occurrence and distribution of microplastics in the scheldt...

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

III

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,

IV

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

V

Copyrights

De auteur en promotoren geven de toelating deze scriptie voor consultatie beschikbaar te stellen en

delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van

het auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk de bron te

vermelden bij aanhalen van resultaten uit deze scriptie.

The author and promotors give the permission to use this thesis for consultation and to copy parts of

it for personal use. Every other use is subject to copyright laws, more specifically the source must be

extensively specified when using results of this thesis.

Gent, 21 september 2015

De promotor

Prof. dr. Colin Janssen

De tutor

De auteur

Lisbeth Van Cauwenberghe

Niels De Troyer

VII

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

VIII

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

IX

Appendix 10: Sedimentation equations (Rhodes, 2008) ................................................................... 98

Appendix 11: Formula derivation of the maximum particle size under laminar flow conditions

(Rhodes, 2008) ................................................................................................................................... 99

XI

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

XII

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


the sediment (%). ................................................................................................................................... 49


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

XIII

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

XV

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

XVII

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

XVIII

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

XIX

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

XX

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.

XXI

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

XXII

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.

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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.

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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 (LDPE)

17.5 Bags, food and drink cartons, computer hardware

High density polyethylene (HDPE)

12.1 Bottle caps, storage containers, bags, bottles, surgery

Polypropylene (PP) 18.9 Car bumper, flower pots, clothing, carpets

Polyvinyl chloride (PVC) 10.4 Boots, windows, pipelines, clothing, insulation

Polystyrene (PS) 7.1 Yoghurt pots, insulation, CD cases, razors

Polyethylene terephthalate (PET)

6.9 Bottles, photovoltaic cells, medical devices

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

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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

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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

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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

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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

Plymouth (UK) Eckman grab and trowel Flotation, filtration, identification with FT-IR; definition MPs: not specified; no contamination analysis.

86 microfibres.kg-1

dry weight* Thompson et al. (2004)

Singapore’s coastline Sediment beach collection Based on method of Thompson et al. (2004), adaptations/additions: vacuum filtration (1.6 µm); definition MPs: > 1.6 µm; no contamination analysis.

16 MPs.kg-1

dry weight Ng & Obbard (2006)

Belgium’s coastal zone Van Veen grab, sediment beach collection and core sampling

Based on method of Thompson et al. (2004), adaptations/additions: sieving on 38 µm; definition MPs: 38 µm - 1 mm; no contamination analysis.

390.7 ± 32.6 MPs.kg-1

dry weight Claessens et al. (2011)

Slovenian coast 5 cm of a 25 cm² quadrat with a metal spatula + 500 mL circular corer

Based on method of Thompson et al. (2004), adaptations/additions: two-step decantation, sieving on 250 µm; definition MPs: 250 µm – 5 mm; no contamination analysis + sample preservation in plastic bags.

155.6 MPs.kg-1

dry weight Laglbauer et al. (2014)

Lagoon of Venice, Italy Box coring of top 5 cm sediment

Based on method of Thompson et al. (2004), adaptations/additions: identification with micro-FT-IR and ESEM; definition MPs: 32 µm – 1 mm; no contamination analysis.

2175 MPs.kg-1

dry weight Vianello et al. (2013)

Porcupine Abyssal Plain

Coring 25 cm² surface area and cutting the cores in 1 cm thick slices. The upper slice was used for analysis

Consecutive wet sieving: first on 1 mm, then on 35 µm; flotation of the > 35 µm fraction with NaI (density = 1.6 kg.L

-1), vacuum filtration over a 5 µm membrane filter,

identification with micro-Raman; definition MPs: 35 µm – 1 mm; no contamination analysis.

400 MPs.m-2

Van Cauwenberghe et al. (2013a)

*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

Northeast Atlantic Sea Continuous plankton recorder (CPR)

Data from CPR (visual determination); definition MPs: not specified; no contamination analysis.

0.042 microfibres.m-3

Thompson et al. (2004)

Singapore’s coastline Water: rotating drum sampler + 12 V DC Teflon pump

Filtration (1.6 µm), FT-IR analysis; definition MPs: > 1.6 µm; no contamination analysis.

2000 MPs.m-3

Ng & Obbard (2006)

Northeast Atlantic 1 mm filtered water from continuous seawater intake system (at 3 m depth)

Sieving on 250 µm sieve, resuspension with a little sieved water and filtration (1.2 µm), microscopic visual identification and Raman analysis; definition MPs: > 250 µm; contamination analysis of airborne particles.

