Comparing Biological Responses to Contaminants

in Darters (Etheostoma spp.) Collected from Rural

and Urban Regions of the Grand River

Watershed, Ontario

A thesis submitted to the Committee on Graduate Studies

in Partial Fulfillment of the Requirements for the Degree of Master of Science in the

Faculty of Arts and Science

TRENT UNIVERSITY

Peterborough, Ontario, Canada.

© Copyright by Sam Diamond 2015

Environmental and Life Sciences M.Sc. Graduate Program

September 2015

ii

Abstract

Comparing Biological Responses to Contaminants in Darters (Etheostoma spp.)

Collected from Rural and Urban Regions of the Grand River Watershed, Ontario.

Sam Diamond

Urban and agricultural activities may introduce chemical stressors, including

contaminants of emerging concern (CECs) and current use pesticides (CUPs) into

riverine systems. The objective of this study was to determine if fish collected from sites

in a river show biomarkers of exposure to these classes of contaminants, and if the

biomarker patterns vary in fish collected from urbanized and agricultural sites. The

watershed selected for this study was the Grand River in southern Ontario, which

transitions from areas dominated by agricultural land use in the north to highly urbanized

locations in the southern part of the watershed. Rainbow darters (Etheostoma caerluem)

and fantail darters (Etheostoma flabellare) were collected from the Grand River in June,

2014 for biomarker analysis from two urbanized sites and three agricultural sites (n=20

per site). Over the same period of time, Polar Organic Chemical Integrative Samplers

(POCIS) were deployed for 2 weeks at each site to monitor for the presence of CUPs and

CECs. The amounts of the target compounds accumulated on POCIS, determined using

LC-MS/MS were used to estimate the time weighted average concentrations of the

contaminants at each site. Data on the liver somatic index for darters indicate site-

specific differences in this condition factor (p<0.05). Significant differences in the

concentrations of thiobarbituric acid reactive substances (TBARS) in gill tissue (p<0.05)

indicate differences in oxidative stress in fish collected from the various sites. Measured

concentrations of ethoxyresorufin-O-deethylase (EROD) in liver tissue were significantly

different between sites (p<0.05), indicating differences in CYP1A metabolic activity.

Finally, acetylcholinesterase (AChE) activity in brain tissue was significantly different

between fish from rural and urban sites (p<0.05). The analysis of these biomarkers

indicates that fish may be experiencing different levels of biological stress related to

different land uses. These data may be useful in developing mitigation strategies to

reduce impacts on fish and other aquatic organisms in the watershed.

Keywords: Biomarkers, darters, land use, POCIS, AChE, TBARS, EROD.

iii

Acknowledgements

A project of this magnitude could not have been completed by one person within

an appropriate amount of time. Many people helped along the way to achieve the goals

set out for this thesis. All efforts large and small were greatly appreciated, whether they

were simple words of encouragement, or help achieving project milestones, I appreciate it

all and thank everybody involved in the completion of this project.

First off, I would like to thank those involved in all field components of this

project. Craig Murray (Institute for Freshwater Science, Trent University) helped with

the deployment and retrieval of all passive samplers. The days were long and exhausting,

but without Craig’s help this would have been a daunting task. Thank you to the Servos

lab group (University of Waterloo) for their assistance in collecting the fish for this

project. If it wasn’t for their knowledge of the watershed and their portable field lab, fish

collection would have been much more difficult.

Next I would like to thank those involved in the laboratory components of this

project. Thank you to Brenda McIlwain for her patience and assistance in teaching me

the POCIS sampler extraction methods. I would also like to thank Tamanna Sultanna for

her assistance in developing the LC-MS/MS method for fungicide analysis. Their

assistance with POCIS extraction and analysis was greatly appreciated. Thank you to

Jonathan Martin for his tutorials on performing many of the bioassay’s used in this

project.

Finally, I would like to thank Chris Metcalfe (Trent University), Mark Servos

(University of Waterloo), and Gary Burness (Trent University) for their help and

guidance as my supervisory committee. Without their vast knowledge and experience I

would not have been able to complete such an intense, multi-disciplinary project within

the appropriate time. This would not have been possible without the NSERC Discovery

Grant awarded to Chris Metcalfe.

Thank you all. This project was truly a team effort from beginning to end.

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Table of Contents

Abstract

ii

Acknowledgements

iii

Table of Contents

iv

List of Figures

vi

List of Tables

vii

List of Abbreviations

viii

Chapter 1: Introduction

1

1.0 General Introduction

1

1.1 Biological Response

3

1.2 Municipal Wastewater

8

1.3 Runoff of Contaminants

9

1.4 Passive Sampling

12

1.5 Influences of Land Cover and Use

13

1.6 Project Overview

14

Chapter 2: Biological Responses in Darters (Etheostoma spp.) Exposed to Rural and Urban Influences in the Grand River Watershed, Ontario, Canada 19

1.0 Introduction

19

2.0 Methods and Materials

21

2.1 Materials and chemicals

21

2.2 Study area

21

2.3 POCIS deployment and extraction

22

2.4 POCIS extract analysis

24

2.5 Darter collection

28

2.6 Positive control treatments

28

2.7 Acetylcholinesterase assay

29

2.8 Ethoxyresorufin-O-deethylase (EROD) assay

30

2.9 2-thiobarbituric acid reactive substances (TBARS) assay 31

2.10 Protein assay

32

2.11 Statistical analyses

33

2.12 OFAT III

33

3.0 Results and Discussion

34

3.1 Site characterization

34

3.2 Contaminants

35

3.3 Fish somatic index data

44

3.4 EROD assay validation

46

3.5 EROD

47

3.6 TBARS

50

v

3.7 AChE

54

4.0 Conclusion

57

Chapter 3: Conclusions and Future Steps

58

1.0 Major findings

58

2.0 Project objectives and hypotheses

59

3.0 Future work

63

References

64

Appendix 1: POCIS Sampling Rates for PPCPs

78

Appendix 2: POCIS Sampling Rates for CUPs

79

Appendix 3: Fungicide LC-MS/MS Parameters

80

vi

List of Figures

Figure 1: Study catchment within the Grand River watershed, showing the locations of

the 5 study sites……………………………………………………………………….. 23

Figure 2: Mean EROD activity ± S.E. (pmol/min/mg protein) in livers of rainbow trout

exposed to BNF, and control groups. Significance noted by different letter code

(p<0.05)………………………………………………………………………...….….. 46

Figure 3: Mean EROD activity ± S.E. (pmol/min/mg protein) in livers of rainbow darters

at each of the five sites. Significance noted by different letter code

(p<0.05)………………………………………………………………………………. 48

Figure 4: Comparison of mean EROD activity ± S.E. (pmol/min/mg protein) in livers of

rainbow (RBD) and fantail (FTD) darters at the rural sites.....…………………..…... 49

Figure 5: Mean levels of TBARS ± S.E. (nmol per g) in gill tissue collected from rainbow

darters sampled from each of the five sites. Significance is noted by different letters

(p<0.05)………………………………………………………………………………. 53

Figure 6: Comparison of mean levels of TBARS ± S.E. (nmol per g) in gill tissue

collected from rainbow (RBD) and fantail (FTD) darters sampled from each of the three

rural sites. ……………………………………………………………………..….…. 53

Figure 7: Mean AChE activity ± S.E. (µmol/min/mg protein) in brain tissue of rainbow

darters at all five sites. Significance noted by different letter (p<0.05)…………..….56

Figure 8: Comparison of mean AChE activity ± S.E. (µmol/min/mg protein) in brain

tissue of rainbow darters and fantail darters collected at the rural sites.

…………………………………………………………………...…………..…….... 56

vii

List of Tables

Table 1: List of all targeted analytes for POCIS analysis…………………………… 26

Table 2: Targeted fungicides, herbicides, biocides, and I.S. with the used MRM

transitions....................................................................................................................... 27

Table 3: OFAT III generated data on catchment area for each of the 5 sampling sites,

with the corresponding area for each land cover type (km2). Sites are arranged from

furthest upstream to the furthest

downstream………………………………………………………………………….... 35

Table 4a: Mean estimated TWA concentrations (ng/L) ±S.D. of CUPs at each of the five

study sites…………………………………………………………………………...… 42

Table 4b: Mean estimated TWA concentrations (ng/L) ±S.D. of CECs at each of the five

study sites………………………………………………………………………….……43

Table 5: Somatic index data for rainbow and fantail darters sampled at 5 locations in the

Grand River, including length, weight, liver somatic index (LSI) and condition factor (k).

Significant differences between sites are shown by a different letter code, and significant

differences between species are shown by a dagger symbol………………………….. 46

Table A1: Mean (±SD) sampling rates (Rs) in litres per day determined for the target

compounds in POCIS in static experiments at 15oC (n=3). Sampling rates were

determined by Li et al. (2010b)……………….………………………………………. 78

Table A2: Mean (±SD) sampling rates (Rs) in litres per day determined for the target

compounds in POCIS in static experiments at 20oC (n=3). Sampling rates were

determined by Metcalfe et al.

