Faculteit Bio-ingenieurswetenschappen

Academiejaar 2014 – 2015

Occurrence and distribution of microplastics in the Scheldt river

Niels De Troyer

Promotor: Prof. dr. Colin Janssen

Tutor: Lisbeth Van Cauwenberghe

Masterproef voorgedragen tot het behalen van de graad van

Master in de bio-ingenieurswetenschappen: milieutechnologie

Faculteit Bio-ingenieurswetenschappen

Academiejaar 2014 – 2015

Occurrence and distribution of microplastics in the Scheldt river

Niels De Troyer

Promotor: Prof. dr. Colin Janssen

Tutor: Lisbeth Van Cauwenberghe

Masterproef voorgedragen tot het behalen van de graad van

Master in de bio-ingenieurswetenschappen: milieutechnologie

Preface

Beste lezer

Al van kinds af aan ben ik bijzonder gefascineerd door de natuur. Die passie heeft zich ondertussen omgevormd tot een levensdoel: op welke manier kan ik mijn steentje bijdragen aan een duurzaam toekomstbeeld? Bio-ingenieur worden is alvast de eerste grote stap. Eerst het hoofd vullen met waardevolle kennis om dan later actie te ondernemen. Het verhaal dat binnen enkele pagina’s verteld wordt, is het resultaat van vijf boeiende jaren op ’t Boerekot. Maar ik ben hier niet op mijn eentje geraakt. Heel wat mensen hebben me vergezeld op die tocht. Je kunt niet zonder de anderen, zoals Zjef Vanuytsel zo mooi verkondigt, en dat kan ik alleen maar beamen. Een klein dankwoordje is dan wel op zijn plaats, vind je niet?

Colin Janssen en Lisbeth van Cauwenberghe hebben me de mogelijkheid gegeven om mijn passie te combineren met wetenschappelijk onderzoek. Doordat het voor ons allemaal vrij nieuw onderzoek was, zag ik soms door de bomen het bos niet meer. Op die momenten stonden Colin en Lisbeth klaar om te luisteren en hulp te bieden. Bedankt daarvoor. Lisbeth zou ik nog eens extra willen bedanken voor haar bereidwilligheid en onevenaarbare ‘verbeterskills’.

Dit onderzoek vroeg heel wat laboratoriumwerk en de juiste uitwerking ervan was me nooit gelukt zonder Nancy De Saeyer, ofte Nancy van de laagste prijsgarantie, zoals Michiel ze ook wel eens durft noemen. Je kent deze laborante wellicht niet, maar ik kan je vertellen dat ze het synoniem is van bereidwilligheid. Het is er eentje uit de duizend. Een dikke ‘dank je wel’ is wel het minste wat ik kan schrijven voor haar. De laboavonturen zouden trouwens nooit hetzelfde geweest zijn zonder Michiel Lecomte, die ik leerde kennen in de 1e bachelor. Zijn droge humor, scherpzinnigheid en behulpzaamheid maakten mijn studiejaren bijzonder aangenaam. Het was fijn met hem samen te studeren en te werken. Vervolgens had ik graag Pieter Boets bedankt voor zijn hulp met het software programma ArcGis. Zonder hem hadden de staalnamelocaties nooit hun weg gevonden naar de landkaart. Eveneens wil ik Sylvia Lycke bedanken voor haar hulp met de micro-Raman spectroscopie en het analyseren van de spectra.

Dankzij mijn familie ben ik deze studie begonnen en, nog belangrijker, zal ik ze ook kunnen afmaken. Elk familielid heeft op zijn unieke manier bijgedragen tot dit werk. De steun die ik van ieder kreeg,

III heeft me heel veel geholpen. Mijn grootouders (meme Wis en pepe Rene & meme Fransine en pepe Eddy) wil ik nog eens extra in de verf zetten. Het is dankzij hen dat ik geworden ben tot wie ik ben. Hun voordeur (en frigodeur) stond steeds open om me met open armen te ontvangen. De zorg die ze met zoveel liefde geven is uiterst uniek en daarom een immens grote ‘dank je wel’!

Naast mijn familie wil ik nog al mijn vrienden in dit bedanklijstje zetten voor hun steun en de geweldige momenten die we samen mochten beleven, met een speciale vermelding voor de JNM’ers van Ninove-Geraardsbergen. De NiGers zijn stuk voor stuk unieke mensen die zich op een wonderbaarlijke manier inzetten voor natuur, milieu én vrienden.

En, last but definitely not least, zou ik de aandacht willen vestigen op iemand die me zeer dierbaar is. Iemand die mijn hart gestolen heeft en waarvoor ik wel eens naar een ander continent reisde. Jenna, liefje, bedankt om er steeds te zijn voor mij.

Veel leesplezier!

Niels De Troyer

IV

Deze pagina is niet beschikbaar omdat ze persoonsgegevens bevat. Universiteitsbibliotheek Gent, 2021.

This page is not available because it contains personal information. Ghent University, Library, 2021.

Table of contents

Preface ...... III Copyrights ...... V Table of contents ...... VII List with figures ...... XI List with tables ...... XV List with abbreviations ...... XVII Abstract ...... XIX Samenvatting ...... XXI Introduction ...... 1 Literature ...... 3 The importance of plastics ...... 3 Plastic waste management ...... 4 Waste hierarchy ...... 4 Dealing with plastic waste in Europe ...... 4 Plastic accumulation in the environment ...... 6 Sources ...... 6 Occurrence and distribution in marine environments ...... 7 Broadening the mind: river-sea interaction ...... 9 Effects on ecosystems ...... 11 Microplastics ...... 14 Definition ...... 14 Primary microplastics ...... 15 Secondary microplastics ...... 16 Presence of microplastics in the environment...... 17 Effects on ecosystems ...... 25 The health of the Scheldt – Research objectives ...... 29 Materials and methods ...... 31 Sampling locations ...... 31 Sampling campaigns ...... 33

VII

Sample processing ...... 33 Contamination analysis ...... 35 Microplastics characterisation ...... 35 Determination of moisture content and organic matter ...... 36 Granulometry ...... 37 Recovery ...... 38 Data analysis ...... 38 Results ...... 41 Microplastics identification ...... 41 River profile of microplastics ...... 44 Particle size distributions ...... 45 Behavioural patterns of microplastics in the freshwater environment ...... 48 Discussion ...... 53 How polluted is the Scheldt river? ...... 53 Predicting the presence of microplastics ...... 56 Spatial distribution of microplastics in the Scheldt river ...... 57 Size of microplastics ...... 62 Conclusion ...... 67 Further research ...... 69 References ...... 71 Appendices ...... 83 Appendix 1: Microplastic concentration used for Lumbriculus variegatus in Imhof et al. (2013) .....83 Appendix 2: Protocol treatment sediment, adapted from Van Echelpoel (2014) ...... 84 Appendix 3: Detailed overview of the used equipment ...... 85 Appendix 4: Pictures of contamination ...... 86 Appendix 5: Spectral analysis of coloured particles ...... 87 Appendix 6: Determination of the average amount of microplastics (MP) and the standard deviation ...... 90 Appendix 7: PSD for every location with width as a characteristic dimension ...... 92 Appendix 8: Results of the normality tests for the PSDs ...... 94 Appendix 9: Data regarding population density and the results of the granulometry and the determination of the organic matter content...... 96

VIII

Appendix 10: Sedimentation equations (Rhodes, 2008) ...... 98 Appendix 11: Formula derivation of the maximum particle size under laminar flow conditions (Rhodes, 2008) ...... 99

IX

List with figures

Figure 1: Sources and movement of plastics in the oceanic environment. Debris accumulates on beaches (1) in the neritic (2) and oceanic zone (3). The curved, grey and stippled arrows respectively indicate the wind-blown litter from land, the water-borne plastics (e.g. ships, sewage and rivers) and the vertical migration of plastics, while the black arrows show the ingestion of marine organisms (Ryan et al., 2009)...... 7 Figure 2: Simulation of a spatial distribution model for drifting marine debris after 10 years of advection by oceanic surface currents. The spatial density of plastic is indicated with colours. Blue means a low density, while red represents a higher abundance (Maximenko et al., 2012)...... 8 Figure 3: Occurrence and distribution of marine litter on the bottom of European seas and the Atlantic ocean (Pham et al., 2014)...... 9 Figure 4: Representation of the average plastic mass flow (g.s-1; middle) in the Danube river in function of the inhabitants (millions; left vertical axis) and the mean discharge (m³.s-1; right vertical axis) (Lechner et al., 2014)...... 11 Figure 5: Entanglement of a grey seal (Halichoerus grypus) by abandoned fishing gear (Allen et al., 2012)...... 12 Figure 6: *Left+ Plastic debris found in the gastrointestinal tract of the sea bird Cory’s shearwater (Rodríguez et al., 2012). [Right] Plastic found in the stomach of a sperm whale (D). Amongst other things, the stomach contained a rope (A), a tub of ice-cream (B) and a flower pot (C) (De Stephanis et al., 2013)...... 13 Figure 7: Coastal microplastic distribution for sediments around the globe (Browne et al., 2011)...... 21 Figure 8: Relationship between neustic microplastic concentration and urbanisation in Chesapeake bay, USA (adapted from Yonkos et al., 2014)...... 23 Figure 9: SEM image of a polystyrene particle with a crack in the surface (white arrow), illustrating the degradation and thus the fragmentation of (micro)plastics (Imhof et al., 2013)...... 24 Figure 10: Potential routes for microplastic ingestion by animals. The blue dots are microplastics with a density smaller than seawater while the red dots are denser polymers (Ivar Do Sul & Costa, 2014).25 Figure 11: Map of the study area. The blue lines represent large rivers and channels in Flanders and Brussels. The bold blue line stands for the Scheldt river upon which the eight sampling points are indicated with black stars. The white triangles are different Belgian cities or municipalities...... 32 Figure 12: Covered glass jar containing sampled sediment...... 33 Figure 13: Equipment used during sample collection and sample processing...... 34 Figure 14: The principle of the sedigraph method (Micromeritics, 2015)...... 37

XI

Figure 15: Three examples of particles present on the contamination filters. The colour and the type (fragment or fibre) are specified for each example...... 41 Figure 16: Micro-Raman analysis of a red bead...... 42 Figure 17: Micro-Raman analysis of a blue fragment...... 42 Figure 18: Pie chart of microplastic colour. Only particles that were positively identified as microplastics (as a result of contamination analysis and micro-Raman spectroscopy) were included. 43 Figure 19: Cumulative distribution functions of length and width of all microplastics. Only particles that were positively identified as microplastics (as a result of contamination analysis and micro- Raman spectroscopy) were included...... 44 Figure 20: River profile of mean microplastic abundance per sampling location. Locations are represented from river mouth to source. Flags represent the standard deviation of the mean...... 44 Figure 21: Map of the spatial evolution of the microplastic abundance. The blue bars represent the average microplastics concentrations...... 45 Figure 22: PSD of microplastics found in Antwerp (ACRB, AAPF and ABPF)...... 46 Figure 23: PSD of microplastics found in Hemiksem...... 46 Figure 24: PSD of microplastics found in Temse...... 46 Figure 25: PSD of microplastics found in Destelbergen (DA and DB)...... 46 Figure 26: PSD of microplastics found in Oudenaarde...... 47 Figure 27: Correlation of microplastic abundance (particles.g-1 dry weight) and fraction of organic matter (%OM)...... 48 Figure 28: Correlation of microplastic abundance (particles.g-1 dry weight) and the < 2 µm fraction of the sediment (%)...... 49 Figure 29: Correlation of microplastic abundance (particles. g-1 dry weight) and the < 20 µm fraction of the sediment (%)...... 49 Figure 30: Correlation of microplastic abundance (particles.g-1 dry weight) and the < 50 µm fraction of the sediment (%)...... 49 Figure 31: Correlation of microplastic abundance (particles.g-1 dry weight) and the < 63 µm fraction of the sediment (%)...... 49 Figure 32: Correlation of microplastic abundance (particles.g-1 dry weight) and the population density (inhabitants.km-2)...... 50 Figure 33: Correlation of fraction of organic matter (%OM) and the < 63 µm sediment fraction (%). . 51 Figure 34: [Left] Microbeads in the St. Lawrence river (Castañeda et al., 2014). [Right] Only brightly coloured spherical particles were considered to be microbeads in this research, such as a blue bead (A), a green bead (B) and a red bead (C). Brown spheres, such as (D), were not taken into account. .. 55

XII

Figure 35: Plastic debris found on the river shores at the convex river bend (ACRB). Plastic pellets in different colours were highly abundant...... 58 Figure 36: River profile of microplastic abundance for the locations Oudenaarde, Hemiksem and the area near the plastic factory in Antwerp...... 59 Figure 37: Particle size distribution of the microbeads found in the St. Lawrence river (Castañeda et al., 2014)...... 63 Figure 38: Number-weighted differential particle size distribution for the microplastics found in every replica sediment sample from Hemiksem...... 65 Figure A1: Visualisation of abundantly present particles and fibres on contamination filters...... 86 Figure A2: Spectral analysis of a red fragment ...... 87 Figure A3: Spectral analysis of a blue bead ...... 87 Figure A4: Spectral analysis of a green fragment ...... 88 Figure A5: Spectral analysis of an orange fragment. The pattern of the spectrum can be assigned to fluorescent orange pigment (Colombini & Kaifas, 2010). Specification is not possible due to the little available reference spectra and the fact that the bands can shift slightly depending on the company’s production ...... 88 Figure A6: Spectral analysis of an orange fragment. Pigment orange 13 (PO13). The reference spectrum can be found in Scherrer et al. (2009) on page 513 ...... 89 Figure A7: Width-based PSD of microplastics found in Antwerp (ACRB, AAPF and ABPF) ...... 92 Figure A8: Width-based PSD of microplastics found in Hemiksem ...... 92 Figure A9: Width-based PSD of microplastics found in Temse ...... 93 Figure A10: Width-based PSD of microplastics found in Destelbergen (DA and DB) ...... 93 Figure A11: Width-based PSD of microplastics found in Oudenaarde ...... 93 Figure A12: Normal Q-Q plot of Antwerp (ACRB, AAPF and ABPF) ...... 94 Figure A13: Normal Q-Q plot of Hemiksem ...... 94 Figure A14: Normal Q-Q plot of Temse ...... 95 Figure A15: Normal Q-Q plot of Destelbergen (DA and DB) ...... 95 Figure A16: Normal Q-Q plot of Oudenaarde ...... 95

XIII

List with tables

Table 1: Overview of the most common plastics in Europe in 2013 (PlasticsEurope, 2015)...... 3 Table 2: Reaction processes acting on plastic in the environment...... 16 Table 3: Abundance of microplastics (MPs) in marine sediments...... 18 Table 4: Abundance of microplastics (MPs) in seawater...... 19 Table 5: Overview of the sampling points...... 31 Table 6: Descriptive statistics of the PSDs of every location...... 47 Table 7: Correlation analysis...... 50 Table 8: Summary of the data needed to calculate the maximal for lake Garda...... 54 Table A1: Description of all used materials and chemicals ...... 85 Table A2: Results of the determination of the dry solids content, the filter analysis and the calculation of the average amount of microplastics and the standard deviation for the locations ACRB, AAPF and ABPF ...... 90 Table A3: Results of the determination of the dry solids content, the filter analysis and the calculation of the average amount of microplastics and the standard deviation for the locations Hem, Tem, DA, DA and Oud ...... 91 Table A4: Results of the Shapiro-Wilk W test for the PSDs ...... 94 Table A5: Data regarding population density from the national Belgian register (2015) ...... 96 Table A6: Results of the granulometry analysis ...... 96 Table A7: Results of the determination of the organic matter content ...... 97

XV

List with abbreviations

BPA Bisphenol A CDF Cumulative distribution function CLP Classification, labelling and packaging DDE Dichlorodiphenyldichloroethylene DDT Dichlorodiphenyltrichloroethylene EC European Commission EPA Environmental Protection Agency ESEM Environmental Scanning Electron Microscopy FAO Food and Agriculture Organisation of the United Nations FTIR Fourier Transform Infrared GEF Global Environment Facility GES Good Environmental Status IEEP Institute for European Environmental Policy IUCN International Union for the Conservation of Nature HDPE High density polyethylene LDPE Low density polyethylene MP Microplastics MSFD Marine Strategy Framework Directive NOAA National Oceanic and Atmospheric Administration NP Nonylphenol PBDE Polybrominated diphenyl ether PCB Polychlorinated biphenyl PCP Personal care product PP Polypropylene PVC Polyvinyl chloride PS Polystyrene PET Polyethylene terephthalate PUR Polyurethane PSD Particle size distribution

XVII

REACH Registration, evaluation, authorisation and restriction of chemicals SEM Scanning Electron Microscopy STP Sewage treatment plant TEP Transparent exopolymer particle UNEP United Nations Environment Programme

XVIII

Abstract

Plastics are widely used in the packaging industry, building and construction, electronics, automotive, agriculture and households. In 2013, 299 million tons of plastic was produced globally, which is approximately 175 times higher than in 1950 (PlasticsEurope, 2015). Due to the high production and consumption rate, it is necessary to treat plastic waste in a sustainable way. However, plastics appear to be abundantly present in natural environments due to littering and illegal dumping, tourism and industrial activities (Bowmer & Kershaw, 2010). The presence of plastics has been mainly reported for the marine environment: beaches, the open sea, coastal ecosystems and abyssal plains. Plastic items can be ingested by animals or these creatures can get entangled in marine debris causing severe adverse effects (e.g. suffocation). Of significant importance are the microplastics (< 1 mm) which originated from the deterioration of larger debris (secondary microplastics) or were industrially produced to be used in e.g. personal care products (primary microplastics). Due to their small size, they are available to lower trophic organisms introducing them into the food web (Wright et al., 2013).