25 MPs.m-3

Lusher et al. (2014)

Northeast Pacific 5 mm filtered water from continuous seawater intake system (at 4.5 m depth)

Consecutively sieved on 250 µm, 125 µm and 62.5 µm, resuspension with a little sieved water, acid digestion and colouring with Nile Red, vacuum filtration (0.45 µm) and microscopic visual identification; definition MPs: > 62.5 µm; no contamination analysis.

9180 MPs.m-3

Desforges et al. (2014)

North Western Mediterranean Sea

Top 10 cm of the water column with a manta trawl net (333 µm mesh size)

Microscopic visual identification; definition MPs: 333 µm – 5 mm; no contamination analysis.

0.892 MPs.m-2

Collignon et al. (2012)

Jade system, Southern North Sea

Filling of well-rinsed PE bottles at 20 cm depth + at some locations: sieving of 6 L seawater on 40 µm

PE bottles with surface water: filtration over 1.2 µm cellulose nitrate filters; 40 µm sieved seawater: extra treatment with hydrogen peroxide (H2O2) and hydrogen fluoride (HF), microscopic visual identification of transparent particles and fibres; definition MPs: not specified; contamination analysis of airborne particles.

2.42 x 106 MPs.m

-3

Dubaish & Liebezeit (2013)

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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

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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.

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

R² = 0.9997

0

50

100

150

200

250

0 100 200 300 400 500 600

Mic

rop

last

ics

con

cen

trat

ion

(g

.km

-2)

Population density (persons.km-2)

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

collected and the samples were afterwards treated with a 1.6-1.7 kg.L-1 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

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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

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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

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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.

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

10 L HDPE bottle with PE cap containing 0.8 µm filtered water

Covered 1.5 L Weck jar with 15 µm nylon sieve

1.6 kg.L-1 NaI solution

Pipette with 10 mL HDPE pipette tip

Petri dish with filter

50 mL PP centrifuge tube with HDPE cap

Syringe with 0.45 µm acrodisc

5 µm cellulose nitrate filters

HDPE funnel

Inox scoop

35 µm nylon sieve

Van Veen grab (250 cm²)

PP centrifuge bottle

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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

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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

With the intensity of the radiation at the end of the cuvette (W.sr-1), 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

terminal settling velocity (m.s-1), the density of the suspended solids (kg.m-3), 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

Where is the ith 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.

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.

42%

35%

21%

2%

Blue

Red

Green

Orange

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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.

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.

0

0.2

0.4

0.6

0.8

1

0 25 50 75 100 125 150

Frac

tio

ns

(%)

Size (µm)

Width

Length

0

20

40

60

80

100

ACRB AAPF ABPF Hem Tem DA DB Oud

Nu

mb

er o

f m

icro

pla

stic

s

(#.g

-1 d

ry w

eigh

t)

Source Mouth

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

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

0

0.05

0.1

0.15

0.2

0.25

0.3

Freq

uen

cy (

-)

Length (µm)

Antwerp (N = 264)

0

0.05

0.1

0.15

0.2

0.25

0.3

Freq

uen

cy (

-)

Length (µm)

Hemiksem (N = 65)

0

0.05

0.1

0.15

0.2

0.25

0.3

Freq

uen

cy (

-)

Length (µm)

Temse (N = 134)

0

0.05

0.1

0.15

0.2

0.25

Freq

uen

cy (

-)

Length (µm)

Destelbergen (N = 394)

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Figure 26: PSD of microplastics found in Oudenaarde.

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

Location Sample

size (-)

Minimum

(µm)

Maximum

(µm)

Mean μ

(µm)

Standard deviation σ

(µm)

Skewness (-)

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:

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Freq

uen

cy (

-)

Length (µm)

Oudenaarde (N = 19)

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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.

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

matter (%OM).

ACRB

AAPF

ABPF

Hem

Tem

DA

DB

Oud

R² = 0.421

0

10

20

30

40

50

60

70

80

0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00%

Nu

mb

er o

f m

icro

pla

stic

s

(#.g

-1 d

ry w

eig

ht)

Organic matter (%)

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Figure 28: Correlation of microplastic abundance (particles.g-1 dry

weight) and the < 2 µm fraction of the sediment (%).