(submitted)……………………………………………..……………………………….79

Table A3.1: Ionization parameters for the pesticides targeted in this study………..….80

Table A3.2: Ionization parameters for all pesticide surrogates used in this study…… 81

viii

Table A3.3: Pesticide analytes with their corresponding I.S. used in the present

study…………………………………………………………………………………....82

ix

List of Abbreviations

7-ER: 7-ethoxyresorufin

Ach: Acetylcholine

AChE: Acetylcholinesterase

AhR: Arylhydrocarbon receptor

BMP: Best management practice

BNF: β-napthaflavone

BSA: Bovine serum albumin

CECs: Contaminants of emerging concern

CUPs: Current use pesticides

CYP450: Cytochrome P450

DMSO: Dimethyl sulfoxide

EROD: Ethoxyresorufin-O-deethylase

FTD: Fantail darter

GIS: Geographical Information Systems

HPLC: High pressure liquid chromatography

I.P. Injection: Intraperitonial injection

I.S.: Internal standard

k: Condition factor

Kow: Octanol-water partition coefficient

LC-MS/MS: Liquid chromatography tandem mass spectrometry

LMB: Liquid municipal biosolids

LOD: Limit of detection

LSI: Liver somatic index

MDA: Malondialdehyde

MRM: Multiple reaction monitoring

MWWE: Municipal wastewater effluent

NSAIDs: Non-steroidal anti-inflammatory drugs

x

OFAT III: Ontario flow assessment tool III

OMNRF: Ontario Ministry of Natural Resources and Forestry

OPs: Organophosphate pesticides

pKa: Acid dissociation constant

POCIS: Polar Organic Chemical Integrative Sampler

PPCPs: Pharmaceuticals and personal care products

RBD: Rainbow darter

Rs: Sampling rate

S.D.: Standard deviation

S.E.: Standard error

TBA: Thiobarbituric acid

TBARS: 2-thiobarbituric acid reactive substances

TWA: Time weighted average

WWTP: Wastewater treatment plant

λ: Wavelength

1

Chapter 1: Introduction

1.0 General introduction

A variety of contaminants are discharged into aquatic ecosystems from both point and

non-point sources (Tetreault et al. 2011; Metcalfe et al. 2008; Li et al. 2009). Many

aquatic organisms, including fish, have been shown to exhibit biological responses when

exposed to organic contaminants discharged in municipal waste water effluent (MWWE)

and in run off from urban and rural areas (Shelley et al. 2012; Lissemore et al. 2006;

Balch et al. 2004). Pharmaceuticals and personal care products (PPCPs) discharged in

MWWE as well as pesticides used to control fungal and plant pests in agriculture and turf

care have been shown to elicit a variety of sub lethal biological responses in fish,

including increased prevalence of gonadal intersex, increased gonadosomatic and

hepatosomatic indices, developmental deformities in early and late life stages, induction

of oxidative metabolism enzymes, inhibition of acetylcholinesterase and, an increase in

reactive oxygen species (Tanna et al. 2013; Tetreault et al. 2011; Smith and Wilson 2010;

Cattaneo et al. 2008).

In order to monitor for the presence of PPCPs and pesticides, it is necessary to

measure the concentrations of these compounds at a variety of sites that are influenced by

point and non-point sources of these contaminants. Although collecting grab samples is a

cost effective method for monitoring contaminants in the aquatic environment, it is often

less reliable when compared to other monitoring methods due to the potential of missing

peak events or only sampling during periods of high input (Bundschuh et al. 2014;

Rujiralai et al. 2011). To obtain data that reflects concentrations over a longer period of

time, the Passive Organic Chemical Integrative Sampler (POCIS) can be deployed for an

2

extended period of time (2 – 4 weeks) and will provide a time weighted average (TWA)

concentration for that site during the time of deployment (Li et al. 2010; Metcalfe et al.

2014). Extracts prepared from the use of POCIS also offer a less complex matrix

compared to extracts from other samples and therefore may result in higher quality

analytical data.

While sampling in surface waters to determine the presence of PPCPs and pesticides

using analytical methods provides data on the concentrations of specific compounds of

interest, these analytical data do not indicate the biological impacts of the contaminants to

aquatic organisms. In addition, analytical monitoring focuses on the target compounds

included in the analytical method and cannot indicate exposure to other unknown

chemicals present in the environment. In situ biomarker studies can indicate whether

there are impacts on biota within the system. While there are many different organisms

within an aquatic system that can be used as viable indicator species, fish are often used

in biomarker studies due to their complex biological responses to contamination (Bolger

and Connolly 1989). When there is a decline in water quality due to the presence

contaminants, fish may show responses through somatic changes, such as liver somatic

index (LSI), gonadosomatic index (GSI), as well as length – weight relationships when

compared to the seasonal average (Bolger and Connolly 1989; Tetreault et al. 2012).

Fish may also react to exposure to contaminants on a biochemical level through increases

or decreases in several biological parameters, including inhibition of neurotransmitter

function, an increase in metabolic enzyme activity and an increase in oxidative stress

(Scornaienchi et al. 2010; Sumith et al. 2012; Cattaneo et al. 2008).

3

As land cover and land use changes across a region, so do the classes and

concentrations of contaminants present in the aquatic environment (Metcalfe et al. 2000;

Countway et al. 2003). For example, higher concentrations of pesticides would be

expected in an agricultural area, although inputs of these compounds may be mitigated by

the use of best management practices such as large riparian buffer zones adjacent to

surface waters. Likewise, higher concentrations of PPCPs would be expected in surface

waters in urban areas that are downstream of municipal wastewater treatment plants,

although investment in advanced wastewater treatment technologies could mitigate these

exposures. Therefore, fish and other aquatic organisms are subject to different levels of

exposure throughout a watershed, so sampling within one land cover type is not

representative of contaminant impacts over the entire watershed. By monitoring

responses in fish across different land covers within a watershed, it is possible to identify

how biological responses are influenced by the contaminants present and which portions

of the watershed exhibit the greatest impacts on fish. This becomes a powerful tool when

trying to identify and prioritize best management practices or technological solutions

across a watershed to reduce the impacts of contaminants to aquatic organisms.

1.1.Biological responses

Exposure of an organism to a biological, chemical or physical stressor induces

some form of biological response (Ings et al. 2011a/b; Uno et al. 2011; da Fonseca et al.

2008). Biological responses observed in situ may be early indicators of a population

based response (Bravo et al. 2011). The two main descriptors for biological responses

are “acute” and “chronic”. Acute responses refer to short term, high intensity events that

usually occur within 96 hours of initial exposure and usually cause organism mortality.

4

While these endpoints are useful for determining the concentrations at which toxic effects

occur, they are typically less commonly observed in the natural environment. Chronic

responses are sublethal endpoints that usually develop at environmentally relevant

concentrations and last over long periods of time, ranging from days to years. Many

organisms, including fish, express a variety of sublethal responses, including: a change in

the length – weight ratio (ie. condition factor) compared to the seasonal mean, an increase

in LSI and GSI, physical abnormalities, gonadal intersex and, a variety of biochemical

fluctuations which regulate metabolism (Jasinka et al. 2015; Rajakumar et al. 2012;

Tetreault et al. 2011; Metcalfe et al. 2001).

Somatic indicators of exposure are rapid and relatively simple methods for

assessing the health of fish (Farkas et al. 2002; Bolger and Connolly 1989). These

methods may be either invasive or non-invasive. Commonly used, non-invasive physical

indicators include the condition factor (i.e. length to weight ratios), the presence of

tumors or other lesions around the mouth or body of the fish, secondary sex

characteristics as indicators of intersex (e.g. male fish with an ovipositor), physical

abnormalities such as spinal curvatures, etc. Invasive indicators include the LSI and GSI

compared to seasonal means, numbers of internal parasites or lesions, and the presence of

gonadal intersex. Many contaminants from municipal waste water (MWW) or from

agricultural origins have been shown to inhibit the growth of fish, as well as increase the

LSI and GSI of the organism (Anderson et al. 2015; Farkas et al. 2002). While somatic

indicators may be an effective measure of exposure to a chemical stressor, they may not

always be observed at environmentally relevant concentrations (Arellano – Aguilar et al.

2009). In addition, somatic changes may be induced by other stressors other than

5

exposure to chemicals, such as changes in water temperature, pathogenic diseases,

invasive species or availability of food resources.

Biochemical responses, usually referred to as “biomarkers”, have been widely

used to evaluate whether fish are responding to exposures to waterborne pollutants.

Many biomarkers of exposure are compound specific and therefore will usually only be

regulated by specific classes of compounds. However many different compounds may

induce multiple biomarkers simultaneously (Smith and Wilson 2010; Sturve et al. 2008).

For example, several different pesticides, including diuron and atrazine have been shown

to induce oxidative stress, as well as inhibit acetylcholinesterase in fresh water fish

(Ahmed et al. 2012; Rossi et al. 2011). Biomarkers responses will vary, depending on

the compounds to which the organisms are exposed. For example, the activity of the

metabolic enzyme, ethoxyresorufin-O-deethylase is upregulated in fish by exposure to β

– napthoflavone, but is down regulated by hexabromo-cyclododecane (Martin et al. 2013;

Du et al. 2015).