It is believed that rivers are significant contributors to the plastic pollution of the oceans due to their estuarine connection. However, data on freshwater ecosystems are scarce. In order to assess the risks associated with plastics, research has to be conducted on the abundance, fate, sources and biological effects in freshwater environments (Wagner et al., 2014). For this reason, river shore sediments of the Scheldt river in Flanders (Belgium) were analysed. The main purpose was to find out how polluted this river is with microplastics. Samples were taken near the city of Oudenaarde, the sewage treatment plant of Destelbergen, the industrial area of Antwerp and at the confluence of the river Rupel and the Scheldt. The abundances ranged from 1 840 ± 2 407 microplastics.kg-1 dry weight to 63 112 ± 24 628 microplastics. kg-1 dry weight, which is much higher than the concentrations found in the marine environment or other freshwater ecosystems. This research also pointed at the importance of sewage treatment plants and industrial areas as sources of microplastics due to the observed increase in microplastic abundance in these areas. Human activities thus impact the Scheldt river, although population density did not appear to be a good predictor for microplastic abundance. On the other hand, the concentration after the river confluence Rupel-Scheldt dropped while it was expected to increase as rivers are believed to be important suppliers of (micro)debris. This points at

XIX other factors influencing the behaviour of microplastics in a freshwater environment. Next to human activities, the microplastic characteristics (e.g. density and sphericity) and the hydrodynamic state of the water determine the microplastic occurrence and distribution (Rocha-Santos & Duarte, 2014). As a result, a fluctuating pattern for the concentrations was observed along the river continuum instead of a continuous increase.

Hydrodynamics were investigated via sediment particle analysis as it is believed that the composition of the sediment is a good approximation for the local (average) hydrodynamic state. A direct proportional relationship was observed for the fine fraction of the sediment (< 63 µm) and the abundance of benthic microplastics. Consequently, considering hydrodynamics is indispensable for explaining patterns of microplastic pollution. Additionally, the < 63 µm sediment fraction was significantly positively correlated with the amount of organic matter. Both variables can thus be used as a predictor for microplastic abundance.

Finally, (micro)plastics in natural environments are susceptible to several degradation processes leading to smaller particles (e.g. photolysis). The fragmentation of microplastics was investigated via analysis of the spatial evolution of particle size distributions along the river continuum. At locations farther downstream, microplastics were significantly smaller than those found at locations closer to the river source indicating fragmentation. This fragmentation should also be taken into account in order to explain changes in microplastics concentrations in the river sediment as it leads to a larger amount of (smaller) microplastics.

In summary, the Scheldt river is a highly polluted freshwater ecosystem. The occurrence and the distribution of microplastics cannot only be ascribed to anthropogenic impacts. Microplastic characteristics and hydrodynamics should be taken into account when conducting microplastic research. Normalisation to matter does matter.

XX

Samenvatting

Plastics zijn niet meer weg te denken uit onze maatschappij. Het wordt veelvuldig gebruikt in de verpakkingsindustrie, automobielsector, elektronica, landbouw en als bouwmateriaal. In 2013 bedroeg de globale plasticproductie 299 miljoen ton, wat 175 keer hoger is dan in 1950 (PlasticsEurope, 2015). Door de hoge productie en consumptie is een goed afvalbeleid onontbeerlijk. Maar dit is eenvoudiger gezegd dan gedaan. Plastics zijn abundant aanwezig in natuurlijke ecosystemen door sluikstorten, illegaal dumpen van afval, waterzuiveringsinstallaties en industriële activiteiten (Bowmer & Kershaw, 2010). Onderzoek naar plasticvervuiling richt zich vooral op het mariene milieu: stranden, open zee, kustecosystemen en abyssale vlaktes. Door hun persistentie kunnen ze heel wat schade aanrichten. Organismen kunnen er in verwikkeld geraken en bijgevolg verdrinken of ze kunnen het aanzien als hun prooi en zo opgenomen worden in het lichaam waardoor het dier kan stikken. Vooral microplastics (< 1 mm) zijn van belang aangezien deze beschikbaar zijn voor organismen op een lager trofisch niveau (e.g. algen). Op deze manier wordt plastic opgenomen in het voedselweb.

Rivieren worden aanzien als belangrijke bronnen van vervuiling. Plastics worden meegevoerd met de rivier tot in de oceanen. Maar er is heel weinig bekend over deze zoetwaterecosystemen. Om de risico’s geassocieerd met plastics te evalueren is er dringend onderzoek nodig naar de hoeveelheden, de bronnen en de biologische effecten in het zoetwatermilieu. Om die reden werd het sediment van de rivier de Schelde in Vlaanderen (België) geanalyseerd. Het hoofddoel van dit onderzoek was om na te gaan hoe vervuild het sediment is met microplastics. Stalen werden genomen nabij de stad Oudenaarde, de rioolwaterzuiveringsinstallatie van Destelbergen, de Antwerpse industrie en aan de samenvloeiing van de Rupel en de Schelde. De hoeveelheden varieerden van 1 840 ± 2 407 microplastics.kg-1 droge stof tot 63 112 ± 24 628 microplastics. kg-1 droge stof. Dit is veel hoger dan in het mariene milieu en andere zoetwaterecosystemen. Dit onderzoek wees ook op het belang van rioolwaterzuiveringsinstallaties en industrie als een belangrijke bron van microplastics aangezien de microplastic concentraties in het sediment steeg in deze gebieden. Menselijke activiteiten hebben dus een belangrijk impact op de rivier, ondanks het feit dat er geen significant verband was tussen de populatiedichtheid en de abundantie aan microplastics. Langs de andere kant daalde de concentratie na de monding van de Rupel in de Schelde terwijl er verwacht werd dat deze zouden stijgen aangezien rivieren plastics aanvoeren. Dit wijst erop dat het voorkomen van microplastics niet alleen

XXI beïnvloedt wordt door menselijke activiteiten. De microplastic eigenschappen en de hydrodynamische toestand van het water moeten ook in rekening gebracht worden om patronen in het voorkomen van microplastics te verklaren. Bijgevolg is er een fluctuerende trend in de microplastic concentraties volgens het verloop van de rivier in plaats van een continue toename.

De hydrodynamiek werd onderzocht door deeltjesanalyse van het sediment. Er werd verondersteld dat de deeltjessamenstelling een goede benadering is voor de lokale (gemiddelde) hydrodynamische toestand. Een recht evenredig verband werd vastgesteld voor de fijne sedimentfractie (< 63 µm) en het aantal benthische microplastics. Eveneens was de < 63 µm fractie significant positief gecorreleerd met de hoeveelheid organisch materiaal. Beide variabelen kunnen dus gebruikt worden om het voorkomen van microplastics te voorspellen.

Microplastics in natuurlijke ecosystemen zijn onderhevig aan allerlei degradatieprocessen (vb. fotolyse) wat leidt tot kleinere deeltjes. De fragmentatie werd onderzocht aan de hand van de evolutie van de deeltjesdistributies volgens het verloop van de rivier. Microplastics waren significant kleiner op locaties verder de rivier dan deeltjes gevonden op locaties dichter bij de bron wat wijst op fragmentatie. Ook fragmentatie dient in rekening gebracht te worden om veranderingen in microplastic concentraties te verklaren aangezien dit leidt tot meer (kleinere) microplastics.

Algemeen kan gesteld worden dat de Schelde sterk vervuild is met microplastics. Het voorkomen van microplastics kan echter niet alleen toegeschreven worden aan menselijke activiteiten. Hydrodynamiek en plasticeigenschappen dienen in rekening gebracht te worden bij microplastic onderzoek.

XXII

Introduction

A world without plastics has become unthinkable. Humans produce and consume tons of plastic each year. Their versatile properties and long life expectancy make them highly desired. In combination with a good waste policy, these polymers are a durable product. However, many plastics are still easily thrown away introducing them in natural environments. In that case, the advantages of plastics turn into serious issues questioning their durability from an ecological point of view. As a consequence of their persistent nature, they pose a major threat to organisms. However, degradation of plastics does occur leading to smaller plastic fragments: microplastics (< 1 mm). These small particles are also industrially produced to serve as an additive in personal care products or as a sand-blasting medium to clean surfaces. An increased usage enhances the risk of polluting the environment with this type of microplastics. The ecosystem effects of microplastics can be even more severe than larger debris as they have the potential of infiltrating the food web via lower trophic organisms.

Microplastics appear to be present in several ecosystems. They were even found in deep sea environments and polar regions indicating their mobility potential. A plastic bottle that is thrown away on land, might end up as microplastics in the ocean, which can be seen as a major sink. Rivers are an important link as they transport debris towards the oceans. In order to fully understand the microplastic pollution, these freshwater environments deserve more attention than they get today. How many microplastics are present in a certain area? Where do they come from? What is their fate? And what about effects on freshwater species? These are all questions that are not yet answered for many rivers. This research is just a small part of a much bigger story. It can be seen as an urgent call for assessing microplastic pollution in freshwater ecosystems and to make people, and especially politicians, aware of the consequences of a poor waste management.

Page 1 of 99

Literature

The importance of plastics

Plastics have become indispensable for human society. As a consequence of its intrinsic properties and its modification potential, these polymers are excellent materials for several purposes such as packaging, clothing, building material and pharmaceutics. This high applicability has led to an increasing production over time. In 1950 approximately 1.7 million tons were produced and ever since there has been a positive exponential growth, with a global plastic production of 299 million tons of in 2013 (PlasticsEurope, 2015). As plastics are made from the naphtha fraction of crude oil, they account for approximately 8% of the global oil production (Thompson et al., 2009). After China, Europe has the biggest market share (20 % in 2013) in the global plastic production (PlasticsEurope, 2015). The most commonly produced plastic types in Europe are listed in Table 1, together with their respective market share and some applications. Especially Belgium plays an important role in the European plastic industry as it is the market leader in plastic production and processing per capita. In 2011, Belgium (2,2% of the EU population) processed 5% and produced 10% of all European plastics (Dalimier, 2012). This success is explained by the high availability of raw materials due to the presence of three major seaports (Antwerp, Zeebrugge and Ghent) and Belgium's centralized location in an economically important pipeline network for transportation (Dalimier, 2012).

Table 1: Overview of the most common plastics in Europe in 2013 (PlasticsEurope, 2015).

Plastic type Market share (%) Applications Low density polyethylene Bags, food and drink cartons, computer 17.5 (LDPE) hardware High density polyethylene Bottle caps, storage containers, bags, 12.1 (HDPE) bottles, surgery Polypropylene (PP) 18.9 Car bumper, flower pots, clothing, carpets Boots, windows, pipelines, clothing, Polyvinyl chloride (PVC) 10.4 insulation Polystyrene (PS) 7.1 Yoghurt pots, insulation, CD cases, razors Polyethylene terephthalate 6.9 Bottles, photovoltaic cells, medical devices (PET) Polyurethane (PUR) 7.4 Sponges, insulation

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Plastic waste management

Waste hierarchy The increasing production trend implies that a good waste policy has to be developed. Waste prevention and reuse are of top priority but some plastics have a rather short lifespan and thus defy this statement. The inexpensive, lightweight and durable character of plastics encourages single use and a high ‘consumption throughput’ (Hammer et al., 2012). For example, the application of single use plastics in packaging products increases the amount of waste and implies inefficient use of resources. The 2013 PlasticsEurope report affirms this by reporting that 62,2% of the total post- consumer plastic waste originates from packaging. Waste is inevitable and it is therefore imperative to design plastic materials that have a high recyclability or that at least can be incinerated with energy recuperation (EC, 2010). Landfill and direct releases to the environment should be avoided as this not only implies a loss of valuable resources, but could also harm the ecosystem in which it is introduced (Cole et al., 2011). The collected waste in Belgium is mainly recycled or burned with energy recuperation thanks to the landfill ban (European Environment Agency, 2013). In 2012, approximately 31% of the collected Belgian waste was recycled and 66% was incinerated (PlasticsEurope, 2015). However, it should be stressed that this only gives information on the treatment of collected waste. Plastics directly released in the environment are more difficult to assess. Once present in the environment, these synthetic polymers can persist for centuries, depending on the environmental factors and the physical and chemical properties (Andrady, 2011). Concerning sustainability, a durable plastic is desirable but once released in nature, it would better not persist too long. It is this ‘plastic paradox’ that makes it difficult to decide how sustainable plastics really are.

Dealing with plastic waste in Europe

Legislation In Europe, the waste hierarchy is depicted in the Waste Framework Directive (2008/98/EC). However, this does not specifically apply to plastic waste. Only the Packaging and Packaging Waste Directive 94/62/EC directly deals with the generation of plastic packaging waste. It emphasizes the value of recycling. This is also where the REACH regulation (1907/2006/EC) can be of importance as certain hazardous chemical additives lowers recyclability. Together with the Classification, Labelling and Packaging Regulation 1272/2008/EC (CLP), REACH contributes to the production of less hazardous plastics with an enhanced recycling potential (European Commision, 2013).

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Since 1988, The International Convention for the Prevention of Pollution from Ships (MARPOL) tries to tackle the pollution in marine environments. Especially annex V deals with the problem of dumping garbage from ships. Other, more regional, conventions dealing with the plastic pollution are the OSPAR, Barcelona, Helcom and Black sea conventions (European Commision, 2013). The issue of marine litter is described in the Marine Strategy Framework Directive 2008/56/EC (MSFD). According to the MSFD, marine waters of the EU member states have to have a good environmental status (GES) by 2020. Marine litter is taken into account in the determination of the GES as the 10th descriptor in Annex I of the MFSD, which is defined as: ‘Marine litter does not cause harm to the coastal and marine environment' (Galgani et al., 2010).

Biodegradable plastic: the solution to pollution? The persistent and non-biodegradable nature of plastics causes accumulation of these pollutants in the environment (Andrady, 2011). Biodegradable plastics attempt to tackle the persistency problem. These materials are made from renewable resources (e.g. starch or cellulose) or fossil fuels and are characterised by a higher biodegradability than conventional plastics (European Bioplastics, 2015). This means that these materials are mineralised more rapidly by microorganisms (Song et al., 2009). It is estimated by measuring the amount and the rate of CO2 released in lab conditions (Narayan, 2006). However, this lab estimation can be a bad representation for real life. Biodegradation tests depend on hot and aerated conditions for the optimisation of the metabolism of bacteria, fungi and insects (Moore, 2008). Additionally, the presence of certain microorganisms is indispensable for the biodegradation, which is not always the case in reality (Hopewell et al., 2009). It is consequently difficult to determine how these plastics will react in the highly variable environment. Next to that, recycling processes may be complicated due to the presence of biodegradable plastics (Ren, 2002). They are more suited for incineration with energy recovery or biological waste treatment such as composting and anaerobic digestion (Song et al., 2009).

End-of-pipe solution To cope with the amounts of plastic already present in the environment, several cleaning systems have been developed. Since 1989 a global cleaning action is organised annually, called the International Coastal Cleanup, where volunteers actively collect trash on beaches (UNEP, 2009). There are also several theoretical concepts to clean the oceans, such as automated drone-based and vessel-based concepts where marine litter is gathered in nets. The main issue with these clean up mechanisms is the economic feasibility. Fuel consumption, technical issues, limited capacity and

Page 5 of 99 number of ships and the size of the area that needs to be cleaned often impede neustic cleaning (Van Schie et al., 2014). These end-of-pipe solutions should therefore be seen as a last possibility. The key words in solving the plastic pollution issue are reduce, reuse and recycle.

Plastic accumulation in the environment

Sources Most plastic waste gets released into the environment due to improper human behaviour (e.g. littering) and/or the lack of a good waste management (Barnes et al., 2009). Plastics have infiltrated the natural environment via several ways. From an ecological and a socio-economic point of view, it is thus of major importance that plastic pollution is thoroughly investigated.

In highly populated or industrialized areas there is a major input from land, especially in the form of packaging material (Derraik, 2002). Street litter, poorly managed waste disposal, plastic manufacturing and processing sites, sewage treatment and overflows, tourism and illegal dumping impact the environment (Bowmer & Kershaw, 2010). For example, the production of many plastic products is accomplished via resin pellets, also known as nurdles or Mermaid’s tears (EPA, 1992). These raw materials are available in different forms and colours. The presence of plastic pellets along shorelines is often an indication of a poor transport of these precursors and the direct loss at the factory (Bowmer & Kershaw, 2010). The plastic accumulated on land may eventually end up in the ocean via riverine or wind-driven transport. On the other hand, tides and wave action bring plastic back to land (Barnes et al., 2009). Regarding the marine environment, land-based sources contribute the most to the plastic pollution, but there are local differences (Andrady, 2003). Fisheries for instance introduce plastics as a result of discarding and losing fishing equipment such as nets and lines. Especially in areas with high fishing intensities (e.g. Alaska) litter mainly originates from fishing gear (Derraik, 2002). Figure 1 represents possible sources and mobilisation of plastic in marine ecosystems. Another source of litter (not shown in Figure 1) is aquaculture installations. Through time, this sector has become an important way of producing fish. In the period 2000-2012 the global fish production via aquaculture had an average annual growth rate of 6,2% (FAO, 2014). Consequently, the contribution to pollution of this sector should not be underestimated. Materials used to hold suspended cultures, such as buoys, ropes and floats, are sometimes released in the environment (Astudillo et al., 2009). Hong et al. (2014) identified styrofoam buoys, used in aquaculture, as the biggest contributor in the pollution of surveyed Korean beaches.

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Figure 1: Sources and movement of plastics in the oceanic environment. Debris accumulates on beaches (1) in the neritic (2) and oceanic zone (3). The curved, grey and stippled arrows respectively indicate the wind-blown litter from land, the water-borne plastics (e.g. ships, sewage and rivers) and the vertical migration of plastics, while the black arrows show the ingestion of marine organisms (Ryan et al., 2009).

Occurrence and distribution in marine environments The global distribution of debris at sea is very patchy and depends on wind and current conditions, geomorphology and anthropogenic influence (Barnes et al., 2009). Across the globe, there are certain areas where the low energy status of the water allows accumulation. Floating plastic is expected to concentrate in regions of low circulation and high sedimentation rates such as frontal zones, enclosed and semi-enclosed areas (Acha et al., 2003). For example, in the North sea plastic hot spots develop due to eddy currents, the import of litter via the gulfstream from the south transporting it northwards and to zones of low turbidity and turbulence (Galgani et al., 2000). Continental shelves are expected to have lower concentrations than areas closer to shore. The rationale behind this reasoning is that a lot of debris on the shelves comes from land and rivers. But there is a high local variability: areas closer to land can experience high currents induced by e.g. strong winds prohibiting the presence of large amounts of plastic (Galgani et al., 2000). On the other hand, deeper shelf waters provide more favourable conditions for sedimentation and allows debris to accumulate. In the open sea there are also specific regions where plastic assembles, known as convergence zones (Cózar et al., 2014). These areas are the result of a rotating oceanic surface current (gyre) induced by the drag forces of the wind, the Coriolis deflection and continental interactions (Pinet, 2005). Floating materials tend to accumulate in areas away from these currents. Figure 2 shows the simulation result of a probabilistic model that predicts the spatial distribution of plastic debris on the oceanic surface. These plastic hotspots are also referred to as garbage patches or oceanic landfills (Cózar et al., 2014).

