Alex Crew 2019 Assessing the Abundance, Diversity and Distribution of Microplastics in the Upper St. Lawrence River Alex

Assessing the abundance, diversity and distribution of microplastics in the Upper St. Lawrence River

Alex Crew

Biology Department

McGill University, Montreal, Quebec, Canada

Submitted April 2019

A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Masters of Science

Table of Contents Table of Contents ...... II

Abstract ...... IV

Résumé ...... V

Acknowledgements ...... VI

Author Contributions ...... VIII

List of Tables ...... IX

List of Figures ...... X

I. General Introduction ...... 1

Plastic pollution: a generational environmental issue...... 1

An emerging contaminant: Microplastics ...... 2

Microplastics in freshwater systems ...... 4

Microplastics in the St. Lawrence River ...... 6

Research objectives ...... 7

Tables and figures ...... 8

Literature Cited – General Introduction ...... 12

Linking statement ...... 22

II. Manuscript: Where does all the plastic go? Investigating the factors that govern the abundance, distribution and diversity of microplastics in the Upper St. Lawrence River ...... 23

Abstract ...... 24

Introduction ...... 24

Microplastics in the St. Lawrence River ...... 26

Methods ...... 27 III

Results ...... 34

Discussion ...... 35

Conclusion ...... 41

Tables and Figures ...... 42

Literature cited - Manuscript ...... 54

III. General Conclusion ...... 64

Literature cited – General conclusion ...... 66

Appendix ...... 67

Abstract

Although microplastics are recognized as globally pervasive pollutants in fresh waters, their presence and fate in riverine environments are still poorly documented. Previous research demonstrated that microplastics in the form of polyethylene microbeads are abundant in the sediments of the St. Lawrence River; however, the extent to which the river is contaminated by microplastics (beads, fibres, fragments) in general and the factors that govern the distribution and abundance of such pollutants remain to be determined. In this thesis, I attempt to bridge this gap by quantifying the abundance and diversity of a broad range of microplastics in the sediments and surface waters and by relating these metrics to environmental variables in the St. Lawrence River. I sampled 21 sites spanning a land use gradient, including 10 wastewater effluent sites, along the fluvial corridor between Montreal and Quebec City. Microplastics were removed from sediments using an oil extraction protocol and enumerated under fluorescent microscopy. The mean concentration of microplastics across all sites was 832 (±150 SE) particles (range 62 to 7562 particles) per kg dry weight. I found that microplastic concentrations in the sediments can be predicted from a small selection of environmental variables. Particle characteristics, proximity to point sources, and environmental filters each play a role in explaining microplastic concentrations in the sediment. In water samples, mean concentrations of microplastics were 0.12±0.01 (SE) particles per litre upstream and 0.16±0.02 (SE) particles per litre downstream of wastewater effluents; but in only one case out of the ten wastewater effluents was the average microplastic concentration higher downstream of the effluent site. Overall, this is the first study to demonstrate empirically which environmental variables best explain the diversity, abundance and distribution of microplastic particles in riverine sediments. Furthermore, I present an interim protocol that can be used to detect microplastics with relatively high efficiency and accuracy, and that could be standardized for large-scale monitoring.

Résumé

Bien que les microplastiques soient reconnus mondialement comme des polluants omniprésents dans les milieux aquatiques d’eaux douces, leur présence et leur sort dans les environnements riverains sont encore peu documentés. Des recherches antérieures ont démontré qu’une forme de microplastique; les microbilles de polyéthylène, sont abondantes dans les sédiments du fleuve Saint-Laurent. Cependant, le degré de contamination du fleuve par les microplastiques (billes, fibres, fragments) et les facteurs qui régissent la répartition et l'abondance de ces polluants restent à déterminer. Pour combler ces lacunes, l’objectif de ma thèse est de quantifier l'abondance et la diversité d'une vaste gamme de microplastiques retrouvés dans les sédiments et les eaux de surface du fleuve Saint-Laurent et de les relier à des variables environnementales. J'ai échantillonné 21 sites représentant différents types d'utilisation du territoire, dont 10 sites associer aux l'effluent des eaux usées, le long du corridor fluvial entre Montréal et la ville de Québec. Les microplastiques ont été retirés des sédiments à l'aide d'une méthode utilisant l’huile de canola et les particules ont été énumérés par la microscopie à fluorescence. La concentration moyenne de microplastiques sur tous les sites était de 832 (± 150 SE) particules (62 à 7562 particules) par kg de poids sec. Suite aux analyses des variable environnementales, j’ai découvert que la concentration de microplastique dans les sédiments peut être prédite et expliquée par les caractéristiques des particules, la proximité des sources ponctuelles et les filtres environnementaux. Dans des échantillons d'eau, la concentration moyenne des microplastiques étaient de 0,12 ± 0,02 (SE) particules par litre en amont et de 0,16 ± 0,02 (SE) particules par litre en aval des effluents d’eaux usées; mais dans seulement un cas sur dix, la concentration de microplastiques était plus élevée en aval du site d'effluent. Cette étude est la première à démontrer empiriquement quelles variables environnementales expliquent le mieux la diversité, l'abondance et la distribution des particules de microplastiques dans les sédiments riverains. De plus, je présente un protocole provisoire qui peut être utilisé pour détecter les microplastiques avec une efficacité et une précision relativement élevée, et qui pourrait être normalisé pour une surveillance à grande échelle.

Acknowledgements

First and foremost, I would like to thank both of my advisors, Anthony Ricciardi and Irene Gregory-Eaves. Their support, compassion, and mentorship made the completion of this thesis possible. They consistently allowed this paper to be my own work but steered me in the right direction whenever they thought I needed it. Their encouragement and passion throughout my degree made it an enjoyable and rewarding experience.

I am also grateful to my supervisory committee, Frédéric Guichard and Nicholas Lapointe, for their guidance as I progressed through my research project. Very special gratitude goes out to The Trottier Institute for Public Policy, the Canadian Wildlife Federation, GRIL, the McGill University Biology Department and the Redpath Museum for providing me with stipend support and travel funding to complete my field work.

I will always be thankful to have worked in two labs with such passionate, brilliant and collaborative group of students. Special thanks to Genevieve D’Avignon whose interest and passion for microplastic research helped me progress through this project. I will miss your look of joy and excitement whenever a significant moment or result was reached, and I will forever be appreciative for having worked with you. I want to thank the other graduate students in both the Ricciardi and Gregory-Eaves labs: Michelle Gros, Jaime Grimm, Paul McKeigan, Jennifer Barrow, Alexandre Baud, Joanna Gauthier, Cindy Paquette, Katie Griffiths, Sunci Avlijas, Vicky Chicatun, and Dustin Raab. You all taught me so much and I will forever be thankful to have worked with you as a colleague. You are all such talented scientists and I wish you all the best in the future.

To the countless undergraduate volunteers, work-study students and summer students who supported me in aiding with field and laboratory work, I thank you. I want to give special thanks to Sandrine Beaumont-Courteau, Yulia Klimento and Imogen Hobbs who each kept a positive attitude in the face of endless hours sieving sediments and completing microplastic extractions. I also want to thank Antonina Scheer for allowing me to supervise her undergraduate thesis and who was pivotal in running pilot tests on many of the methods used in this research. You are one of the most positive, compassionate and motivated people I’ve ever had the joy of working with. VII

I want to thank my wonderful friends who supported me with endless laughs and interest in my research. To my brother Jean-Marc and parents Line and Terry: thank you for always providing me with endless moral and emotional support, especially when I needed it most. Without you all, I would have never been able to reach my goals in life and for that, I am grateful. Finally, I want to thank Ashley Piché, who has patiently stood by every mistake, triumph and roadblock that came my way. She has consistently been my greatest sounding board and supporter, without whom I’m not sure I would have made it to the end of this thesis.

VIII

Author Contributions

The main chapter of my thesis is written as a manuscript for submission to a peer- reviewed journal. This thesis was completed under the supervision of Prof. Anthony Ricciardi and Prof. Irene Gregory-Eaves at McGill University, who will both be co-authors on the submitted manuscript. Both Prof. Ricciardi and Prof. Gregory-Eaves were instrumental in suggesting research questions, experimental designs and providing laboratory space and equipment. In this thesis, all sampling, laboratory and statistical analysis were completed by me. I wrote all initial and final drafts of the thesis, while Prof. Ricciardi & Prof. Gregory-Eaves provided feedback and editing. This being the case, reflecting the intention to submit this chapter for publication, plural pronouns are used throughout the thesis.

List of Tables

I.1 – Microplastics in freshwater systems ...... 8

1.1 – Environmental predictors chosen for this study ...... 42 1.2 – Forward selected environmental variables: microplastic concentrations by type...... 43 1.3 – Forward selected environmental variables: microplastic concentrations by fluorescence ... 43 1.4 – Results of paired t-tests examining difference in microplastic concentrations up and downstream of wastewater effluents ...... 44 1.5 – Output from AIC table comparing two linear mixed effect models examining microplastic abundance as a function of sampling location (upstream or downstream) ...... 45 1.6 – Summary output for the fixed effects in linear mixed effect model ...... 45 1.7 – Output table outlining the confidence intervals for the slope of the mixed effect model .... 45

A.1 – Sediment size fractions used in this study ...... 65

A.2 – Summary table of maximum and mean microplastic concentrations (items/kg dry weight) documented in other published studies ...... 66

A.3 – Table of raw plastic data for all sediment sites ...... 73

A.4 – First table displaying raw environmental data for sediment samples ...... 74

A.5 – Second table displaying raw environmental data for sediment samples ...... 76

A.6 – Table of raw plastic data for all surface water samples ...... 78

List of Figures

I.1 – Conceptual framework illustrating the research objectives and potential implications ...... 12

1.1 – Map of the Upper St. Lawrence River, displaying locations where sediment samples and surface water samples were collected ...... 46 1.2 – Map indicating the density of microplastics in the sediments collected across the network of sites...... 47 1.3 – Ordination biplot of seven environmental variables and microplastic taxa types obtained by RDA ...... 48 1.4 – Variance partitioning diagrams examining which group of environmental variables control for the variance in microplastic concentrations ...... 49 1.5 – An ordination biplot of three environmental variables and microplastic fluorescence types obtained by RDA...... 50 1.6 – Boxplots displaying concentration of microplastics in surface water samples ...... 51 1.7 – Square-root transformed microplastic concentrations in upstream and downstream sampling locations with slopes of mixed effect model coefficients ...... 52 1.8 – Logarithmic graph displaying mean concentration of microplastics reported in published studies that measured plastics per kg sediment ...... 53

A.1 – Photos of sample collection ...... 80

A.2 – Global identification tree used to identify microplastic particles ...... 81

A.3 – Fragment identification tree used to identify microfragments ...... 82

A.4 – Microbead identification tree used to identify microbeads ...... 83

A.5 – Fibre identification tree used to identify microfibres ...... 84

A.6 – Laboratory workflow for the extraction of microplastics from samples ...... 85

A.7 – Data analysis workflow for the enumeration of microplastics from samples ...... 86 1

I. General Introduction

Plastic pollution: a generational environmental issue

Plastic products have been a characteristic of modern society for over 150 years. The first man-made plastic ever produced is attributed to an English metallurgist, Alexander Parkes, who developed and later patented Parkesine, a material made from pyroxylin and oil in Birmingham, England in 1856 (Friedel 1983). According to Pioneer Plastic, Robert Friedel’s book on the making and selling of Celluloid, Parkes was an ambitious entrepreneur who claimed to the London Society of Arts in 1865 that he could produce ‘any quantity’ of Parkesine for less than one shilling per pound, by a process that cheapens the materials of manufacture through the addition of castor oil as a plasticizer. While this significantly reduced the cost of manufacturing, materials made of Parkesine, like hair combs, became so wrinkled and contorted in only a few weeks that they essentially became worthless. Owing to an inability to produce lasting products, the company founded in 1866 by Alexander Parkes to manufacture and sell Parkesine, went bankrupt only two years later.

Despite Parkes’ failures to manufacture plastic on an industrial scale, it is evident that the desire to make durable, mouldable products for the lowest cost possible has flourished. Surely Alexander Parkes would be amazed at the scale and magnitude to which plastic products are now integrated into modern society and the world economy. Plastic products are increasingly used in diverse commercial sectors such as packaging, construction, transportation, agriculture and consumer products, owing to their low cost, versatility, and durability (Andrady & Neal 2009). Since 1964, plastic production has grown 20-fold from 15 million tonnes to 322 million tonnes in 2015 (Plastics Europe 2016) and this increase in production is predicted to continue where, by 2050, plastic production is projected to be well over 1 billion tonnes per year (WEF 2016).

Although plastic has provided us with great societal and economic benefits, it is difficult to dispute that plastics are now a pervasive form of pollution. In the marine environment, floating plastic debris was first documented nearly fifty years ago, when detectable concentrations of plastics were observed on the surface of the Sargasso Sea (Carpenter et al. 1972). However, it was not until the discovery of the ‘garbage patch’ in the North Pacific Gyre by Captain Charles Moore in the mid-1990s that the issue of plastic contamination became a 2 pervasive concern amongst the public, the scientific community, and government agencies (Rochman et al. 2016).

Since this discovery, we have begun to understand the magnitude of waste plastic products present in our society. Using the example of packaging materials, 95% of all plastic packaging material, valued at $80–120 billion annually, is lost to the economy after a short first use and only 14% of such material is collected for recycling (WEF 2016). More troubling is that 72% of plastic packaging is not recovered at all, as 40% is deposited in landfills and 32% leaks out of the collection system entirely (WEF 2016). The magnitude of this pollution is not limited to plastic packaging materials; 192 coastal countries generated 275 million tonnes of plastic waste in 2010, of which 4.8 to 12.7 million tonnes have leaked into the ocean (Jambeck et al. 2015). For perspective, this is roughly equivalent to dumping the contents of one garbage truck of plastic into the ocean every minute (WEF 2016). Without a solution to reduce the influx of such waste, it is predicted that by 2050 the mass of plastics in the ocean will equal that of fish (WEF 2016).

An emerging contaminant: Microplastics

As research on plastic contamination has increased, developed, and expanded over the past few decades, so has the classification of the different types of plastic pollution. Particularly, over the past decade, there has been growing concern among the public and scientific community surrounding the potential environmental effects of microscopic plastic particles, commonly known as microplastics. Microplastics are plastics <5mm in maximum dimension and can include sub-classes of large microplastics (<5mm-1mm), small microplastics (<1mm-1µm) and nanoplastics (<1µm) (Crawford & Quinn 2016). Microplastics, like all plastics, are comprised of many different types of synthetic polymers in various forms including spheres, fragments, fibres, and foams (Law & Thompson 2014).

The type and source of microplastic will vary significantly depending on their origin and their intended purpose. Generally, researchers have classified microplastics into two groups: primary microplastics and secondary microplastics. Primary microplastics are those that are manufactured to be microscopic in size (Cole et al. 2011) and are released from discrete point sources. Well known examples are engineered polyethylene microbeads used in cosmetic products (Zitko & Hanlon 1991; Gregory 1996; Fendell & Sewell 2009) and released from 3 wastewater treatment plants along rivers, lakes, and coastlines (Sutton et al. 2016; Mason et al. 2016b; Murphy et al. 2016). These microbeads are commonly used as abrasives and were initially introduced as a replacement for more expensive natural exfoliating materials (Fendell & Sewell 2009). Less attention has been given to primary microplastics introduced through industrial manufacturing, even though their presence in the natural environment has been known for decades, with some of the earliest studies of microplastics describing industrial plastic pellets on marine beaches (Gregory 1978; Shiber 1979; Shiber 1982). Plastic manufacturing was identified as the source of these pellets, which entered the natural environment through losses or spillage during shipment, handling, and unloading at ports and from spills at inland factories (Gregory 1978). Other examples of the industrial link are the accumulation of virgin production pellets and powders found near plastic production and processing plants along the Rhine and Danube Rivers (Lechner et al. 2014; Mani et al. 2015; Lechner & Ramler 2015) and resin pellets found in large abundances on a beach in Lake Huron, which was attributed to local plastic industry activity (Zbyszewski & Corcoran 2011).