In this study, the biomarkers used to assess exposure to contaminants in fish

included an indicator of oxidative stress, inhibition of an enzyme associated with

neurotransmission with cholinergic nerves, and changes in enzyme activity associated

with two classes of cytochrome P450 microsomal enzymes. The mechanisms by which

these biomarkers ca be used to indicate exposure to contaminants of specific classes are

discussed below.

1.1.1 Acetylcholinesterase

Acetylecholinesterase (AChE) is critical to the transmission of the acetylcholine

(ACh) as it responsible for terminating transmission at the synaptic cleft. As AChE

6

inhibition increases in severity, the organism’s nervous system becomes overstimulated,

resulting in mortality, generally as a result of respiratory failure (Bretaud et al. 2000).

For fish exposed to chemicals that inhibit AChE, respiration is impaired and swimming

becomes more erratic, and this results in mortality (Bretaud et al. 2000).

Organophosphate (OPs), neonicotinoid and, carbamate insecticides are commonly used

throughout agricultural and urban areas and have been shown to inhibit AChE activity in

freshwater fish (Anderson et al. 2015; Xing et al. 2013; Xuereb et al. 2009). Due to the

extensive use of pesticides throughout diverse landscape, AChE can be used as a

biomarker of exposure to pesticides across different land uses.

1.1.2. 2-thiobarbituric acid reactive substances (TBARS)

Oxidative stress is the result of an overwhelming assault of reactive oxygen

species (ROS) on fatty, lipid rich substrates within an organism (Oakes et al. 2004; Kelly

et al. 1998). When ROS overwhelms the endogenous protection mechanisms within the

cells of an organism such as antioxidants and specific degradative enzymes, the result is

cellular damage. There are many ways to quantify oxidative stress within an organism

(Rossi et al. 2011; Oakes and Van der Kraak 2003). One of the most common methods is

through the production of 2-thiobarbituric acid reactive substances (TBARS). TBARS

are measured by the reaction of malondialdehyde (MDA), a degradation product of lipid

peroxidation, with 2-thiobarbituric acid (TBA). An elevation in TBARS within an

organism may be induced by exposure to a variety of organic contaminants, including

PPCPs, pesticides and, domestic and industrial wastewater (Nunes et al 2015; Scarcia et

al. 2012). Since the induction of TBARS has been observed in fish exposed to a variety

7

of different contaminants, it is a viable indicator of exposure to a range of waterborne

contaminants.

1.1.3. Cytochrome P450

The cytochrome P450 (CYP450) family of monooxygenase enzymes is

responsible for the oxidative metabolism of xenobiotics in organisms, including fish

(Maier et al. 2014; Smith and Wilson 2010; Codi et al. 2004). There are 50 different

isoforms in the CYP450 family of enzymes are more easily induced by certain chemical

classes than others. For instance, changes in the activity of CYP450 enzymes have been

used as biomarkers of exposure to PPCPs, fungicides and herbicides (Scornaienchi et al.

2010; Hernandez-Moreno et al. 2008). For example, CYP4501A (CYP1A) is usually

induced by exposure to agonists of the aryl hydrocarbon receptor (AhR), such as

polycyclic aromatic hydrocarbons (PAHs), dioxins and other planar halogenated

compounds, and some pesticides such as organochlorine pesticides, OPs and carbamates

(Karaca et al. 2014; Whyte and Tillit 2000; Hodson et al., 1996). Other CYP450

isoforms such as CYP4503B (CYP3B) have been shown to indicate exposure to

pharmaceuticals. While all members of the CYP450 family may show some response to

PPCPs, CYP3B is the primary indicator of exposure in vertebrates (Smith et al. 2012;

Smith and Wilson 2010). Thus, the superfamily of CYP450 enzymes can be used as an

indicator of exposure to a variety of contaminants originating from rural and urban

sources.

The increase in catalytic activity of ethoxyresorufin-O-deethylase (EROD) is a

common biomarker of CYP1A activity in an organism (Hodson et al. 1996). EROD

activity describes the rate at which CYP1A catalyzes the de-ethylation of the substrate, 7-

8

ethoxyresorufin (7-ER) to form the fluorescent product, resorufin (Maier et al. 2014;

Martin et al. 2013; Whyte and Tillit 2000). The catalytic activity is an indication of the

amount and/or activity of the enzyme present in the tissue sample (Whyte et al. 2000).

This study will focus on CYP1A in the livers of fish to monitor for differences in

oxidative metabolism between study sites.

1.2. Municipal wastewater

A major point source of contaminants in aquatic ecosystems is the discharges

from municipal wastewater treatment plants (Helm et al. 2012; Wang et al. 2012;

Metcalfe et al. 2009). Several contaminants of emerging concern (CEC) associated with

municipal wastewater effluents (MWWE) have been linked to biological responses in

aquatic organisms, including fish (Helena et al. 2013; Togunde et al. 2012; Tetreault et al.

2011; Gagne et al. 2006; Metcalfe et al. 2001). Wastewater treatment plants (WWTPs)

operate at several different levels of efficiency and are diverse in the technologies

employed between municipalities (Holeton et al 2011). The wastewater treatment

process begins by removing large debris including gravel, sand and, garbage utilizing a

screen and settling tank. Secondary treatment focuses on removing the remaining

suspended and particulate matter from the wastewater using clarifiers (i.e. physical

settling), followed by aerobic digestion (i.e. microbial activity). Solid matter (i.e. sludge)

that settles out of suspension is often sent to anaerobic digesters for treatment, which can

include production of biogas which is used to generate electricity. Some WWTPs

employ a tertiary treatment step designed to remove dissolved organic matter,

phosphorous and other nutrients, or residual contaminants. However, this step is not

commonly used in WWTPs in North America. The final effluent then undergoes a

9

disinfection process, which is usually by chlorination or UV-irradiation in North

American WWTPs.

In Canada, most municipal WWTPs typically do not include a tertiary treatment

step; although many of these plants are now being upgraded. Municipal WWTPs that use

chlorination for disinfection have been shown to have higher concentrations of CECs in

their effluent, compared to WWTPs that disinfect using ozonation or UV-irradiation prior

to effluent discharge (Gurke et al. 2015a; Rodayan et al. 2014; Larcher et al. 2012).

Although there is a trend in Canada to improve treatment, the introduction of CECs into

the aquatic environment and into sources of drinking water through discharges of

MWWE still remains a concern (Metcalfe et al. 2014; Servos et al. 2005). The presence

of PPCPs in Canadian surface waters is generating concern due to the capacity of PPCPs

to induce sublethal responses in fish at low concentrations (Jasinka et al. 2015; Johnson et

al. 2015; Ings et al. 2011b; Tetreault et al. 2011). In some cases, wastewater treatment

has been shown to increase the concentrations of PPCPs throughout the treatment

process. For example, many prescribed pharmaceuticals are conjugated in the human

body with large biomolecules (e.g. glutathione, glucuronide) in order to facilitate

excretion (Gurke et al. 2015a/b; Gracia – Lor et al. 2012). During the treatment process,

these compounds may de-conjugate and are released as the parent compound in the final

effluent (Gurke et al. 2015a).

1.3. Runoff of contaminants

Runoff of contaminants from the terrestrial environment into the aquatic

environment is a continuing concern in urban and rural areas (Kurt-Karakus et al. 2011;

Li et al. 2009; Metcalfe et al. 2008) Typically the peak season for runoff of contaminants

10

in Canada is from April to late May, as this coincides with the spring freshet, as well as

the pre-growing season of most crops, and the post-winter maintenance of turf (Moreau-

Guigon et al. 2007). For chemicals applied to agricultural souls, both surface and sub-

surface (typically through tile drainage) runoff peaks after the first major rain event. The

rates of chemical runoff are mediated by several factors including the physical and

chemical properties of the chemicals, the quality and extent of riparian buffer zones, the

volume and duration of precipitation and, the permeability of the soils (Moore et al. 2014;

Struger et al. 2004).

The physicochemical properties of a compound strongly influence whether or not

it is readily transported to aquatic ecosystems (Palma et al. 2015). Attributes such as

water solubility, volatility, KOW, pKa and, chemical degradation rates greatly influence

the potential for runoff of a pesticide (Struger et al 2004; Poissant et al 2008). Certain

compounds that have a lower water solubility, short half-life and a higher affinity for

binding to the organic matter present in soils will be less susceptible to runoff during a

rainfall event.

Riparian buffers are considered a best management practice (BMP) for mitigating

surface runoff into the aquatic environment (Weissteiner et al. 2014; Bereswill et al.

2012). A healthy riparian buffer zone consists of a substantially vegetated area that

separates clear cut areas from waterbodies. In the absence of a healthy riparian buffer,

there is an increase in runoff directly entering the aquatic environment, which introduces

higher volumes of particulate matter, including soils, along with recently applied current

use pesticides (CUPs) that are still present on the surface or that have bound to surface

soils.

11

Many of the CUPs used in Canada have been shown to have impacts on

freshwater fish. Sub-lethal responses including endocrine disruption, oxidative stress,

AChE inhibition and, CYP450 enzyme induction have been observed in fish exposed to

CUPs in toxicity studies (Rajakumar et al 2012; Shelley et al. 2012; da Fonseca et al

2008; Metcalfe et al. 2008). Fish express toxic responses to many CUPs at low

concentrations. CUPs have been detected in both rural and urban surface waters in

Canada, and they are not necessarily geographically confined to areas of high use (Byer

et al. 2011; Garcia-Ac et al 2009; Poissant et al 2008).