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Figure 2: Simulation of a spatial distribution model for drifting marine debris after 10 years of advection by oceanic surface currents. The spatial density of plastic is indicated with colours. Blue means a low density, while red represents a higher abundance (Maximenko et al., 2012).

Plastic distribution is not only limited to the ocean surface, but they also show scattering deeper down the water column. Therefore, spatial variability should be seen in three dimensions. When plastic is released in the environment, it is rapidly fouled by sediment and organisms. (Bio)fouling increases the density of the material and initially buoyant plastic sinks to the bottom where it may be incorporated into the sludge. Despite the fact that almost 40% of the total plastic produced is neutrally buoyant, this does not imply that these plastics cannot be found in the sediment. Waterlogging induces similar effects (Wabnitz & Nichols, 2010). Research regarding the quantification of marine litter has mainly focused on coastal areas and surface waters as deep sea sampling entails technical difficulties and a high cost (Pham et al., 2014). However, this field is gaining more attention. In 2012, Bergmann & Klages, for example, investigated the amount of marine litter on the deep sea floor in the Arctic (2500 m) with camera observation. Based on the photographs the densities were estimated. They found that marine debris, of which 59% was plastic, increased from 3635 to 7710 items.km-2 between 2002 and 2011. Benthic debris has also been quantified in European waters by Galgani et al. in 2000. This was done with otter trawls and pole trawls with 20 mm mesh size at the cod end. There was a high spatial variability as a result of local differences in currents, hydrodynamics and human influence. Values ranged from 64 ± 51 plastics.km-1 (Bay of Seine) to 2630 ± 1080 plastics.km-1 (Adriatic sea). A more recent study is that of Pham et al. (2014) where the litter density was determined with video surveying and the usage of two trawls (20 mm and 40 mm mesh size respectively). Figure 3 summarizes their results. Plastics accounted for 41% of all litter. This research

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highlighted the importance of fouling as they reported higher densities for the seabed in comparison with surface waters. A generality that follows from all these studies is that marine litter distribution depends on human activity and oceanographic processes.

Figure 3: Occurrence and distribution of marine litter on the bottom of European seas and the Atlantic ocean (Pham et al., 2014).

Broadening the mind: river-sea interaction Scientific studies regarding plastic pollution have mainly focused on a quantitative description of marine areas and effects on marine organisms (Derraik, 2002). It is stated that rivers contribute significantly to the plastic pollution of the oceans due to their estuarine connection with the marine environment (Bowmer & Kershaw, 2010; Williams & Simmons, 1997). However, there are little data available for freshwater and terrestrial ecosystems (Thompson et al., 2009). In order to gain better insight in the mechanisms behind the plastic pollution of the environment assessments should also be made for freshwater ecosystems (Wagner et al., 2014).

As in the marine environment, the spatial distribution of freshwater litter depends on human activities, hydrodynamics and geomorphology. Especially geographical differences in human activities determine the specific litter profile of a river (Rech et al., 2014). The transport of litter via rivers depends on several factors. For instance, the balance between freshwater outflow and seawater inflow creates specific conditions that influences pollutant mobility. This was demonstrated by Acha et al. (2003), who described how materials accumulate in estuarine surface fronts originating from

Page 9 of 99 the encounter of salt and freshwater. There is a proportional relationship between river flow rate and the waste transport towards the sea: large rivers, characterised by high surface flow rates and the presence of bottom currents, export more litter in comparison with smaller rivers (Galgani et al., 2000).

The research of Williams & Simmons (1997) describes the interaction between ecosystems. They investigated the amount of litter washed ashore on estuarine beaches in the Bristol Channel in the UK. The largest amount was found on a river flowing into the estuary, known as the Taff river, indicating the importance of riverine transport of litter to marine areas. Plastic dominated the debris at every site and most of it didn’t have a marine origin. The urbanised areas around the river could be a possible reason for the pollution with fly-tipping and sewage inputs as main sources. In 2014, Rech et al. conducted an analogue research for Chilean rivers. To estimate the riverine contribution to marine pollution the composition and the abundance of litter at beaches near the mouth and at the river banks were compared. Once again, plastics, classified as persistent buoyant items, were the most abundant pollutants on beaches and riversides. The composition of the stranded debris on river banks bore resemblance to that found on the adjacent coastal beaches. Estuaries are characterised by tides according to the definition of Fairbridge (1980). This can have an influence on the distribution and transport patterns of debris, as investigated by Sadri & Thompson (2014) for the Tamar estuary. During neap tide, there was a distribution shift observed to smaller debris. However, it is not correct to ascribe this only to the tides as other variables could have had an influence on the outcome (e.g. wind, and phytoplankton concentration).

In the UK in 2014, the river Thames was also assessed but instead of just collecting stranded material Morritt et al. used nets to characterize debris dragged along the river. Sanitary products had a relative high abundance which pointed at the fact that consumer behaviour influenced the pollution of rivers. Additionally, this suggested that a significant source of litter in rivers is land-based. Low findings of plastics bags were reported due to the design of the nets. Gasperi et al. (2014) came to the same conclusion for the Seine river in France. The sampling method is thus of importance to conduct this type of research in a representative way. Additionally, a broad time-integrated sampling approach is advisable (Gasperi et al., 2014). Lechner et al. (2014) paid attention to this remark by sampling two years (2010 – 2012) with stationary nets in the Danube river in Austria. Industrial pre- production pellets showed the highest contribution due to industrial activity (approx. 80% on

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average). However, the amount varied between the two years. Besides a quantification of the drifting debris, the mass flow of plastic to the Black sea was estimated.

Figure 4 summarizes the averaged discharge for the period 2010-2012. The mass flow increases along the river continuum. The increasing population and mean discharge towards the mouth can be linked to this.

Figure 4: Representation of the average plastic mass flow (g.s-1; middle) in the Danube river in function of the inhabitants (millions; left vertical axis) and the mean discharge (m³.s-1; right vertical axis) (Lechner et al., 2014).

Remarkable is that the above mentioned rivers indeed have their own unique litter profile, as stated by Rech et al. (2014). Comparing the different results to each other is not easy due to the usage of different units and other sampling techniques. This problem has already been described for assessments in the marine environment by Ryan et al. (2009) and standardized protocols should be developed in the future to solve this issue (Galgani et al., 2013).

Effects on ecosystems One of the most important questions concerning plastic pollution is how wildlife and the functioning of a certain ecosystem is impacted. Together with the quantification of marine litter, effect assessment was one of the first topics studied regarding plastic pollution (Barnes et al., 2009). For

Page 11 of 99 example, Kenyon & Kridler (1969) were one of the first scientists investigating swallowed material by Laysan albatrosses. Besides pumice, rocks, squid beaks and nuts different kinds of plastics were found, such as plastic bags, caps and toys. Up to now, there have been numerous studies that dealt with the effects of large plastic debris on biota (Derraik, 2002). For the marine environment, one of the most pronounced effects is entanglement by lost fishing gear, six-pack plastic rings and packing strapping bands (Katsanevakis, 2008). For example, wandering nets continue capturing marine organisms, known as ghost fishing. This phenomenon is a cyclic happening according to the IEEP report from 2005 (Institute for the European Environmental Policy) (Brown et al., 2005). Whilst ghost fishing, the net gets heavier and eventually sinks to the bottom where scavenging organisms clean it and consequently reduce the weight. This leads to a resuspension of the net allowing ghost fishing to resume. Animals are attracted to drifting debris as a consequence of their normal behaviour. Predacious fish may be lured to this ‘gathering’ and thus risk getting entangled as well. These animals may drown, get injured or may experience difficulties to catch food or to evade predators (Laist, 1987). This issue is seen as an important cause of death for mammals, fish, turtles and birds (Katsanevakis et al., 2007). Figure 5 shows the severity of entanglement (Allen et al., 2012). Particularly slow-growing animals with a low fecundity and a relative long life span, such as cetaceans, are vulnerable to this threat (Read et al., 2006). Additionally, entanglement enhances the extinction risk of species listed on the IUCN red list (Gall & Thompson, 2015). Karamanlidis et al. (2008), for example, state that accidental entanglement contributes significantly to the population decline of the Mediterranean monk seal (Monachus monachus), a currently endangered species. Even deep-sea creatures, such as anglerfishes and deep-water sharks, are jeopardized (Large et al., 2009).

Figure 5: Entanglement of a grey seal (Halichoerus grypus) by abandoned fishing gear (Allen et al., 2012).

Besides getting entangled in plastic debris, animals may ingest these synthetic polymers. This may occur due to a misidentification of the litter or may be ingested accidently during feeding

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(Katsanevakis, 2008). The ingestion of plastic bags by sea turtle species is a well-known example (e.g. Tomás et al., 2002). This debris is mistaken for jelly fish leading to a blockage of the gullet (Derraik, 2002). Figure 6 illustrates the ingestion of plastic debris by the sea bird Cory’s shearwater (Calonectris diomedea) and a sperm whale (Physeter macrocephalus).

Figure 6: [Left] Plastic debris found in the gastrointestinal tract of the sea bird Cory’s shearwater (Rodríguez et al., 2012). [Right] Plastic found in the stomach of a sperm whale (D). Amongst other things, the stomach contained a rope (A), a tub of ice-cream (B) and a flower pot (C) (De Stephanis et al., 2013).

The Global Environment Facility (2012) report that 663 species are known to be affected by debris entanglement or ingestion. Next to internal and external injuries, suffocation, starvation or a general weakening of affected organisms plastics can cause intoxication due to the chemical additives they contain (Katsanevakis, 2008). Chemicals such as phthalates, organotins, polybrominated diphenyl ethers (PBDE), bisphenol A (BPA) and nonylphenols (NP) are used during the production process to give the synthetic polymer specific properties (Teuten et al., 2009).The leaching and natural degradation of these additives is determined by polymer characteristics and environmental conditions. For example, BPA is readily biodegraded in aerobic conditions (Zhang et al., 2007) but in anoxic zones BPA is more persistent (Ike et al., 2006). Besides leaching of additives, hydrophobic compounds such as polychlorinated biphenyls (PCB), dichlorodiphenyldichloroethylene (DDE) and NPs adsorb on plastic (Mato et al., 2001). These pollutants tend to be more attracted to plastics than to natural sediments. The presence of plastics in a certain area consequently leads to an

Page 13 of 99 upconcentration of these chemical pollutants (Teuten et al., 2009). Exposure of toxicants to organisms via plastic is most likely due to the ingestion of these polymers introducing them in the food chain (Galgani et al., 2013). For example, Ryan et al. (1988) found a positive correlation between the amount of ingested plastic and the DDT (dichlorodiphenyltrichloroethylene) concentration in the fat tissue of the sea bird Great Shearwater (Puffinus gravis). An analogue, more recent research is that of Tanaka et al. (2013). The PBDE analysis of the plastics found in the stomach of the Short-tailed Shearwater (Ardenna tenuirostris) and the fat tissues showed resemblance indicating the transfer of this pollutant from plastic to animal. Animals can experience severe adverse effects from exposure to toxicants. BPA and NPs, for example, are endocrine disruptors which interfere with the natural hormone balances (Careghini et al., 2014).

Another threat to ecosystems by plastic debris is the invasion of alien species (Gregory, 2009). Species such as bryozoans, barnacles, polychaete worms, hydroids and molluscs attach themselves to the highly mobile floating litter (Barnes, 2002). Barnes & Milner (2005) demonstrated the potential of alien invasion as they found an exotic barnacle on flotsam in the Shetland islands.

Plastic accumulation in and on the sediment can alter the ecosystem functioning. For example, Katsanevakis et al. (2007) showed that litter serves as a new substratum. This increased the abundance of certain species and changed the megafauna community structure. On the other hand, plastic on the bottom of the sea can interfere with the oxygen exchange of the sediment and the overlying water leading to reduced oxygen concentrations in the sediment (Goldberg, 1994). Based on the model of Pearson & Rosenberg (1978) this may alter the abundance, biomass and biodiversity of the benthic community.

Microplastics

Definition The National Oceanic and Atmospheric Administration (NOAA) defines microplastics as particles smaller than 5 mm. However, this is not used unambiguously in research. Claessens et al. (2011), for example, used 1 mm as a boundary as this is a more intuitive value (i.e. the start of the micrometre range). The latter is used throughout this dissertation. Based on their origin, microplastics can be further classified as primary or secondary microplastics. The first category covers all manufactured microscopic plastic particles while secondary particles are born from larger debris.

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Primary microplastics Personal care products (PCP) like scrubs, toothpaste and shower gels can contain small plastic particles. In the last years, manufacturers of cosmetics fabricate more products with plastic particles, known as microbeads, instead of using natural exfoliators, such as pumice and oatmeal (Fendall & Sewell, 2009). This consequently leads to an increasing plastic use by consumers. Gouin et al. (2011) estimated that one inhabitant of the United States of America consumed 2.4 mg plastic per day in 2009. Primary microplastics are also indirectly formed by human activities. For example, used PET bottles can be recycled into polymer fibres via an extrusion process which are applied in e.g. clothing, carpets and furniture (Park & Kim, 2014). Upon washing synthetic textile, plastic fibres detach from the material and consequently contribute to the pollution of the environment. In 2011, Browne et al. discovered that one garment released up to more than 1900 fibres per wash.

It is not expected that primary microplastics are retained efficiently in filtration mechanisms at wastewater facilities due to their small size and buoyancy (Fendall & Sewell, 2009). However, several studies are inconsistent with this statement. For example, Magnusson & Norén (2014) report removal efficiencies of more than 99%. However, they only investigated the fraction larger than 300 µm. The Helcom Base pilot project in 2014 took a minimal particle size of 20 µm into account for which also high removal efficiencies (more than 90%) was found (Talvitie & Heinonen, 2014). It should be stressed, however, that these results cannot be compared due to differences in waste streams, technical installations, sampling, sample processing techniques and analysis procedures. Nonetheless, both studies observed retention of plastics in wastewater sludge indicating the removal potential of activated sludge systems. The development of a correct sludge treatment process is consequently imperative. Sludge disposal on land, for example, is no option as this releases the retained microplastics to the environment (Zubris & Richards, 2005).

Primary microplastics are also used in air blasting technology. Polyester, melamine and acrylic particles are fired towards surfaces which need cleaning (Cole et al., 2011). As these powders maintain their effectiveness for a longer time than sand does, there’s a tendency to use plastics (Leslie et al., 2011). After usage, the medium is vacuumed to be reused, but losses are inevitable (Roex et al., 2009). Furthermore, they get contaminated with heavy metals, such as lead and chromium, posing an additional threat to ecosystems upon loss (Cole et al., 2011).

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Secondary microplastics Plastics in the environment are susceptible to several physical and chemical processes. These degradation reactions convert larger plastic debris into smaller particles, known as secondary microplastics (< 1 mm).

Physical abrasion (e.g. wave action) enhances the fragmentation of macroplastic and leads to shifts in particle size distribution as time passes (Barnes et al., 2009). Biological activity also reduces particle size via boring, shredding or grinding mechanisms (Bowmer & Kershaw, 2010). Plastic is also vulnerable to chemical degradation which realizes a decline in specific polymer properties, such as molecular weight (Yousif & Haddad, 2012). According to Andrady (2011) there are four possible plastic degradation mechanisms occurring in the environment, based on the agent causing it (Table 2).

Table 2: Reaction processes acting on plastic in the environment.

Reaction type Agent Biological degradation Organisms (e.g. bacteria) Photolysis and photooxidative degradation Light (UV) Thermooxidative degradation Oxygen at moderate temperature Hydrolysis Water

In lab conditions decomposition of plastics can be achieved with several bacterial and fungal strains (Bhardwaj et al., 2012). But in reality, the presence of these species tends to be low and microbial ecological processes (e.g. competition) impede biodegradation (Andrady, 2011). Additionally, the activity of microorganisms is determined by environmental conditions, such as temperature and pH (Kaiser & Attrill, 2011).

Photodegradation takes care of a rather rapid material transformation in contrast to e.g. hydrolysis and biodegradation (Andrady, 2011). In the absence of oxygen solar radiation reorganises the molecular positions via chain scissions and cross-linking. This is known as photolysis (Yousif & Haddad, 2012). When oxygen is available UV light starts the photooxidation. This autocatalytic reaction involves the formation of radicals and shows similarities with thermal oxidations (Yousif & Haddad, 2012). The synergism of these two reactions leads to an accelerated degradation that can even be enhanced if temperature is increased (Andrady, 2011). The environmental conditions and the polymer properties highly determine the process and the rate of

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degradation. For example, photodegradation is significant if a plastic polymer is exposed to the sun (e.g. on beaches, river banks, streets) and if it contains chromophoric groups (e.g. dyes) as these molecules are needed for the absorption of photons to initiate the break-up (Yousif & Haddad, 2012). Plastic floating on the water surface is less susceptible to rapid degradation processes due to lower temperatures, lower oxygen availability and (bio)fouling (Andrady, 2011).

Presence of microplastics in the environment

Marine ecosystems Microplastic research has been a hot topic the past decade (Cole et al., 2011). Ivar Do Sul & Costa (2014) distinguished 4 classes of research based on the main focus of 101 peer-reviewed papers:

1. Microplastics in the water column (via plankton samples) 2. Microplastics in sediment 3. Microplastics ingestion 4. Interactions of microplastics and pollutants.