Secondary microplastics are tiny fragments derived from the breakdown of discarded larger plastic debris that occurs over time owing to physical, biological, and chemical weathering (Barnes et al. 2009). These processes can reduce the structural integrity of plastic, resulting in fragments continuing to become progressively smaller, until they are of microscopic size (Browne et al. 2007; Cole et al. 2011). Examples of secondary microplastics include disintegrating macroplastics such as water bottles, grocery bags and Styrofoam (Cole et al. 2011). Given the long duration of plastic disintegration, secondary source microplastics are likely to accumulate over long timescales. It has been suggested that even if all inputs of plastic to aquatic environments were to cease immediately, an increase in microplastic particles would still occur owing to the fragmentation of legacy plastic items already present within the aquatic environment (Eerkes-Medrano & Thompson 2018).

There is also a third category that is commonly grouped with secondary microplastics. These microplastics arise as a consequence of wear during the product lifetime as opposed to those generated from discarded materials (Eerkes-Medrano & Thompson 2018). They include acrylic and polyester fibres released from fleece garments when they are washed (Napper & Thompson 2016; Hernandez et al. 2017), carpet fibres from textile manufacturing companies 4

(Ziajahromi et al. 2016), particles released from plastic mulching, rubber fragments released by abrasion from car tires, and the release of plastic flakes from the abrasion of synthetic paints (Duis & Coors 2016).

As our knowledge of the diversity of microplastics has grown, so has the concern over their environmental prevalence and potential risk to biota. In the marine environment, microplastics have been discovered on nearly every beach in the world (Andrady 2011; Cole et al. 2011; Nelms et al. 2017), on the surface of every open ocean (Eriksen et al. 2014), in the sediments of the deep sea (Van Cauwenberghe et al. 2013; Woodall et al. 2014), and are even found incorporated within Arctic Sea ice (Obbard et al. 2014). Studies have determined that these polymers can be a transport vector for toxic metals (Ashton et al. 2010; Holmes et al. 2012; Nakashima et al. 2012) and persistent organic pollutants (Frias et al. 2010; Rochman et al. 2013), which could amplify the impact of ingested microplastics on aquatic biota. Additional adverse health effects could also arise from the leaching of constituent contaminants such as monomers and plastic additives, capable of causing carcinogenesis and endocrine disruption (Oehlmann et al. 2009; Talsness et al. 2009). This is concerning as these microplastics have been documented in over 100 species of biota (GESAMP 2016), many of which are filter feeding invertebrates near the base of aquatic food webs. Therefore, an understanding of the types, diversity, distribution, and source of these plastic particles is critical to assessing their potential risk.

Microplastics in freshwater systems

In contrast to numerous studies on the abundance, transport and fate of microplastics in the marine environment, freshwater systems have only recently begun to be assessed with most studies being published within the last five years, beginning with lakes and since expanding to rivers, tributaries and reservoirs (Table I.1). Research on lakes has largely been focused on the Laurentian Great Lakes (Zbyszewski & Corcoran 2011; Eriksen et al. 2013; Zbyszewski et al. 2014; Corcoran et al. 2015; Mason et al. 2016a; Dean et al. 2018), European lakes (Faure et al. 2012; Imhof et al. 2013; Fischer et al. 216; Vaughan et al. 2017; Sighicelli et al. 2018; Imhof et al. 2018) and Chinese lakes (Su et al. 2016; Zhang et al. 2016; Wang et al. 2017b; Xiong et al. 2018; Wen et al. 2018; Wang et al. 2018a). They have also been documented in the surface water of a remote lake in Mongolia (Free et al. 2014) and in the sediments of a lake in India (Sruthy & Ramasamy 2017). These studies have begun to reveal the extent to which microplastics are 5 contaminating inland waters and have provided a preliminary understanding of the potential sources and behaviours of microplastics in aquatic environments. For instance, in the Great Lakes, microplastics have been found to be accumulating in the shoreline sediments of Lake Ontario for nearly 40 years (Corcoran et al. 2015), having entered the environment via the industrial sector or wastewater effluents (Zbyszewski & Corcoran 2011; Eriksen et al. 2013). Once in the lake, microplastics tend to accumulate in areas of high urban density and shipping traffic (Baldwin et al. 2016; Dean et al. 2018) or are transported to various areas by surface water circulation patterns (Dean et al. 2018).

While our understanding of microplastics in lakes is still growing, recent studies have now focused on riverine environments, driven by widely cited estimates that rivers transport >60 billion plastic particles to the ocean every day (GESAMP 2016) and that 1.15 to 2.41 million tonnes of plastic waste currently enters the ocean every year from rivers (Lebreton et al. 2017). Similar to research that has occurred in lakes, the majority of microplastic research focused on rivers has taken place in Europe and North America with recent studies emerging from Asia (Table I.1). Early studies focused on river systems have highlighted the ability for rivers to act a conveyer belt system for transporting plastics to enter the ocean (Gasperi et al. 2014; Yonkos et al. 2014; Klein et al. 2015; Schmidt et al. 2017). More recent studies have revealed that rivers can also act as sinks for microplastics (Castañeda et al. 2014). Modeling studies have suggested that fate of microplastics in rivers are controlled by particle characteristics and river hydrodynamics such that areas of low flow are likely hotspots for microplastic deposition (Nizzetto et al. 2016; Besseling et al. 2017).

To date, a small number of studies have examined microplastic distribution within river systems, and these have determined that local abundance and distribution are predicted by the sources of microplastics – such as proximity to industrial areas and areas with high urban density (Mani et al. 2015; Baldwin et al. 2016; Rodrigues et al. 2018; Peng et al. 2018) and the proximity to wastewater effluents (Mani et al. 2015; Leslie et al. 2017). However, high concentrations of microplastics have been found in the sediments of rivers in remote areas that lack these point sources (Klein et al. 2015). Only a handful of studies have examined the role that environmental filters play in governing the diversity, distribution and abundance of microplastics in riverine environments, and their results are equivocal. For example, Nel et al. (2018) found that water 6 flow, substrate type and sediment organic matter may determine microplastic distribution within the sediments of a South African river; conversely, Vermaire et al. (2017) found that sediment organic matter and substrate grain size were not significant predictors of microplastic abundance in the Ottawa River (Canada), suggesting that the environmental variables governing microplastics distribution remain to be identified. To improve environmental monitoring and assessment of microplastics in our aquatic environment, it is essential to identify environmental variables that best predict the diversity, abundance and distribution of microplastic particles.

Microplastics in the St. Lawrence River

To date, one study has reported on the abundance of microplastics in the St. Lawrence River (Castañeda et al. 2014) and found polyethylene microbeads (400µm - 2mm in diameter) widely distributed in sediments across the river bed, in concentrations of thousands of microbeads per square meter at one site – a magnitude that rivals some of the world’s most contaminated sediments in both marine and freshwater systems (Hurley et al. 2018). Based on the size, shape and coloration of the particles, Castañeda et al. (2014) suggested that a large fraction of these microplastics originated from personal care products (shower gels, toothpaste and facial scrubs).

The media attention that followed this research provided impetus for the Canadian federal government to prohibit the sale of personal care products containing plastic microbeads in Canada, effective July 2018, with a ban on use of microplastics in natural health products and non-prescription drugs scheduled for July 2019. Similar bans have been enacted or proposed in various other countries (McDevitt et al. 2017; Lam et al. 2018), but such bans target one specific type of microplastic, which may not be the most abundant type in the environment. Microbeads may also be found in other products that are not covered by existing regulations, including printer toners, industrial products such as abrasive media (e.g., plastic blasting), industry (e.g., oil and gas exploration, textile printing, and automotive molding), other plastic products (anti- slip, anti-blocking applications), and medical applications (Environment Canada 2015).

Currently, we have limited information about the diversity of microplastic pollution in large river systems. As a large urbanized and heterogeneous watershed, the St. Lawrence River offers important information regarding the extent to which such ecosystems are contaminated by microplastics and the factors that govern the distribution and abundance of such pollutants. The 7

St. Lawrence River is also likely a major conduit for the transport of plastic from urban centers around Lake Ontario and the Island of Montreal downstream into the marine environment.

Research objectives

The overall goal of my research is to explore which factors govern the abundance, diversity, and distribution of microplastic contamination within the sediments of the fluvial section of the upper St. Lawrence River (Figure I.1). Under this framework, my objectives were as follows:

1. Determine the concentration and composition of microplastics within St. Lawrence River sediments; 2. Determine if the microplastics found within the St. Lawrence River sediments can be predicted by various local site characteristics – point sources and environmental filters.

Tables and Figures

Table I.1: Publications reporting the presence of microplastics in freshwater systems. The sample matrix is shown as either surface water (SW), beach sediment (BCH) or bottom sediment (BS)

Authors Location Study system Samples taken Zbyszewski & Corcoran 2011 Lake Huron, Ontario, Canada Lake BCH Faure et al. 2012 Lake Geneva, Lake Maggoire, Switzerland Lake SW Imhof et al. 2013 Lake Garda, Italy Lake BCH Eriksen et al. 2013 Laurentian Great Lakes, USA Lake SW Zbyszewski et al. 2014 Lake Erie, Lake St. Clair, Ontario Lake BCH Free et al. 2014 Lake Hovsgol, Mongolia Lake SW Corcoran et al. 2015 Lake Ontario, Canada Lake BS Fischer et al. 2016 Lake Bolsena and Lake Chiusi, Italy Lake SW/BS Su et al. 2016 Taihu Lake, China Lake SW/BS Mason et al. 2016a Lake Michigan, USA Lake SW Zhang et al. 2016 Siling Co basin, China Lake BS Sruthy & Ramasamy 2017 Vembanad Lake, Kerala, India Lake BS Anderson et al. 2017 Lake Winnipeg, Canada Lake SW Vaughan et al. 2017 Edgbaston Pool, Birmingham, UK Lake BS Sighicelli et al. 2018 Lake Maggiore, Iseo and Garda, Italy Lake SW Hendrickson et al. 2018 Lake Superior, Canada Lake SW Imhof et al. 2018 Lake Garda, Italy Lake BCH 9

Wang et al. 2018b Dongting Lake and Hong Lake, China Lake SW Dean et al. 2018 Lake Erie, Ontario Lake/ Tributaries BCH/BS Gasperi et al. 2014 River Seine, Paris, France River SW Castañeda et al. 2014 St. Lawrence River, Quebec River BS McCormick et al. 2014 North Shore Channel, Chicago, USA River SW Lechner et al. 2014 Danube River, Austria River SW Mani et al. 2015 Rhine River, Germeny River SW Yonkos et al. 2014 Chesapeake Bay, U.S.A Tributaries SW Klein et al. 2015 Rhine River, Germany River BS Horton et al. 2017 River Thames, UK River BS Leslie et al. 2017 Rhine and Meuse Rivers River BS Miller et al. 2017 Hudson River, New York, USA River SW Vermaire et al. 2017 Ottawa River, Ontario, Canada River SW/BS Wang et al. 2017a Beijiang River, China River BS Rodrigues et al. 2018 Antuã River, Portugal River SW/BS Peng et al. 2018 Shanghai, China River BS Kapp & Yeatman 2018 Snake and Lower Columbia rivers, USA River SW Hurley et al. 2018 Greater Manchester, UK River BS Lin et al. 2018 Pearl River, Guangzhou City, China River SW/BS Nel et al. 2018 Bloukrans River system, South Africa River BS Wang et al. 2018a Wenzhou, China River BS Tibbets et al. 2018 River Tame, Birmingham, UK River BS 10

Zhang et al. 2015 Three Gorges Dam, China Reservoir SW Di & Wang 2018 Three Gorges Reservoir, China River Reservoir SW/BS Lahens et al. 2018 Saigon River, Vietnam River/estuary SW Zhang et al. 2017 Xiangxi River, China River Reservoir SW/BS Ballent et al. 2016 Lake Ontario, Ontario, Canada Lakes & Rivers SW/BCH/BS Baldwin et al. 2016 29 Great Lakes Tributaries, USA Lakes & Rivers SW Wang et al. 2017b Wuhan, China Lakes & Rivers SW Xiong et al. 2018 Qinghai Lake, China Lakes & Rivers SW/BS Wen et al. 2018 Changsha, China Lakes & Rivers BS

Figure I.1: Conceptual framework illustrating the research objectives, key variables, and potential implications.

Literature Cited – General Introduction

Anderson, P.J., S. Warrack, V. Langen, J.K. Challis, M.L. Hanson and M.D. Rennie. 2017. Microplastic contamination in lake Winnipeg, Canada. Environmental Pollution 225: 223-231.

Andrady, A. L. and M.A. Neal. 2009. Applications and societal benefits of plastics. Philosophical Transactions of the Royal Society of London B: Biological Sciences 364(1526): 1977-1984.

Andrady, A. L. 2011. Microplastics in the marine environment. Marine Pollution Bulletin, 62(8):1596-1605.

Ashton, K., L. Holmes and A. Turner. 2010. Association of metals with plastic production pellets in the marine environment. Marine Pollution Bulletin 60(11): 2050-2055.

Baldwin, A.K., S.R. Corsi and S.A. Mason. 2016. Plastic debris in 29 Great Lakes tributaries: relations to watershed attributes and hydrology. Environmental Science & Technology 50(19): 10377-10385.

Ballent, A., P.L. Corcoran, O. Madden, P.A. Helm and F.J. Longstaffe. 2016. Sources and sinks of microplastics in Canadian Lake Ontario nearshore, tributary and beach sediments. Marine Pollution Bulletin, 110(1): 383-395.

Barnes, D. K., F. Galgani, R.C. Thompson and M. Barlaz. 2009. Accumulation and fragmentation of plastic debris in global environments. Philosophical Transactions of the Royal Society of London B: Biological Sciences 364(1526): 1985-1998.

Besseling, E., J.T. Quik, M. Sun and A.A. Koelmans. 2017. Fate of nano-and microplastic in freshwater systems: A modeling study. Environmental Pollution 220: 540-548.

Browne, M.A., T.S. Galloway and R.C. Thompson. 2007. Microplastic—an emerging contaminant of potential concern? Integrated Environmental Assessment and Management 3(4): 559-561.

Carpenter, E. J., S.J. Anderson, G.R. Harvey, H.P. Miklas and B.B. Peck. 1972. Polystyrene spherules in coastal waters. Science, 178(4062): 749-750. 13

Castañeda, R.A., S. Avlijas, M.A. Simard and A. Ricciardi. 2014. Microplastic pollution in St. Lawrence river sediments. Canadian Journal of Fisheries and Aquatic Sciences, 71(12):1767- 1771.

Cole, M., P. Lindeque, C. Halsband and T.S. Galloway. 2011. Microplastics as contaminants in the marine environment: a review. Marine Pollution Bulletin 62(12): 2588-2597.

Corcoran, P.L., T. Norris, T. Ceccanese, M.J. Walzak, P.A. Helm and C.H. Marvin. 2015. Hidden plastics of Lake Ontario, Canada and their potential preservation in the sediment record. Environmental Pollution 204 :17-25.

Crawford, C.B. and B. Quinn. 2017. Microplastic Pollutants: 159-178. Elsevier.

Dean, B.Y., P.L. Corcoran and P.A. Helm. 2018. Factors influencing microplastic abundances in nearshore, tributary and beach sediments along the Ontario shoreline of Lake Erie. Journal of Great Lakes Research 44: 1002-1009.

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Linking statement

In the preceding section, I reviewed the literature to link the current prevalence of microplastics to the long-term pattern of production and diversification of use of plastics in society. I cited evidence demonstrating the ubiquitous presence of microplastics in aquatic environments, the concern they raise as an emerging environmental contaminant, and the limited scope of understanding we have on the factors that govern their occurrence in riverine sediments. In the next section of this thesis, I present the results of a research study that examines the diversity, abundance, and distribution of microplastics within the sediments of the St. Lawrence River and relates these variables to a suite of environmental filters and proximity to point sources. The next section is formatted as a manuscript to be submitted for publication, with my supervisors listed as co-authors.