Some agricultural lands are subject to the application of biosolids, which typically

consists of treated sludge from WWTPs (Wallace et al. 2013). Often biosolids contain

residuals of PPCPs from the wastewater treatment process (Wallace et al. 2013;

Gottschall et al. 2012). Rainfall events occurring post application of biosolids may lead

to the introduction of PPCPs, artificial sweeteners and other compounds commonly found

in MWWE. Many of these compounds, such as triclosan, have the potential to move into

the tile drainage of an agricultural field during a rain event where it is then transported

into aquatic environment (Topp et al 2008; Lapen et al. 2008). The potential for transport

into surface waters is greatly dependant on the application method used (Edwards et al

2009; Sabourin et al. 2009). Liquid municipal biosolids (LMB) applied via injection

have been shown to facilitate quick movement of PPCPs through soils into tile drainage

systems where they are easily released into surface waters following rain events (Lapen et

al. 2008). Dewatered biosolids have been shown to have a slower release rate of PPCPs

when compared to LMB, which aids in the degradation of PPCPs prior to entry into

surface waters (Edwards et al. 2009; Sabourin et al. 2009).

12

1.4. Passive sampling

Monitoring for the presence of CECs within a watershed can be conducted using a

variety of sampling techniques and technologies. Many WWTPs employ time flow

proportional (TFP) sampling to actively monitor for target compounds present in different

stages of treatment (Gurke et al. 2015a; Verlicchi et al. 2012). TFP sampling has the

benefit of being accurate for generating 24-h composite samples. However it is generally

too expensive and difficult to use in a field study due to its running costs and need for

electrical power. Grab samples are an inexpensive method for acquiring sample but there

is a high risk of sampling bias towards the time of collection and the risk of missing

critical events, such as increases in flow rates and chemical input (Bundschuh et al 2014).

Passive sampling offers a relatively inexpensive, low maintenance and, reliable option for

estimating contaminant concentrations over a period of time (Helm et al. 2012; Li et al.

2009).

The Polar Organic Compounds Integrative Sampler (POCIS) is a passive

sampling technology widely used to monitor for PPCPs, endocrine disrupting chemicals

and pesticides over a time period of several weeks (Metcalfe et al. 2014; Kaserzon et al.

2014; Miege et al. 2012; Mazzella et al. 2008). POCIS passive samplers contain a solid

phase sorbent trapped between two permeable membranes, held together with a stainless

steel washer and, placed in a stainless steel cage designed to allow water to freely flow

through the sampler while keeping out debris that may damage the POCIS. Organic

pollutants are concentrated into the solid phase sorbent, where they remain relatively

stable throughout their deployment period.

13

After deployment, the POCIS are disassembled and the sorbent is extracted using

an organic solvent, which is typically methanol. The extracts are then evaporated and

brought to final volume where they can be analyzed via liquid chromatography coupled

with tandem mass spectrometry (LC-MS/MS). Data from the analysis of POCIS extracts

can be used to estimate the time weighted average (TWA) concentration of target

contaminants at a site over the period of deployment. Sampling rates (Rs) in litres per

day for each target compound are determined experimentally in the lab at different

temperatures to generated TWA concentrations under different environmental conditions.

Due to their ability to remain deployed unattended for an extended period of time,

POCIS offer an inexpensive alternative to TFP sampling, and a more robust alternative to

grab samples. Extended periods of deployment mean that peak events occurring during

that time will be accounted for, and the sample is ultimately more representative of the

site concentration when compared to the snap shot provided by using conventional grab

sampling techniques.

1.5. Influences of land cover and use

As outlined in previous sections of this chapter, there are multiple sources

contributing to the contamination of the aquatic environment, particularly in riverine

systems. Rural and urban areas both have the potential to introduce contaminants into

riverine systems (Kurt-Karakus et al 2011; Metcalfe et al. 2010). However, due to

differences in land use and population sizes between rural and urban areas, contaminants

may be introduced into watersheds at different rates. For instance, there may be

differences in PPCP concentrations in effluents of WWTPs due to differences in

population size and prescription rates (Gurke et al. 2015b). Areas of higher population

14

are more likely to have upgraded WWTPs to manage the higher volume of municipal

influent. On the other hand, in rural areas, concentrations of CUPs will most likely be

higher than in urban areas due to land usage for agriculture (Byer et al. 2011; Garcia-Ac

et al. 2009). Golf courses and other urban turf care practises may introduce contaminants

into riverine systems (Metcalfe et al., 2008); but it would be expected that the total

application volume of CUPs in urban areas will be less than in rural areas.

When considering these factors it becomes clear that land use patterns and land

cover within watersheds may have an influence on biological responses in aquatic

organisms, including fish. In order to monitor biological responses in fish from areas

with different land uses and covers, it is first necessary to characterize exactly what

differences in land use are occurring within a watershed. The Ontario Flow Assessment

Tool III (OFAT) is a coarse, watershed delineation tool developed by the Ontario

Ministry of Natural Resources and Forestry (OMNRF). By analyzing topographic

features and archived flow data, OFAT is an effective tool for creating maps of catchment

areas and calculating the different land uses, as well as land cover types within a

catchment.

1.6.Project overview

By analyzing in situ biomarkers of exposure to contaminants, it is possible to

evaluate the impacts of chemical contaminants on fish in a watershed between sites

dominated by rural or urban influences. Physical traits, such as deformities and intersex,

along with biochemical indicators such as oxidative stress, metabolic enzyme induction

and, neurotransmitter inhibition can be used to monitor sub-lethal responses associated

with exposure to waterborne pollutants. In this study, two species of darters (Etheostoma

15

sp.) were collected from sites primarily experiencing agricultural or urban influences in

the Grand River watershed in southern Ontario. The rainbow darter (E. caerulem) was

collected across all sites and the fantail darter (E. flabellare) was collected only at the

agricultural sites, allowing for response comparisons between the two species. Darters

were selected due to their overall abundance and range throughout the Grand River

watershed, as well as their previous use as indicator organisms for exposure to chemical

contaminants (Wang et al. 2012; Tetreault et al. 2011; Brown et al. 2011). Rainbow and

fantail darters feed on small aquatic invertebrates in the benthic community of rivers and

streams. Both species of darters reach sexual maturity within one to two years, and

change microhabitats from rapids into cobble pools within the stream during their mating

season (Hubbs 1985). In addition, these fish species are philopatric and stay within a

home range of a few square meters throughout their entire life history (Hubbs 1985).

The Grand River watershed covers a substantial portion of southern Ontario, with

a total catchment area of 6,965 km2 (GRCA 2005). With a total reach of around 300 km,

the Grand River watershed stretches from as far north as Dundalk, Ontario and ultimately

drains at Port Maitland into Lake Erie. As a result of its large size, the Grand River

watershed is highly diversified in terms of its land use and coverage, having a range of

activity from low intensity agriculture to dense urban centers. Due to this diverse

landscape, the watershed is impacted by several classes of chemical contaminants which

enter through point and non-point sources.

Many contaminants associated with urban and agricultural land use types have

been shown to induce toxic responses in aquatic organisms, including fish (Vajda et al.

2008; Whitehead et al. 2004). Since the Grand River watershed is so diverse in terms of

16

waterborne pollutants and land uses, it is essential to monitor responses across sites

experiencing both rural and urban influences in order to identify the sources of the

contaminants that impact the watershed. Once these responses are understood, it may be

possible to develop mitigation strategies, such as the use of BMPs in agricultural areas

and installation of advanced wastewater treatment technologies in urban areas. While it is

useful to monitor waters for contaminants known to induce toxic responses using

analytical techniques, this approach cannot indicate whether there are biological impacts

on fish and other aquatic organisms.

Two approaches to monitoring were employed for this project: i) contaminant

monitoring, and ii) biomarker analysis. Sites were monitored for contaminants using

POCIS passive samplers, as they offer the ability to calculate the estimated TWA during

their time of deployment for a diverse group of organic compounds (i.e. PPCPs and

CUPs). Analysis of the POCIS extracts was conducted using LC-MS/MS, which allows

for the detection of organic compounds at trace concentrations. Biomarker analysis was

performed for several known indicators of exposure to waterborne contaminants. Two

species of darters were sampled for brain, liver and, gill tissue, which were analyzed for

AChE inhibition, CYP1A induction and, TBARS respectively, using spectrophotometric

and fluorescence methods. Biomarker responses were compared across sites influenced

by rural (n=3) and urban (n=2) land uses within the Grand River watershed. To assess the

severity of the response, the measured biomarker responses were compared to data from

the literature where fish were similarly exposed. Hepatosomatic indices and condition

factors were analyzed to compliment biomarker response data.