Approximately 80% of the considered papers was published in the last 15 years and 60 % in the last 5 years. The occurrence and the distribution of microplastics has been studied for several places on Earth. Table 3 and Table 4 give an overview of the results of a selection of papers. The sampling method and the analysis procedure are also concisely specified as this is indispensable for the comparison of results. One of the main issues in microplastic research is the wide variety of sampling and processing procedures, the usage of different units and no ambiguously used definition for microplastics. These inconsistencies make comparison of results of different studies nearly impossible and a standardisation is thus urgently needed (Hidalgo-ruz et al., 2012).

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Table 3: Abundance of microplastics (MPs) in marine sediments.

Location Sampling method Analysis procedure Max. abundance Reference

Flotation, filtration, identification with FT-IR; definition Plymouth (UK) Eckman grab and trowel 86 microfibres.kg-1 dry weight* Thompson et al. (2004) MPs: not specified; no contamination analysis.

Based on method of Thompson et al. (2004), Singapore’s coastline Sediment beach collection adaptations/additions: vacuum filtration (1.6 µm); 16 MPs.kg-1 dry weight Ng & Obbard (2006) definition MPs: > 1.6 µm; no contamination analysis.

Van Veen grab, sediment Based on method of Thompson et al. (2004), Belgium’s coastal zone beach collection and core adaptations/additions: sieving on 38 µm; definition 390.7 ± 32.6 MPs.kg-1 dry weight Claessens et al. (2011) sampling MPs: 38 µm - 1 mm; no contamination analysis.

Based on method of Thompson et al. (2004), 5 cm of a 25 cm² quadrat adaptations/additions: two-step decantation, sieving on Slovenian coast with a metal spatula + 500 250 µm; definition MPs: 250 µm – 5 mm; no 155.6 MPs.kg-1 dry weight Laglbauer et al. (2014) mL circular corer contamination analysis + sample preservation in plastic bags.

Based on method of Thompson et al. (2004), Box coring of top 5 cm adaptations/additions: identification with micro-FT-IR Lagoon of Venice, Italy 2175 MPs.kg-1 dry weight Vianello et al. (2013) sediment and ESEM; definition MPs: 32 µm – 1 mm; no contamination analysis.

Consecutive wet sieving: first on 1 mm, then on 35 µm; Coring 25 cm² surface area flotation of the > 35 µm fraction with NaI (density = 1.6 and cutting the cores in 1 cm Van Cauwenberghe et Porcupine Abyssal Plain kg.L-1), vacuum filtration over a 5 µm membrane filter, 400 MPs.m-2 thick slices. The upper slice al. (2013a) identification with micro-Raman; definition MPs: 35 µm was used for analysis – 1 mm; no contamination analysis.

*The initial unit is: microfibres.(50 mL)-1 sediment. Assuming an average sediment density of 1600 kg.m-3 and an average wet sediment.(dry sediment)-1 ratio of 1.25, the initial unit converts to microfibres.kg-1 dry weight (Claessens et al., 2011).

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Table 4: Abundance of microplastics (MPs) in seawater.

Location Sampling method Analysis procedure Max. abundance Reference

Continuous plankton Data from CPR (visual determination); definition MPs: Northeast Atlantic Sea 0.042 microfibres.m-3 Thompson et al. (2004) recorder (CPR) not specified; no contamination analysis.

Water: rotating drum Filtration (1.6 µm), FT-IR analysis; definition MPs: Singapore’s coastline sampler + 12 V DC Teflon 2000 MPs.m-3 Ng & Obbard (2006) > 1.6 µm; no contamination analysis. pump

Sieving on 250 µm sieve, resuspension with a little 1 mm filtered water from sieved water and filtration (1.2 µm), microscopic visual Northeast Atlantic continuous seawater intake 25 MPs.m-3 Lusher et al. (2014) identification and Raman analysis; definition MPs: system (at 3 m depth) > 250 µm; contamination analysis of airborne particles.

Consecutively sieved on 250 µm, 125 µm and 62.5 µm, 5 mm filtered water from resuspension with a little sieved water, acid digestion Northeast Pacific continuous seawater intake and colouring with Nile Red, vacuum filtration 9180 MPs.m-3 Desforges et al. (2014) system (at 4.5 m depth) (0.45 µm) and microscopic visual identification; definition MPs: > 62.5 µm; no contamination analysis.

Top 10 cm of the water North Western Microscopic visual identification; definition MPs: 333 column with a manta trawl 0.892 MPs.m-2 Collignon et al. (2012) Mediterranean Sea µm – 5 mm; no contamination analysis. net (333 µm mesh size)

PE bottles with surface water: filtration over 1.2 µm Filling of well-rinsed PE cellulose nitrate filters; 40 µm sieved seawater: extra Jade system, Southern bottles at 20 cm depth + at treatment with hydrogen peroxide (H O ) and hydrogen Dubaish & Liebezeit 2 2 2.42 x 106 MPs.m-3 North Sea some locations: sieving of 6 fluoride (HF), microscopic visual identification of (2013) L seawater on 40 µm transparent particles and fibres; definition MPs: not specified; contamination analysis of airborne particles.

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Sediment sampling differs strongly for the papers mentioned in Table 3 but sample processing and sample analysis is mainly based on the method of Thompson et al. (2004) where microplastics are isolated in three steps. Firstly, adding 1.2 kg.L-1 NaCl solution to the sample allows flotation of particles with a density smaller than 1.2 kg.L-1. Secondly, after stirring and allowing the sediment to settle, the supernatant is filtered (Whatman GF/A) and the obtained filters are put in the oven to dry. Thirdly, FT-IR spectroscopy needs to make certain whether or not the microscopic particles on the filters are plastic. Sometimes only a visual identification with a stereomicroscope is performed instead of a spectroscopic analysis. Most of the articles in Table 4 visually identify microplastics present in the water column. This is more susceptible for misidentification and thus underestimation or overestimation of the abundance of microplastics as there is no solid evidence that a certain suspicious particle is plastic or not (Hidalgo-ruz et al., 2012). The visual classification of particles differs between papers and comparing results can thus lead to false conclusions. For example, Dubaish & Liebezeit (2013) only looked at transparent particles and fibres while Lusher et al. (2014) identified microplastics based on criteria such as unnatural shapes and colours. In most of the articles in Table 3 and Table 4 no contamination analysis was performed. During sampling and sample processing contamination should be avoided as much as possible. Lusher et al. (2014) tried to minimize contamination by wearing cotton clothes, covering and cleaning lab material with filtered water. They also analysed airborne particles by analysing filters that were exposed to air. On the contrary, Laglbauer et al. (2014) used plastic bags to preserve sediment samples which might have caused interferences caused by contamination.

The research of Thompson et al. (2004), Ng & Obbard (2006) and Claessens et al. (2011) show similarities in sampling and processing sediment which facilitates comparing their results. Relative higher abundances are reported for the Belgian coastal zone. A more global approach of coastal microplastic prevalence is the research of Browne et al. (2011). The sediment was collected from 18 sandy beaches and analysed according to the method of Thompson et al. (2004). Figure 7 illustrates their results.

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Figure 7: Coastal microplastic distribution for sediments around the globe (Browne et al., 2011).

By assuming an average sediment density of 1600 kg.m-3 and a wet to dry conversion factor of 1.25, the unit in Figure 7 can be converted to number of microplastics per kilogram dry weight. This reveals that the observed concentrations are in the range of 3 to 125 microplastics.kg-1 dry weight. The spatial differences (in both sediment and water) can be explained by the fact that the occurrence and the distribution of microplastics in the aquatic environment depends on hydrodynamics, anthropogenic factors, meteorological factors and geographical conditions, as is the case for larger plastic debris (Rocha-Santos & Duarte, 2014). For example, Vianello et al. (2013) found a positive proportional relationship between the amount of microplastics in the sediment and the mud fraction, indicating the tendency of microplastics to settle in low dynamic areas. On the contrary, meteorological phenomena can resuspend settled microplastics. Lattin et al. (2004) found higher concentrations in the water column after a storm event. This phenomenon was observed more in near-shore areas than in places farther away from land due to a stronger vertical mixing and an increased input from land and rivers.

Other coastal ecosystems impacted by plastic pollution are mangrove forests. Nor & Obbard (2014), for example, analysed the sediment of seven tide-dominated mangrove forests in Singapore. The amounts varied from 3 particles.kg-1 dry weight to 62.7 particles.kg-1 dry weight. Mangrove forests are ecosystems with a high ecological value. These unique, highly productive, tropics-limited forests provide nursery grounds for fishes and are important for the protection and the conversation of coral reefs (Kaiser & Attrill, 2011). Not even the shallow water coral reef, which is the most diverse and productive marine ecosystem on Earth, is safe from plastic pollution (Hall et al., 2015).

Even the most pristine ecosystems on Earth are polluted with microplastics. Van Cauwenberghe et al. (2013b) have found microplastics in deep sea sediments, reporting values of 400 particles.m-2 in the

Page 21 of 99 sediment of the Porcupine Abyssal Plain (4843 m depth). Plastic can invade the deep sea via (bio)fouling or via the formation of marine snow. The latter is composed of polymers excreted by small pelagic organisms, such as algae and bacteria, that caused the aggregation of suspended material (Kaiser & Attrill, 2011). These microscopic units are called transparent exopolymer particles (TEPs) in which microplastics can get stuck. Fischer et al. (2015) also found microplastics in the deep sea. The maximum observed concentration in the Kuril-Kamchatka trench area was five times higher than that of Van Cauwenberghe et al. (2013b). This can even be an underestimation as they only looked at particles larger than 300 µm while the minimum value in the research of Van Cauwenberghe et al. (2013b) was 35 µm. On the other hand, Fischer et al. (2015) analysed the top 20 cm of the sediment and 75% of the detected microplastics were fibres while Van Cauwenberghe et al. (2013b) only investigated the first cm of the sediment and neglected fibrous particles. It is thus difficult to compare the results of these two studies.

Freshwater ecosystems Data on the presence of microplastics in freshwater ecosystems are more scarce than for the marine environment (Wagner et al., 2014). As for larger debris, the microplastic pollution of both environments should be seen as a whole due to the estuarine connection (Rech et al., 2014). Transport of microplastics via rivers is of significant importance regarding marine microplastic pollution (Bowmer & Kershaw, 2010). In 2014, Zhao et al. examined this statement by investigating the occurrence and the distribution of microplastics in the Yangtze estuary and the adjacent East China Sea. They reported concentrations of 10 200 particles.m-3 and 0.455 particles.m-3 for the estuary and the adjacent sea respectively. A higher prevalence of microplastics was thus observed in the estuary. However, the larger mesh size of the neuston net might lead to a wrong premise. Remarkable was the significantly higher concentration of microplastics along a transect in the extension of the estuary in comparison with transects farther away from the river. Zhao et al. (2014) also mentioned the influence of river tributaries and population density on microplastic abundance in river ecosystems. Klein et al. (2015) investigated this in more detail by sampling shore sediments in areas with high and low population densities, industrial places and nature reserves along the continuum of the river Rhine and the tributary Main in Germany. They chose to analyse sediment as this allows to determine the presence of non-buoyant particles in contrast to water samples. By applying a modified version of the sample processing method of Thompson et al. (2004) the abundance of plastic particles between 63 µm and 5 mm was measured. The amount ranged from 228 particles.kg-1 dry weight to 3763 particles.kg-1 dry weight. Remarkable was the 2.5 times higher

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abundance of microplastics in the sediment after the confluence of the two rivers. Furthermore, the hypothesis that microplastic abundance depends on population density, industrial activities and the presence of sewage treatment plants could not be confirmed. The neglecting of hydrodynamic effects is a possible explanation for the missing correlations. However, Yonkos et al. (2014) did find a distinct positive relationship between the prevalence of microplastics and the population density (Figure 8), just as Eriksen et al. (2013). Both studies sampled surface water with a 333 µm mesh Manta trawl.

250 R² = 0.9997

200

) 2

- 150

(g.km 100

50 Microplastics concentration Microplastics 0 0 100 200 300 400 500 600

Population density (persons.km-2)

Figure 8: Relationship between neustic microplastic concentration and urbanisation in Chesapeake bay, USA (adapted from Yonkos et al., 2014).

Eriksen et al. (2013) concentrates attention on the pollution of lentic ecosystems as their research focused on the Laurentian Great Lakes in the USA and Canada. For lake Erie, a remarkable maximum value of 466 355 microplastics.km-2 was reported. Especially multi-coloured spheres were detected in the water samples. These were considered as microbeads due to the similarities they showed with the analysed microbeads from consumer products. The presence of these primary microplastics in the environment points at the consequences of using such products. Their small size makes them bioavailable and usage of products with microbeads should therefore be discouraged (Fendall & Sewell, 2009). This type of microplastics is also found in sedimentary depositions, as shown by Castañeda et al. (2014) for the St-Lawrence river in Canada. The presence of neutrally buoyant polyethylene microplastics indicate the importance of (bio)fouling in the downward transport of microplastics. The reported densities ranged from 7 ± 7 microplastics.km-2 to 136 926 ± 83 947 plastics.km-2, but this could be an underestimation because only particles larger than 500 µm were taken into account. In 2013, the touristic lake Garda in Italy was also examined for microplastic

Page 23 of 99 pollution by Imhof et al. Beach sediment from the north and the south of the lake was randomly

-1 collected and the samples were afterwards treated with a 1.6-1.7 kg.L ZnCl2 solution. After decantation and filtration on a 0.3 µm quartz filter paper the retained microplastics (< 5 mm) were identified using Raman spectroscopy. For the south shore the authors reported a density of 108 ± 55 microplastics per m². The high touristic activity and the narrowing of the lake towards the north accompanied by a strong south to north wind resulted in an approximately ten times higher microplastic density (# particles.m-2) at the north shore than at the south. The authors also performed a scanning electron microscopy (SEM) which revealed degradation marks on the surface of microplastics (Figure 9).

Figure 9: SEM image of a polystyrene particle with a crack in the surface (white arrow), illustrating the degradation and thus the fragmentation of (micro)plastics (Imhof et al., 2013).

Free et al. (2014) pointed at the importance of a proper waste management in order to protect the environment from microplastic pollution. They investigated the presence of microplastics in lake Hovsgol in Mongolia. This is a large, remote lake that is characterized by a low population density and little industrial and agricultural activities. It can therefore be seen as a near-pristine ecosystem. However, the absence of wastewater treatment facilities, the inappropriate disposing of waste (burning, burying or dumping) and increasing tourism threatens the natural environment. The surface water was sampled with a 333 µm mesh Manta trawl. The retained material was consecutively sieved on 4.75 mm, 1 mm and 355 µm sieves. Microplastic density ranged from 997 particles.km-2 to 44 435 particles.km-2. These values are higher than those for lake Huron and lake Superior, which are located in more developed and densely populated areas (Eriksen et al., 2013). The authors mentioned three possible explanations for this surprising observation. Firstly, lake Hovsgol has a higher lake retention time in comparison with the Great Lakes, leading to a smaller displacement of the microplastics.

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Secondly, the small surface area of the Mongolian lake relative to the Great Lakes could lead to a concentration of microplastics. The most probable reason, however, is the lack of a proper waste management. This is verified by the prevalence of mainly fragmented household plastics in the encountered microdebris.

Effects on ecosystems Microplastics are in the same size range as plankton and are therefore available for uptake by many (marine) organisms (Browne et al., 2008). Figure 10 illustrates possible pathways of microplastic ingestion.

Figure 10: Potential routes for microplastic ingestion by animals. The blue dots are microplastics with a density smaller than seawater while the red dots are denser polymers (Ivar Do Sul & Costa, 2014).

Deposit feeders (e.g. lugworms) and detritivores (e.g. amphipods) are exposed to microplastics in sedimentary depositions (Table 3). Additionally, microplastics in the water column might be mistaken for planktonic prey by filter feeders (e.g. barnacles) and suspension feeders. Van Cauwenberghe & Janssen (2014), for example, detected microplastics in the blue mussel (Mytilus edulis) and the Pacific oyster (Crassostrea gigas). The consumption of these suspension feeders by human beings poses a threat to food safety. On the other hand, ingestion of microplastics can have negative effects on the affected organisms, as demonstrated for the blue mussel (Mytilus edulis) by von Moos et al. (2012). They found that microplastic uptake provoked a strong inflammatory response leading to changes in

Page 25 of 99 cells and tissues. Additionally, leached out plastic additives and adsorbed pollutants (e.g. heavy metals, persistent organic pollutants and endocrine disrupters) may intoxicate organisms (Cole et al., 2011).

The uptake of microplastics by marine invertebrates has mainly been investigated under controlled lab experiments where organisms are exposed to rather unrealistic high amounts of prefabricated microplastics with a size range of a few micrometre to a few millimetre (Ivar Do Sul & Costa, 2014). On the other hand, the presence of microplastics in marine vertebrates is determined via field campaigns where contaminated animals are collected. To illustrate the latter, Lusher et al. (2013) collected five pelagic and five demersal fish species from coastal waters near Plymouth in the UK and investigated their digestive tract. Out of the 504 fishes, 36.5% contained plastics of which approximately 30% were plastic particles smaller than 1 mm. The main encountered polymers were polyamide and polyester. The fishing industry is most likely responsible due to the frequent usage of those materials. Microplastics have even been detected in carnivorous marine mammals. In 2015, Lusher et al. found microplastics in the digestive tract of three stranded True’s beaked whales (Mesoplodon mirus). It is most probable that these piscivorous cetaceans ingested microplastics whilst hunting. Whether the microplastics were accidentally ingested or via trophic transfer could not be determined. Trophic transfer can be seen as the indirect ingestion of microplastics. Setälä et al. (2014) demonstrated this by feeding mysids (macrozooplankton) with mesozooplankton that contained 10 µm fluorescent polystyrene microspheres. The concentrations ranged from 109 to 1010 microplastics per m3, which is much higher than found in the environment (Table 4). After 3h incubation the microplastics present in the mysids were visualized with an epifluorescence microscope. Plankton is especially susceptible for plastic ingestion due to their indiscriminate feeding behaviour (Moore, 2008). This strengthens the hypothesis of trophic transfer as these organisms are at the base of the food web upon which the entire marine ecosystem depends. However, ingested microplastics are not necessarily retained in the affected organism. Instead of being taken up in the tissues, as demonstrated by Browne et al. (2008) for the blue mussel (Mytilus edulis), the plastic particles can be egested via defecation. This was observed by Setälä et al. (2014) as mysids and copepods, that had ingested microspheres, contained less plastic particles after putting them for 12h in seawater free of microplastics. Consequently, trophic transfer does not necessarily imply biomagnification.