II. Manuscript

Where does all the plastic go? Investigating the factors that govern the abundance, distribution and diversity of microplastics in the Upper St. Lawrence River

Alex Crew1,2,3, Irene Gregory-Eaves1,3, Anthony Ricciardi1,2,3

1 Department of Biology, McGill University, Montréal, QC, Canada 2 Redpath Museum, McGill University, Montréal, QC, Canada 3 Groupe de Recherche Interuniversitaire en Limnologie (GRIL), Montréal, QC, Canada

Key words: Microplastics, sediments, freshwater, river, distributions, diversity, abundance, RDA, variance partitioning

Highlights:

• Quantified abundance of a broad range of microplastic types in sediments and water of the St. Lawrence River • In sediments, MP concentration ranged from 62 to 7561 items·kg−1, mean value of 828 items·kg−1 • Environmental filters, point sources and particle characteristics influence MP fate in sediments

Target Journals: Environmental Pollution, Science of the Total Environment or Canadian Journal of Fisheries and Aquatic Sciences

Abstract

Microplastics are pervasive pollutants in fresh waters, but their presence and fate in riverine environments are still poorly documented. Previous research has demonstrated that polyethylene microbeads were abundant in the sediments of the St. Lawrence River. Here we extend this work by quantifying the abundance and diversity of a broad range of microplastics in the sediments and water samples, and relate these metrics to environmental variables. We sampled 21 sites spanning a land use gradient (including 10 wastewater effluent sites) along the fluvial corridor between Montreal and Quebec City from July to August 2017. Microplastics were removed from sediments using an oil extraction protocol and enumerated under fluorescent microscopy. We tested predictions that environmental filters and known point sources affect microplastic concentrations in the river. We found that the average concentration of microplastics across all sediment sampling sites was 832 (±150 SE) plastics per kg dry weight (range 62 to 7562 plastics per kg dry weight) and that microplastic concentrations in the sediments were significantly related to a handful of environmental variables. We showed that particle characteristics, proximity to point sources and environmental filters each explain a significant fraction of variation in microplastic concentrations in the sediment; however, environmental filters likely have an overriding influence. This is the first study to demonstrate empirically which environmental variables best predict the diversity, abundance and distribution of microplastic particles in riverine sediments. Furthermore, we present an interim protocol that could be used to accurately detect microplastics until a standardized protocol that can be applied to large-scale monitoring of microplastic pollution is established.

Introduction

Over the past 50 years, annual plastic production has grown 20-fold from 15 million tonnes to >300 million tonnes (Plastics Europe 2016). Plastic debris is accumulating rapidly in the natural environment (Corcoran et al. 2014), and it is now recognized as a global contaminant that has potential impacts on ecosystems, food security and human health (Derraik 2002; Thompson et al. 2009; Law et al. 2017). In particular, there is growing concern among the public and scientific community surrounding the pervasiveness and effects of microscopic plastic particles, i.e. microplastics. Microplastics are defined as synthetic polymer particles <5mm in size. They typically occur in the form of spheres, fragments, fibres, and foams, which are either 25 manufactured for use in industrial and biotechnological applications, or as components in pharmaceuticals, cosmetics and personal care products, or are otherwise produced from the breakdown of larger plastic debris (Law & Thompson 2014).

Diffusing from terrestrial sources mainly through flowing waters (Lebreton et al. 2017), microplastics have become ubiquitous within aquatic environments. They have been discovered on virtually every beach sampled worldwide (Andrady 2011; Cole et al. 2011; Nelms et al. 2017), in ocean surface waters (Eriksen et al. 2014), in deep sea sediments (Van Cauwenberghe et al. 2013; Woodall et al. 2014), and within Arctic sea ice (Obbard et al. 2014). In rivers, they are accumulating in sediments (Castañeda et al. 2014; Klein et al. 2015) and can outnumber fish larvae within the water column (Lechner et al. 2014).

The ecological implications of the widespread influx of microplastics have recently begun to emerge. Microplastic particles are found to be consumed by over 100 species of invertebrates and vertebrates (GESAMP 2016; Auta et al. 2018), which is probably a tiny fraction of the actual number of species that remain to be shown to ingest the material, and this material can be passed on to higher levels of the food web (Setälä et al. 2014). Heavy ingestion of microplastics may carry energetic and toxicological costs (Wright et al. 2013; Rochman et al. 2013), especially as plastic surfaces readily adsorb contaminants, including toxic metals (Aston et al. 2010; Holmes et al. 2012; Nakashima et al. 2012) and persistent organic pollutants (Frias et al. 2010; Rochman et al. 2013). Accurate risk assessment of exposure to this burgeoning form of pollution requires a comprehensive understanding of the types, diversity, distribution, and sources of microplastics in the aquatic environment.

While the presence, abundance and behaviour of microplastics in marine systems is becoming well recognized (Andrady et al. 2011; Cole et al. 2011; do Sul & Costa 2014; Auta et al. 2018), the vast majority of studies on freshwater systems have emerged within the past five years (Hurley et al. 2018). The fate of microplastic particles in riverine environments is likely to become a fertile area of research, as rivers are governed by unique physical, chemical, and biological processes whose roles in dispersing plastic particles remain largely unstudied. The few studies that have examined microplastic distribution within river systems have suggested that sources of microplastics – such as proximity to industrial areas, areas with high urban density, and wastewater effluents (Mani et al. 2015; Baldwin et al. 2016; Leslie et al. 2017; Rodrigues et 26 al. 2018; Peng et al. 2018) – are significant predictors of local abundances and distributions. However, studies have also found high concentrations of microplastics in the sediments of rivers in remote areas that lack such sources (Klein et al. 2015). Nevertheless, little attention has yet been given to the role environmental filters in governing the diversity, distribution and abundance of microplastics in rivers. Although early evidence from modeling studies suggested that fate of microplastics are controlled by particle characteristics and hydrodynamics (Nizzetto et al. 2016; Besseling et al. 2017), the results are equivocal when examined empirically. For example, Nel et al. (2018) found that water flow, substrate type and sediment organic matter may determine microplastic distribution within the sediments of a South African river; conversely, Vermaire et al. (2017) found that sediment organic matter and substrate grain size were not significant predictors of microplastic abundance in the Ottawa River (Canada), suggesting that important environmental variables governing microplastics distribution remain to be identified. A better understanding of which environmental variables best predict the diversity, abundance and distribution of microplastic particles is crucial for adapting environmental monitoring and risk assessment of this form of pollution.

Microplastics in the St. Lawrence River

Currently, we have limited information about the diversity of microplastic pollution in large river systems. The St. Lawrence River is large urbanized watershed that can offer important information regarding the extent to which such ecosystems are contaminated by microplastics and the factors that govern the distribution and local abundance of such pollutants. The St. Lawrence River is also likely a major conduit for the transport of plastic from urban centers in Lake Ontario and the Island of Montreal downstream to the marine environment. To date, one study has reported on the abundance of microplastics in the St. Lawrence River (Castañeda et al. 2014) and found polyethylene microbeads (400–2000 µm diameter) widely distributed in sediments across the river bed, in concentrations of thousands of microbeads per square meter at one site – a magnitude that rivals some of the world’s most contaminated sediments in both marine and freshwater systems (Hurley et al. 2018). However, this study focused exclusively on microbeads, thus the extent to which the St. Lawrence River in contaminated with microplastics of various types remains to be determined. 27

The objective of this study is to determine factors affecting the abundance, diversity, and distribution of microplastics within the sediments and surface water of the upper St. Lawrence River. We predict that local microplastic concentrations within the sediments are correlated with site characteristics (sources and filters), such that depositional areas and areas with high urban land use will yield the highest microplastic concentrations. We also compared the concentration of microplastics to that previously recorded in the river (Castañeda et al. 2014) and in various other aquatic systems.

Methods

Sediment sampling

Site selection

Accounting for land use types, a GIS-based site selection technique was employed to choose sediment sampling sites a-priori across the length of the river using ArcGIS 10.5. A 30-m resolution raster land cover map (obtained from the Québec Ministère du Développement durable, de l'Environnement et de la Lutte contre les changements climatiques - MDDELCC) contained 130 distinct land cover classification types (MDDELCC 2016). Using the ‘reclass’ tool in ArcGIS, these land cover types were reclassified into 8 groups: urban areas, agricultural areas, natural areas, industrial areas, grassland areas, bare ground areas, other (snow & clouds) and water.

Once reclassified, 100 random sampling points were placed on the St. Lawrence River and a 5km buffer was placed around each point. Using the ‘extract by mask’ tool, each buffer zone extracted the land cover in the area. The exact sampling location was then determined by placing the point at the most downstream portion of the buffer zone to encapsulate the land cover 10km directly upstream from the sampling location. From these 100 random points, 24 sampling locations were chosen that best represented the diversity of land cover types on the river and were divided into six land cover types: urbanized areas (>50% urban land cover), agricultural areas (>50% agriculture land cover), forested areas (>50% forested land cover), agricultural & urban mix (>30% agricultural and urban land cover), agriculture and forested mix (>30% agricultural and forested land cover) and a mixed area (>20% urban, >20% agriculture, >20% forested land cover). In addition to the 24 sediment sites determined by land cover, one site 28

(Gentilly-2 power plant) was added to the study as this location contained the highest density of microbeads found in the study conducted in 2014 (Castañeda et al. 2014), resulting in 25 total sediment sample locations (Figure 1.1).

Sediment sampling

The fluvial section of the upper St. Lawrence River, from Ile de Salaberry to Quebec City, was sampled during July and August 2017. At each location, three petite ponar grabs and a suite of physical and limnological variables were taken ~100m apart. Each sediment grab sample was transferred into a clean capped bucket and transported back to the lab for analysis. The samples were left to rest for 12–24 hrs to allow for the sediment to settle to the bottom of the bucket. Once settled, the excess water was removed from the bucket (Appendix 1; Figure A.1). The total volume of the sample was measured, the sample was mixed, and a 250 mL subsample was taken for the analysis of microplastics. The remaining sediment was transferred into WhirlPak bags for storage in a refrigerator at 4°C.

Laboratory protocol for the extraction of microplastics from sediment

Sediment sample preparation

Sediment samples were sieved into 8 different sediment size fractions according to the Wentworth scale (Wentworth 1922): clay (<4 μm), silt (4–63 μm), very fine sand (63–125 μm), fine sand (125–250 μm), medium sand (250–500 μm), coarse sand (500–1000 μm), very coarse sand (1–2 mm), and very fine gravel (2–4 mm) and fine gravel (>4mm). The clay and silt fractions were combined as clay/silt (<63 microns) (Appendix 1; Table A.1). Once sieved, each size fraction was transferred into aluminum trays and dried in a drying oven at 55°C. All dried material for each retained size fraction was weighed to determine the dry weight of the sediment. Each size fraction was then transferred into WhirlPak bags to be stored in the laboratory before undergoing microplastic extraction.

Microplastic extraction

Microplastics were obtained from each size fraction of sediment using an oil extraction method following Crichton et al. (2017); this method was chosen because it is independent of plastic density, had the shortest processing time, and resulted in a lower cost per sample 29 compared to other extraction methods. Since each sediment sample contained eight different size fractions, it was determined that the six lower size fractions (Appendix 1; Table A.1) would undergo this extraction, and the two remaining size fractions (fine gravel, very fine gravel) would be examined for microplastics under a light stereoscope (Leica MS5).

The sediment size fractions that underwent an extraction were transferred from the WhirlPak bags into a 250 ml Erlenmeyer flask. Since the protocol outlined by Crichton et al. (2017) is designed for a maximum of 50 g dry sediment, size fractions were split if they were greater than 50g, with a maximum of 6 extractions for one size fraction (up to 300g). If a size fraction was larger than 300g, this sample would not be included in the analysis (n=1). Following extraction, all retained material within the oil layer was filtered through a borosilicate filter (with 1µm pore size) using a vacuum pump. The retained material was then backwashed and transferred into a 20mL scintillation vial, which was subsequently placed in a drying oven at 55°C to evaporate the excess water added during the backwashing step.

Surface water sampling and processing

Wastewater effluent sites within the upper St. Lawrence River were identified by the Quebec government (MDDELLC) and ten of these sites were sampled (Figure 1.1). For water collection, the bottle sampling method developed by Vermaire et al. (2017) was applied in which 4L acid-washed plastic jugs were used to filter 100L of water through a new piece of 100μm nylon mesh. This was repeated three times at each location, both 500m upstream and 500m downstream of the effluent. After each 100 L sample of water was filtered, the filters were carefully removed from the plastic cylinder and placed directly into a Whirl-Pak bag and sealed. Samples were then transported back to McGill University where the samples were kept at -20°C until processed in the laboratory. Sampling blank samples were collected to account for any plastic particles added during the sampling procedure (see procedure verification and contamination). Microplastics were extracted from water samples following the same extraction protocol applied to the sediment samples (Crichton et al. 2017).

Sample digestion and staining

Organic digestion

Owing to the large size of our samples, organic digestion was applied after oil extraction, thus digesting only the material retained on the filters. The same organic digestion process was applied for both water and sediment samples. Each vial received 10ml of 30% hydrogen peroxide, the samples were then digested for 24 hours at room temperature before undergoing an 8-hour digestion at 55°C. Once digested, the samples were transferred back onto a borosilicate filter (pore size 1m) by vacuum filtration and prepared for analysis.

Nile Red staining

To quantify the microplastic particles retained on each filter, Nile Red staining and fluorescent microscopy was applied – a technique that has been deemed to accurately quantify microplastics from environmental samples owing to its ability to differentiate between microplastic and natural particles such as silica fragments or natural fibres, and has been validated to be similar in accuracy to FT-IR and Raman Spectroscopy (Shim et al. 2016; Maes et al. 2017; Erni-Cassola et al. 2017; Catarino et al. 2018). The staining method followed the protocol first described by Erni-Cassola et al. (2017) where the filters are dyed with a few drops of Nile Red dissolved in 95% methanol (at 1 μg/mL), and then filters were covered with a glass cover slip to protect samples from airborne contamination. Following staining, microplastics were analyzed under a fluorescent compound microscope (Olympus BX43), coupled with a GFP filter (excitation max at 490 nm and emission max at 525 nm) at 100× magnification.

Microplastic identification & enumeration

Microplastic identification followed a protocol that identifies plastic particles depending on the intensity of fluorescence (high, moderate, none), colour and plastic type. High fluorescence particles are those that surpass a pixel brightness threshold in ImageJ outlined by Erni-Cassola et al. (2017) and represent four plastic polymers: polyethylene, polypropylene, polystyrene and Nylon-6. However, not all plastic particles fluoresce or are able to surpass this pixel brightness threshold (Erni-Cassola et al. 2017). As such, for moderately and non- fluorescent particles, identification trees for individual plastic forms (fibres, fragments and microbeads) was developed to be used in combination with the fluorescence microscopy to 31 differentiate plastic particles from other hydrophobic particles (Appendix 1; Figure A.2-A.5). The identification trees were built using identification characteristics described or depicted within the literature (Norén 2007; Nor & Obbard 2014; Eerkes-Medrano et al. 2015; Cheung et al. 2016; Catarino et al. 2018) to aid in differentiating a plastic particle or fibre from those that are naturally occurring or are semi-synthetic (e.g. silk, cotton). This resulted in plastics being quantified into three groups: those that are highly fluorescent (i.e. surpass the pixel brightness threshold), those that were moderately fluorescent but identified as plastic owing to their physical characteristics, and non-fluorescent particles that were identified owing to their physical characteristics.

Microplastics of each size fraction were pooled by plastic type (fibre, microbead, fragment, film, foam) and level of fluorescence (high, moderate and none). Very small (<10µm) particles that could not be identified were classified as unidentifiable fragments. Microplastic counts were then adjusted by the retention rate of the spiked plastics within the sample to give the estimated count for each sample.

Procedure verification and contamination

Verification of the laboratory procedure was assessed by spiking the samples with five different plastic types to determine the retention rate. Samples were spiked with known microplastics of varying densities and included PVC fragments (1.3–1.45 g/cm3), Nylon fibres (1.11–1.18 g/cm3), Polyester fibres (1.39–1.44 g/cm3), Polyethylene microbeads (0.91–0.94 g/cm3) and Polypropylene fragments (0.91 g/cm3). All spiked microplastics were virgin and easily distinguished from environmental microplastics owing to their bright colour and/or lack of weathering.

A variety of steps were taken to mitigate possible contamination of samples. In particular, all microplastic extractions were completed under a laminar flow hood. We also used only reverse osmosis water during sample extractions and washed all glassware and tools between samples with soap and water, and then rinsed thoroughly to ensure all soap was removed. In addition, work surfaces were regularly wiped with distilled H2O and then 70% ethanol. Cotton lab coats were worn at all times, and only glass or stainless-steel tools were used during sample processing in the lab. All reagents and solutions were filtered through a borosilicate filter (1µm pore size) prior to use, and all reagents and solutions were stored in clean glass containers prior 32

to use. Samples were kept covered with aluminum foil or a lid, whenever possible. To quantify potential contamination of samples from microplastics present within the laboratory or between samples, both procedural and contamination blanks underwent the full laboratory procedure. For both the water and sediment samples, procedural blanks were run in parallel after every 7-8 samples (10%) to account for potential cross contamination during the extraction procedure and provided a measure of any contamination from reagents and equipment. The contamination blanks, which quantified the microplastics present within the lab, were obtained by placing wet borosilicate filter near all work not undertaken under the laminar flow hood. Plastic totals quantified in environmental samples were adjusted by removing the number of plastics observed in the contamination blanks.