17

Differences in biomarker responses are expected between the rural and urban

study sites. Inhibition of AChE is expected in the agricultural portions of the watershed

due to a greater presence of CUPs, while greater induction of CYP450 enzymes and

TBARS are expected at sites more strongly influenced by urban development, including

WWTPs. A relationship is expected between targeted contaminants measured in POCIS

extracts and biomarker responses in darters. However, there may be impacts on fish from

contaminants that were not monitored using the passive samplers. Overall a trend of

increasing biomarker response is expected heading downstream, due to cumulative

effects from agricultural and urban areas.

It is expected that the cumulative influence of contaminants from the different

land types will result in increased biomarker responses in darters collected in the Grand

River. Fish collected at the urban sites are expected to have more severe biological

responses compared to those collected at the rural sites due to an increase in contaminant

inputs and cumulative effects from upstream and downstream sources. Fish collected at

the most downstream sites in both rural and urban areas are expected to have the highest

levels of biological response due to cumulative effects from several contaminant inputs

(i.e. agriculture, WWTPs, golf courses, etc.).

The objectives for this study are:

1) To determine the presence of several targeted organic chemical contaminants

linked to agricultural and municipal wastewater sources at study sites within the

Grand River watershed.

2) To evaluate biomarkers of exposure to contaminants in darter species collected

from agriculturally and urban impacted sites.

18

3) To compare biomarker responses between two species of darters collected at the

agriculturally impacted sites.

My hypotheses are as follows:

H1: Darters collected at the study sites will display biological responses indicative of

exposure to chemical contaminants from agricultural and urban origins.

H2: Biomarker responses in fantail and rainbow darters collected at the same locations

in the Grand River will not be significantly different; indicating similar sensitivities of

these species to exposure to chemical contaminants.

H3: Estimated concentrations of chemical contaminants at the darter collection sites in

the Grand River will increase with distance downstream due to cumulative exposures,

and biomarker responses will reflect exposure to specific chemicals.

19

Chapter 2: Biological Responses to Contaminants in Darters

(Etheostoma spp.) Collected from Rural and Urban Regions of

the Grand River Watershed

1.0 Introduction

Contaminants from both point and non-point sources are discharged into riverine

systems (Tetreault et al. 2012; Metcalfe et al. 2008). As land use patterns change, so do

the primary chemical stressors (Byer et al. 2011; Countway et al. 2003). Municipal

wastewater treatment plants (WWTPs) and surface runoff are common contributors of

pharmaceuticals and personal care products (PPCPs), artificial sweeteners, and current

use pesticides (CUPs) into riverine systems (Li et al. 20009; Edwards et al. 2009; Struger

et al. 2004). Several contaminants of emerging concern (CECs), including PPCPs and

CUPs have been shown to induce sublethal responses in fish at environmentally relevant

concentrations (Jasinka et al. 2015; Shelley et al. 2012; Bereswill et al. 2012). Common

biological indicators of exposure in fish include increased prevalence of gonadal intersex,

increased gonadosomatic and hepatosomatic indices, physiological deformities in early

and later life stages, induction of oxidative detoxification enzymes, inhibition of

acetylcholinesterase (AChE) and an increase in impacts from reactive oxygen species

(Tanna et al. 2013; Tetreault et al. 2011; Smith and Wilson 2010; Cattaneo et al. 2008).

The sampling and analysis of surface waters for the presence of CECs is an

important tool in understanding the frequency and magnitude of concentrations within

riverine systems, but this approach does not indicate impacts on biota. In situ biomarker

studies in combination with environmental sampling allows for a more holistic approach

to monitoring the impacts of CECs in riverine systems (Jasinska et al., 2015; Fonseca et

al., 2011; Vajda et al. 2008). When there is a degradation of water quality due to the

20

presence contaminants, fish may show responses through somatic changes such as liver

somatic index (LSI), as well as length – weight relationships when compared to the

seasonal average (Bolger and Connolly 1989; Tetreault et al. 2012). Fish may also react

to exposure to contaminants on a biochemical level through increases or decreases in

several biological parameters, including neurotransmitter inhibition, an increase in

metabolic detoxification activity and an increase in oxidative stress (Scornaienchi et al.

2010; Sumith et al. 2012; Cattaneo et al. 2008).

This project aims to monitor the biological responses in fish collected at

agriculturally impacted and urban impacted sites throughout the Grand River watershed

in southern Ontario, Canada. Female rainbow darters (Etheostoma caerluem) and fantail

darters (Etheostoma flabellare) were sampled across five study sites, including three rural

sites and two urban sites. The darters were analyzed for the induction of Phase I

microsomal enzymes (CYP1A), the TBARS indicator of oxidative stress, and AChE

inhibition. The passive organic chemical integrative sampler (POCIS) passive sampler

was used to monitor for CECs at each of the five study sites. Previous studies have

typically focused on the response of a single species in areas influenced by either rural or

urban chemical stressors. This study aims to compare the responses of darters exposed to

chemical contaminants in urban and rural sites. It is expected that there will be

differences in the biological responses of darters between the five study sites, and that

they will be reflective of the estimated time weighted average concentrations of

contaminants known to induce the selected biomarkers.

21

2.0 Methods and Materials

2.1. Materials and chemicals

Analytical standards for all target compounds were purchased from Sigma

Aldrich Canada (Oakville, ON, Canada) and their stable isotope surrogates were

purchased from C/D/N Isotopes (Pointe-Claire, QC, Canada), except for the surrogate for

sucralose, which was purchased from Toronto Research Chemicals (Toronto, ON,

Canada). Ammonium acetate was also purchased from Sigma Aldrich Canada. All stock

solutions for analysis were made up in HPLC grade methanol (VWR International,

Mississauga, ON, Canada). Acetonitrile and hexane were HPLC grade and were

purchased from VWR International, Mississauga, ON, Canada). Glacial acetic acid and

reagent grade acetone were purchased from ACP Chemicals (Montreal, QC, Canada).

The POCIS containing Waters OASIS HLB sorbent were purchased from Environmental

Sampling Technologies (St. Joseph, MO, USA). For protein determination, the Bio-Rad

Protein Assay Reagent was purchased from Bio-Rad Laboratories Canada (Mississauga,

ON, Canada). The sources of the bioassay reagents are listed in the sections describing

these methods.

2.2. Study area

This study focused on sampling at sites located within the Grand River watershed

in southern Ontario, Canada. The Grand River watershed covers a total area of 6965 km2

and is comprised of densely populated urban centers, small towns and a large agricultural

landscape (GRCA 2005), although the landscape is changing due to an increasing

population. By the year 2051 the Grand River Conservation Authority predicts that there

22

will be 1,530,000 people living within the watershed compared to approximately 960,000

inhabitants residing there today.

Figure 1 illustrates the locations of the 5 study sites. The three upstream sites at

Gordonville (43°54’15.70” N, 80°32’48.32” W), Petherton (43°52’20.72” N,

80°35’21.91” W), and FMCDS (43°49’32.60” N, 80°36’58.58” W) are primarily

dominated by agricultural and rural land use, with small rural communities. The two

downstream sites at Horse Ranch (43°24’05.43” N, 80°25’44.57” W) and Pioneer Tower

(43°23’55.87” N, 80°24’58.32” W) are located within the city of Kitchener, Ontario.

Horse Ranch is located 500 m upstream from the Doon WWTP serving the city of

Kitchener, and Pioneer Tower is located approximately 500 m downstream of the Doon

wastewater discharge. According to the Ontario Clean Water Agency’s Performance

Assessment Report in 2008, the Doon WWTP services approximately 211,000 people,

with an average daily discharge of about 74,000 m3/day. In 2012-13, the Doon WWTP

was upgraded with a new UV-irradiation disinfection system, and an upgrade to Plant 2

with the installation of fine bubble diffusers to the aerobic treatment system.

2.3. POCIS deployment and extraction

All POCIS were stored at -20°C prior to the deployment. The POCIS were

deployed in stainless steel cages which had been previously solvent washed with HPLC

grade hexane and reagent grade acetone. All POCIS were deployed from June 18th to

July 7th, 2014. POCIS cages were secured between two steel posts, facing the direction

of flow, with the cages positioned approximately 20 cm from the bottom. . One cage

containing three POCIS was deployed at each of the five sites. During the deployment

23

and retrieval of the POCIS, trip blanks were exposed to the atmosphere to monitor for

contamination that may occur when preparing the samplers.

Figure 1: Study catchment within the Grand River watershed, showing the locations of

the 5 study sites.

Upon retrieval, all POCIS were gently cleared of any debris, wrapped in clean foil

and temporarily stored in a cooler for transport. Once transported to the laboratory, all

POCIS were then stored at -20°C until extraction. The extraction procedures for POCIS

were similar to those described by Li et al (2009). Each POCIS was briefly rinsed with a

Gordonville

Petherton

FMCDS

Horse Ranch

Pioneer Tower

24

gentle stream of water to remove any debris adhering to the outside of the membrane.

