modelling process started. Since the affected areas are clearly visible in the model results, it could be a good starting point for further research such as fieldwork. Data from the specified zones should be collected regarding plastic size, type and origin. Such data can be used for validation as well as input for a future reduction plan in plastic pollution and policy making. The possibilities for research are present, expanding the modelling process and making it more precise and accurate should be the goal for further research on the topic of plastic litter modelling. We propose to continue the research and the modelling process by expanding more the usage of the probabilistic approach with a more detailed description of plastic particle characteristics and transport processes. So far this method has presented good results, which combined with the measured data can be validated and verified to provide further clarity. The model results can be compared to the plastics measurements resulted from the KIMO initiative for marine litter monitoring (Kimo, 2014). The model and the data can then support each other and create a sound basis to identify plastic accumulation areas within the North Sea. Effort is being put into improve the model by connecting it with the available sources of data. In this direction, additional information regarding the amount of micro plastics entering the North Sea environment via the river Maas has been collected within the CleanSea project (CleanSea Project, 2013). Such information can be used to adjust the current considered amount (which is calculated as a function of river discharge and plastic concentration in the fresh water input).

7. Acknowledgments

We would like to offer special thanks to all of the parties involved in the CleanSea FP7 European Union project, in particular, to Heather Leslie, the coordinator, for her help in bringing all the involved parties together. We would also like to show gratitude to the members of the Fishing for litter project for providing us with field data and measurements.

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