Statistical analysis

Statistical software

All statistical analysis of the data was undertaken in R version 3.5.1 (R Core Team 2018), with the exception of our calculation of median Phi scores for grain size analysis. We used the GRADISTAT particle size analysis software (Blott and Pye 2001) to calculate median particle size statistics for sieved samples.

Data analysis

For each sampling site, the concentration of microplastics in surface water and sediment was calculated by dividing the number of identified microplastic particles by the total volume of water (L-1) measured and the dry weight (kg-1 dry weight) of the sediment sample, respectively. Of the 25 sediment sites sampled, we excluded four sites (bringing our total number to 21) because the sample volume was too large (n=1), we had poor (<10%) retention rates or other laboratory challenges (n=3). The retention rate for sediment samples varied among samples and among microplastic types; the mean retention rate was 67% ± 2.3 (SE) for fibres, 63% ± 3.5 (SE) for microbeads, and 61% ± 2.2 (SE) for fragments. For all data analyses, results are expressed as corrected microplastic concentrations, such that we re-scaled data to account for variation in retention rates.

Microplastic particles were sorted into three categories – fibres, microbeads and fragments –for which separate concentrations were determined. Variation in these concentrations 33 were related to a suite of environmental variables using multivariate canonical ordination (redundancy analysis, RDA) using the rda function from the {vegan} package in R (Oksanen et al. 2017). Before applying the RDA, concentration data were Hellinger transformed (Legendre and Gallagher 2001), and all environmental variables were Box-Cox transformed (Sakia 1992) to eliminate the influence of extreme values on ordination scores and normalize the environmental variables. It was assumed that microplastics would exhibit different patterns when analyzed as their form (fibre, fragment bead), compared to their polymer type for which fluorescence is a proxy. Therefore, we conducted two separate analyses, where microplastic concentrations were grouped: 1) as the three major forms: fibres, fragment and microbeads; and 2) based on their level of fluorescence: high, moderate and no fluorescence.

To determine which variables best explained variance in plastic concentrations, 34 environmental variables (Table 1.1; Appendix 1; Table A.4-A.5) were chosen to represent sources, as well as hydrological and limnological filters that may influence microplastic abundance, diversity and distribution. Environmental variables with high correlation coefficients (r > 0.75) were excluded in the final RDA analysis, leaving 22 environmental variables to be analyzed in the global model. To determine the most parsimonious set of predictors for plastic datasets, forward selection of environmental variables was applied using the Blanchett et al. (2008) double stopping criterion. When more than four environmental variables were selected, the variables were grouped based on the type of environmental proxy they represent (e.g. depositional, environmental filter, point source) and analyzed in variance partitioning analysis (Borcard et al. 1992).

To determine if the concentrations of microplastics in water are different between sampling sites 500m upstream of wastewater effluents compared to 500m downstream, we conducted paired t-test for each individual effluent station. Additionally, a linear mixed effect model was developed to see if microplastic concentrations can be predicted by sampling location:

푀푃 푐표푛푐푒푛푡푟푎푡푖표푛 ~ 푠푎푚푝푙푖푛푔 푙표푐푎푡푖표푛 + (1 + 푠푎푚푝푙푖푛푔 푙표푐푎푡푖표푛| 푒푓푓푙푢푒푛푡 푙표푐푎푡푖표푛) + 휀

In this model, sampling location (either upstream or downstream) is our fixed effect, whereas effluent location in the river is our random effect, allowing us to examine if the intercepts and 34 slopes of the microplastic concentration and sampling location relationship varies by effluent location.

Results

Sediment samples

Microplastics were found in all sediment samples. After the removal of microplastics found in the contamination blanks, the mean and median values of microplastic particles across all sediment sampling sites was 831.9 ±149.9 (SE) and 428.5±187.9 (SE) per kg dry weight respectively. Across all sites, concentrations ranged between 61.7 to 7561.7 plastics per kg dry weight. Procedural blank samples had a mean and median values of 290.7±137.2 (SE) and 136.1 ±171.45 (SE) per kg dry weight, respectively. The variation in concentrations in the procedural blank samples were largely driven by one sample (blank 6) which contained a high concentration of cross-contaminated microbeads (882.79 microbeads/kgDW; Appendix 1; Table A.3). Microbeads were the most abundant type (mean + SE = 488.7±120.2 microplastics per kg dry weight), followed by fragments (220.0±81.4 (SE)) and fibres (122.1±17.9 (SE)). Foam and films were rare within the sediments, comprising <1% of the total microplastic composition. Small microbeads (<400µm) made up ~95% of all identified microbead types. Microplastics were very heterogenous within the sediments, with concentrations varying in magnitude, even within the same sampling site (Figure 1.2). The highest concentration of microplastics was found in a site just downstream of the Island of Montreal, largely dominated by unidentifiable (<10µm), highly fluorescent fragments and small, moderately fluorescent microbeads (Figure 1.2).

The complete suite of environmental variables explained 34.3% of the variance in plastic concentrations (RDA, p=0.002). Seven of the 23 variables that were chosen through forward selection (Table 1.2) accounted for over 75% of this explained variance (26.68%; p=0.001) (Figure 1.3). Fibres were strongly associated with variables characterizing depositional environments (e.g. % organic carbon, % silt & clay), whereas fragments were associated with areas of high urban land use, and microbeads were associated with greater proportions of medium sized sands (250–500 µm). Variance partitioning analysis found that urban land use variables explained 14.99% (p=0.046), depositional variables explained 12.2% (p=0.056), and the other remaining variables explaining 16.4% (p=0.061) of the variance (Figure 1.4a). A separate variance partitioning analysis, in which variables were divided into environmental filters 35 and microplastic sources, found that the former explained 22.58% (p=0.016) whereas the microplastic sources (urban land cover) explained 14.99% (p=0.046) (Figure 1.4b).

In the second RDA, microplastics were analyzed by level of fluorescence. When all environmental variables were placed in the global model, the model was not significant (p=0.07). However, once three environmental variables (% inorganic carbon, % medium sand and % natural land use; Table 1.3) were chosen through forward selection, a significant amount of the variance was explained in the reduced RDA (15.9%; p=0.003) (Figure 1.5). High fluorescence particles were inversely correlated with areas of high natural land use, moderately fluorescent particles were associated with substrates dominated by medium sand (500-250µm), and non- fluorescent particles were associated with substrates that contain high carbonate content.

Water samples

After the removal extremely high measurements (one upstream and one downstream, but not from the same effluent site), the mean and median values of microplastic particles across all sampling sites were 0.118±0.011 (SE) and 0.119±0.014 (SE) microplastics per litre upstream of wastewater effluents and 0.158±0.018 and 0.157±0.022 (SE) microplastics per litre downstream of wastewater effluents, respectively. Procedural blank samples contained a mean and median concentration of 0.075±0.02 (SE) and 0.06±0.03 (SE) microplastics per litre (Figure 1.5). In only one case out of the ten waste water effluents was the average microplastic concentration significantly higher downstream compared to upstream of the effluent site (p=0.024) and marginal differences in mean concentrations of microplastics when comparing all upstream and downstream concentrations (p= 0.06; Table 1.4). With linear mixed effect modelling, we found that the model treating waste water effluent location as a random effect had higher predictive power than the linear model (Table 1.5). However, examining the confidence interval of the slope from the linear mixed effect model overlapped with zero, therefore was not significantly different from zero at the 0.05 level (Table 1.6; Figure 1.7).

Discussion

Concentration of microplastics in the sediments

The abundance of microplastics in the sediments of the St. Lawrence River were spatially variable, ranging from 62 to 7561 items·kg−1, with a mean value of 832 items·kg−1. In 36 comparison to other studies using similar metrics, the mean concentration of microplastics in the St Lawrence River was among the highest recorded globally (Figure 1.8; Appendix 1; Table A.2). The mean concentration is of the same order of magnitude as concentrations measured in highly contaminated rivers and lakes found near densely populated cities in China (Peng et al. 2018; Wen et al. 2018). Within the eastern Canadian region, concentrations in the St Lawrence River are slightly higher than those measured in the Lake Ontario tributaries (610 items·kg−1) (Ballent et al. 2016) and nearly four orders of magnitude higher than in the Ottawa River (220 items·kg−1) (Vermaire et al. 2017).

Concentrations of microplastics measured in this study were generally higher than those previously recorded in 2013 by Castañeda et al. (2014), who found large microbeads (>400µm) along the upper the St. Lawrence River at a median concentration of 52 microbeads·m−2. Castañeda et al. (2014) also found a local concentration of 1.4×105 microbeads·m−2 at one site near the Gentilly-2 power plant, which rivalled some of the most contaminated marine sites known at the time. In the same study, concentrations of all microplastic types varied from 993 to 1.38×105 items·m−2 with mean and median concentrations of 1.4×104 items·m−2 and 6035 items·m−2, respectively. However, it is worth noting that the methodology we adopted for this study allowed us to identify microplastics that were much smaller and present in the organic-rich fraction of the sediment. Indeed, we found that 95% of the microbeads in our study were smaller than the lower limit of 400µm used by Castañeda et al. (2014). Likewise, we did not find the same level of abundance at the Gentilly-2 site as has been previously reported by Castañeda et al. (2014), possibly due to the introduction of our organic digestion step (cf. Munno et al. 2017). To determine if the difference in large microbeads is attributable to a difference in methodology, we applied the same method used in Castenada et al. (2014) on sediment samples collected in 2017 from the Gentilly-2 site and found that there is indeed a high concentration of these larger microbeads still present at this site (216 microbeads/kg DW or 3.69x104 microbeads/m2). Presuming that this large fraction of microbeads was excluded from our analysis across sampling sites owing to our procedures, the concentrations reported here may represent only a minimum abundance of microbeads in the river. Further investigations are required to determine the effect of different methodological procedures on the estimated abundances of microplastics. 37

As has been observed recently in other lotic systems (Ballent et al. 2016; Peng et al. 2018; Wen et al. 2018), fragments and microbeads were more abundant in river sediments than fibres, which tend to dominate in lake and marine bottom sediments (Fischer et al. 2016; Vaughn et al. 2017; Pagter et al. 2018; Abidli et al. 2018; Mu et al. 2019). Since fibres are often comprised of polymers that are denser than water, they will remain in suspension in turbulent mixing systems, but will sink in stable, open waters (Cable et al. 2017). Indeed, microplastics of fibrous shapes exhibit a slower settling velocity than fragments and spheres (Khatmullina and Isachenko (2018), thus hindering their deposition in lotic systems.

Factors that govern microplastic abundance and distributions

To assess the risk of biota being exposed to microplastics, and to determine the efficacy of policy interventions, biomonitoring programs need to focus their sampling effort on habitats where microplastics abundance is maximal and must consider factors that govern temporal and spatial variability of microplastics in aquatic environments (Syberg et al. 2015; Koelmans et al. 2017). Previous research has suggested that particle distribution may be governed by their proximity to point sources (Lechner et al. 2014; Mani et al. 2015; Ballent et al. 2016; Leslie et al. 2017) as well as by various factors such as particle characteristics, topography, water flow, water depth, organic content and substrate type (Castañeda et al. 2014, Eerkes-Medrano et al. 2015, Nel et al. 2018). Over a quarter of the variance in microplastic concentration in the sediments were explained by seven variables, and different microplastic types correlated with different environment types (Figure 1.4). Fibre abundance was correlated with characteristics commonly associated with depositional environments (% organic content, % silt & clay, phi score), as has been observed in previous studies (Fisher et al. 2016; Nel et al. 2018). Fragment abundance was correlated with urban land use, which has also been identified as an important variable in some studies (Mani et al. 2015; Peng et al. 2018). Fragments occurred most often as small (<10µm) particles that were highly fluorescent, and thus are significant for risk assessment as the severity of toxicity increases with decreasing particle size (Anbumani & Kakkar 2018). In contrast, microbead abundance correlated with medium-sized sand, which settle to the sediments in faster flowing waters than finer materials, and there was also a weak inverse correlation with median phi score. To our knowledge, no such relationships have previously been reported for riverine 38 systems. In a shallow coastal marine environment, Alomar et al. (2016) found microplastics consistently present in coarse sands, but observed no trend with sediment grain size.

When microplastics were examined as a function of their fluorescence, less variance was explained, but similar relationships were found relative to those observed based on microplastic form. In total, only 15% of the variance in the concentration of different microplastic fluorescence types in the sediments was explained by three variables: % inorganic carbon, % medium sand, and % natural land use. Non-fluorescent plastic particles (mostly fibres; Erni- Cassola et al. 2017) were correlated with % inorganic carbon. Moderately fluorescent particles (mostly microbeads) were correlated with % medium sand, whereas highly fluorescent microplastic fragments were inversely correlated with % natural sand use. Therefore, based on our comparative analyses of two approaches for characterizing plastics, we suggest that more information can be deciphered from analyses of morphology rather than by their fluorescent intensity. When microplastics were classified by morphology, we found that that environmental filters and point sources (urban land use) explained a significant fraction of the variation in microplastic composition in the sediments.

Surprisingly, proximity to wastewater effluents – a suggested point source of microplastics into rivers (Murphy et al. 2016; Mason et al. 2016) – was not a significant predictor of microplastic abundances in the sediments. We found a significant increase in microplastic concentration downstream of the wastewater effluent in water samples in only one of ten effluent sites (Table 1.4; Figure 1.6). A linear mixed effect model examining microplastic concentration as a function of sampling location was also found to be non-significant (Table 1.5- 1.7; Figure 1.7). This may be an effect of location, as effluents are often strategically placed in areas of the river that are fast flowing, to enable rapid waste dilution. Similarly, Hoellein et al. (2017) found that microplastic concentrations in surface water did not correlate with distance downstream from wastewater effluent location.

A call for standardization

There is growing recognition of the need to develop and adapt standardized protocols for sampling, extraction, separation and identification of microplastics (Twiss 2016; Prata et al. 2018). Each of these steps can be costly, time-consuming processes. In addition, many factors contribute to the varying efficiency of identification methods, thereby challenging large-scale 39 monitoring programs (Yu et al. 2018). To enable evidence-based policy, and to increase comparability of disparate studies, researchers must adopt cost-effective standardized protocols that enable efficient and accurate enumeration of microplastics in a broad range of environmental situations. In our study we adapted a protocol that uses methods that are inexpensive, require a short processing time, are not limited in the scope of microplastic types that can be identified, and can be applied to water and sediment samples. The protocol combined an oil extraction protocol (Crichton et al. 2017) and the use of Nile red tagging and fluorescent microscopy (Erni- Cassola et al. 2017).

In recent years, many techniques have been developed to extract microplastics from environmental media. The most common extraction method is density separation, which involves mixing the sample with a liquid that has a defined density and is normally a saturated salt solution (Van Cauwenberghe et al. 2015; Prata et al. 2018). This procedure enables high-density particles (such as sand or clay) to sink to the bottom, whereas low-density particles such as microplastics to float and thus be recovered from the supernatant. Other methods include combining density separations or various solvents with a mechanical apparatus to stimulate separation – such as the Munich Plastic Sediment Separator (Imhof et al. 2012) or pressurized fluid extraction (Fuller and Gautam 2016). These solutions and solvents are often expensive, inefficient at extracting a broad range of microplastics, and require special handling because they are toxic (Li et al. 2018). Conversely, the oil extraction protocol used in this study uses safe, low-cost solutions that are readily purchased (e.g. canola oil). Furthermore, the oil extraction approach is density independent, as it manipulates the oleophilic properties of plastic, thereby allowing for all plastic types to be separated. Additionally, the processing time for oil extraction is much shorter (a few minutes) compared with density separation (a few hours) and thus allows a greater number of samples to be processed in a given period. Our retention rate was lower than expected for this method, likely as a result of conducting organic digestion post-extraction (Crichton et al. 2017). To improve the retention rate, future research should attempt to digest their sediment samples prior to microplastic extraction, or consider alternatives for the digestion step (e.g. KOH).