POCIS membranes were then disassembled and the sorbent medium was carefully

transferred to a glass chromatography column (1cm ID x 30cm) which was fitted with

glass wool and stop cocks. The membranes were then gently rinsed with HPLC grade

methanol to transfer any remaining sorbent into the column. Immediately, each sample

was spiked with 100 µL of 500 ng/mL internal standard (I.S.) mixture and allowed to sit

for three minutes before elution. The sorbent was eluted with 100 mL of HPLC grade

methanol into a 250 mL flask which was then reduced to 1 mL using rotary evaporation,

transferred to a centrifuge tube and evaporated to near dryness. This volume was then

centrifuged to remove any remaining particulate matter and then transferred into an

amber autosampler vial with HPLC grade methanol and brought up to a final volume of

400 µL for analysis. Final volumes were stored at -20°C until analysis.

2.4. POCIS extract analysis

All target compounds (Table 1) were analyzed using liquid chromatography

coupled with tandem mass spectrometry (LC-MS/MS) and an electrospray ionization

source (ESI) using an AB Sciex QTrap 5500 instrument equipped with an Agilent 1100

series HPLC separation system (Applied Biosystems-Sciex, Mississauga, ON, Canada).

The analytes were separated chromatographically using a reverse phase Genesis – C18

column (150 mm x 2.1 mm ID; 4µm particle size) with a guard column (Genesis C18,

10mm x 2.1 mm ID; 4µm particle size) of the same stationary phase (Chromatographic

Specialties, Brockville, ON, Canada).

PPCPs and the artificial sweetener, sucralose were separated by HPLC using a

gradient elution with a total run time of 18 minutes. The mobile phases used for

25

chromatographic separation were [A] MilliQ Water (100%) with 0.1% acetic acid, and

[B] Acetonitrile (100%) with 0.1% acetic acid, as described by Metcalfe et al. (2014).

The gradient used for both positive and negative polarity modes was adapted from the

method described by Topp et al. (2008). The LC-MS/MS was run in multiple reaction

monitoring mode (MRM) for the detection of precursor and product ions. The ion

transitions used for analysis of the target compounds and their corresponding IS, as well

as the POCIS sampling rates (Rs) for PPCPs were previously described by Li et al.

(2010b). The Rs value for sucralose was determined in Metcalfe et al. (2014). The Rs

values selected for this study are listed in Appendix 1.

Fungicides, herbicides, and biocides were separated by HPLC using a gradient

elution with a total run time of 18 minutes. The mobile phases used for chromatographic

separation were [A] 10 mM Ammonium Acetate with 0.1% acetic acid and, [B] 100%

Acetonitrile. The gradient performed as follows for both positive and negative polarity

modes: [B] 30% for 1 minute, increasing to [B] 90% over 5 minutes and held for an

additional 5 minutes, then dropped back to [B] 30% over another 5 minutes and held for

an additional 2 minutes. The LC-MS/MS was run in MRM mode for the detection of

precursor and product ions. These ion transitions for the target CUPs and their

corresponding I.S. are listed in Table 2. Ionization parameters are listed in Appendix 3.

An external standard method was used for quantification, with adjustments made using

analytical responses for the respective surrogate internal standards. The POCIS sampling

rates of CUPs were previously described by Metcalfe et al. (submitted). The sampling

rates used for this study are described in Appendix 2.

26

Table 1: List of all target analytes for monitoring by POCIS and their sources.

Chemical Family Compound Source

Fungicide

Tebuconazole Agricultural

Propiconazole Agricultural

Fluconazole Pharmaceutical

Ketoconazole Personal care product

Climbazole Personal care product

Carbendazim Turf Care/Biocide

Azoxystrobin Agricultural/Turf Care

Myclobutanil Agricultural/Turf Care

Iprodione Agricultural/Turf Care

Herbicide

Mecoprop Agricultural/Turf Care

Atrazine Agricultural

Dicamba Agricultural/Turf Care

2,4-D Agricultural/Turf Care

Biocide

Irgarol 1051 Antifouling

Terbutryn Antifouling

PPCP

Estrone Hormone

Androstenedione Hormone

Ibuprofen Anti-inflammatory

Naproxen Anti-inflammatory

Acetaminophen Pain Killer

Sucralose Artificial Sweetener

Trimethoprim Antibiotic

Gemfibrozil Cholesterol regulator

Carbamazapine Anti-convulsant

27

Table 2: Target pesticides and internal standards, with the MRM transitions used for

analysis.

Compound Formula MRM Transition Polarity

Azoxystrobin C22H17N3O5 404 → 86 +

Fluconazole C13H12F2N6O

307 → 238 +

Climbazole C15H17ClN2O2 293 → 197 +

Myclobutanil C15H17ClN4 289 → 70 +

Propiconazole C15H17Cl2N3O2 342 → 159 +

Tebuconazole C16H22ClN3O 308 → 70 +

Carbendazim C9H9N3O2 192 → 160 +

Ketoconazole C26H28Cl2N4O4 531 → 489 +

Iprodione C13H13Cl2N3O3 328 → 141 -

Atrazine C8H14ClN5 216 → 174 +

Dicamba C8H6Cl2O3 219 → 161 -

2,4-D C8H6Cl2O3 220 → 162 -

Mecoprop C10H11ClO3 213 → 141 -

Irgarol C11H19N5S 254 → 198 +

Terbutryn C10H19N5S 242 → 186 +

Ketoconazole-d4 C26H24D4Cl2N4O4 535 → 81 +

Carbendazim-d4 C9H5D4N3O2 196 → 164 +

Fluconazole-d4 C13H8D4F2N6O 311 → 70 +

Propiconazole-d5 C15H17Cl2N3O2D5 347 → 279 +

Atrazine-d5 C8H9D5ClN5 221 → 72.9 +

2,4-D-d3 C8H3Cl2D3O3 222 → 164 -

2,4-C-d3 C10H8D3ClO3 216 → 144 -

3,6-D-d3 C8D3H3Cl2O3 222 → 178 -

Iprodione-d5 C13H8D5Cl2N3O3 333 → 97 -

Terbutryn-d5 C10D5H14N5S 247 → 173 +

28

2.5. Darter collection

The two species of darters were collected at the study sites in the Grand River

during June 23rd 2014 to June 27th, 2014, which compliments the time of POCIS

deployment from June 19 to July 7, 2014. All collections were conducted according to

Animal Care Protocols approved by the Trent Animal Care Committee. A total of 20

female rainbow darters (E.caeruleum) were collected at each of the five study sites. A

total of 15 female fantail darters (E. flabellare) were collected at each of the three stations

in the upper watershed (i.e. rural sites), due to their regional distribution. Darters were

collected using a backpack electrofishing unit (Smith-Root LR-24, Vancouver,

Washington, USA) by a three person team consisting of two netters and one electrofisher.

The darters were kept alive in aerated buckets containing site water, before they were

sacrificed for dissection. The length and mass of the fish were measured to calculate

condition factor before the fish were sacrificed by spinal severance. Liver weight was

taken for the calculation of LSI. Each fish was dissected for brain, gill and, liver tissue

samples, which were immediately placed in cryovials (Cole-Parmer Canada, Montreal,

QC, Canada) and snap frozen in liquid nitrogen. All tissue samples were stored in an

ultra-low temperature freezer at -80°C until the time of analysis.

2.6. Positive control treatments

In order to validate the methods used for measuring the hepatic assay response for

EROD in darters, juvenile rainbow trout (Oncorhynchus mykiss) ranging from 25 to 30

cm in size were exposed in the wet lab at Trent University to chemicals known to induce

these biomarkers and liver tissue collected from these fish was evaluated for the

29

biochemical responses. All procedures were conducted according to Animal Care

Protocols approved by the Trent Animal Care Committee. Trout were purchased from

Linwood Acres Trout Farm (Campbellcroft, Ontario, Canada) and acclimated for a period

of two weeks in a 600 L tank with fresh water flow through at a temperature of 11 °C.

The trout were exposed via intraperitoneal (I.P.) injection to a known CYP1A inducer.

The I.P. injections were performed with a 25G tuberculin needle and 1 mL syringe in the

ventral-posterior section of the fish beneath the anal fin. Stocks of the positive control

compound, β – napthoflavone (BNF) were prepared using DMSO and then diluted in

corn oil to a final concentration with less than 10% DMSO. Treatments consisted of six

fish for each of the treatments with BNF at a dose of 10 µg/g for CYP1A induction

(Martin et al. 2014; Smith and Wilson 2010; Hodson et al. 1996). One negative control

group exposed to the corn oil and <10% DMSO and one blank (no injection) group were

tested simultaneously. After 72 hours, the fish were sacrificed by spinal severance. Liver

samples were collected in a similar fashion as outlined in section 2.5.

2.7. Acetylcholinesterase assay

Acetylcholinesterase (AChE) was determined using the Ellman et al (1961)

protocol adapted to a 96 well microplate spectrophotometric reader, as described by

Fisher et al (2000). In this assay, AChE activity is determined by cholinesterase

catalyzing the breakdown of acetylcholine into acetate and thiocholine. Enzyme activity

associated with this reaction was monitored at λ405 nm using a SpectraMAX Plus 384

UV-Vis plate reader (Molecular Devices, Sunnyvale, CA, USA) at λ405 nm. An increase

in yellow colouration marks the production of thiocholine when it reacts with the added

30

reagent 5,5’-dithio bis-2-nitrobenzoate which was purchased from Sigma-Aldrich Canada

(Oakville, ON).