Figure 29: Correlation of microplastic abundance (particles. g-1 dry






ACRB

AAPF

ABPF

Hem

Tem

DA

DB

Oud

R² = 0.1367

0

10

20

30

40

50

60

70

80

0% 5% 10% 15% 20% 25% 30%

Nu

mb

er o

f m

icro

pla

stic

s (#

.g-1

dry

wei

ght)

< 2 µm fraction (%)

ACRB

AAPF

ABPF

Hem

Tem

DA

DB

Oud

R² = 0.2251

0

10

20

30

40

50

60

70

80

0% 10% 20% 30% 40% 50%

Nu

mb

er o

f m

icro

pla

stic

s

(#.g

-1 d

ry w

eig

ht)


ACRB

AAPF

ABPF

Hem

Tem

DA

DB

Oud

R² = 0.4071

0

10

20

30

40

50

60

70

80

0% 10% 20% 30% 40% 50% 60% 70%

Nu

mb

er o

f m

icro

pla

stic

s

(#.g

-1 d

ry w

eig

ht)


ACRB

AAPF

ABPF

Hem

Tem

DA

DB

Oud

R² = 0.4244

0

10

20

30

40

50

60

70

80

0% 20% 40% 60% 80%

Nu

mb

er o

f m

icro

pla

stic

s

(#.g

-1 d

ry w

eig

ht)


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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.

Dependent variable Coefficient of

determination R² (-)

Pearson’s

correlation R (-)

Correlation

(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

Antwerp

Hemiksem

Temse

Destelbergen

Oudenaarde

R² = 0.0122

0

10

20

30

40

50

60

70

0 500 1000 1500 2000 2500 3000

Nu

mb

er o

f m

icro

pla

stic

s

(#.g

-1 d

ry w

eig

ht)

Population density (inhabitants.km-2)

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

in the sediment.

Figure 33: Correlation of fraction of organic matter (%OM) and the < 63 µm sediment fraction (%).

ACRB

AAPF ABPF

Hem

Tem DA

DB

Oud

R² = 0.5576

0%

2%

4%

6%

8%

10%

12%

0% 20% 40% 60% 80%

Org

anic

mat

ter

(%)


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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

Where is the concentration of microplastics particles (no fibres) in the sediment (microplastics.kg-1

dry weight), the sampled surface area (m²), the fraction of fibres (-), the microplastic

concentration on the sediment surface (microplastics.m-2), the sediment bulk density (kg.m-3),

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

microplastics (1 µm to 5 mm) and that they used a ZnCl2 solution with a density of 1.6 kg.L-1 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)

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

0.002 m³ Imhof et al. (2013)

1500 kg.m-3 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

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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

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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).

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

0

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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

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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

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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

Where is the particle Reynolds number (-), the fluid density (kg.m-3), the average

microplastic diameter (m), the terminal settling velocity (m.s-1) 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.

Figure 38: Number-weighted differential particle size distribution for the microplastics found in

every replica sediment sample from Hemiksem.

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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

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microplastics research, normalisation to matter does matter (Strand et al., 2013), as acknowledged by

Klein et al. (2015).

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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

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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

Mass of added food = 5.10-6 kg

Ratio food particles to plastic particles =10:1

Density fish food (Tetraphyll, Tetra GmbH, Germany) = 205 kg.m-3

Mass of sandy sediment = 2.10-3 kg

Formula derivation

( )

Equation A1

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

(m³.particle-1) 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.

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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.

Location Mass of wet sampled sediment (g wet weight)

Dry solids

content (%)

Mass of dry sampled sediment (g dry weight)

MPs

count (#)

MPs concentration (#.g-1 dry weight)

Average ± standard deviation

(#.g-1 dry weight)

ACRB Sample 1 10.15 76 7.74 7 0.96

1.84 ± 2.41 Sample 2 3.62 72 2.60 12 4.57 Sample 3 15.80 76 12.04 0 0.00 AAPF Sample 1 3.26 44 1.42 53 37.42

63.11 ± 24.63 Sample 2 2.85 44 1.25 81 65.39 Sample 3 3.22 38 1.23 107 86.52 ABPF Sample 1 3.22 45 1.44 40 27.83

35.19 ± 9.33 Sample 2 3.02 39 1.17 53 45.68 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.