Many studies have relied upon visual identification coupled with spectroscopic testing (FT-IR, Raman) or chromatographic methods (pyrolysis GC/MS, liquid chromatography) to 40 quantify plastic particles within a study (Appendix 1; Table A.2). Such studies are limited in their ability to detect small microplastics (<300µm; the ‘lost microplastic fraction’) which could be easily identified using the Nile red tagging method (Shim et al. 2016; Erni-Cassola et al. 2017). The costs of Nile red and a fluorescent microscope are lower than that of obtaining and using spectroscopic or chromatographic equipment, and both are nearly as accurate in terms of quantification (Shim et al. 2016). The principal limitation of the Nile red tagging method is that it reveals no information on the chemical composition of the sample. In order to obtain a standardized protocol, methods should be suitable for a range of researchers from academia to NGOs and using the Nile red tagging method will aid in this effort as it can allow for quick and accurate detection of microplastic particles, without the need to be coupled with time-intensive, expensive, analytical equipment.

Finally, there is a need to standardize an identification protocol. Generally, microplastics are identified using physical factors (e.g., their elasticity when squeezed by tweezers, how well putative fibres bend and fold, and whether they deform under sufficient force) and visual factors (e.g., such as whether fibres taper towards the ends, their transparency, or whether they are homogenously coloured) (Noren 2007; Cheung et al. 2016). However, visual sorting is strongly affected by variation in researcher identification, microscopy quality and the sample matrix (Li et al. 2018). Additionally, some studies have decided to include any fibres within their analysis, including those suspected of being of a cellulosic or semi-synthetic origin, resulting in an overestimation of microplastic fibres in their samples (Lusher et al. 2014). In this study we applied the above criteria and developed identification trees that, in combination with Nile red tagging, should aid in more accurate identification microplastic particles. Our identification trees apply commonly-used visual characteristics for plastic particles to identify cellulosic or semi-synthetic fibres and they build in criteria to avoid overestimation of plastic from other fluorescent particles such as organic debris that might be stained by the dye. These identification trees took a conservative approach by excluding white, or translucent, fragments and fibres, because they could be bleached natural particles that did not fully digest in peroxide (Erni-Cassola et al. 2017; Li et al. 2018). Indeed, our identification trees could potentially result in an underestimation of microplastics as previous studies have found that white and translucent plastics can comprise a significant portion of microplastic composition (Peng et al. 2018; Lin et al. 2018; Zhu et al. 41

2018). Nevertheless, these identification trees provide an interim tool to consistently quantify microplastic particles in sediment and water samples.

Conclusion

This study provided the first comprehensive quantification of microplastics in the sediments of the St. Lawrence River. The abundance and distribution of microplastics in the river is explained, to some extent, by point sources and environmental filters, which may inform monitoring programs regarding which areas are vulnerable to microplastic accumulation and, consequently, which biotic communities are subject to maximal exposure. The results of this study may help inform the design of more environmentally-relevant experiments testing the impacts of exposure of aquatic biota to microplastics. Finally, in contrast to the previous study in the St. Lawrence River (Castañeda et al. 2014), 95% of the microbeads in our study were smaller than 500µm and we did not find the same level of abundance at some sites; these differences appear to be largely due to difference in methodology and highlight the need for standardized methods are needed. Future research in large rivers should assess the mass balance of microplastic loads and their fate during transport to the ocean, to determine whether river sediments are best viewed as permanent or transient sinks for microplastic pollution. 42

Tables and Figures

Table 1.1: Environmental predictors chosen for this study.

Environmental predictor type Environmental predictors Rationale (point source or environmental filter)

Point source of plastic: Urban, industry and Land cover classes: Urban, Agricultural, agricultural land use. Land use Natural, Industry, Grassland Background reference sites: grasslands and natural areas. Used to examine a unidirectional effect (increase Spatial variables Asymmetic Eigenvector Maps (AEM) in MP with distance downstream).

%Gravel, % Very Fine Gravel, %Very Dynamic environments: characterized by higher % Course Sand, % Course Sand, % Medium of gravel and coarse sand. Sediment variables Sand, % Fine Sand, % Very Fine Sand, % Depositional environments: characterized by Silt & Clay, Median Phi score higher % of silt and clay.

Depositional environment: typically characterized by high organic content. Organic content could aid in depositing microplastics. Organic content % Organic content, % Inorganic content Flat river-bottom: typically characterized by limestone bedrock which contains high inorganic content; could facilitate organic production. Physical variables Depth, Secchi Depth, Distance to shore Hydrological variables River velocity (m/s), Specific Riverine variables Hydrological variables; environmental chemistry conductivity 43

Table 1.2: Seven environmental variables chosen by forward selection when examining microplastic concentrations by type. Significant values are bolded.

Variable Adjusted R2 Adjusted R2 Cumulative F-value p-value % Inorganic Carbon 0.05 0.05 3.916 0.041* % Urban land use 0.04 0.09 3.444 0.048* % Medium sand 0.03 0.12 3.167 0.045* %Organic Carbon 0.04 0.16 3.801 0.045* Distance to shore 0.04 0.20 3.583 0.044* Phi score 0.04 0.24 4.330 0.026* % Silt & Clay 0.03 0.27 3.066 0.047*

Table 1.3: Environmental variables identified by forward selection when examining microplastic concentrations were sorted by categories based on their fluorescence. Significant values are bolded.

Variable Adjusted R2 Adjusted R2 Cumulative F-value p-value

% Medium Sand 0.06 0.06 4.721 0.018*

% Natural Land Use 0.05 0.11 4.668 0.026*

% Inorganic carbon 0.05 0.16 3.952 0.034* 44

Table 1.4: P-values arising from paired t-tests looking at the mean difference in microplastic concentrations upstream and downstream of wastewater effluents. Effluent sites are defined by their location and treatment type. Marginally significant p-values are shown in italics and significant values are bolded.

Site ID Effluent location Type of Paired t-test at Paired t-test all upstream Treatment individual vs. downstream effluents effluents (p-value) (p-value) WWTP2 Notre-Dame-de-l'Île-Perrot, Québec Aerated ponds 0.1788 WWTP8 Montréal, Québec Physico-chemical 0.4506 WWTP9 Vareens, Québec Aerated ponds 0.9806 WWTP10 Repentigny, Québec Physico-chemical 0.9012 WWTP13 Lanoraie, Québec Aerated ponds 0.8335 0.0552 WWTP14 Sorel Tracy, Québec Aerated ponds 0.4732 WWTP15 Saint-Ignace-de-Loyola, Québec Aerated ponds 0.2872 WWTP17 Nicolet, Québec Aerated ponds 0.0241* WWTP21 Lotbinière, Québec Aerated ponds 0.3504 WWTP30 Quebec City, Québec Biofiltration 0.8541 45

Table 1.5: Output from AIC table comparing a linear model and a linear mixed effect model examining microplastic abundance as a function of sampling location (upstream or downstream). The linear model does not treat effluent location as a random effect whereas the mixed effect model does.

Model AIC Linear model -69.014 Linear mixed effect model -82.144

Table 1.6: Summary output for the fixed effects in linear mixed effect model.

Model Estimate Std. Error Df t-value Intercept 0.328 0.025 47 11.976 Sampling location (US/DS) 0.049 0.035 47 1.411

Table 1.7: Output table outlining the confidence intervals for the slope of the mixed effect model. Confidence intervals overlap with 0, therefore, the slope is not significantly different from zero at the 0.05 level.

Confidence interval Slope Std.error Value Upper CI 0.049 + 0.035 * 1.96 = 0.118 Lower CI 0.049 - 0.035 * 1.96 = -0.019 46

Figure 1.1: Map of the Upper St. Lawrence River, displaying locations where sediment samples and surface water samples were collected.

Figure 1.2: Density of microplastics in the sediments collected across our network of sites. Circle size indicates the concentration of microplastics. All density measurements have been adjusted based on laboratory retention rates. 48

2 AdjR = 26.68%

Figure 1.3 An ordination biplot of environmental variables and microplastic taxa obtained by RDA (black arrows depict plastic types and red arrows depict environmental predictors). Fibres were found to be associated with variables commonly linked with depositional environments, whereas fragments were found in areas of high urban land use. Microbeads were associated with medium sized sand (250-500 µm), typical of dynamic areas 49

Figure 1.4: A) Variance partitioned into three groups of environmental variables; depositional variables (% silt & clay, % organic carbon and % inorganic carbon), urban land use and other environmental variables (Phi score, % medium sand, distance from shore). Only urban land use contributed in explaining a significant portion of the variance (p<0.05) whereas depositional variables and other variables were marginally significant (p<0.065). B) Variance partitioned into two groups; environmental filters (Phi score, % medium sand, % silt and clay, % carbon, % inorganic carbon and distance from shore) and point sources (% urban land use). Both groups contributed in explaining a significant portion of the variance (p<0.05). 50

Figure 1.5: An ordination biplot of three environmental variables and microplastic fluorescence types obtained by RDA (black arrows depict plastic fluorescence and red arrows depict environmental variables). High fluorescence particles were found to be inversely correlated with areas of high natural land use, moderately fluorescent particles were found substrates dominated by medium sand (500-250µm) and non-fluorescent particles associated with substrates that contain high inorganic content. 51

Figure 1.6: Boxplots displaying concentration of microplastics in procedural blank, upstream and downstream samples.

Upstream Downstream

Figure 1.7: Square-root transformed microplastic concentrations in upstream and downstream sampling locations. Grey lines refer to the slopes of individual waste water effluents coefficients generated by the model. The black line represents the slope of the coefficient for the mixed effect model 53

Figure 1.8: Mean concentration of microplastics documented in studies that measured plastics per kg sediment. *** represents studies that used Nile Red to identify microplastics and the number in brackets beside the sampling location refers to the reference number in this paper. 54

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General Conclusions

Implications of predicting microplastic distributions and abundances

Microplastics are pervasive pollutants in freshwaters, but their presence and fate in riverine environments are still poorly documented. Given the need to develop accurate risk assessments for microplastic pollution, it is critical to fully understand the factors that govern the abundance and distribution of microplastic particles in aquatic environments. In this thesis, I attempt to bridge this gap by expanding on previous research in the St. Lawrence River by quantifying a broader range of microplastics found in the sediments. In addition, I demonstrate the importance of point sources and environmental filters in explaining the abundance and distributions of microplastics in the river. My results offer insight for biomonitoring programs regarding which areas should be prioritized for sampling and which environmental variables should be considered when conducting risk assessments for riverine systems. I advocate the development of hypotheses that incorporate environmental factors, such as hydrological variables, to make more environmentally relevant generalizations about the potential risks of microplastics to aquatic species in large urbanized river systems.

Implications for policy on plastic pollution

In response to rising public concern over the threat microplastics pose to the marine environment and potentially to human health, comprehensive legislation has been adopted in numerous countries (Lam et al. 2018), including Canada and the United States, where bans for microbeads in personal care products have been put into place. However, the plastic pollution issue will not be solved by banning the production of all problematic products, as plastic will continue to remain a useful and important material for future generations. Our results suggest that even if current bans were imposed decades earlier, as microplastics were accumulating in the environment, at least 50% would still be present in the river in the form of fibres or fragments.

Recently it has been suggested that effective mitigation of plastic pollution requires coordination across sectors, stakeholder groups, and nations through a Global Convention, akin to the Stockholm Convention for POPs (Worm et al. 2017). To achieve similar results to the Stockholm Convention, we need a better understanding of the sources and sinks of plastic and the ecological risks posed to a broad range of aquatic species. I recommend that future studies 65 aim to create a standardized protocol, as this would greatly enhance comparability among research studies. Currently, there is a mixed-bag of techniques employed globally to sample, extract and quantify microplastic particles. By developing standardized methods that can be applied to a broad variety of environmental contexts, we could more efficiently and accurately assess the extent of microplastic contamination in aquatic systems on a global scale.

General Conclusion – Literature Cited

Castañeda, R.A., S. Avlijas, M.A. Simard and A. Ricciardi. 2014. Microplastic pollution in St. Lawrence river sediments. Canadian Journal of Fisheries and Aquatic Sciences, 71(12):1767- 1771.

Worm, B., H.K. Lotze, I. Jubinville, C. Wilcox and J. Jambeck. 2017. Plastic as a persistent marine pollutant. Annual Review of Environment and Resources 42: 1-26.

Appendix

Table A.1: Sediment size fractions (Crawford and Quinn 2016) used in this study.

Aggregate Grain diameter of Sieving size fractions Microplastic interest for microplastic applied in this study extraction research

Fine Gravel 8-4mm >4mm No extraction

Very Fine Gravel 4-2mm 4-2mm No extraction

Very Course Sand 2-1mm 2-1mm Oil extraction

Coarse Sand 1mm -500 µm 1-500 µm Oil extraction

Medium Sand 500-250 µm 500-250 µm Oil extraction

Fine Sand 250-125 µm 250-125 µm Oil extraction

Very Fine Sand 125-62.5 µm 125-63 µm Oil extraction

Silt 62.5-3.9 µm <63 µm Oil extraction

Clay 3.9-1µm 68

Table A.2: List of maximum and mean microplastic abundances documented worldwide (plastic items per kilogram dry weight).

Study Max (items kg-1) Mean (items kg-1) Sampling location Identification method Claessens et al. 2011 390.7 166.7 ± 92.1 (SD) Harbours off the coast of Belgium Visual + FTIR Claessens et al. 2011 269.5 97.2 ± 18.6 (SD) Belgian Continental Shelf Visual + FTIR Vianello et al. 2013 2175 1445±458.4 (SD) Lagoon of Venice, Italy Visual + μ-FTIR Leslie et al. 2013 3600 3300 ±420 (SD) Rhine Estuary, Netherlands Visual Leslie et al. 2013 720 440 ±160 (SD) North Sea, Netherlands Visual Leslie et al. 2013 770 770±N/A Wadden Seam Netherlands Visual Wagner et al. 2014 64 N/A Elbe, Mosel, Neckar, and Rhine Rivers, Visual Germany. Klein et al. 2015 1368 N/A Main River, Germany Visual + FTIR Klein et al. 2015 3763 N/A Rhine River, Germany Visual + FTIR Alomar et al. 2016 N/A 163.68 ± 93.02 (SD) 1) Andratx, Mallorca Island, Mediterranean Visual

Alomar et al. 2016 N/A 122.80 ± 98.18 (SD) 2) Andratx, Mallorca Island, Mediterranean Visual Alomar et al. 2016 N/A 897.35 ± 103.31 (SD) 1) Santa Maria, Cabrera Island, Visual Mediterranean Alomar et al. 2016 N/A 244.01 ± 33.46 (SD) 2)Santa Maria, Cabrera Island, Mediterranean Visual Alomar et al. 2016 N/A 100.78 ± 55.49 (SD) 1)Es Port, Cabrera Island, Mediterranean Visual Alomar et al. 2016 N/A 100 ± 30 (SD) 2)Es Port, Cabrera Island, Mediterranean Visual Ballent et al. 2016 2790 980 ± 1125.9 (SD) Lake Ontario Visual +Raman + XRF Ballent et al. 2016 27830 610 ± 706.9 (SD) Lake Ontario Tributaries Visual + Raman + XRF 69

Fischer et al. 2016 117 112 ± 32 (SD) Lake Bolsena, Italy Visual + SEM Fisher et al. 2016 266 234 ± 85 (SD) Lake Chuisi, Italy Visual + SEM Su et al. 2016 234.6 N/A Lake Taihu, China Visual + μ-FT-IR Frere et al. 2017 2.03 0.40 ± 0.67 (SD) Brest Bay, Brittany, France (Autumn) Visual + Raman Frere et al. 2017 8.74 1.53 ± 2.84 (SD) Brest Bay, Brittany, France (Winter) Visual + Raman Clunies-Ross et al. 2017 5.1 1.7 ± 2.4 (SD) Greater Canterbury, New Zealand (Estuarine Visual + µ-Raman sediments) Clunies-Ross et al. 2017 15.1 3.9 ± 3.5 (SD) Greater Canterbury shorelines, New Zealand Visual + µ-Raman (Harbour sediments) Tsang et al. 2017 458 157.8±118.2 (SD) Hong Kong, China Visual + ATR-FTIR Naji et al. 2017 125 ± 25 61 (Avg of Means) Persian Gulf, Iran Visual + FT-IR Peng et al. 2017 340 121± 9 (SD) Changjiang Estuary, China Visual + μ-FT-IR Maes et al. 2017 3,146 585.29± 1133.70 (SD) Belgian Continental shelf Visual Maes et al. 2017 561 203.43± 183.52 (SD) Dutch Continental Shelf Visual Maes et al. 2017 643 306± 267.27 (SD) UK Continental shelf Visual Maes et al. 2017 1509 481.20± 586.56 (SD) French Continental shelf Visual Vaughn et al. 2017 260 N/A Urban Lake, Birmingham, UK Visual Vermaire et al. 2017 450 220±N/A Ottawa River, Canada Visual Bergmann et al. 2017 6595 4356± 2025.4 (SD) Fram Strait, Svalbard, Norway FlowCam + ATR- FT-IR Zobkov et al. 2017 48.4 34 ± 10 (SD) Baltiysk Strait, Baltic Sea, Russia Visual Wang et al. 2017 544 312.5 (avg of means) Beijiang River, China Visual + μ -FTIR Horton et al. 2017 660 350 (avg of means) River Thames, UK Visual + Raman 70