Approximately 10 mg of brain tissue was homogenized in 350 µL of ice cold

0.02M phosphate buffer (PB) containing 1% Triton-X-100 (Sigma Aldrich Canada) at

pH 8.0 in a 1.5 mL polypropylene centrifuge tube. Samples were then centrifuged at

13,000 g for 10 minutes at 3°C. Supernatants were removes and put in a clean 1.5 mL

centrifuge tube and stored on ice prior to analysis. Each plate included a quality control

consisting of Electric Eel (Electrophorus electricus) AChE purchased from Sigma

Aldrich (Oakville, ON, Canada). Additions to each well were as follows: 1) 100 µL of 8

mM 5,5’-dithio bis 2-nitrobenzoate in PB, 2) 50 µL of undiluted homogenate, quality

control enzyme, or assay blank (PB with 1.0% Trition-X-100) 3) 50 µL 16 mM

acetylcholine Iodide in PB. All samples were run in triplicate. Plates were then

incubated at 30°C for 3 minutes to initiate the reaction. Samples were read in the plate

reader over a 10 minute period, with intermittent shaking. Protein determination was

performed as outlined in section 2.13. Enzyme activity in µmoles/min/mg protein was

calculated using Equation 1 from Ellman et al (1961):

𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = ∆𝑂𝐷/𝑚𝑖𝑛

𝑀𝐸𝐶 𝑋 𝐶

Where: MEC = molar extinction coefficient (1.36 x 104/cm), OD = Optical density as

determined using the spectrophotometric method and, C = supernatant protein

concentration in the assay well (mg/L).

(1)

31

2.8. Ethoxyresorufin-O-deethylase (EROD) assay

The EROD assay procedure using S9 preparation used for this study was adapted

from Hodson et al. (1996). For this assay, darter liver tissues from two fish were pooled,

resulting in a sample size of 10 for rainbow darters and a sample size of 7 for fantail

darters collected at each site. Approximately 30 mg of liver tissue was homogenized in

250 µL of HEPES grinding buffer (pH 7.5) and then brought up to 750 µL by adding an

additional 500 µL of HEPES solution. These samples were then centrifuged at 9,000 g

for 20 minutes at 3°C to generate an S9 preparation. The supernatant (i.e. S9) layer was

gently aspirated and transferred into a clean cryovial and stored at -80°C until analysis.

The frozen homogenates were thawed on ice and mixed using a vortex mixer prior to

analysis. A standard curve ranging from 0 to 5 ug/mL resorufin dissolved in DMSO was

prepared and stored in the dark at room temperature until analysis.

Additions to the microplate wells were as follows: 1) 50 µL vortexed S-9 fraction,

in triplicate, 2) 50 µL 7-ER/HEPES solution. Plates were then incubated in the dark at

room temperature for 10 minutes. Finally 10 µL of nicotinamide adenine dinucleotide

phosphate (NADPH) was added quickly to each well to initiate the reaction.

Fluorescence was read every 30 seconds for 12 minutes at λexc 530 nm and λem 586 nm

using a Gemini EM fluorescence plate reader (Molecular Devices, Sunnyvale, CA, USA.

Protein was determined as outlined in section 2.13. All reagents for this assay were

purchased from Sigma-Aldrich (Oakville, Ontario, Canada).

32

2.9. 2-thiobarbituric acid reactive substances (TBARS) assay

The TBARS assay measures lipid peroxidation that results from oxidative stress

within an organism. The procedure for TBARS analysis followed in this study was

adapted from the protocol described by Hermes-Lima et al. (1995). Approximately 10

mg of gill tissue was homogenized in 1.1% H3PO4 (19 µL mg-1 tissue) in a 1.5 mL

centrifuge tube. The reaction mixture for the assay consisted of: 150 mg thiobarbituric

acid (TBA), 1.5 mL of 1mM butylated hydroxytoluene (BHT), and 13.5 mL of 56 nM

NaOH. Assay results were compared against a standard curve of malondialdehyde

(MDA) ranging from 0 to 20 mM concentrations in 1.1% H3PO4. Additions to clean

centrifuge tubes were made as follows: 1) 200 µL of reaction mixture 2) 200 µL of

homogenate, standard or 1.1% H3PO4 blank 3) 100 µL of 7% H3PO4. All samples and

standards were placed in a boiling water bath for 15 minutes, then immediately cooled

down on ice. Each sample was brought up to volume with 750 µL of butanol and mixed

with a vortex mixer to initiate the reaction. Immediately after mixing the samples were

centrifuged at 10,000 g for 5 minutes. Standards were run in duplicate and homogenates

were run in triplicate by adding 200 µL and 100 µL per well, respectively. Absorbance

was then read at λ 532 nm using a SpectraMAX Plus 384 UV-Vis plate reader. All

reagents for the TBARS assay were purchased from Sigma Aldrich Canada, Oakville,

Ontario.

2.10. Protein assay

For protein determination for the AChE and EROD assays, the protein

concentrations were compared against a standard curve of BSA in MilliQ water at

33

concentrations ranging from 0 to 0.25 mg mL-1. For the EROD assay, S-9 homogenates

were diluted twenty-one-fold in MilliQ water, to 50 µL to 1000 µL respectively. Brain

homogenates for AChE were diluted using the same proportions. Standards were run in

unison and samples were run in triplicate. Additions to the microplate wells were as

follows: 1) 10 µL dilute homogenate or standards 2) 200 µL dilute Bio-Rad (1:4 MilliQ).

The plates were then incubated in the dark at room temperature for 5 minutes.

Absorbance was read λ 600 nm using a SpectraMAX Plus 384 UV-Vis plate reader.

2.11. Statistical analyses

When sample groups had equal variances, a One Way Analysis of Variance

(ANOVA) was performed. In cases where statistically significant differences (p < 0.05)

were observed, the Tukey Honestly Significant Difference post hoc test was applied to

test for differences between specific study sites. If the data for the sample groups did not

meet the criteria to perform a one way ANOVA, the Kruskal – Wallis test was substituted

with Nemenyi post hoc test, in case significant differences (p < 0.05) were observed.

When comparing the data on biological responses for rainbow darters to fantail darters, a

Two Way ANOVA was performed with a pairwise post hoc in the event of statistical

significance. All statistical analysis was conducted using the statistical software R

(v3.2.0; The R Foundation).

2.12. OFAT III model

The OFAT III model was developed by the Ontario Ministry of Natural Resources

and Forestry (OMNRF) for the detailed delineation of watersheds. Catchments were

generated for each of the five study sites based on the site of the POCIS cage deployment.

34

Based on the surrounding topography, detailed descriptions of each study site were

generated. OFAT III is capable of modelling average and seasonal flow dynamics, land

geography, and land use patterns based off of existing GIS databases. For this study,

OFAT III was used to generate land cover and land use data for each of the five study

sites to illustrate differences in land cover and catchment sizes.

3.0 Results and Discussion

3.1. Site characterization

When comparing the land cover types and associated coverage, it becomes clear

that the catchments influencing each site are primarily agricultural, but the two urban

sites are located within the city of Kitchener and therefore are expected to be primarily

influenced by contaminants of urban origin. Table 3 displays the watershed

characterization as determined by the OFAT III model. Communities and infrastructure

land use ranges from 1.09 km2 at the most upstream site to 166.03 km2 for the most

downstream site. Although the agricultural proportions remain dominant at the

downstream sites (Table 3), agriculture may have a lesser influence because these land

use patterns are over 100 km away, while the urban infrastructure is immediately adjacent

to the sampling sites. Gordonville, Petherton and FMCDS are primarily agricultural,

with approximately equivalent coverage by small rural communities (~2.1%). Although

there are several small WWTPs and septic systems throughout the upper Grand River

watershed, agricultural land usage is expected to have the most significant influence on

the watershed at these sites. Wetlands cover approximately 8-11% of each catchment.

Previous studies have shown that wetlands have a significant impact on the degradation

35

of CUPs and therefore, may aid in mediating their transport into a riverine system

(Maillard et al 2011; Poissant et al. 2008).

Table 3: OFAT III generated data on catchment area for each of the 5 sampling sites, with

the corresponding area for each land cover type (km2). Sites are arranged from furthest

upstream sample site to the furthest downstream site.

Criteria (% coverage) Gordonville Petherton FMCDS

Horse

Ranch

Pioneer

Tower

Drainage Area (km2) 52.06 71.78 117.18 2502.71 2504.23

Clear Open Water 1.05 0.76 0.54 0.90 0.90

Marsh 0.67 0.66 0.54 1.45 1.45

Swamp 10.89 9.34 8.09 10.35 10.35

Fen 0.00 0.00 0.00 0.01 0.01

Bog 0.00 0.00 0.00 0.23 0.23

Treed Upland 0.60 0.54 0.56 0.37 0.37

Deciduous Treed 1.49 1.38 1.27 2.28 2.29

Mixed Treed 0.17 0.14 0.10 0.08 0.08

Coniferous Treed 0.15 0.14 0.24 0.45 0.45

Plantations 2.68 1.96 1.31 1.37 1.37

Hedge Rows 0.51 0.65 0.83 0.42 0.42

Sand/Gravel 0.08 0.06 0.04 0.00 0.23

Infrastructure 2.09 2.04 2.09 6.60 6.63

Agriculture 79.61 82.34 84.39 75.27 75.23

3.2. Contaminants

The estimated TWA concentrations estimated for each analyte are listed in Tables

4a and 4b. A total of 28 analytes were measured at each of the five study sites. Of these

target analytes, 10 compounds were not detected at any of the sites, including the steroid

hormones, estrone and androstenedione (Table 4b). Atrazine was found at the highest

estimated TWA concentrations (70 – 420 ng/L) and was detected at all five sites.