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Ingestion by limnetic organisms is not as documented as for marine animals. Imhof et al. (2013) are one of the few scientists that have investigated this for freshwater macroinvertebrates. They observed ingestion of prefabricated red non-floating fluorescent microplastics by annelids, crustaceans, ostracods and gastropods. It should be mentioned that realistic circumstances were not well represented in this lab experiments as the concentration to which the animals were exposed was higher than those found in the natural environment. This statement is verified in Appendix 1 where the microplastic concentration used for Lumbriculus variegatus (benthic organism) is estimated to be 107 – 108 plastic particles per kilogram of sediment. In 2014, Sanchez et al. provided the first evidence of microplastic ingestion by river fishes. They collected gudgeons (Gobio gobio) from eleven French rivers and analysed the digestive tract. Microplastics were only detected in fishes from urbanised sites and not in fishes obtained in rural areas, pointing at the influence of human activities on freshwater ecosystems.

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The health of the Scheldt – Research objectives

The 355 km long Scheldt river has its origin in Saint-Quentin (France) and flows into the North Sea near Vlissingen (the Netherlands). The river part from Saint-Quentin to Ghent is not influenced by the tides and is known as the Upper Scheldt. The tidal based part ranges from Ghent (160 km inland) to Vlissingen. According to Fairbridge (1980), the latter is referred to as the estuary of the river. The Scheldt estuary is divided into a Belgian and a Dutch part, known as the Sea Scheldt and the Western Scheldt respectively. The tidal regime has led to a freshwater tidal area which is a unique ecosystem (Meire et al., 2005). Generally seen, estuaries are considered to be one of the most valuable ecosystems in the world (Costanza et al., 1997). The ecosystem functions of estuaries are manifold: transformation, immobilization and elimination of nutrients, biogeochemical cycling, water purification, mitigation of floods, animal nursery grounds etc. (Meire et al., 2005). Despite the ecological and economic importance of estuaries, they have been subjected to prolonged cumulative anthropogenic impacts. The Scheldt river is threatened by densely populated areas and industrial activities. Land reclamation, land use and water management, discharge manipulations, canalization, installation of sluices, channel deepening and a general sea level rise severely impacts the ecosystem (Van Den Bergh et al., 2005).

The microplastic pollution of the Scheldt river is investigated in this dissertation. A first objective is to map the quantity and the particle size distribution of microplastics at specific sites along the river continuum. Secondly, the importance of different areas (industry, sewage treatment plants and river confluences) as a source of microplastics are analysed. Whilst flowing towards the sea, the Scheldt passes several cities and is continuously impacted by human activities. The river has been more exposed to human impacts at locations closer to the mouth than at sites farther inland. Therefore, it is expected that the amount of microplastics increases towards the mouth of the Scheldt. The presence of microplastics depends on population density, as demonstrated by Yonkos et al. (2014), and is therefore investigated in this research. Additionally, the particle size distribution (PSD) probably shifts to smaller values due to fragmentation of (micro)plastics. Therefore, the evolution of the PSDs along the river transect is investigated.

It was chosen to sample sediment instead of water as the mobility of microplastics at the water surface was expected to be higher than near the sediment. The calmer water near the river bed in

Page 29 of 99 contrast to the surface and the fact that benthic microplastics are heavier than neustic microplastics possibly reduces their transport. This implies a lower temporal variability in the amount of microplastics incorporated in the sediment in comparison with neustic microplastics. However, the assumption of lower temporal variability in sediments needs to be confirmed by further research. Another advantage of sediment analysis is that it allows to investigate the influence of hydrodynamics, which, along with particle characteristics, determine the sedimentation rate, on the presence of microplastics. Particle size analysis of the sediment gives information about the average hydrodynamic conditions. For example, sediment that mainly consists of silt and clay represents calmer and less turbulent conditions allowing microplastics to accumulate. On the other hand, sandy sediment reveals stronger hydrodynamics which hampers microplastic sedimentation. In summary, the abundance of microplastics is expected to be directly proportional to the percentage of a fine sediment fraction (< 2 µm, < 20 µm, < 50 µm or < 63 µm). Vianello et al. (2013) verified this hypothesis for the Lagoon of Venice (Italy). Additionally, hydrodynamics influence the sedimentation of organic matter, as verified by Incera et al. (2003) for intertidal sediments of the Iberian Peninsula. In areas with stronger hydrodynamics, less accumulation of organic matter occurs and vice versa. Due to the directly proportional relationship between these two variables, a positive correlation is expected for the microplastic abundance and the amount of organic matter.

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Materials and methods

Sampling locations

The first location is the industrial area of Antwerp. Samples were taken in a convex river bend and before and after a plastic producing plant. The influence of river tributaries is investigated by sampling before and after the confluence of the Rupel and the Scheldt (i.e. Temse and Hemiksem respectively). The connection with many Belgian rivers (Gete, Demer, Kleine Nete, Grote Nete, Dijle and Zenne), and thus the coverage of a large area, has led to the selection of the Rupel for this purpose. To examine the extent to which wastewater facilities pollute river ecosystems with microplastics, sediment was sampled before and after the sewage treatment plant (STP) of Destelbergen. The final sampling location was Oudenaarde, an urban area situated outside the tidal range. Table 5 gives an overview of the locations and Figure 11 illustrates the study area.

Table 5: Overview of the sampling points.

Location Abbreviation Coordinates Industrial area of Antwerp Convex river bend ACRB 51°15'26.0"N, 4°18'55.1"E After plastic producing company AAPF 51°14'28.7"N, 4°22'03.1"E Before plastic producing company ABPF 51°14'26.9"N, 4°22'41.4"E Confluence of Scheldt and Rupel Hemiksem Hem 51°08'42.6"N, 4°19'51.2"E Temse Tem 51°07'28.0"N, 4°16'32.3"E Sewage Treatment Plant of Destelbergen After discharge point of STP DA 51°03'00.1"N, 3°46'35.3"E Before discharge point of STP DB 51°03'00.1"N, 3°46'28.0"E Urban area Oudenaarde Oud 50°50’21.6”N, 3°36’13.8”E

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Figure 11: Map of the study area. The blue lines represent large rivers and channels in Flanders and Brussels. The bold blue line stands for the Scheldt river upon which the eight sampling points are indicated with black stars. The white triangles are different Belgian cities or municipalities.

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Sampling campaigns

The method applied for sediment sampling was influenced by the tidal range of the river. For areas under the influence of the tides, sludge of the river bank could be collected at low tide with an inox scoop. These locations included Antwerp, Hemiksem, Temse and Destelbergen. At the sampling location in Oudenaarde, the metal scoop was not appropriate for sediment sampling as this location was not situated within the tidal range of the Scheldt river, and thus constantly submerged. Therefore, a Van Veen grab with a sampling surface of 250 cm² was used. Per location three replicates were collected. To avoid plastic contamination, the sediment samples were transferred into 1 L glass jars with glass covers(Figure 12). The samples were stored at 4°C to reduce biological activity.

Figure 12: Covered glass jar containing sampled sediment.

Sample processing

After homogenizing the sample with a metal spoon, a small amount of well mixed sediment sample

(3 to 5 g) was oxidized with 20 mL 30% hydrogen peroxide (H2O2) to reduce the organic content. After 24h of oxidation, the sample was diluted 1:4 (v:v) with 0.8 µm filtered deionised water and consecutively sieved. Firstly, the oxidized sample was sieved over a 35 µm or 50 µm sieve. The residue (> 35 or 50 µm) was then transferred into a centrifuge tube (50 mL) using a sodium iodide solution (NaI) with a density of approximately 1.6 kg.L-1. This high-density solution is used for a density separation of lighter particles (including microplastics) from the heavier sediment particles. Secondly, the filtrate was sieved over a 15 µm sieve. In this way, two additional size fractions were obtained: the residue containing particles between 35/50 µm and 15 µm and the filtrate containing particles smaller than 15 µm (< 15 µm). Once again, the residue was suspended in the dense NaI. The < 15 µm fraction was transferred to a 750 mL centrifuge bottle. To complete the separation of

Page 33 of 99 microplastics from sediment, the two centrifuge tubes and the centrifuge bottle were centrifuged for 5 minutes at 3500 rpm (Claessens et al., 2013). Afterwards, the top 10 mL of every tube was collected in a new one. This centrifugation step was repeated three times. The obtained NaI solutions were then filtered over a 5 µm filter (Whatman AE98 cellulose nitrate membrane filter). The < 15 µm size fraction was centrifuged in the same way. To ensure a maximal recovery for this small size fraction, NaI was added to the residue in the centrifuge bottle after filtrating the supernatant. Finally, the filters were transferred to a petridish and dried in an oven at 40°C, for at least 24h. The complete protocol for sediment sample processing is shown in Appendix 2. The equipment used throughout the entire protocol (from sampling to extraction) is shown in Figure 13. Appendix 3 contains some more detailed information on this.

Van Veen grab Covered 1.5 L Weck jar 10 L HDPE bottle with PE cap 35 µm nylon sieve (250 cm²) with 15 µm nylon sieve containing 0.8 µm filtered water

PP centrifuge bottle

-1 Inox scoop 1.6 kg.L NaI solution

Pipette with 10 mL HDPE pipette tip

Petri dish with filter

HDPE funnel

5 µm cellulose Syringe with 50 mL PP centrifuge nitrate filters 0.45 µm acrodisc tube with HDPE cap

Figure 13: Equipment used during sample collection and sample processing.

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Contamination analysis

The laboratory is an environment where samples are highly susceptible to contamination. To reduce this risk during sample processing several measures were taken:

 Treating samples under a fume hood and covering them as much as possible  Repeatedly rinsing of used equipment with 0.8 µm filtered water  All solutions and liquids used during extraction were filtered over a 0.45 µm or 0.8 µm filter  Preference of materials made of glass instead of plastic  No synthetic garments

Despite the reducing actions, contamination proved inevitable. Therefore, an analysis was performed to qualify this interference. Firstly, two beakers with filtered water were placed on locations in the lab where samples were mostly processed. These included the fume hood and the filtration area. After three days the cups were filtered over a 0.8 µm filter, dried in the oven at 40 °C and visually analysed with the microscope. Secondly, to get a better idea of the contamination during sample processing a blank sample was analysed. The sediment that remained in the centrifuge tube after centrifugation of different samples was collected and mixed. The mixture served as the blank sample as it was assumed that this was free of microplastics. Nonetheless, to ensure excellent removal the sample was treated with a NaI solution that had a density of 1.8 kg.L-1. Afterwards, the entire procedure (from oxidation to filtration) was repeated on this blank sample. Lastly, as synthetic clothing could contribute to the contamination of samples with fibres, clothes worn in the lab were scraped with a scalpel over a beaker of water. This was then filtered over a 0.8 µm membrane filter, dried in the oven at 40°C and microscopically analysed.

Microplastics characterisation

All filters were analysed with an Olympus BX41 microscope (10x10 magnification) and an Olympus UC30 camera to record suspicious particles. Brown and segmented fragments or fibres appeared to be of natural origin and were not taken into account, just as black and shiny particles or beads that were considered to be fly ashes (Eriksen et al., 2013). Only brightly coloured fragments or fibres were considered to be of synthetic/anthropogenic origin. To eliminate contamination, fragments or fibres that showed resemblance to those found on the contamination filters, were not taken into account during filter analysis. After visual identification of probable microplastics, a final identification step was performed to specify the plastic type of the microplastics. Micro-Raman spectroscopy was

Page 35 of 99 applied on a subset of particles that had a high abundance. This spectroscopic technique gave information on the molecular structure due to the interaction of infrared radiation resulting in changes in vibrational state (Larkin, 2011). The Raman spectrometer (Bruker Optics ‘Senterra’ dispersive Raman spectrometer coupled with an Olympus BX51 microscope) was operated at a laser wavelength of 785 nm (diode) and high resolution spectra were recorded in three spectral windows, covering 80–2660 cm-1. The microscope had 5x, 20x, and 50x objectives, with spot sizes of approximately 50, 10, and 4 µm, respectively. The instrument was controlled via the OPUS 6.5.6 software.

Determination of moisture content and organic matter

Per replicate, 5 g of the well-mixed sample was put in a porcelain cup which was first dried in the oven at 100°C, cooled in a desiccator and weighed with a precision of 0.01 g. These cups were then placed in a 100°C oven for 12 hours. After cooling down in a desiccator, the mass of the cup containing the dried sediment was determined. To ensure that the evaporation was complete, the cups were once again placed in the 100°C oven for 1 hour and subsequently cooled in a desiccator and weighed. This step was repeated until no change in mass occurred anymore. The moisture percentage was then calculated according to Equation 1.

( ) Equation 1

With the relative amount of water (%), the mass of the dried sediment (g) and the mass of the wet sediment (g).

The percentage of organic matter was determined in a similar way. The only difference is that the dried cups were put in a high temperature oven at 550°C for 16 hours. The oxidized samples were also reheated multiple times until no change in mass occurred anymore. The calculation of the amount of organic matter (Equation 2) is similar to Equation 1.

( ) Equation 2

With the relative amount of organic matter (%) and the mass of the oxidized sediment (g).

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Granulometry

Granulometry analysis of the sediment samples were performed at an external laboratory (Al-West BV). This Dutch lab achieved an acknowledgement for the analysis of solids and soil samples by OVAM (i.e. Public Waste Agency of Flanders). For every location, one sample of approximately 100 g was prepared by mixing an equal amount of each replicate. Five size fractions were determined: < 2 µm, 2 to 20 µm, 20 to 50 µm, 50 to 63 µm and particles larger than 63 µm. The analysis was done via the sedigraph method. Figure 14 is a representation of the methods’ principle. This device emits X-ray radiation through a well-mixed sample. The suspended solids concentration can be calculated using Beer-Lambert’s law, which relates the concentration to the absorption of X-ray radiation by the sample (Equation 3).

Equation 3

-1 With the intensity of the radiation at the end of the cuvette (W.sr ), the intensity of the radiation entering the cuvette (W.sr-1), the molar attenuation coefficient (L.mol-1.cm-1), the concentration (mol.L-1) and the width of the cuvette (cm).

The settling rate is calculated based on the position of the measuring area and the elapsed time since the beginning of sedimentation. Stokes’ law allows the determination of the Stokes’ equivalent sphere diameter for particles with a certain terminal settling velocity, as described in Equation 4.

√ Equation 4 ( )

With the Stokes’ equivalent sphere diameter (m), the dynamic viscosity (Pa.s), the -1 -3 terminal settling velocity (m.s ), the density of the suspended solids (kg.m ), the density of the liquid (kg.m-3) and the acceleration of gravity (m.s-2).

Figure 14: The principle of the sedigraph method (Micromeritics, 2015).

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The SediGraph measures the concentration and the terminal settling velocity at specific times during settling. The concentration at a specific time (Equation 3) represents the amounts of particles smaller than or equal to the Stokes’ equivalent sphere diameter determined at the same time with the measured terminal settling velocity (Equation 4). This provides a distribution of concentrations for different particle sizes. Based on this, the particle fractions can be determined.

Recovery

To determine the efficiency of the extraction protocol, three samples were spiked with a fixed amount of spherical polystyrene microplastics (Coulter Standard Latex Beads, Analis). The spike solution contained 730 plastic beads (90 µm diameter) per mL. This was first diluted 1:4 (v:v) with 0.8 µm filtered water. One mL of this diluted solution was transferred to a counting chamber and the number of beads present were accurately counted. Afterwards, the content of the counting chamber was added entirely to a 24h-oxidized sample. This procedure was repeated three times, each for a different sample. The samples used for the recovery determination were three replicates collected at the convex river bend in Antwerp. These spiked samples were then processed to filters according to the protocol for sample processing. The filters were analysed (i.e. beads counted) with the Olympus BX41 microscope and an ocular with a grid to avoid double counting. The recovery (%) was the proportion of beads detected after sample processing relative to the initial amount.

Data analysis

The raw data, i.e. particle counts after filter analysis, had to be first adjusted by eliminating possible contamination and those particles that were not identified as microplastics by micro-Raman spectroscopy. After updating the data, the spatial evolution of the microplastic abundance could be visualised. For every replica, the total abundance was corrected with the recovery of microplastics from the samples. After normalising this to the dry weight, the arithmetic mean and the standard deviation per location was calculated (N=3). Due to the fact that there were only three data points per location no statistical analysis was performed.

Fragmentation of (micro)plastics was investigated via the evolution of particle size distributions (PSD) along the river transect. As this was examined at a rather large scale (kilometres), the data for the three locations in Antwerp (ACRB, AAPF, ABPF) were analysed together. The same was done for Destelbergen, where the data consisted of DA and DB. A class width of 10 µm and a range of 5 µm to 320 µm was used for constructing number-weighted PSDs for every location. As a single microplastic

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was characterised by its longest dimension (length) and its smallest dimension (width), two PSDs were made per location. In comparison to the data for constructing the river profile of microplastics, the sample size of the PSD data was larger which made statistical analysis more reliable. All statistical analyses were performed with IBM SPSS statistics 22 software. As length is most important regarding effects assessments of microplastics on biota, statistical analysis was only performed for the length- based PSDs. In order to apply a parametric test, the assumptions of normality had to be checked. The normality distribution was verified via the Shapiro-Wilk W test, which provides better power than the Lilliefors corrected Kolmogorov-Smirnoff test (Steinskog et al., 2007). The test was evaluated on the 5% significance level. Additionally, a Q-Q plot allowed to graphically verify the normality condition. As the condition of normality wasn’t met, a non-parametric test had to be used. In this case, the Mann- Whitney U rank test was applied to detect a significant difference. Once again, this was evaluated on the 5% significance level.