Leslie et al. 2017 10,500 2071 ±4146 (SD) Amsterdam Canal, Netherlands Visual + FT-IR Pagter et al. 2018 N/A 104±N/A Galway Bay, Ireland Visual Hurley et al. 2018 26,800 3434.6± 5678.7(SD) River Irwell, UK (Pre-flood) Visual + FT-IR Hurley et al. 2018 3400 1019.2± 797.5(SD) River Irewell, UK (Post-flood) Visual + FTIR Hurley et al. 2018 62200 11764.3±19433.9 (SD) River Mersey, UK (Pre-flood) Visual + FT-IR Hurley et al. 2018 72400 6142.9±19082.9 (SD) River Mersey, UK (Post-flood) Visual + FT-IR Zhao et al. 2018 340 171.8 ± 55.4 (SD) Bohai Sea, China Visual + μ -FT-IR Zhao et al. 2018 280 123.6 ± 71.6 (SD) Northern Yellow Sea, China Visual + μ -FT-IR Zhao et al. 2018 140 72.0± 27.2 Southern Yellow Sea, China Visual + μ -FT-IR Zhu et al. 2018 N/A 37.1 ± 42.7 (SD) North Yellow Sea, China Visual + μ -FT-IR Peng et al. 2018 N/A 723 ± 306 Huangpu River branch, Shanghai, China Visual + μ-FT-IR Peng et al. 2018 N/A 765 ± 276 Shajinggang river, Shanghai, China Visual + μ-FT-IR Peng et al. 2018 N/A 1535 ± 771 Caohejing river, Shanghai, China Visual + μ-FT-IR Peng et al. 2018 N/A 1600 ± 191 Beishagang river, Shanghai, China Visual + μ-FT-IR Peng et al. 2018 N/A 1120 ± 56 Jiangjiagang river, Shanghai, China Visual + μ-FT-IR Peng et al. 2018 N/A 410 ± 127 Yujiabang river, Shanghai, China Visual + μ-FT-IR Peng et al. 2018 N/A 53 ± 12 Nanhuizui tidal flat, Shanghai, China Visual + μ-FT-IR Abidii et al. 2018 461.25 ± 29.74 316.0 ± 123.7 Northern Tunisian coast, Tunisia Visual + FTIR-ATR Lin et al. 2018 9597 1669±N/A Pearl River, Guangzhou City, China Visual + μ-FTIR Nel et al. 2018 563.8 160.1 ± 139.5 (SD) Bloukrans River, South Africa (winter) Visual Nel et al. 2018 14.6 6.3± 4.3 (SD) Bloukrans River, South Africa (summer) Visual Rodrigues et al. 2018 629 392± 268.7 (SD) Antuã River, Portugal (Spring) Visual + ATR–FTIR 71

Rodrigues et al. 2018 514 245± 250.6 (SD) Antuã River, Portugal (Fall) Visual + ATR–FTIR Dean et al. 2018 391 90.3± 107.0 (SD) Lake Erie Nearshore, Ontario Visual+ FTIR + Raman Dean et al. 2018 462 116.6 ±193.5 (SD) Lake Erie Tributaries, Ontario Visual+ FTIR + Raman Wen et al. 2018 N/A 270.2 ± 48.2 (SD) Xianjia Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 536.3 ± 88.6 (SD) Yue Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 557.6 ± 65.1 (SD) Nianjia Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 866.6 ± 38.0 (SD) Yuejin Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 779.1 ± 252.1 (SD) Meixi Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 375.5 ± 175.2(SD) Yang Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 635.2 ± 197.20 (SD) Dong Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 468.0 ± 147.9(SD) Donggua Lake, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 401.8 ± 177.2 (SD) Jinjiang River, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 307.6 ±94.7 (SD) Longwanggang River, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 580.8 ±310.4 (SD) Laodao River, Changsha, China SEM (visual) + μ-FTIR Wen et al. 2018 N/A 364.90 ±244.69 (SD) Liuyang River, Changsha, China SEM (visual) + μ-FTIR Wang et al. 2018 74800 32947 ± 15342 (SD) Wen-Rui Tang River, China Nile red + μ-FT-IR Phuong et al. 2018 102 67 ±76 (SD) Pays de la Loire, France μFT-IR microscope Tibbets et al. 2018 350 166.7± 113.4 (SD) Tame River, Birmingham, UK Visual Li et al. 2019 2310 ± 29 (SE) 2096 (Avg. of means) Maowei Sea entrance zones, China Visual + µ-Raman Li et al. 2019 940 ±17 (SE) 665 (Avg. of means) Maowei Sea River Estuary, China Visual + µ-Raman Li et al. 2019 990 ± 3 (SE) 510 (Avg. of means) Maowei Sea, China Visual + μ -Raman Mu et al. 2019 68.78 22.9±23.9 (SD) Bering-Chukchi Sea shelf, Arctic Visual + μ-FT-IR 72

Table A.3: Table of raw plastic data for all sediment sites including total counts and concentrations.

Site Microbead Fibre Fragment Revised Revised Revised Revised Revised Modified Modified Modified Modified Dry Fibre/ Micro- Fragments/ Plastics / Retention Retention Retention fibre count micro-bead fragment foam/ film count Fibres micro-beads fragments plastic weight kg DW beads/ kg kg DW kg DW Rate (%) rate (%) rate (%) count count count counts (kg) DW Site1A 100 70 75 2 2 10 0 16 2.86 2 13.33 20.19 0.0392 72.89 51.02 340.14 464.04 Site1B 90 70 80 9 8 14 0 34 12.86 8.89 17.5 42.24 0.0452 284.45 196.66 387.17 868.28 Site1C 90 80 75 7 3 1 0 12 8.75 3.33 1.33 14.42 0.039 224.36 85.47 34.19 344.02 Site2A 100 75 90 54 42 18 1 115 72 42 20 135 0.1008 714.29 416.67 198.41 1340.39 Site2B 100 55 65 10 6 8 0 25 18.18 6 12.31 37.49 0.3272 55.57 18.34 37.62 111.52 Site2C 100 90 85 28 107 8 1 143 31.11 107 9.42 147.53 0.2705 115.01 395.56 34.79 549.72 Site3A 20 90 70 15 3 48 3 67 16.67 15.00 68.57 101.24 0.28 60.08 54.07 247.19 376.80 Site3B 80 90 80 3 0 19 0 24 3.33 0.00 23.75 29.08 0.29 11.55 0.00 82.27 93.81 Site3C 60 95 65 12 4 21 0 38 12.63 6.67 32.31 52.61 0.23 56.12 29.62 143.53 229.26 Site4A 100 60 80 49 63 30 1 142 81.67 63.00 37.50 182.17 0.35 230.37 177.72 105.78 517.40 Site4B 70 65 85 21 13 26 0 60 32.31 18.57 30.59 81.47 0.36 89.15 51.25 84.40 224.80 Site4C 100 60 65 14 16 43 0 74 23.33 16.00 66.15 106.49 0.25 94.77 64.99 268.70 428.46 Site5A 40 25 40 8 146 21 3 175 32.00 365.00 52.50 449.50 0.22 143.63 1638.24 235.64 2051.17 Site5B 20 60 15 48 306 39 0 393 80.00 1530.00 260.00 1870.00 0.25 323.49 6186.82 1051.35 7561.67 Site5C 40 40 85 30 147 17 1 194 75.00 367.50 20.00 462.50 0.29 258.89 1268.55 69.04 1600.54 Site6A 100 75 95 17 35 29 0 81 22.67 35.00 30.53 88.19 0.24 95.72 147.80 128.91 372.44 Site6B 80 75 70 16 11 23 0 50 21.33 13.75 32.86 67.94 0.28 76.57 49.35 117.94 243.86 Site6C 70 80 50 18 39 438 0 495 22.50 55.71 876 954.21 0.1804 124.72 308.84 4855.88 5289.44 Site7A 100 75 70 40 1 14 2 56 53.33 1 20 75.33 0.074 717.81 13.46 269.18 1038.90 Site7B 100 60 70 21 2 48 0 71 35.00 2 68.57 105.57 0.11 332.38 18.99 651.20 1002.58 Site7C 100 100 85 38 737 24 1 799 38.00 737 28.24 803.24 0.28 134.85 2615.33 100.20 2854.55 Site9A 80 55 70 8 337 5 0 350 14.55 421.25 7.14 442.94 0.32 46.35 1342.42 22.76 1411.53 Site9B 30 50 35 8 87 15 2 110 16 290 42.86 348.86 0.257 62.26 1128.40 166.76 1379.66 Site9C 10 50 65 12 2 4 4 18 24 20 6.15 50.15 0.277 86.64 72.20 22.22 203.28 Site10A 40 70 60 14 5 15 1 34 20 12.5 25.00 57.50 0.1759 113.70 71.06 142.13 336.37 Site10B 40 50 60 5 5 21 0 31 10 12.5 35.00 57.50 0.2095 47.73 59.67 167.06 274.46 Site10C 50 75 60 15 4 19 0 40 20 8 31.67 61.67 0.1634 122.40 48.96 193.80 365.16 Site11A 10 75 85 2 0 44 1 47 2.67 0 51.76 55.43 0.3037 8.78 0.00 170.45 183.10 Site11B 10 80 55 2 0 9 0 14 2.5 0 16.36 21.86 0.2621 9.54 0.00 62.43 71.97 Site11C 20 65 55 1 0 29 0 32 1.54 0 52.73 56.27 0.2759 5.58 0.00 191.11 196.69 Site12A 80 75 75 13 44 6 0 63 17.33 55 8.00 80.33 0.2767 62.64 198.77 28.91 290.33 Site12B 50 35 35 6 8 3 0 17 17.14 16 8.57 41.71 0.2922 58.67 54.76 29.33 142.76 Site12C 100 70 20 9 102 3 0 114 12.86 102 15.00 129.86 0.2699 47.64 377.92 55.58 481.13 Site13A 90 60 75 15 91 7 0 113 25 101.11 9.33 135.44 0.1412 177.05 716.08 66.10 959.24 Site13B 80 70 45 24 152 3 0 179 34.29 190 6.67 230.95 0.2496 137.36 761.22 26.71 925.29 Site13C 50 70 50 20 191 7 0 218 28.57 382 14.00 424.57 0.3126 91.40 1222.01 44.79 1358.19 Site14A 20 35 50 5 69 19 0 94 14.29 345.00 38.00 398.29 0.31 46.04 1111.83 122.46 1280.33 73

Site14B 40 40 45 5 124 12 0 141 12.50 310.00 26.67 349.17 0.33 37.96 941.39 80.98 1060.33 Site14C 20 45 40 2 5 6 2 13 4.44 25.00 15.00 44.44 0.28 15.95 89.73 53.84 177.47 Site16A 80 80 60 7 0 24 0 34 8.75 0.00 40.00 51.75 0.31 27.80 0.00 127.11 154.91 Site16B 20 60 40 7 2 6 2 18 11.67 10.00 15.00 39.67 0.25 45.95 39.39 59.08 164.11 Site16C 60 75 50 5 2 16 0 23 6.67 3.33 32.00 42.00 0.22 30.67 15.33 147.19 193.19 Site19A 80 90 40 11 42 9 0 62 12.22 52.50 22.50 87.22 0.13 95.41 409.84 175.64 680.89 Site19B 30 45 40 22 44 10 0 76 48.89 146.67 25.00 220.56 0.16 307.86 923.59 157.43 1388.89 Site19C 90 25 65 5 1 9 0 15 20.00 1.11 13.85 34.96 0.12 160.13 8.90 110.86 279.88 Site20A 60 55 60 10 34 2 0 46 18.18 56.67 3.33 78.18 0.25 71.55 223.01 13.12 307.68 Site20B 20 80 35 8 90 0 0 98 10.00 450.00 0.00 460.00 0.32 31.74 1428.12 0.00 1459.85 Site20C 90 100 45 44 97 31 0 172 44.00 107.78 68.89 220.67 0.19 235.17 576.04 368.19 1179.40 Site22A 60 60 85 2 1 15 1 18 3.33 1.67 17.65 22.65 0.37 9.08 4.54 48.08 64.91 Site22B 100 70 85 4 0 17 0 21 5.71 0.00 20.00 25.71 0.33 17.42 0.00 60.98 78.40 Site22C 30 60 85 6 2 24 0 32 10.00 6.67 28.24 44.90 0.39 25.71 17.14 72.60 115.46 Site23A 40 35 45 11 3 9 0 23 31.43 7.50 20.00 58.93 0.25 123.39 29.45 78.52 231.36 Site23B 90 40 60 11 0 3 0 14 27.50 0.00 5.00 32.50 0.34 80.34 0.00 14.61 94.95 Site23C 50 60 60 18 0 4 0 22 30.00 0.00 6.67 36.67 0.17 175.75 0.00 39.05 214.80 Site24A 50 60 60 22 3 16 0 41 36.67 6.00 26.67 69.33 0.29 125.14 20.48 91.01 236.63 Site24B 30 35 35 18 42 18 0 78 51.43 140.00 51.43 242.86 0.30 171.09 465.74 171.09 807.91 Site24C 90 55 40 2 87 3 0 92 3.64 96.67 7.50 107.80 0.25 14.53 386.20 29.96 430.70 Site26A 90 80 75 14 27 20 1 61 17.50 30.00 26.67 74.17 0.35 49.69 85.18 75.71 214.37 Site26B 60 70 70 10 199 16 1 225 14.29 331.67 22.86 368.81 0.23 63.18 1466.90 101.09 1637.50 Site26C 70 85 50 15 215 23 0 253 17.65 307.14 46.00 370.79 0.25 70.90 1234.00 184.81 1489.71 G2BA 100 100 80 24 19 50 0 93 24.00 19.00 62.50 105.50 0.22 111.58 88.33 290.56 490.47 G2BB 40 90 75 24 35 33 0 92 26.67 87.50 44.00 158.17 0.16 166.25 545.51 274.31 986.08 G2BC 60 90 80 18 67 23 3 108 20.00 111.67 28.75 160.42 0.19 103.09 575.60 148.20 846.22 Blank 1 40 90 30 2 0 8 0 10 2.22 0.00 26.67 28.89 0.24 9.03 0.00 108.41 117.45 Blank 2 100 95 55 0 3 4 0 7 0.00 3.00 7.27 10.27 0.24 0.00 12.20 29.57 41.76 Blank 3 90 40 80 4 1 18 0 23 10.00 1.11 22.50 33.61 0.24 40.65 4.52 91.47 136.65 Blank 4 70 70 45 11 3 6 0 20 15.71 4.29 13.33 33.33 0.24 63.89 17.42 54.21 135.52 Blank 5 60 55 30 6 11 19 0 36 10.91 18.33 63.33 92.58 0.24 44.35 74.53 257.48 376.37 Blank 6 70 70 55 8 152 1 0 161 11.43 217.14 1.82 230.39 0.24 46.46 882.79 7.39 936.65

Table A.4: First table displaying raw environmental data for sediment samples. Environmental data included in this table includes sediment data, distance and depth data and land cover percentages.