Location Mass of wet sampled sediment (g wet weight)

Dry solids

content (%)

Mass of dry sampled sediment (g dry weight)

MPs count (#)

MPs concentration (#.g-1 dry weight)

Average ± standard deviation

(#.g-1 dry weight)

Hem Sample 1 2.81 46 1.30 40 30.67

23.06 ± 8.11 Sample 2 2.84 50 1.42 33 23.99 Sample 3 3.23 47 1.53 22 14.53 Tem Sample 1 2.46 45 1.12 64 57.05

48.65 ± 7.43 Sample 2 2.82 49 1.38 59 42.96 Sample 3 3.17 52 1.64 76 45.95 DA Sample 1 3.44 42 1.44 92 63.75

71.96 ± 7.34 Sample 2 3.07 37 1.14 89 77.88 Sample 3 3.05 40 1.22 90 74.25 DB Sample 1 3.21 35 1.14 102 89.85

61.10 ± 25.63 Sample 2 6.01 37 2.24 119 52.81 Sample 3 5.77 39 2.26 92 40.63 Oud Sample 1 3.13 54 1.70 16 9.61

6.85 ± 2.43 Sample 2 1.71 51 0.88 4 5.05 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

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

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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Freq

uen

cy (

-)

Width (µm)

Antwerp (N = 264)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Freq

uen

cy (

-)

Width (µm)

Hemiksem (N = 65)

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

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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

5-1

5

25

-35

45

-55

65

-75

85

-95

10

5-1

15

12

5-1

35

14

5-1

55

16

5-1

75

18

5-1

95

20

5-2

15

22

5-2

35

24

5-2

55

26

5-2

75

28

5-2

95

30

5-3

15

Freq

uen

cy (

-)

Width (µm)

Temse (N = 134)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

5-1

5

25

-35

45

-55

65

-75

85

-95

10

5-1

15

12

5-1

35

14

5-1

55

16

5-1

75

18

5-1

95

20

5-2

15

22

5-2

35

24

5-2

55

26

5-2

75

28

5-2

95

30

5-3

15

Freq

uen

cy (

-)

Width (µm)

Destelbergen (N = 394)

00.05

0.10.15

0.20.25

0.30.35

0.40.45

0.50.55

5-1

5

25-3

5

45-5

5

65-7

5

85-9

5

10

5-1

15

125

-135

14

5-1

55

16

5-1

75

18

5-1

95

20

5-2

15

22

5-2

35

24

5-2

55

26

5-2

75

28

5-2

95

30

5-3

15

Freq

uen

cy (

-)

Width (µm)

Oudenaarde (N = 19)

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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

Belgian register (2015).

Table A6: Results of the granulometry analysis.

Location Population

(inhabitants)

Area

(km²)

Population density

(inhabitants.km-2)

Antwerp 511 711 204.5 2 505.4

Hemiksem 11 034 5.4 2029.0

Temse 29 155 39.9 730.4

Destelbergen 17 849 26.6 671.9

Oudenaarde city 30 754 68.1 451.9

Location < 2 µm (%) < 20 µm (%) < 50 µm (%) < 63 µm (%)

ACRB 1.5 1.8 2.3 2.4

AAPF 17.0 35.0 55.0 57.0

ABPF 24.0 40.0 53.0 56.0

Hem 17.0 27.0 40.0 43.0

Tem 19.0 30.0 44.0 46.0

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

1.49 ± 1.12 Sample 2 4.31 4.19 2.78 Sample 3 4.42 4.38 0.90 ABPF Sample 1 1.98 1.76 11.11

10.71 ± 0.41 Sample 2 2.33 2.09 10.30 Sample 3 1.96 1.75 10.71 AAPF Sample 1 2.12 1.93 8.96

10.19 ± 1.06 Sample 2 1.67 1.49 10.78 Sample 3 2.31 2.06 10.82 Hem Sample 1 1.94 1.75 9.79

8.83 ± 1.47 Sample 2 2.52 2.34 7.14 Sample 3 2.51 2.27 9.56 Tem Sample 1 2.36 2.18 7.63

6.98 ± 1.54 Sample 2 2.47 2.27 8.10 Sample 3 3.06 2.90 5.23 DA Sample 1 1.96 1.83 6.63

8.37 ± 0.40 Sample 2 2.13 1.93 9.39 Sample 3 2.41 2.24 7.05 DB Sample 1 2.02 1.86 7.92

7.69 ± 1.49 Sample 2 1.99 1.82 8.54 Sample 3 2.08 1.90 8.65 Oud Sample 1 3.17 2.98 5.99

5.49 ±0.44 Sample 2 2.82 2.67 5.32 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

Where is the terminal settling velocity (m.s-1), 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

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

(3) Turbulent conditions = Newton’s region (400 < < 2 x 105):

√ | |

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

Where is the terminal settling velocity (m.s-1), 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),

the fluid density (kg.m-3), 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

occurrence and distribution of microplastics in the scheldt...

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