The presence of microplastics in the environment depends on meteorological and geographical conditions, anthropogenic factors and hydrodynamics (Rocha-Santos & Duarte, 2014). The relationship between microplastic pollution and hydrodynamics (i.e. %OM and granulometry) and microplastic pollution and population density was investigated in this research. These relationships were analysed by means of a linear regression and the determination of the Pearson’s correlation. Before constructing the graphs, a Q-test was performed to detect possible outliers. After sorting the data from low to high numbers, the Q-value was calculated using Equation 5. It is for 95% sure that a data point was not an outlier if was smaller than the reference value on the 5% significance level

(0.526 for N = 8).

Equation 5

th Where is the i of the N elements.

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Results

Microplastics identification

Elimination of possible contamination was the first step in the raw data modification. If particles showed resemblance with those found on the contamination filters, they were removed from the data. Figure 15 and Appendix 4 visualize the encountered particles. Especially fibres were highly abundant (approximately 70 % of all particles) on the contamination filters. As a precautionary approach, all fibres were excluded. Consequently, only fragments and beads were taken into account.

Blue and pink fibre Multi coloured fragment Brown particle

Figure 15: Three examples of particles present on the contamination filters. The colour and the type (fragment or fibre) are specified for each example.

After contamination elimination, the encountered suspicious particles could be classified into six classes according to colour: red, blue, green, orange, purple and pink. In order to conclude that these particles were in fact microplastics, micro-Raman spectroscopy was applied on several particles from each class. The obtained spectra were then compared to reference spectra from PP, HDPE, LDPE, PET, PVC, PS, Teflon and nylon. None of the scanned particles could be identified as plastic. However, the colour of the particle indicated the presence of pigments which might have interfered with the measurements. This was observed for the particles in the classes red, blue, green and orange as the spectra of the particles corresponded to the spectra of PR254 (Pyrrole Red) or PR112 (Naphthol Red AS-D), PB15 (Phthalocyanine Blue), PG7 (Phthalocyanine Green G) and PO13 (Benzidine Orange) respectively. Figure 16 and Figure 17 show the results of the micro-Raman analysis for a red bead and a blue fragment respectively. The other results are shown in Appendix 5.

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Figure 16: Micro-Raman analysis of a red bead.

Figure 17: Micro-Raman analysis of a blue fragment.

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These pigments do not naturally occur and thus indicate the anthropogenic origin of these particles. Additionally, these pigments are most commonly used in the plastic industry (Lewis, 2004). They are used for the coloration of PP, LDPE, HDPE and PVC (Colors India, 2015). The particles are therefore considered to be microplastics. The spectra from the classes purple and pink didn’t show distinct similarities with spectra from organic colorants and, therefore, it could not be concluded that these particles were microplastics. They were removed from further analyses. Figure 18 represents the share of the different colour classes of all updated data.

2%

21%

Blue 42% Red Green Orange

35%

Figure 18: Pie chart of microplastic colour. Only particles that were positively identified as microplastics (as a result of contamination analysis and micro-Raman spectroscopy) were included.

Microplastics were characterised by a length (longest dimension) and a width (smallest dimension). The upper size limit was set at 1 mm, the lower at 15 µm. To give an idea of the microplastic sizes, a cumulative distribution function (CDF) was constructed for each dimension (Figure 19). The CDF based on the length of the microplastics is most steep between 25 µm and 30 µm, while the curve based on the microplastic width is steepest between 20 µm and 25 µm. Additionally, most of the microplastics are smaller than or equal to 100 µm (93.6% for the length-curve). Finally, fragments were most abundantly present. Only 4% of the encountered microplastics were beads.

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1

0.8

0.6

0.4 Width

Fractions (%) Fractions Length 0.2

0 0 25 50 75 100 125 150 Size (µm)

Figure 19: Cumulative distribution functions of length and width of all microplastics. Only particles that were positively identified as microplastics (as a result of contamination analysis and micro- Raman spectroscopy) were included.

River profile of microplastics

One of the purposes of this research was to map the spatial evolution of the microplastic abundance along the Scheldt river. Figure 20 and Figure 21 visualise the results regarding this objective. The calculation of the average abundance of microplastics and the standard deviation, which were used to construct Figure 20, is summarized in Appendix 6.

100

80

60

dryweight)

1 40 -

(#.g 20 Number of microplastics microplastics ofNumber

0 ACRB AAPF ABPF Hem Tem DA DB Oud Mouth Source

Figure 20: River profile of mean microplastic abundance per sampling location. Locations are represented from river mouth to source. Flags represent the standard deviation of the mean. Page 44 of 99

Figure 21: Map of the spatial evolution of the microplastic abundance. The blue bars represent the average microplastics concentrations.

Particle size distributions

As for the microplastic abundance, the particle size distributions are expected to change along the river transect as a result of increased fragmentation with increased residence time. The length-based particle size distribution (PSD) of Antwerp, Hemiksem, Temse, Destelbergen and Oudenaarde are shown in Figure 22, Figure 23, Figure 24, Figure 25 and Figure 26 respectively. For every size class (class width of 10 µm), the frequency is displayed. This is the number of microplastics found in a respective size class divided by the total number. The data from ACRB, AAPF and ABPF were merged together to construct the PSD for Antwerp. The same was done for Destelbergen, which consisted of DA and DB. The descriptive statistics of every location are summarized in Table 6. The PSDs based on the microplastic width can be viewed in Appendix 7. Only the length-based PSDs were further analysed as this is more relevant for effects assessments of microplastics on biota and will therefore be referred to as PSD from now on.

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Antwerp (N = 264) Hemiksem (N = 65) 0.3 0.3

0.25 0.25

)

) 0.2 0.2

- -

0.15 0.15

0.1 0.1

Frequency ( Frequency ( Frequency 0.05 0.05

0 0

Length (µm) Length (µm)

Figure 22: PSD of microplastics found in Antwerp (ACRB, AAPF and ABPF). Figure 23: PSD of microplastics found in Hemiksem.

0.3 Temse (N = 134) 0.25 Destelbergen (N = 394)

0.25 0.2

)

) 0.2 - - 0.15 0.15 0.1

0.1

Frequency ( Frequency ( Frequency 0.05 0.05

0 0

Length (µm) Length (µm)

Figure 24: PSD of microplastics found in Temse. Figure 25: PSD of microplastics found in Destelbergen (DA and DB).

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Oudenaarde (N = 19) 0.4 0.35

0.3

) - 0.25 0.2 0.15

Frequency ( Frequency 0.1 0.05 0

Length (µm)

Figure 26: PSD of microplastics found in Oudenaarde.

Table 6: Descriptive statistics of the PSDs of every location.

Location Sample Minimum Maximum Mean μ Standard deviation σ Skewness size (-) (µm) (µm) (µm) (µm) (-) Antwerp 264 15 301 43.129 32.859 4.350 Hemiksem 65 16 195 50.585 35.182 2.249 Temse 134 15 222 41.896 25.556 4.043 Destelbergen 394 16 320 53.525 35.987 2.882 Oudenaarde 19 16 101 41.526 21.780 2.087

At first sight, the PSDs don’t look normally distributed. This was statistically verified with Shapiro-Wilk W tests and Q-Q plots (Appendix 8). On the 5% significance level, no evidence was found that the data were normally distributed. The Q-Q plots confirm this as the data points deviate from the straight line for every location. Additionally, as the skewness of every PSD is larger than zero (Table 6), it can be stated that the PSDs are positively skewed distributions.

As the condition of normality wasn’t met for all locations the Mann-Whitney U test was used in order to detect a significant difference in microplastic size of two locations. There are three significant differences on the 5% significance level between two locations:

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(1) The microplastic size is significantly higher in Hemiksem (μHemiksem = 50.6 µm) than in Antwerp

(μAntwerp = 43.1 µm) (p = 0.034)

(2) The microplastic size is significantly higher in Destelbergen (μDestelbergen = 53.5 µm) than in

Antwerp (μAntwerp = 43.1 µm) (p < 0.001)

(3) The microplastic size is significantly higher in Destelbergen (μDestelbergen = 53.5 µm) than in

Temse (μTemse = 41.9 µm) (p < 0.001)

Behavioural patterns of microplastics in the freshwater environment

The dependency of microplastic presence on hydrodynamics and human activities is investigated via the relationships between the average microplastic abundance and the average organic matter, the sediment particle size distributions (< 2 µm, < 20 µm, < 50 µm and < 63 µm) and the population density. The results of the determination of the amount of organic matter and the sediment particle fractions are summarized in Appendix 9, along with the data used for the population density. Before constructing the graphs, a Q-test was performed in order to find any outliers. No outliers were detected on the 5% significance level. Figure 27 to Figure 32 visualise the results of the linear regression analysis.

80 R² = 0.421

70 DA

AAPF

60 DB

50 Tem 40

ABPF

dry weight) dry

1 30 - Hem

(#.g 20

Number of microplastics of Number 10 Oud 0 ACRB 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00%

Organic matter (%)

Figure 27: Correlation of microplastic abundance (particles.g-1 dry weight) and fraction of organic matter (%OM).

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80 80 R² = 0.1367 R² = 0.2251

70 DA 70 DA

AAPF

60 AAPF DB 60 DB

50 Tem 50 Tem 40 40

ABPF ABPF dry weight) dry

30 30

dry weight) dry

1

- 1

20 Hem - 20 Hem

(#.g (#.g

10 10 Number of microplastics of Number

Oud microplastics of Number Oud 0 ACRB 0 ACRB 0% 5% 10% 15% 20% 25% 30% 0% 10% 20% 30% 40% 50% < 2 µm fraction (%) < 20 µm fraction (%) Figure 28: Correlation of microplastic abundance (particles.g-1 dry Figure 29: Correlation of microplastic abundance (particles. g-1 dry weight) and the < 2 µm fraction of the sediment (%). weight) and the < 20 µm fraction of the sediment (%). 80 80 R² = 0.4071 R² = 0.4244

70 DA 70 DA

AAPF AAPF

60 DB 60 DB

50 Tem 50 Tem 40 40 ABPF ABPF

30 weight) dry 30

dry weight) dry

1

- 1 - Hem

20 Hem 20

(#.g (#.g

10 10 Number of microplastics microplastics of Number

Number of microplasticsof Number Oud Oud 0 ACRB 0 ACRB 0% 10% 20% 30% 40% 50% 60% 70% 0% 20% 40% 60% 80% < 63 µm fraction (%) < 50 µm fraction (%) Figure 30: Correlation of microplastic abundance (particles.g-1 dry Figure 31: Correlation of microplastic abundance (particles.g-1 dry weight) and the < 50 µm fraction of the sediment (%). weight) and the < 63 µm fraction of the sediment (%).

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70 Destelbergen R² = 0.0122

60

50

40

Temse Antwerp dry weight) dry

30

1 -

(#.g 20 Hemiksem Number of microplastics of Number 10 Oudenaarde 0 0 500 1000 1500 2000 2500 3000 Population density (inhabitants.km-2)

Figure 32: Correlation of microplastic abundance (particles.g-1 dry weight) and the population density (inhabitants.km-2).

To determine the best predictor, the correlation is evaluated via the coefficient of determination and Pearson’s correlation (Table 7). Table 7: Correlation analysis.

Coefficient of Pearson’s Correlation Dependent variable determination R² (-) correlation R (-) (Dancey & Reidy, 2004) Organic matter 0.421 0.649 Moderate < 2 µm 0.137 0.370 Weak < 20 µm 0.225 0.474 Moderate < 50 µm 0.407 0.638 Moderate < 63 µm 0.424 0.651 Moderate Population density 0.012 -0.110 Negligible

The amount of organic matter and the percentage of the < 63 µm sediment fraction show the highest coefficient of correlation. Based on their p-values, which are both 0.08, no significant correlation was detected at the 5% significance level. Hydrodynamics and the amount of organic matter are also related to each other, as shown in Figure 33 where the amount of organic material is directly proportional to the < 63 µm fraction of the sediment. There’s a significant correlation on the 5% significance level (p = 0.033). As for microplastics, sedimentation of organic material is thus more favourable in calmer water and therefore the < 63 µm sediment fraction, which reflects the average

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hydrodynamic state of the water, can be seen as the best predictor for the presence of microplastics in the sediment.

12% R² = 0.5576 AAPF

10% ABPF

Hem DB 8% DA Tem 6% Oud

4% Organic matter (%) matter Organic 2% ACRB 0% 0% 20% 40% 60% 80% < 63 µm fraction (%) Figure 33: Correlation of fraction of organic matter (%OM) and the < 63 µm sediment fraction (%).

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Discussion

How polluted is the Scheldt river?

Microplastics were highly abundant in the sediment of the Scheldt river. The concentrations ranged from 1 840 ± 2 407 microplastics.kg-1 dry weight to 63 112 ± 24 628 microplastics. kg-1 dry weight (Appendix 6). In comparison with the amounts found for the marine environment (Table 3), the Scheldt river is more polluted with microplastics at first sight. However, comparing concentrations is not that straightforward due to differences in sampling and processing procedures, upper and lower size limits and units. For example, Claessens et al. (2011) analysed sediment from the Belgian coastal zone by applying a modified method of Thompson et al. (2004). As no sodium iodide (NaI) was used for the density separation the extraction efficiency of microplastics from the sediment matrix was most likely lower than in this research. This statement is confirmed by the research of Claessens et al. (2013). Additionally, they looked at microplastics with a size between 38 µm (i.e. the mesh size of the smallest sieve) and 1 mm. Many microplastics in the sedimentary depositions of the Scheldt river were however smaller than 38 µm (Figure 19). If only the particles larger than 38 µm are considered, the concentrations for the Scheldt river range from 566 ± 741 microplastics.kg-1 dry weight (ACRB) to 37 932 ± 15 913 microplastics.kg-1 dry weight (DB) for the length-characterised microplastics. The maximum abundances in the Scheldt river are still higher than the maximum amount of microplastics found by Claessens et al. (2011) for the Belgian coastal sediment (390.7 ± 32.6 microplastics.kg-1 dry weight). The Scheldt river is thus more polluted than the Belgian coastal environment. This is even more confirmed by the fact that in this research no fibres were considered, while the maximum amount of microplastics in Claessens et al. (2011) consisted of approximately 35% fibres indicating a lower abundance of particles (256.4 ± 21.4 microplastic fragments.kg-1 dry weight).

As for the marine environment, the lack of a standardised protocol hinders comparison of results for different freshwater ecosystems. However, by means of data modification some conclusions can be made. For example, in lake Garda 1 108 ± 983 microplastics.m-2 were found at most (Imhof et al., 2013). In order to state whether or not this is a higher concentration than the maximum abundance for the Scheldt river (i.e. 63 112 ± 24 628 microplastics. kg-1 dry weight), the unit has to be converted to microplastics.kg-1 dry weight by means of Equation 6.

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( ) Equation 6

-1 Where is the concentration of microplastics particles (no fibres) in the sediment (microplastics.kg dry weight), the sampled surface area (m²), the fraction of fibres (-), the microplastic -2 -3 concentration on the sediment surface (microplastics.m ), the sediment bulk density (kg.m ), the volume of the sampled sediment (m³) and the wet to dry sediment ratio (-).

Applying the data from Table 8 to Equation 6, the maximum for lake Garda is approximately 18 ± 16 microplastics.kg-1 dry weight , which is much lower than the maximum abundance reported for the Scheldt river. Additionally, the fact that Imhof et al. (2013) looked at a larger size range of

-1 microplastics (1 µm to 5 mm) and that they used a ZnCl2 solution with a density of 1.6 kg.L to 1.7 kg.L-1 as a separation liquid, which is similar to this research, confirms this conclusion.

Table 8: Summary of the data needed to calculate the maximal for lake Garda.

Parameter/variable Value Reference 0.04 m² Imhof et al. (2013)

0.023 Imhof et al. (2013) -2 1 108 ± 983 microplastics.m Imhof et al. (2013)

0.002 m³ Imhof et al. (2013) -3 1500 kg.m Fettweis et al. (2007) 1.25 Claessens et al. (2011)

Microbeads were not very abundantly present in the Scheldt river (4% of all microplastics). The highest concentration (2 799 ± 742 microbeads.kg-1 dry weight) was found for the location in Antwerp before the plastic factory (ABPF). Castañeda et al. (2014) reported a maximum abundance of 136 926 ± 83 947 microbeads.m-2 for the St. Lawrence river in Quebec (Canada). The location for which this amount was reported is similar to Antwerp as it was situated in an industrial area. The unit conversion is similar to that for lake Garda. The authors sampled sediment with a petite Ponar grab. The volume sampled with this sediment sampler is 2.4 L (Thermo Fisher Scientific, 2015), the sampled surface area is 0.0225 m² and the fraction of fibres equals 0. The bulk density of the sediment and the wet to dry ratio remain unchanged. This calculation leads to a maximum amount of 1 069 ± 656 microbeads.kg-1 dry weight, which is lower than that found in the sediment of the Scheldt river for the industrial area of Antwerp. At first sight, the difference in amounts of microbeads between the St.

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Lawrence river and the Scheldt river is not so high. However, Castañeda et al. (2014) did also take brown and black microbeads into account, while this research only focused on coloured ones (Figure 34). This points at an even lower maximum abundance of coloured microbeads in the St. Lawrence river which enlarges the difference.

Figure 34: [Left] Microbeads in the St. Lawrence river (Castañeda et al., 2014). [Right] Only brightly coloured spherical particles were considered to be microbeads in this research, such as a blue bead (A), a green bead (B) and a red bead (C). Brown spheres, such as (D), were not taken into account.

Klein et al. (2015) investigated the microplastic pollution for the Rhine river in Germany in a very similar way as was done for the Scheldt river in this research. They reported abundances of 228 particles.kg-1 dry weight to 3 763 particles.kg-1 dry weight for the size range 63 µm – 5 mm. The amount of microplastics larger than 63 µm in the Scheldt river ranged from 142 ± 185 microplastics.kg-1 dry weight to 18 481 ± 1 885 microplastics.kg-1 dry weight (length-based). Once again, it can be concluded that the Scheldt river is more polluted than the Rhine river. Note that the amounts of microplastics in the Rhine river sediments are in the same range as marine sediments. Consequently, the conclusion that the Scheldt is a heavily polluted river in comparison with the marine environment should not be generalized for freshwater ecosystems.