Site % Fine % Very % Very % % % Fine % Very %Silt Depth Distance Distance to Secchi Urban Natural Agriculture Industrial Grassland Gravel Fine coarse Coarse Medium sand fine sand and (m) to shore WWTP (km) depth land use land use land use (%) land use land use (%) Gravel sand sand sand Clay (m) (m) (%) (%) (%) Site1A 1.53 1.79 1.28 1.53 5.61 59.18 11.73 17.35 3.80 190.00 15.62 3.10 28.51 12.77 55.17 1.85 0.01 Site1B 0.44 1.55 0.88 0.66 12.39 19.47 18.81 45.80 7.50 79.00 15.83 3.81 28.51 12.77 55.17 1.85 0.01 Site1C 1.28 1.03 0.26 0.77 11.54 24.62 25.38 35.13 9.50 73.00 15.92 4.10 28.51 12.77 55.17 1.85 0.01 Site2A 3.27 0.10 0.20 0.40 0.50 3.08 2.98 89.48 4.70 240.00 54.10 0.96 17.78 61.83 20.03 0.11 0.00 Site2B 8.83 3.45 3.85 9.26 46.76 8.86 6.23 12.74 4.40 227.00 54.10 1.07 17.78 61.83 20.03 0.11 0.00 Site2C 0.85 0.59 1.66 45.51 29.76 5.29 3.03 13.31 3.60 243.00 54.10 1.08 17.78 61.83 20.03 0.11 0.00 Site3A 1.59 0.97 2.49 5.48 36.09 28.84 10.71 13.84 3.20 376.00 13.75 1.46 98.37 0.46 0.00 0.66 0.00 Site3B 3.01 1.39 2.18 4.61 43.12 21.82 12.37 11.50 0.35 3.30 360.00 13.86 1.48 98.37 0.46 0.00 0.66 Site3C 4.35 1.42 1.42 3.60 9.68 28.70 13.77 37.05 0.35 3.10 272.00 13.94 1.45 98.37 0.46 0.00 0.66 Site4A 51.42 9.76 4.82 7.36 13.23 6.83 2.03 4.54 1.66 3.80 273.00 5.15 2.31 95.57 1.66 0.30 1.93 Site4B 39.05 23.07 7.70 6.73 14.24 5.27 1.68 2.26 1.66 1.80 136.00 5.30 1.51 95.57 1.66 0.30 1.93 Site4C 2.52 2.64 5.16 20.92 32.17 14.78 8.00 13.81 1.66 2.80 152.00 5.47 2.25 95.57 1.66 0.30 1.93 Site5A 1.71 0.45 2.83 7.05 37.16 35.73 7.36 7.72 0.67 1.40 50.00 2.92 1.10 55.81 7.61 33.81 1.53 Site5B 0.36 0.44 0.53 0.65 16.46 54.55 12.86 14.15 0.67 3.50 20.00 3.00 1.17 55.81 7.61 33.81 1.53 Site5C 0.24 0.17 0.17 0.31 54.47 29.62 3.69 11.32 0.67 2.00 42.00 3.10 1.13 55.81 7.61 33.81 1.53 Site6A 0.34 0.46 0.89 2.41 17.10 52.66 12.71 13.43 1.48 2.00 116.00 7.37 1.54 45.03 2.59 49.80 1.55 Site6B 0.79 0.68 0.29 1.26 44.29 27.60 11.59 13.50 1.48 2.10 145.00 7.51 1.42 45.03 2.59 49.80 1.55 Site6C 2.22 1.33 1.22 3.44 19.24 37.58 21.23 13.75 1.48 2.90 58.00 7.62 1.32 45.03 2.59 49.80 1.55 Site7A 3.63 2.15 0.81 1.62 2.02 7.27 13.19 69.31 0.47 3.10 127.00 9.04 0.72 7.39 14.35 78.26 0.00 Site7B 3.04 1.61 0.76 0.76 1.33 7.03 7.98 77.49 0.47 3.60 144.00 9.24 0.70 7.39 14.35 78.26 0.00 Site7C 1.21 0.64 0.92 0.75 31.94 45.28 5.57 13.70 0.47 3.20 56.00 9.38 0.61 7.39 14.35 78.26 0.00 Site9A 1.24 0.13 0.16 0.19 49.49 33.37 8.29 7.14 0.89 2.20 237.00 6.55 0.72 8.76 58.49 30.35 1.62 Site9B 0.89 0.43 0.12 0.27 7.51 44.75 26.03 20.00 0.89 2.10 166.00 6.60 0.75 8.76 58.49 30.35 1.62 Site9C 1.62 0.14 0.11 0.29 44.84 33.97 11.30 7.73 0.89 2.10 135.00 6.70 0.73 8.76 58.49 30.35 1.62 Site10A 0.40 0.23 1.99 1.59 22.29 33.94 25.36 14.21 0.80 2.00 96.00 10.08 0.71 26.64 36.05 34.76 1.98 Site10B 0.53 1.48 1.43 3.87 7.45 45.58 24.73 14.94 0.80 2.10 89.00 10.16 0.71 26.64 36.05 34.76 1.98 Site10C 0.12 0.12 0.18 0.37 18.60 17.38 34.21 29.01 0.80 1.90 82.00 10.29 0.76 26.64 36.05 34.76 1.98 Site11A 1.02 0.07 0.20 0.26 56.47 30.42 5.73 5.83 1.62 1.80 140.00 2.37 1.20 36.44 17.09 44.75 1.19 Site11B 0.76 0.31 0.27 0.53 45.06 30.68 11.45 10.95 1.62 2.10 122.00 2.44 1.16 36.44 17.09 44.75 1.19 Site11C 0.72 0.40 0.51 0.40 47.01 29.76 8.26 12.94 1.62 1.70 106.00 2.55 1.26 36.44 17.09 44.75 1.19 Site12A 0.33 0.25 1.05 33.83 56.31 3.76 1.41 0.03 0.96 2.30 1648.00 16.27 1.70 2.98 63.54 32.56 0.40 Site12B 0.27 0.14 0.48 0.92 45.17 40.11 8.32 0.05 0.96 2.90 1672.00 16.37 1.70 2.98 63.54 32.56 0.40 Site12C 0.89 0.41 0.26 0.26 71.73 16.19 6.19 0.04 0.96 2.60 1607.00 16.43 1.59 2.98 63.54 32.56 0.40 75

Site13A 0.42 0.50 0.42 1.20 15.51 39.31 17.85 24.79 0.23 2.40 155.00 27.22 0.85 7.88 27.32 62.23 0.76 Site13B 0.32 0.64 0.96 2.76 44.39 20.47 8.25 22.20 0.23 2.30 158.00 27.32 0.80 7.88 27.32 62.23 0.76 Site13C 0.16 0.13 0.13 0.42 36.60 29.59 14.91 18.07 0.23 2.30 158.00 27.39 0.70 7.88 27.32 62.23 0.76 Site14A 3.00 0.39 0.45 1.00 70.58 15.34 5.00 4.25 0.13 3.80 58.00 7.59 0.55 33.76 19.87 44.59 1.24 Site14B 0.58 0.33 0.21 0.73 56.57 27.82 8.02 5.74 0.13 3.50 76.00 7.64 0.56 33.76 19.87 44.59 1.24 Site14C 4.16 0.61 0.61 0.97 30.29 38.94 12.81 11.59 0.13 4.20 73.00 7.78 0.48 33.76 19.87 44.59 1.24 Site16A 0.16 0.25 0.48 0.60 0.89 47.79 33.46 1.01 0.41 4.20 64.00 3.73 1.18 33.62 39.67 24.83 1.32 Site16B 0.98 0.24 0.35 0.43 0.95 48.40 26.23 2.31 0.41 2.50 69.00 3.84 1.18 33.62 39.67 24.83 1.32 Site16C 0.87 0.37 0.32 0.60 18.17 23.09 35.83 1.53 0.41 2.30 69.00 3.95 0.95 33.62 39.67 24.83 1.32 Site19A 4.14 1.33 1.17 1.48 2.19 9.37 27.09 14.80 0.66 1.40 512.00 12.13 1.40 11.78 27.59 58.15 1.41 Site19B 29.16 0.57 0.69 2.20 7.62 13.41 22.04 9.08 0.66 1.40 509.00 12.23 1.10 11.78 27.59 58.15 1.41 Site19C 18.33 0.08 0.80 0.96 3.92 18.49 19.62 46.77 0.66 1.70 462.00 12.38 1.00 11.78 27.59 58.15 1.41 Site20A 6.53 2.75 5.79 25.11 28.22 11.81 5.00 3.79 0.66 2.00 163.00 9.49 1.60 2.18 32.58 64.89 0.21 Site20B 9.14 3.11 4.03 18.47 44.05 7.93 4.19 3.08 2.00 164.00 9.57 1.81 2.18 32.58 64.89 0.21 0.00 Site20C 0.53 0.43 1.82 28.92 15.45 4.38 1.71 1.16 1.90 171.00 9.63 1.68 2.18 32.58 64.89 0.21 0.00 Site22A 2.04 1.63 5.89 65.61 12.18 6.70 2.15 18.85 2.20 224.00 33.77 1.31 6.59 44.48 47.33 0.86 0.00 Site22B 0.46 1.80 6.49 51.77 28.17 7.35 0.88 7.57 2.10 211.00 33.84 1.27 6.59 44.48 47.33 0.86 0.00 Site22C 0.28 4.11 6.94 76.34 4.91 5.63 0.62 29.82 2.00 243.00 34.06 1.16 6.59 44.48 47.33 0.86 0.00 Site23A 21.36 13.43 3.73 4.08 21.99 11.19 5.38 13.60 2.60 411.00 16.05 1.54 10.02 51.92 35.92 1.20 0.00 Site23B 40.78 23.05 5.87 7.89 7.01 5.05 2.78 13.22 2.60 409.00 16.15 1.55 10.02 51.92 35.92 1.20 0.00 Site23C 10.37 11.54 5.39 6.33 14.59 10.72 11.25 17.28 2.50 412.00 16.27 1.53 10.02 51.92 35.92 1.20 0.00 Site24A 26.72 11.06 12.42 17.13 19.32 9.04 2.18 0.02 2.00 585.00 8.77 1.01 19.03 35.02 44.02 0.69 0.00 Site24B 36.19 11.01 4.06 6.09 19.26 15.40 3.39 0.05 1.40 166.00 8.83 1.06 19.03 35.02 44.02 0.69 0.00 Site24C 87.85 2.52 0.88 0.80 1.40 4.08 1.20 0.01 1.30 115.00 8.90 1.10 19.03 35.02 44.02 0.69 0.00 Site26A 36.20 16.52 5.59 2.90 3.58 12.63 8.97 37.28 2.90 114.00 3.48 1.62 76.26 16.46 5.38 1.46 0.00 Site26B 18.80 22.69 6.46 7.34 10.13 12.87 8.49 38.90 2.80 99.00 3.42 1.61 76.26 16.46 5.38 1.46 0.00 Site26C 9.92 6.43 6.91 9.20 16.63 13.18 20.45 44.90 2.60 97.00 3.35 1.59 76.26 16.46 5.38 1.46 0.00

Table A.5: Second table including raw environmental data. Environmental data in this table includes carbon data, environmental

chemistry data, phi scores and AEM values.

Site % % Conductivity Conductivity Specific Specific Avg. Avg. Avg. Max Max Max Min Min Min Median AEM Organic Carbonates (us/cm) (us/cm) conductivity conductivity Velocity Velocity Velocity Velocity Velocity Velocity Velocity Velocity Velocity Phi Score values carbon (Bottom – (top - 1m) (us/cm) (us/cm) (top (m/s) (m/s) (m/s) (3ft) (2ft) (1ft) (3ft) (2ft) (1ft) 1m) (bottom -1m ) - 1m) (3ft) (2ft) (1ft) Site1A 10.60 4.03 289.20 289.50 302.10 302.60 0.30 0.30 0.30 0.50 0.40 0.40 0.20 0.20 0.20 2.81 1.45 Site1B 10.60 4.03 290.00 289.80 304.10 303.20 0.20 0.20 0.20 0.40 0.30 0.30 0.10 0.10 0.10 3.87 0.32 Site1C 10.60 4.03 289.60 289.65 303.10 302.90 0.10 0.10 0.10 0.30 0.30 0.30 0.00 0.00 0.00 3.69 0.19 Site2A 4.58 1.28 102.00 102.50 108.10 107.90 0.00 0.10 0.10 0.10 0.20 0.10 0.00 0.00 0.00 5.76 0.13 Site2B 4.58 1.28 102.05 102.65 107.95 107.85 0.10 0.10 0.10 0.20 0.20 0.10 0.00 0.00 0.00 1.76 0.09 Site2C 4.58 1.28 102.10 102.80 107.80 107.80 0.10 0.00 0.00 0.20 0.20 0.20 0.00 0.00 0.00 1.52 0.06 Site3A 2.75 0.35 207.10 216.50 216.70 223.70 0.10 0.10 0.00 0.20 0.20 0.20 0.00 0.00 0.00 2.54 0.05 Site3B 2.75 0.35 289.40 289.50 301.40 301.50 0.30 0.30 0.40 0.50 0.50 0.50 0.10 0.20 0.20 -2.01 0.05 Site3C 2.75 0.35 289.70 289.90 301.50 301.65 0.30 0.30 0.30 0.40 0.40 0.50 0.10 0.10 0.20 -1.25 0.05 Site4A 4.21 1.66 290.00 290.30 301.60 301.80 0.30 0.30 0.30 0.40 0.40 0.40 0.20 0.20 0.20 1.79 0.05 Site4B 4.21 1.66 192.30 192.80 204.40 205.20 0.10 0.20 0.20 0.40 0.40 0.40 0.00 0.00 0.10 2.49 0.05 Site4C 4.21 1.66 196.05 196.45 207.15 209.15 0.10 0.10 0.30 0.40 0.40 0.50 0.00 0.00 0.10 2.78 0.05 Site5A 3.58 0.67 199.80 200.10 209.90 213.10 0.20 0.20 0.20 0.40 0.30 0.40 0.10 0.10 0.00 1.95 0.05 Site5B 3.58 0.67 277.20 277.70 298.00 298.30 0.10 0.10 0.10 0.30 0.20 0.20 0.00 0.00 0.00 2.76 0.04 Site5C 3.58 0.67 279.05 279.50 298.55 298.90 0.10 0.10 0.10 0.30 0.20 0.20 0.00 0.00 0.00 2.53 0.04 Site6A 4.53 1.48 280.90 281.30 299.10 299.50 0.10 0.10 0.10 0.20 0.20 0.20 0.00 0.00 0.00 2.79 0.04 Site6B 4.53 1.48 145.30 144.80 149.20 148.70 0.30 0.70 0.70 0.50 0.80 0.80 0.30 0.50 0.60 5.11 0.03 Site6C 4.53 1.48 147.05 151.65 150.65 150.50 0.60 0.60 0.60 0.90 0.90 0.80 0.40 0.50 0.50 5.41 0.03 Site7A 2.32 0.47 148.80 158.50 152.10 152.30 0.50 0.50 0.40 0.60 0.50 0.50 0.40 0.40 0.40 2.64 0.03 Site7B 2.32 0.47 171.10 171.60 199.10 199.40 0.40 0.40 0.40 0.50 0.50 0.50 0.20 0.30 0.20 1.99 0.02 Site7C 2.32 0.47 171.00 171.85 189.10 189.70 0.10 0.10 0.10 0.20 0.30 0.40 0.00 0.00 0.20 2.95 0.02 Site9A 1.96 0.89 170.90 172.10 179.10 180.00 0.30 0.30 0.30 0.40 0.50 0.50 0.10 0.20 0.20 2.52 0.02 Site9B 1.96 0.89 182.40 182.70 191.50 191.30 0.10 0.10 0.10 0.30 0.30 0.30 0.00 0.00 0.00 2.84 0.02 Site9C 1.96 0.89 182.05 182.20 190.75 190.70 0.10 0.10 0.10 0.20 0.20 0.20 0.00 0.00 0.00 2.88 0.01 Site10A 3.44 0.80 181.70 181.70 190.00 190.10 0.10 0.10 0.10 0.20 0.30 0.30 0.00 0.00 0.00 3.67 0.01 Site10B 3.44 0.80 226.00 225.70 240.30 240.00 0.30 0.30 0.30 0.40 0.40 0.40 0.20 0.20 0.20 1.93 0.01 Site10C 3.44 0.80 224.80 224.95 239.00 239.35 0.30 0.30 0.30 0.40 0.50 0.50 0.00 0.00 0.10 2.53 0.01 Site11A 1.82 1.62 223.60 224.20 237.70 238.70 0.10 0.10 0.20 0.30 0.30 0.40 0.00 0.00 0.10 2.49 0.01 Site11B 1.82 1.62 220.80 220.80 228.50 228.30 0.10 0.10 0.10 0.20 0.20 0.20 0.10 0.00 0.10 1.62 0.01 Site11C 1.82 1.62 221.00 221.20 228.20 228.30 0.10 0.10 0.10 0.20 0.20 0.10 0.00 0.00 0.10 2.51 0.01 Site12A 3.72 0.96 220.90 221.00 228.35 228.30 0.10 0.10 0.10 0.20 0.20 0.20 0.00 0.00 0.10 1.83 0.01 Site12B 3.72 0.96 135.40 135.60 142.30 142.40 0.10 0.00 0.00 0.30 0.30 0.30 0.00 0.00 0.00 2.90 0.01 77

Site12C 3.72 0.96 135.85 136.10 142.55 142.70 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.00 0.00 2.50 0.01 Site13A 0.83 0.23 136.30 136.60 142.80 143.00 0.00 0.00 0.00 0.00 0.10 0.20 0.00 0.00 0.00 2.70 0.01 Site13B 0.83 0.23 215.60 221.40 229.10 236.30 0.50 0.40 0.40 0.60 0.50 0.60 0.20 0.30 0.20 1.82 0.01 Site13C 0.83 0.23 210.00 213.05 221.95 227.35 0.40 0.50 0.40 0.60 0.60 0.60 0.20 0.40 0.10 1.92 0.01 Site14A 1.30 0.13 215.60 221.40 229.10 236.30 0.50 0.40 0.40 0.60 0.50 0.60 0.20 0.30 0.20 1.82 0.01 Site14B 1.30 0.13 210.00 213.05 221.95 227.35 0.40 0.50 0.40 0.60 0.60 0.60 0.20 0.40 0.10 1.92 0.01 Site14C 1.30 0.13 204.40 204.70 214.80 218.40 0.40 0.40 0.50 0.60 0.60 0.60 0.20 0.20 0.40 2.65 0.01 Site16A 4.15 0.41 262.10 261.10 278.30 277.10 0.30 0.30 0.30 0.40 0.40 0.40 0.20 0.20 0.10 3.00 0.01 Site16B 4.15 0.41 262.15 261.45 278.45 277.15 0.10 0.20 0.20 0.20 0.30 0.30 0.00 0.00 0.00 2.99 0.01 Site16C 4.15 0.41 262.20 261.80 278.60 277.20 0.10 0.10 0.10 0.20 0.20 0.20 0.00 0.00 0.00 3.57 0.00 Site19A 6.42 0.66 255.30 255.30 299.30 299.30 0.00 0.00 0.00 0.20 0.10 0.10 0.00 0.00 0.00 4.23 0.00 Site19B 6.42 0.66 254.90 254.90 297.10 297.10 0.00 0.00 0.10 0.10 0.20 0.20 0.00 0.00 0.00 2.86 0.00 Site19C 6.42 0.66 255.10 255.10 298.20 298.20 0.10 0.10 0.10 0.20 0.20 0.20 0.00 0.00 0.00 3.67 0.00 Site20A 3.55 0.66 266.00 266.30 266.30 266.60 0.20 0.20 0.20 0.30 0.30 0.20 0.10 0.10 0.10 1.67 0.00 Site20B 3.55 0.66 266.50 266.70 266.90 267.05 0.20 0.20 0.20 0.30 0.30 0.30 0.10 0.10 0.10 1.67 0.00 Site20C 3.55 0.66 267.00 267.10 267.50 267.50 0.10 0.10 0.20 0.30 0.30 0.30 0.00 0.00 0.00 2.82 0.00 Site22A 0.68 0.24 115.30 116.00 125.70 126.40 0.20 0.20 0.20 0.30 0.30 0.30 0.10 0.10 0.10 0.81 0.00 Site22B 0.68 0.24 114.50 115.50 124.60 125.60 0.20 0.20 0.20 0.20 0.30 0.30 0.10 0.10 0.10 0.90 0.00 Site22C 0.68 0.24 113.70 115.00 123.50 124.80 0.20 0.20 0.20 0.30 0.20 0.30 0.10 0.10 0.10 0.75 0.00 Site23A 3.71 1.99 265.40 266.40 284.90 285.40 0.00 0.00 0.00 0.20 0.10 0.10 0.00 0.00 0.00 1.66 0.00 Site23B 3.71 1.99 265.70 266.55 285.25 285.70 0.00 0.00 0.00 0.10 0.10 0.10 0.00 0.00 0.00 -1.29 0.00 Site23C 3.71 1.99 266.00 266.70 285.60 286.00 0.00 0.10 0.10 0.20 0.20 0.10 0.00 0.00 0.00 2.56 0.00 Site24A 3.72 0.96 177.70 177.90 193.60 193.80 0.20 0.20 0.30 0.30 0.30 0.40 0.10 0.10 0.20 -0.01 0.00 Site24B 3.72 0.96 171.35 168.55 185.85 185.50 0.10 0.20 0.20 0.30 0.30 0.30 0.10 0.10 0.10 -0.15 0.00 Site24C 3.72 0.96 165.00 159.20 178.10 177.20 0.20 0.20 0.20 0.40 0.30 0.30 0.10 0.10 0.20 -2.21 0.00 Site26A 2.98 0.66 260.10 255.70 277.80 277.10 0.10 0.10 0.10 0.20 0.20 0.20 0.00 0.00 0.00 -1.08 0.00 Site26B 2.98 0.66 260.30 258.10 277.70 277.35 0.10 0.10 0.10 0.20 0.20 0.20 0.00 0.10 0.10 0.64 0.00 Site26C 2.98 0.66 260.50 260.50 277.60 277.60 0.10 0.10 0.10 0.20 0.20 0.20 0.10 0.10 0.10 2.51 0.00

Table A.6: Table of raw plastic data for all surface water samples including total counts and concentrations. * Represents samples that were removed from the analysis as outliers.

Microbead Fibre Fragment Revised Revised Modified Sampling Revised Revised Modified Modified Modified Micro- Fragments/ Plastics / Site Retention Rate Retention Retention rate micro-bead fragment plastic Fibres/L location fibre count count Fibres micro-beads fragments beads/ L L L (%) rate (%) (%) count count counts WWTP2 Upstream 80 80 70 1 0 0 1 1.25 0 0 1.25 0.01 0 0 0.013 WWTP2 Upstream 50 80 50 0 0 0 0 0 0 0 0 0 0 0 0 WWTP2 Upstream 70 80 80 4 1 0 5 5 1.43 0 6.43 0.05 0.014 0 0.06 WWTP2 Downstream 100 80 80 3 0 3 6 3.75 0 3.75 7.5 0.04 0 0.04 0.08 WWTP2 Downstream 70 90 80 8 0 1 9 8.89 0 1.25 10.14 0.09 0 0.01 0.10 WWTP2 Downstream 70 100 90 29 0 8 37 29 0 8.89 37.89 0.29 0 0.09 0.38 WWTP8 Upstream 90 100 80 6 1 2 9 6 1.11 2.5 9.61 0.06 0.01 0.03 0.10 WWTP8* Upstream 100 70 100 10 88 1 99 14.29 88 1 103.29 0.14 0.88 0.01 1.03 WWTP8 Upstream 90 100 70 13 0 4 17 13 0 5.71 18.71 0.13 0 0.06 0.19 WWTP8 Downstream 90 100 80 12 2 2 16 12 2.22 2.5 16.72 0.12 0.02 0.03 0.17 WWTP8 Downstream 80 90 100 18 2 5 25 20 2.5 5 27.5 0.20 0.03 0.05 0.28 WWTP8 Downstream 100 70 70 8 2 1 11 11.43 2 1.43 14.86 0.11 0.02 0.01 0.15 WWTP9 Upstream 100 80 100 4 0 1 5 5 0 1 6 0.05 0 0.01 0.06 WWTP9 Upstream 60 30 30 1 0 1 2 3.33 0 3.33 6.67 0.03 0 0.03 0.07 WWTP9 Upstream 100 70 80 4 1 5 10 5.71 1 6.25 12.96 0.06 0.01 0.06 0.13 WWTP9 Downstream 100 80 50 1 2 0 3 1.25 2 0 3.25 0.01 0.02 0 0.03 WWTP9 Downstream 80 90 60 4 3 4 11 4.44 3.75 6.67 14.86 0.04 0.04 0.07 0.15 WWTP9 Downstream 60 60 70 3 0 2 5 5 0 2.86 7.86 0.05 0 0.03 0.08 WWTP10 Upstream 90 90 80 4 0 4 8 4.44 0 5 9.44 0.04 0 0.05 0.09 WWTP10 Upstream 80 100 90 4 2 1 7 4 2.5 1.11 7.61 0.04 0.03 0.01 0.08 WWTP10 Upstream 60 70 80 8 2 0 10 11.43 3.33 0 14.76 0.11 0.03 0 0.15 WWTP10 Downstream 80 60 70 7 0 7 14 11.67 0 10 21.67 0.12 0 0.1 0.22 WWTP10 Downstream 80 100 80 3 0 0 3 3 0 0 3 0.03 0 0 0.03 WWTP10 Downstream 90 70 70 2 0 1 3 2.86 0 1.43 4.29 0.03 0 0.01 0.04 WWTP13 Upstream 70 80 70 5 3 5 13 6.25 4.29 7.14 17.68 0.06 0.043 0.07 0.18 WWTP13 Upstream 70 100 80 2 1 1 4 2 1.43 1.25 4.68 0.02 0.014 0.01 0.05 WWTP13 Upstream 80 90 80 2 0 5 7 2.22 0 6.25 8.47 0.02 0 0.06 0.08 WWTP13 Downstream 80 100 90 5 0 2 7 5 0 2.22 7.22 0.05 0 0.02 0.07 WWTP13 Downstream 80 100 60 2 1 0 3 2 1.25 0 3.25 0.02 0.013 0 0.03 WWTP13 Downstream 80 90 80 7 0 7 14 7.78 0 8.75 16.53 0.08 0 0.09 0.17 WWTP14 Upstream 100 80 70 3 1 5 9 3.75 1 7.14 11.89 0.04 0.01 0.07 0.12 WWTP14 Upstream 100 100 60 7 3 3 13 7 3 5 15 0.07 0.03 0.05 0.15 WWTP14 Upstream 50 70 90 13 0 1 14 18.57 0 1.11 19.68 0.19 0 0.01 0.20 WWTP14* Downstream 60 100 90 3 137 5 145 3 228.33 5.56 236.89 0.03 2.28 0.06 2.37 WWTP14 Downstream 90 100 80 5 1 1 7 5 1.11 1.25 7.36 0.05 0.01 0.01 0.07 WWTP14 Downstream 100 100 90 6 2 0 8 6 2 0 8 0.06 0.02 0 0.08 WWTP15 Upstream 80 90 100 9 0 3 12 10 0 3 13 0.1 0 0.03 0.13 WWTP15 Upstream 70 90 100 14 0 5 19 15.56 0 5 20.56 0.16 0 0.05 0.21 WWTP15 Upstream 100 70 70 8 2 0 10 11.43 2 0 13.43 0.11 0.02 0 0.13 WWTP15 Downstream 80 90 70 21 4 3 28 23.33 5 4.29 32.62 0.23 0.05 0.04 0.33 WWTP15 Downstream 60 100 100 7 1 7 15 7 1.67 7 15.67 0.07 0.017 0.07 0.16 WWTP15 Downstream 80 100 70 25 1 8 34 25 1.25 11.42 37.68 0.25 0.013 0.11 0.38 WWTP17 Upstream 100 100 70 6 1 2 9 6 1 2.86 9.86 0.06 0.01 0.03 0.10 WWTP17 Upstream 90 100 100 6 3 3 12 6 3.33 3 12.33 0.06 0.03 0.03 0.12 WWTP17 Upstream 70 80 100 10 0 4 14 12.5 0 4 16.5 0.13 0 0.04 0.17 WWTP17 Downstream 100 100 90 13 2 3 18 13 2 3.33 18.33 0.13 0.02 0.03 0.18 WWTP17 Downstream 50 100 100 11 1 6 18 11 2 6 19 0.11 0.02 0.06 0.19 WWTP17 Downstream 60 80 100 12 2 3 17 15 3.33 3 21.33 0.15 0.03 0.03 0.21 WWTP21 Upstream 100 100 40 10 1 0 11 10 1 0 11 0.1 0.01 0 0.11 WWTP21 Upstream 90 100 70 3 2 0 5 3 2.22 0 5.22 0.03 0.02 0 0.05 WWTP21 Upstream 100 100 40 8 0 2 10 8 0 5 13 0.08 0 0.05 0.13 WWTP21 Downstream 90 100 80 8 1 7 16 8 1.11 8.75 17.86 0.08 0.01 0.09 0.18 WWTP21 Downstream 70 90 40 4 0 6 10 4.44 0 15 19.44 0.04 0 0.15 0.19 79

WWTP21 Downstream 100 100 100 2 5 3 10 2 5 3 10 0.02 0.05 0.03 0.1 WWTP30 Upstream 40 100 90 11 1 10 22 11 2.5 11.11 24.61 0.11 0.025 0.11 0.25 WWTP30 Upstream 80 70 90 3 2 2 7 4.29 2.5 2.22 9.01 0.04 0.025 0.02 0.09 WWTP30 Upstream 70 80 100 16 1 2 19 20 1.43 2 23.43 0.2 0.014 0.02 0.23 WWTP30 Downstream 60 100 100 13 4 2 19 13 6.67 2 21.67 0.13 0.07 0.02 0.22 WWTP30 Downstream 80 80 100 10 2 2 14 12.5 2.5 2 17 0.13 0.03 0.02 0.17 WWTP30 Downstream 70 100 80 9 1 4 14 9 1.43 5 15.43 0.09 0.014 0.05 0.15 Blank 1 N/A 90 100 80 1 0 0 1 1 0 0 1 0.01 0 0 0.01 Blank 2 N/A 100 100 70 6 0 0 6 6 0 0 6 0.06 0 0 0.06 Blank 3 N/A 50 90 90 3 1 3 7 3.33 2 3.33 8.67 0.03 0.02 0.03 0.09 Blank 4 N/A 80 20 40 2 3 2 7 10 3.75 5 18.75 0.1 0.04 0.05 0.19 Blank 5 N/A 80 80 80 2 0 8 10 2.5 0 10 12.5 0.03 0 0.1 0.13 Blank 6 N/A 100 100 100 2 0 0 2 2 0 0 2 0.02 0 0 0.02 Blank 7 N/A 90 80 90 2 1 0 3 2.5 1.11 0 3.61 0.03 0.01 0 0.041

Figure A.1: A sediment sample being removed from the River with a petite ponar grab (left). The sediment being deposited from the petite ponar into a transfer bucket (top middle) and the excess water being removed after the sediment has settled (bottom middle). Example of the filtering mechanism post filtering water (right), the material on the mesh is retained and examined back in the laboratory for analysis. Prior to sampling, filters were carefully removed from the Whirl-Pak bag and placed on top of a plastic Nalgene bottle that had been cut in half. The lid that Nalgene bottle had its centered cored, so it could act as a filtering mechanism when a mesh filter was placed over the lid and screwed into place. 81

Figure A.2: Global identification tree that is applied to all plastic particles. The characteristics outlined at the end of the tree are provided from the scientific literature (Norén 2007; Nor & Obbard 2014; Eerkes-Medrano et al. 2015; Cheung et al. 2016; Catarino et al. 2018) and can be applied to all particle types to identify if they are plastic. 82

Figure A.3: Microbead identification tree. The characteristics outlined are provided from the scientific literature (Norén 2007; Nor & Obbard 2014; Eerkes-Medrano et al. 2015; Cheung et al. 2016; Catarino et al. 2018) and can be applied to all particles that resemble microbeads to identify if they are plastic. 83

Figure A.4: Fragment identification tree. The characteristics outlined are provided from the scientific literature (Norén 2007; Nor & Obbard 2014; Eerkes-Medrano et al. 2015; Cheung et al. 2016; Catarino et al. 2018) and can be applied to all fragment particles to identify if they are plastic. 84

Figure A.5: Fibre identification tree. The characteristics outlined are provided from the scientific literature (Norén 2007; Nor & Obbard 2014; Eerkes-Medrano et al. 2015; Cheung et al. 2016; Catarino et al. 2018) and can be applied to all fibrous particles to identify if they are plastic. 85

Figure A.6: Laboratory workflow for the extraction of microplastics from sediment and water samples. 86

Figure A.7: Data analysis workflow for the enumeration of microplastics from sediment and water samples.