There are two possible reasons explaining the high amounts of microplastics in the Scheldt river. Firstly, sediment was sampled in easily accessible areas as it was collected in the vegetation along the shores, except for the convex river bend in Antwerp. As vegetation strongly reduces hydrodynamics, microplastics were most likely retained in these zones. In contrast, there’s a higher throughput near

Page 55 of 99 the centre of the river probably leading to a lower residence time of microplastics in that part of the river. Shores are thus ideal places for microplastics to gather. Consequently, due to accumulation near shores, degradation (i.e. fragmentation) of microplastics increases the chance of encountering small particles, while (bio)fouling enables them to be abundantly present in the sediment. The sediment particle size distribution reflects the low energy status of the shore areas in the Scheldt river as most of the sediment consisted of particles smaller than 63 µm, which can be classified as silt and clay particles (Wentworth, 1922).

Secondly, the sample processing can also explain the high amounts. The search for microplastics was conducted to a particle size of 15 µm, which is a very low detection limit in comparison with other studies (Table 3). Additionally, contamination analysis, testing the recovery of microplastics during sample processing and analysing them with micro-Raman spectroscopy to cope with the low reliability of visual identification contribute to predicting microplastic abundance in an accurate way. However, not every single suspicious particle was analysed with micro-Raman spectroscopy and the particles in a certain colour class (red, blue, green or orange) didn’t entirely have the same colour leading to potential differences in composition. It was also not possible to identify any measured particle as plastic due to pigment interference. However, it was assumed that the particles were microplastics due to the anthropogenic origin of these pigments and their application in plastic colouring. Consequently, it can be stated that the Scheldt river is highly polluted with microplastic debris.

Predicting the presence of microplastics

In this research, three factors were investigated that affect the presence of microplastics: organic matter, hydrodynamics and population density. The presence of microplastics was most strongly correlated to the < 63 µm sediment fraction and the organic matter. As the latter depends on the first (Figure 33), the < 63 µm sediment fraction can be used to predict the benthic microplastic abundance. Strand et al. (2013) confirmed these directly proportional relationships. They found a strong correlation (R = 96%) between the abundance of microplastics and the amount of total organic carbon (TOC) for the marine environment around Denmark. Next to that, a Pearson’s correlation of 81.8% was reported for the number of microplastics (#.(10 g)-1 dry weight) and the percentage of fine fraction in the sediment (< 63 µm). However, it should be stressed that several data points were eliminated before the correlation was tested. These locations were considered to be more or less affected areas of which the microplastic abundance could not only be ascribed to the average

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hydrodynamics and the amount of organic matter. As sedimentation also depends on particle characteristics (e.g. density and sphericity), next to hydrodynamics, this has to be taken into account in order to explain the abundance of microplastics in the sediment. For example, in the research of Strand et al. (2013) the amount of microplastics found in location Nyborg Fjord 26 (harbour) was much higher than predicted by the regression line, which was constructed based on the hydrodynamic state of several other locations. The presence of more dense microplastics (e.g. PVC and (bio)fouled microplastics) in the water column in comparison with locations on the regression line is a plausible explanation for the higher abundance in the sediment.

It was not possible in this research to find a clear relationship between the amount of microplastics and the population density in contrast to Yonkos et al. (2014) and Eriksen et al. (2013). As the population density is an average value based on the entire district it doesn’t represent the actual local anthropogenic impact very well. For example, in Oudenaarde the sediment was sampled before the city. The pressure on the Scheldt river is most likely higher in the city in comparison to the sampling location. Consequently, an improvement of the relationship can be achieved if this reasoning is taken into account. Klein et al. (2015) also weren’t able to prove the dependency of microplastic presence on the population density. However, they did report higher abundances for densely populated areas in comparison with sites near nature reserves.

Spatial distribution of microplastics in the Scheldt river

It was expected that the abundance of microplastics would increase towards the mouth due to the longer exposure time of the river to human impacts. This means that the highest and the lowest concentration is expected in Antwerp and Oudenaarde respectively. This hypothesis is not entirely confirmed by Figure 20, which shows the average abundance of microplastics from mouth to source. Instead of a continuously increasing trend whilst moving towards the river mouth, there is a fluctuating pattern. The differences in hydrodynamics, anthropogenic pressure and microplastic characteristics between the different locations can explain this. For example, the lowest concentration was observed for the convex river bend in Antwerp despite the fact that it was closest to the mouth which can be explained by the hydrodynamic state at that location. Based on Figure 31, it can be stated that the sediment in ACRB had the lowest amount of organic matter and fine sediment particles or a higher abundance of coarser material (such as sand). This reveals stronger hydrodynamics which made sedimentation more difficult and thus led to the lowest amount of microplastics. Whilst sampling at that location, wave action and tides were observed pointing at the

Page 57 of 99 hydrodynamics state of the water. Consequently, (micro)plastics got rather washed ashore instead of being deposited in the sediment, as shown in Figure 35. Browne et al. (2010) confirms this reasoning as they found higher abundances on downwind shores.

Figure 35: Plastic debris found on the river shores at the convex river bend (ACRB). Plastic pellets in different colours were highly abundant.

Large differences in abundances were also observed at smaller scale. This can be seen in the high standard deviation for certain locations (e.g. AAPF and DB) meaning that the concentrations can strongly vary at a local scale. The hydrodynamic conditions can highly differ locally which possibly explains this phenomenon. For example, hydrodynamics depend on the biological activities in the sediment. Especially the balance between two functional groups of biota, the bio-stabilisers and the bio-destabilisers (or bioturbators) has an influence on the sediment stability (Widdows & Brinsley, 2002). Bio-stabilising activity modify the immediate physical environment by increasing sediment cohesiveness and reducing currents, wave action and sediment resuspension. An example of such an ecosystem engineer is the microphytobenthos which produce a biofilm on the surface of the sediment that increases its smoothness. On the other hand, bioturbators are organisms that increase the roughness of the bed or feed on bio-stabilisers leading to an increased erodability of sediment and consequently also microplastics. Due to the high local variability in microplastic abundance for the Scheldt river it is more difficult to prove any significant differences between locations on a large scale. Additionally, no highly reliable statistical results can be obtained based on three replica samples. The sample size is too small to provide enough information of the statistical population. To overcome this issue, more samples are required per location. For example, at least six samples are necessary to perform a Mann-Whitney U test to draw meaningful and reliable conclusions (Kennedy,

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2011). The conclusions drawn from Figure 20 should rather be seen as an indication instead of solid proof. Nonetheless, Figure 20 has some distinct patterns. The expected continuous increase in microplastic abundance along the river continuum can be observed when only Oudenaarde, Hemiksem and the plastic factory in Antwerp are considered (Figure 36).

100 Towards the mouth 90 80 70

60 dryweight)

50

1 -

40 (#.g 30 Number of microplastics microplastics ofNumber 20 10 0 AAPF ABPF Hem Oud

Figure 36: River profile of microplastic abundance for the locations Oudenaarde, Hemiksem and the area near the plastic factory in Antwerp.

As microplastics settle down they are removed from the water column which results in a lower concentration of pelagic/neustic microplastics. An increased abundance of benthic microplastics, which depends on the amount of microplastics in the water column, at a location farther downstream points at the input of microplastics from land into the river. On the other hand, this phenomenon can be ascribed to differences in hydrodynamics. A lower amount of microplastics can be the result of a stronger hydrodynamic state of the water. However, for the locations in Figure 36, the percentages of the < 63 µm sediment fraction are almost the same assuming approximately equal hydrodynamics (Figure 31). The influence of hydrodynamics on the presence of microplastics seems rather limited in this case. However, notice that the sediment represents an averaged hydrodynamic state at a certain location. Hydrodynamics tend to be very variable in function of time as it is influenced by e.g. tides, wave action and currents. Therefore, the actual state of the water could have been more or less turbulent than predicted by the sediment fraction influencing the sedimentation and thus the benthic microplastic abundance. For example, it could have been that the water near the sampling location of Hemiksem was more turbulent than in Antwerp during the high tide before sampling. Sedimentation

Page 59 of 99 was thus more favoured in Antwerp at that time whereas an increased erosion of microplastics could have occurred in Hemiksem leading to higher abundances of microplastics in the sediment in Antwerp. The sediment sampled at low tide afterwards didn’t take this temporal variability into account. In summary, similar average hydrodynamics for every location in Figure 36 does not necessarily imply that the hydrodynamic state should be neglected in order to explain an increase in abundance. This can be investigated by comparing the microplastic abundance in the sediment at different times (e.g. high tide versus low tide).

Next to hydrodynamics, fragmentation of larger microplastics has to be taken into consideration in order to explain the increase in abundance in Figure 36. Fragmentation leads to a higher abundance of smaller particles towards the mouth, as indirectly confirmed by the significantly larger particles at a location closer to the source in comparison with those found more downstream. For example, the microplastics in Hemiksem are significantly larger than in Antwerp (p = 0.034). Additionally, differences in the microplastic characteristics between the locations should not be neglected as it influences the sedimentation process . For example, the higher abundance of microplastics in the sediment at a specific location could be due to a higher portion of particles in the water column with a density larger than water. Local human activities could have polluted the river with such plastics (e.g. PS, PET, PVC, PUR) in a direct way (e.g. via littering) or indirectly via e.g. wastewater facilities. On the other hand, microplastics more downstream could have been present in the river ecosystem for a longer time than those farther upstream. Consequently, they have been longer exposed to (bio)fouling leading to an increase in density facilitating sedimentation. In summary, predicting the presence of microplastics is a complex phenomenon that involves a lot of processes and it is therefore difficult to look for the causes of a certain pattern. Nonetheless, it can be stated that microplastics enter the river ecosystem due to human activities, despite the fact that a clear relationship between the microplastic abundance and the population density could not be verified. Population density is one way for describing possible impacts on river ecosystems by humans, but it does not comprise everything.

Based on Figure 20 it can be concluded that there are two more important sources: industry and sewage treatment plants. For the industrial area of Antwerp, the abundance of benthic microplastics increases when the river has passed the plastic factory (AAPF versus ABPF). The port of Antwerp is the European leader when it comes to plastic production and storage of plastic granulates (Port of Antwerp, 2012). This high activity enhances the risk of polluting the Scheldt with plastics. Finding

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plastic granules at the convex river bend reflects the industrial activities (Figure 35). Next to industry, wastewater facilities are an important source of microplastics. The highest amount of microplastics in the Scheldt river was detected in the sediment near the sewage treatment plant (STP) of Destelbergen. In 2015, Lecomte investigated the behaviour of microplastics in the STP of Destelbergen. He reported a removal efficiency of 51% and 44% for fragments and microbeads respectively and an average release of 4.1 x 108 microplastics per day, which is similar to the results of Van Echelpoel (2014) who investigated the same STP in 2014. In contrast to the wastewater treatment plant Långeviksverket in Sweden (Magnusson & Norén, 2014) the STP in Destelbergen doesn’t efficiently remove microplastics which results in a high pollution of the adjacent river. The effluent of the STP is dispersed in the water from the river that flows perpendicular to the outlet of the wastewater. There’s an increasing trend in abundance from DA to DB. The sediment contains thus more microplastics after the STP. Based on this, the STP can be seen as a source of microplastics. However, other factors have to be taken into account. Firstly, as the < 63 µm particle fraction of the sediment is larger for DA than for DB it is assumed that the average hydrodynamics are stronger before the STP making sedimentation more difficult (Figure 31). Secondly, the characteristics of the plastics entering the Scheldt river via the STP can be different from those found in the sediment before the STP. This has an influence on the sedimentation process and thus on the abundance in the sediment. Finally, as the distance between DA and DB was relatively small (a few hundred metres), fragmentation, and thus the production of more smaller microplastics, is considered to be negligible. Despite these considerations, it is difficult to prove the increase due to the large standard deviation which involves a high uncertainty. Once again, this can be resolved by taking more samples at the respective locations.

In contrast to this research, Klein et al. (2015) weren’t able to verify an increase in abundance for the considered industrial area in the Rhine river and there were no STPs in the proximity of the most polluted areas. This points at the complexity of identifying microplastic sources due to the influence of many factors (e.g. hydrodynamics) on the presence of microplastics in the sediment, as acknowledged by Klein et al. (2015).

Finally, the decrease in concentration after the confluence with the river Rupel is remarkable. It was expected that the abundances would increase as the Rupel supplies the Scheldt river with water that has passed many urban and industrial areas (e.g. Brussels, Leuven and Hasselt) indicating a supply of microplastics. Once again, this points at the importance of local conditions (e.g. hydrodynamics and

Page 61 of 99 microplastic characteristics). According to Figure 31, the average hydrodynamic state is similar for Temse and Hemiksem indicating potential differences in microplastic characteristics. In other words, the lower concentration in the sediment of Hemiksem can be explained by the fact that the particles in the water column were not yet heavy enough to settle down. Based on this hypothesis, sediment more downstream the river and thus farther away from the sampling location can show higher abundances of benthic microplastics as the microplastics in the water column could have become more dense on its way towards the sea due to an increased residence time. In comparison with Hemiksem, the higher average abundance at ABPF, which is farther down the river, is an indication for this reasoning. However, this can also be the consequence of weaker average hydrodynamics at ABPF (Figure 31). On the contrary, many of the particles supplied by the Rupel could have settled down before Hemiksem leading to a reduced concentration in the water column and thus also for the sediment in Hemiksem. Klein et al. (2015) did find evidence for the influence of river confluences on microplastic pollution. They sampled sediment before and after the confluence of the rivers Main and Rhine, just as in this research for the Scheldt and the Rupel. The abundances in the sediment increased after passage of the river Main by the river Rhine indicating the river-to-river transport of microplastics.

Size of microplastics

The PSDs in this research were positively skewed distributions. This distribution type for benthic microplastics has been reported in several other studies. Firstly, Klein et al. (2015) report finding the most particles in the smallest size fraction (i.e. 63 – 200 µm). The contribution of the largest size fraction is negligible. Secondly, the microbeads in the St. Lawrence river sediment showed a similar number-weighted distribution, as illustrated in Figure 37 (Castañeda et al., 2014). Based on these papers and this research it appears that the positively skewed distribution is a good approximation for the microplastic size distribution in sediments.

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Figure 37: Particle size distribution of the microbeads found in the St. Lawrence river (Castañeda et al., 2014).

Next to an increasing abundance in microplastics along the river continuum, it was expected to find smaller particles towards the mouth due to fragmentation. The results of the PSDs indicate that larger particles are found in a location farther upstream the river in comparison with the sediment in an area more downstream. This information provides indirect evidence of microplastic fragmentation in a riverine environment. However, it tends to be more complex than this straightforward conclusion. It can be stated that the microplastics in Hemiksem aren’t significantly smaller than in Temse, despite the fact that the latter is located farther upstream. When only considering fragmentation, a possible explanation is the relative small distance between Temse and Hemiksem indicating that the microplastics have not been deteriorated enough. But the microplastics found in Temse and Antwerp also don’t show any significant difference in size while these locations are much farther away from each other. Once again, the microplastic characteristics and the hydrodynamic conditions should be taken into account in order to understand the evolution of the microplastic size along the river continuum.

Regarding hydrodynamics, it is expected to find larger particles in areas with stronger hydrodynamics (and vice versa), as verified by Stokes’ law for laminar conditions, which shows a quadratic relationship of the terminal settling velocity to the particle size (Appendix 10). Notice that the terminal settling velocity is just directly proportional to the density, meaning that the particle size has a larger influence. It should be stressed that this is only valid for laminar conditions, which can be verified by calculating the Reynolds number of particles (Equation 7). The maximum value of this

Page 63 of 99 dimensionless number is equal to one under laminar conditions. When it is larger than one, the water is in a transient or a turbulent regime. The terminal settling velocity is less influenced by the particle diameter in those conditions (Appendix 10).

Equation 7

-3 Where is the particle Reynolds number (-), the fluid density (kg.m ), the average -1 microplastic diameter (m), the terminal settling velocity (m.s ) and the dynamic viscosity of the fluid (Pa.s).

Calculating Reynolds number is not straightforward as the particle diameter or the terminal settling velocity is often not known. Therefore, Equation 8 can be used to estimate the state of the water, derived from the Reynolds number and the drag coefficient (Appendix 11). If the size of a certain particle is smaller than sedimentation occurs in laminar conditions.

√ Equation 8 | |

Where the maximum size of a particle in order to have laminar conditions (m) and the difference in density between the fluid and the particle (kg.m-3).

Assuming a water temperature of 4°C reveals a dynamic viscosity of 1.5674 mPa.s (Bingham, 1922) and a density of 1000 kg.m-3 (Liley et al., 1999). The density of the microplastics is set at 1 380 kg.m-3, which is the density of PVC (Andrady, 2011), the most dense commonly encountered microplastic in the environment (Wagner et al., 2014). Based on this, the maximum particle size in laminar regime

( ) is 228 µm. As 99.4% of the microplastics (length-based) is smaller than 228 µm, it can be assumed that the sedimentation of microplastics occurred in laminar conditions. The presence of reed is a possible explanation for this fluid regime. Consequently, Stokes’ law can be applied to explain certain patterns in particle size with hydrodynamics. For example, the fact that the microplastics in Hemiksem were significantly larger in comparison with Antwerp can be explained by the weaker average hydrodynamic state in the latter. This is verified by the dimensionless particle Reynolds number, which is 1.6 times higher for Hemiksem than for Antwerp when an equal microplastic density is assumed and the average microplastic size is used for each location. This is also observed in Figure 31 as the sediment in Hemiksem contains less fine particles (< 63 µm) than the

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sediment in Antwerp. If the same reasoning is applied to Destelbergen and Antwerp, there appears to be a contradiction. As a consequence of the significantly larger particles in Destelbergen, the Reynolds number is twice as high as in Antwerp predicting stronger hydrodynamics in Destelbergen. However, according to Figure 31 lower average hydrodynamics are observed for Destelbergen due to a larger amount of < 63 µm sediment particles. The prediction of the hydrodynamic state only based on the particle size thus leads to wrong conclusions pointing at the importance of considering other particle characteristics in the sedimentation process (e.g. density and sphericity). For example, the

in Destelbergen becomes smaller with decreasing microplastic density.

Finally, as for the microplastic abundance, variability is also observed for the PSDs which can be ascribed to local differences in hydrodynamics. This is illustrated in Figure 38 which shows the number-weighted differential particle size distributions of every replica sample in Hemiksem.

0.5 0.45 Replica 1 Replica 2 Replica 3 0.4

0.35

) - 0.3 0.25 0.2

Frequency ( Frequency 0.15 0.1 0.05

0

0

40 20 30 50 60 70 80 90

150 100 110 120 130 140 160 170 180 190 200 210 220

Length (µm)

Figure 38: Number-weighted differential particle size distribution for the microplastics found in every replica sediment sample from Hemiksem.

Page 65 of 99

Conclusion

One of the major issues in microplastic research is the lack of standardisation which makes it difficult to compare results of different studies. Nonetheless, it can be concluded that the Scheldt river is a highly polluted freshwater ecosystem in comparison with several marine and freshwater environments. Especially small microplastic fragments (< 100 µm) are abundantly present in the Scheldt river, as is the case in the lagoon of Venice (Vianello et al., 2013). Size is a key factor regarding bioavailability of microplastics and smaller microplastics are more bioavailable to organisms at the base of the food web (Wright et al., 2013). This poses a potentially significant threat to the ecosystem functioning of the river. There’s even a tendency of microplastics to become smaller towards the mouth of the river. Microplastics closer to the mouth have been longer exposed to degradation processes (e.g. photolysis) leading to fragmentation. Next to finding smaller particles whilst moving towards the river mouth, the abundance appears to increase as well. As the river has travelled a longer distance, the anthropogenic pressure on the ecosystem (e.g. input from land) has augmented which is a plausible explanation for this pattern. This research also highlights the importance of sewage treatment plants (STP) and industrial activity as microplastics source. The highest concentrations are reported for the STP of Destelbergen, while the abundance increases in the vicinity of the industrial area. Human activities thus significantly impact the river in a direct and an indirect way, despite the fact that no clear relationship was observed for microplastic abundance and population density. Additionally, the contribution of river transport to microplastics pollution could not be verified in contrast to Klein et al. (2015). Consequently, this does not imply that river transport isn’t an important contributor to estuarine microplastic pollution.

Reality tends to be more complex than the straightforward conclusions regarding fragmentation and an increasing anthropogenic pressure. The particle size distribution and the abundance of microplastics also depends on the average hydrodynamics and the microplastic characteristics at a certain location. This is also valid for a more local scale due to the relative high spatial variability for the particle size distributions and the abundances. As hydrodynamics are very variable in function of time, the sediment composition is a good approximation of the average hydrodynamic state. Relating the amount of benthic microplastics to the fine sediment fraction (< 63 µm) or to the amount of organic matter allows to investigate the influence of average hydrodynamics on the presence of microplastics, which is indispensable for explaining microplastic abundance in the sediment. In

Page 67 of 99 microplastics research, normalisation to matter does matter (Strand et al., 2013), as acknowledged by Klein et al. (2015).

Page 68 of 99

Further research

Some intuitively obvious relationships could not be verified in this research, such as the contribution of river to river transport by microplastics and the dependency on population density. A possible way of investigating the first is by examining more locations before and after a confluence of two rivers. Researchers should consider the hydrodynamic state (and the percentage of organic matter) and the microplastic characteristics in order to discover any pattern. The relationship between microplastic abundance and the population density can be improved by using local population densities (i.e. at a smaller scale) instead of average values.

Due to the high local variability in this research, patterns on local scale have to be investigated in more detail. Once again, the hydrodynamic state and the microplastic characteristics should be taken into account. For example, it would be interesting to map the abundance of microplastics along a cross- section of the river. This will involve more technical requirements than in this research. Less microplastics are expected in the centre of the river in contrast to the shores with vegetation due to a lower residence time. Next to spatial distribution of microplastics, research should also focus on temporal variability of microplastic abundance in sedimentary depositions and the water column. In this research, for example, it was assumed that the temporal variability in the water column was larger than in the sediment, but this still needs to be confirmed. Additionally, the tides, which are coupled with temporal variability in the water column, have an influence on the hydrodynamics state of the water and thus affects the presence of microplastics making it something worth investigating.

To overcome the lack of data for the freshwater environment, assessments of other riverine/estuarine and limnetic ecosystems need to be done. For Belgium, another economically important river, the Meuse, could be investigated in order to gain a better insight in the impacts of human activities on such rivers. On the other hand, rivers that are not highly influenced by human activities can serve as a reference value to those that appear to be very polluted. However, for Belgium this may prove difficult due to the densely populated areas. Additionally, research on the effects of microplastics on freshwater biota is necessary to elucidate the impacts of this kind of pollution on freshwater ecosystems. This information will be very valuable for river management and conservation. Finally, there’s an urgent need for standardisation. An ambiguously used definition and characterisation of microplastics and a

Page 69 of 99 standardized protocol for sampling and sample processing are necessary steps to facilitate comparison of results which may prove important for decision making regarding priority areas.

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Appendices

Appendix 1: Microplastic concentration used for Lumbriculus variegatus in Imhof et al. (2013)

Assumptions  Perfectly spherical plastic particles  Food particles have the same dimensions as plastic particles  Particle size (i.e. diameter) is normally distributed with mean and standard deviation Data (SI-units)  Mean = 29.5.10-6 m  Standard deviation = 26.10-6 m -6  Mass of added food = 5.10 kg  Ratio food particles to plastic particles =10:1 -3  Density fish food (Tetraphyll, Tetra GmbH, Germany) = 205 kg.m -3  Mass of sandy sediment = 2.10 kg Formula derivation

Equation A1 ( )

-1 Where (particles.kg sediment) is the particle concentration in the sediment, (m³) the volumetric amount of microplastics and -1 (m³.particle ) the volume of one plastic sphere.

in Equation A1 is derived from the assumption that food particles and microplastics are characterised by the same dimensions. The ratio added food particles to plastic particles ( ) equals the volumetric ratio of food to plastic. This means that the volume occupied by microplastics is ten times smaller than that by food particles. The latter is converted to mass ( ) with the food density . Concentration determination

Applying the data to Equation A1 reveals a concentration of 9.07 x 107 ± 1.66 x 107 microplastics.kg-1 sandy sediment.

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Appendix 2: Protocol treatment sediment, adapted from Van Echelpoel (2014)

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Appendix 3: Detailed overview of the used equipment

Table A1: Description of all used materials and chemicals.

Equipment Additional information Glass jars 1.5 L glass bottles, 1750 Rundrand – Glas 100 Syringes 10 mL volume, BD PlastipakTM Hydrogen peroxide 30 vol% AnaloR NORMAPUR, VWR Chemicals BDH prolabo® Acrodisc 0.45 µm mesh size, hydrophilic polyethersulfone membrane, acrylic enclosure, 32 mm diameter, Supor® Pall life science Sodium iodide 3.67 g.cm-3, Chem-Lab NV Funnel HDPE Sieves PVC tubes with monodur gauze (nylon) of 15 µm, 35 µm or 50 µm Centrifuge tubes 50 mL volume, PP with HDPE cap, VWR international Labcon north America Centrifuge Thermoscientific, Heraeus megafuge 40R centrifuge Centrifuge bottles 750 mL volume, PP Bio-bottles with PP sealing lid Cellulose nitrate filters 5 µm mesh size, 47 mm diameter, WhatmanTM GE healthcare life sciences AE 98 membrane filters Membrane filters 0.8 µm mesh size, hydrophilic polyethersulfone membrane, 47 mm diameter, Supor® Pall life science

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Appendix 4: Pictures of contamination

Orange fibre Green fibre Black particle

Black fibre Translucent fibre Translucent fragment

Figure A1: Visualisation of abundantly present particles and fibres on contamination filters.

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Appendix 5: Spectral analysis of coloured particles

Figure A2: Spectral analysis of a red fragment.

Figure A3: Spectral analysis of a blue bead. Page 87 of 99

Figure A4: Spectral analysis of a green fragment.

Figure A5: Spectral analysis of an orange fragment. The pattern of the spectrum can be assigned to fluorescent orange pigment (Colombini & Kaifas, 2010). Specification is not possible due to the little available reference spectra and the fact that the bands can shift slightly depending on the company’s production.

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Figure A6: Spectral analysis of an orange fragment. Pigment orange 13 (PO13). The reference spectrum can be found in Scherrer et al. (2009) on page 513.

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Appendix 6: Determination of the average amount of microplastics (MP) and the standard deviation

 The dry solids content could be calculated with Equation A2, which was derived from Equation 1.

Equation A2

With the dry solids content of the sediment (%), the relative amount of water (%), the mass of the dried sediment (g) and

the mass of the wet sediment (g).

 The recovery was tested three times: 83%, 59% and 60%. This gave an average recovery of 68% which was used to correct the amount of microplastics found in the sediment.

Table A2: Results of the determination of the dry solids content, the filter analysis and the calculation of the average amount of microplastics and the standard deviation for the locations ACRB, AAPF and ABPF.

Mass of wet sampled Dry solids Mass of dry sampled MPs MPs concentration Average ± standard deviation Location -1 sediment (g wet weight) content (%) sediment (g dry weight) count (#) (#.g dry weight) (#.g-1 dry weight) ACRB Sample 1 10.15 76 7.74 7 0.96 Sample 2 3.62 72 2.60 12 4.57 1.84 ± 2.41 Sample 3 15.80 76 12.04 0 0.00 AAPF Sample 1 3.26 44 1.42 53 37.42 Sample 2 2.85 44 1.25 81 65.39 63.11 ± 24.63 Sample 3 3.22 38 1.23 107 86.52 ABPF Sample 1 3.22 45 1.44 40 27.83 Sample 2 3.02 39 1.17 53 45.68 35.19 ± 9.33 Sample 3 2.59 45 1.16 37 32.06

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Table A3: Results of the determination of the dry solids content, the filter analysis and the calculation of the average amount of microplastics and the standard deviation for the locations Hem, Tem, DA, DA and Oud.

Mass of wet sampled Dry solids Mass of dry sampled MPs MPs concentration Average ± standard deviation Location sediment (g wet weight) content (%) sediment (g dry weight) count (#) (#.g-1 dry weight) (#.g-1 dry weight) Hem Sample 1 2.81 46 1.30 40 30.67 Sample 2 2.84 50 1.42 33 23.99 23.06 ± 8.11 Sample 3 3.23 47 1.53 22 14.53 Tem Sample 1 2.46 45 1.12 64 57.05 Sample 2 2.82 49 1.38 59 42.96 48.65 ± 7.43 Sample 3 3.17 52 1.64 76 45.95 DA Sample 1 3.44 42 1.44 92 63.75 Sample 2 3.07 37 1.14 89 77.88 71.96 ± 7.34 Sample 3 3.05 40 1.22 90 74.25 DB Sample 1 3.21 35 1.14 102 89.85 Sample 2 6.01 37 2.24 119 52.81 61.10 ± 25.63 Sample 3 5.77 39 2.26 92 40.63 Oud Sample 1 3.13 54 1.70 16 9.61 Sample 2 1.71 51 0.88 4 5.05 6.85 ± 2.43 Sample 3 2.39 53 1.26 7 5.89

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Appendix 7: PSD for every location with width as a characteristic dimension

Antwerp (N = 264)

0.45 0.4

0.35

) - 0.3 0.25 0.2

Frequency ( Frequency 0.15 0.1 0.05 0

Width (µm)

Figure A7: Width-based PSD of microplastics found in Antwerp (ACRB, AAPF and ABPF).

Hemiksem (N = 65) 0.45 0.4

0.35

) - 0.3 0.25 0.2

Frequency ( Frequency 0.15 0.1 0.05 0

Width (µm)

Figure A8: Width-based PSD of microplastics found in Hemiksem.

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Temse (N = 134) Destelbergen (N = 394) 0.45 0.4 0.4 0.35

0.35 0.3

)

) - 0.3 - 0.25 0.25 0.2 0.2 0.15

0.15 Frequency ( Frequency 0.1 ( Frequency 0.1 0.05 0.05

0 0

5-15

5-15

25-35 45-55 65-75 85-95

25-35 45-55 65-75 85-95

105-115 125-135 145-155 165-175 185-195 205-215 225-235 245-255 265-275 285-295 305-315

105-115 125-135 145-155 165-175 185-195 205-215 225-235 245-255 265-275 285-295 305-315 Width (µm) Width (µm)

Figure A9: Width-based PSD of microplastics found in Temse. Figure A10: Width-based PSD of microplastics found in Destelbergen (DA and DB).

Oudenaarde (N = 19) 0.55 0.5 0.45

0.4 ) - 0.35 0.3 0.25 0.2

Frequency ( Frequency 0.15 0.1 0.05

0

5-15

25-35 45-55 65-75 85-95

125-135 105-115 145-155 165-175 185-195 205-215 225-235 245-255 265-275 285-295 305-315 Width (µm)

Figure A11: Width-based PSD of microplastics found in Oudenaarde.

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Appendix 8: Results of the normality tests for the PSDs

Table A4: Results of the Shapiro-Wilk W test for the PSDs.

Location Shapiro-Wilk W (-) Power (-) Conclusion Antwerp 0.606 <0.001 Reject normality hypothesis Hemiksem 0.744 <0.001 Reject normality hypothesis Temse 0.663 <0.001 Reject normality hypothesis Destelbergen 0.748 <0.001 Reject normality hypothesis Oudenaarde 0.721 <0.001 Reject normality hypothesis

Figure A12: Normal Q-Q plot of Antwerp (ACRB, AAPF and ABPF). Figure A13: Normal Q-Q plot of Hemiksem.

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Figure A14: Normal Q-Q plot of Temse. Figure A15: Normal Q-Q plot of Destelbergen (DA and DB).

Figure A16: Normal Q-Q plot of Oudenaarde.

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Appendix 9: Data regarding population density and the results of the granulometry and the determination of the organic matter content

 Regarding population density, the three locations in Antwerp and the two sites in Destelbergen were used to calculate the average number of microplastics for the respective area. The national Belgian register provided information on the average population density.

Table A5: Data regarding population density from the national Table A6: Results of the granulometry analysis. Belgian register (2015).

Population Area Population density Location < 2 µm (%) < 20 µm (%) < 50 µm (%) < 63 µm (%) Location -2 (inhabitants) (km²) (inhabitants.km ) ACRB 1.5 1.8 2.3 2.4 Antwerp 511 711 204.5 2 505.4 AAPF 17.0 35.0 55.0 57.0 Hemiksem 11 034 5.4 2029.0 ABPF 24.0 40.0 53.0 56.0 Temse 29 155 39.9 730.4 Hem 17.0 27.0 40.0 43.0 Destelbergen 17 849 26.6 671.9 Tem 19.0 30.0 44.0 46.0 Oudenaarde city 30 754 68.1 451.9 DA 23.0 40.0 64.0 67.0

DB 21.0 35.0 55.0 58.0 Oud 27.0 44.0 58.0 59.0

 The organic matter can be calculated with Equation A3, which is the same as Equation 2.

( ) Equation A3

With the relative amount of organic matter (%), the mass of the oxidized sediment (g) and the mass of the dried sediment (g).

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Table A7: Results of the determination of the organic matter content.

Location (g) (g) (%) Mean ± standard deviation (%) ACRB Sample 1 5.12 5.08 0.78 Sample 2 4.31 4.19 2.78 1.49 ± 1.12 Sample 3 4.42 4.38 0.90 ABPF Sample 1 1.98 1.76 11.11 Sample 2 2.33 2.09 10.30 10.71 ± 0.41 Sample 3 1.96 1.75 10.71 AAPF Sample 1 2.12 1.93 8.96 Sample 2 1.67 1.49 10.78 10.19 ± 1.06 Sample 3 2.31 2.06 10.82 Hem Sample 1 1.94 1.75 9.79 Sample 2 2.52 2.34 7.14 8.83 ± 1.47 Sample 3 2.51 2.27 9.56 Tem Sample 1 2.36 2.18 7.63 Sample 2 2.47 2.27 8.10 6.98 ± 1.54 Sample 3 3.06 2.90 5.23 DA Sample 1 1.96 1.83 6.63 Sample 2 2.13 1.93 9.39 8.37 ± 0.40 Sample 3 2.41 2.24 7.05 DB Sample 1 2.02 1.86 7.92 Sample 2 1.99 1.82 8.54 7.69 ± 1.49 Sample 3 2.08 1.90 8.65 Oud Sample 1 3.17 2.98 5.99 Sample 2 2.82 2.67 5.32 5.49 ±0.44 Sample 3 2.91 2.76 5.15

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Appendix 10: Sedimentation equations (Rhodes, 2008)

(1) Laminar conditions = Stokes’ law region ( < 1):

| | Equation A4

-1 Where is the terminal settling velocity (m.s ), the particle diameter (m), the standard acceleration of gravity (m.s-2), the difference in density between the fluid and the particle (kg.m-3) and the dynamic viscosity of the fluid (Pa.s).

Equation A4 is also referred to as Stokes’ law.

(2) Transient conditions (1 < < 400):

| | Equation A5 ( )

-3 Where is the fluid density (kg.m ).

5 (3) Turbulent conditions = Newton’s region (400 < < 2 x 10 ):

| | √ Equation A6

For every flow regime, the terminal settling velocity depends on the particle diameter. The dependency decreases from the laminar to the turbulent region:

Laminar region

Transient region

Turbulent region

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Appendix 11: Formula derivation of the maximum particle size under laminar flow conditions (Rhodes, 2008)

| | √ Equation A7

Equation A8

Equation A9

-1 Where is the terminal settling velocity (m.s ), the particle diameter (m), the standard acceleration of gravity (m.s-2), the difference in density between the fluid and the particle (kg.m-3),

-3 the fluid density (kg.m ), the drag coefficient (-), the particle Reynolds number (-) and the dynamic viscosity of the fluid (Pa.s).

Equation A7 is the general equation for the terminal settling velocity (independent of flow regime).

Equation A8 is only applicable for a laminar regime. In that case, the is smaller than one.

Consequently, in order to have laminar conditions the maximum value of equals one:

⇔ Equation A10

| | Equation A11 √

Combining Equation A10 and Equation A11 leads to Equation A12.

√ Equation A12 | |

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