Naproxen (0.1 – 2.60 ng/L) and carbamazepine (0.1 – 4.20 ng/L) were also found at each

of the five sites. Typically, atrazine is applied to corn crops as a pre-emergence broad

36

leaf herbicide in the early spring and has a half-life of approximately 30 days in soils

(Nwani et al 2010). The highest estimated TWA concentration of atrazine was below the

current Canadian Council of Ministers of the Environment (CCME) guidelines for

aquatic organism toxicity (i.e. 1.8 µg/L). However, due to the late deployment of the

POCIS, the measured concentrations are estimated to be about one month post

application and therefore may only account for the end of the peak runoff period. A 55%

increase in atrazine was observed downstream of the urban WWTP compared to the site

immediately upstream. The increase in atrazine downstream of the WWTP may be due

to the introduction of contaminated sewage from septic systems as well as through storm-

water overflows (Gerecke et al 2002). Kolpin et al. (2006) examined the concentrations

of atrazine throughout a municipal wastewater treatment process, and the results

indicated that atrazine is minimally removed from WWTP effluent before discharge.

There is a large golf course upstream of the urban sites that may also be a source of

atrazine, even though there is a municipal ban on the pesticides application. Arlos et al.

(2014) reported atrazine at the urban sites in Kitchener at concentrations around 207 ± 36

ng/L. Tanna et al. (2013) reported atrazine concentrations at urban sites in Kitchener to

be around 50 ng/L. The concentrations of atrazine observed in the present study fall

within the ranges reported in the literature. Differences in the detected concentrations of

atrazine at the urban study sites may be due to difference in seasonal sampling times,

sampling technologies used (i.e. grab samples vs POCIS), and the river flows during

sample collection.

The pharmaceuticals, naproxen and carbamazepine were also detected at all five

sites, with the highest concentrations detected immediately downstream of the WWTP

37

(Table 4b). Low TWA concentrations of naproxen and carbamazepine were estimated at

sites in the rural portions of the watershed. These pharmaceuticals may have been

introduced at the rural sites through septic leakage or through biosolid applications to

agricultural lands. Carbamazepine is poorly removed in WWTPs and is highly persistent

in surface waters, while naproxen is moderately persistent in WWTPs, but has been

widely detected in surface waters (Gurke et al. 2015b; Lissemore et al., 2006; Tixier et al.

2003). The removal of naproxen is believed to be seasonally influenced, as the majority

of its degradation is due to photodegradation, rather than the wastewater treatment

process itself (Hijosa-Valsero et al. 2010). Both carbamazepine and naproxen are mobile

in surface runoff or tile drain leachate after applications of biosolids onto agricultural

fields (Topp et al., 2008; Lapen et al., 2008). Lissemore et al. (2006) monitored the

presence of several pharmaceuticals at rural and urban study sites in a southern Ontario

watershed. When comparing the concentrations measured in the present study, they are

generally in the lower range of those reported by Lissemore et al. (2006). The differences

in concentrations may be due to dilution caused by river flow, or the choice of sampling

technology. Lissemore et al. (2006) collected grab samples, which may have been

collected during peak runoff events or low flow events, whereas the passive samplers

used in the present study monitor over an extended period of time and provide an

estimated time-weighted-average. Gillis et al. (2014) monitored for PPCPs in the Grand

River watershed using POCIS samplers, and the estimated TWA at each site are similar

to those observed in the present study.

The differences in the concentrations of CUPs across the rural sites are most

likely influenced by riparian buffer quality, application method and rate, and stream flow,

38

which will be discussed later in this section. The furthest upstream site (Gordonville) had

the smallest buffer zone of the agricultural sites and had two active and intensive

agricultural fields adjacent to it, with less than 10 m of buffer on either side. The second

rural site (Petherton) was adjacent to a single, moderately intense agricultural field with

over 15 m of riparian buffer zone between the field and stream bank. The furthest

downstream site (FMCDS) was adjacent to two active, low intensity agricultural fields,

with over 30 m of riparian buffer on one side, and less than 10 m on the opposite side.

The composition and quality of riparian buffer have been shown to mediate the runoff of

pesticides into the aquatic environment, but they are not entirely effective (Weissteiner et

al. 2014). The sites with a lower quality of riparian buffer exhibit higher in situ

concentrations of contaminants compared to those sites with a higher quality of buffer

zone. However, the intensity of agricultural practices in these areas may also influence

concentrations. It can be assumed that the application rates of pesticides are proportional

to the size and intensity of the farm (Boithias et al. 2014; Sattler et al. 2007). While it is

possible that local site conditions, including riparian buffers, may have had an influence

on the contaminants detected at each study site, the influence of upstream factors cannot

be ignored. The quality of buffer zones, and agricultural intensity upstream of the

sampling sites may have had a compounding effect on the concentrations of the targeted

analytes detected at each of study sites.

The physicochemical properties and application methods of these fungicides

greatly influence their ability to be transported into adjacent surface waters (Bundschuh

et al. 2014). Atrazine, tebuconazole, and propiconazole are all highly water soluble at

concentrations up to 35, 36, and 100 mg/L, respectively (U.S. Environmental Protection

39

Agency 2015). The relatively high water solubility of these pesticides suggests that they

are easily leached into surface runoff during precipitation events. Iprodione is less water

soluble at 12 mg/L (U.S. Environmental Protection Agency 2015) and is therefore more

likely to remain in soils when it is applied. Since pesticide application was not observed

at the study sites, it is assumed that they were applied following best management

practices, including reasonable control of drift, and respecting no spray zones. However,

given the properties and functions of the pesticides used, it can be assumed that they were

applied via broadcast spray and that drift into nearby surface water is a possibility

(Bereswill et al. 2012; Reichenberger et al. 2007).

The agricultural herbicides 2,4-D and dicamba were detected downstream of the

urban WWTP at concentrations of 38.9 and 31.1 ng/L, respectively. Neither compound

was detected at any other site, including those within the rural watershed. Previous

studies have shown the presence of these two herbicides in municipal wastewater effluent

generally around the concentrations estimated in the present study (Kuster et al. 208;

Petrovic et al. 2003). As mentioned earlier with regards to the presence of atrazine at

urban sites, this is most likely due to contaminated septic waste or storm water overflow

that entered the WWTP. The influence of distant agricultural applications must also be

considered as the source of atrazine in the urban watershed. Atrazine may be transported

downstream from the agricultural sites to the urban sections of the Grand River.

Carbendazim, a broad spectrum fungicide widely used on golf courses, was also detected

downstream of the WWTP. A golf course is located adjacent to the Horse Ranch site,

upstream of the WWTP and roughly 1 km away from the site of POCIS deployment.

Carbendazim was below the LOD at Horse Ranch, and therefore, the point of entry of this

40

fungicide is located somewhere between the upstream and downstream sites. The

introduction of carbendazim and propiconazole are typically influenced by wet-dry

events (Bollman et al. 2014; Singer et al. 2010). Golf courses typically apply

carbendazim early in the season as a casting worm control agent. Carbendazim was only

detected downstream of a large golf course and the Doon WWTP, but not at the site

immediately adjacent to the golf course. This suggests that the primary source of

carbendazim at this site is through discharges of WWTP effluent (Singer et al. 2014).

Propiconazole is commonly mixed with permethrin as a wood preserver that may be

found in barn board and wood fencing. Since propiconazole was only detected at the

rural study sites, it was likely introduced by leachate of treated woods in the upper

portion of the watershed. Byer et al. (2011) monitored for the presence of atrazine across

surface waters in Ontario. The concentrations measured in southern Ontario in this study

are comparable to those measured in the present study.

Studies have shown that ibuprofen is efficiently removed at a rate of 75-90% in

WWTPs (Hijosa-Valsero et al. 2010; Tixier et al. 2003), and therefore, it appears that

there was little addition of this compounds from the Doon WWTP. The primary

contributor of ibuprofen to these sites may be a less efficient WWTP located upstream of

the study sites (Arlos et al. 2014). The pharmaceutical fungicides, climbazole and

fluconazole were detected at the site below the WWTP at concentrations estimated to be

in the low ng/L range. Azole class fungicides have been shown to have poor removal

efficiencies during the wastewater treatment process (Stamatis et al. 2010). However,

WWTPs that employ UV-irradiation as a part of their treatment process have

significantly higher success when eliminating azole fungicides as many of them are

41

readily removed by photodegradation (Chen et al. 2014). The antibiotic trimethoprim

was detected upstream and downstream of the WWTP with a higher estimated

concentration below the outfall (~400%). Concentrations were still in the low ng/L range

(< 5ng/L) downstream of the outfall. Trimethoprim has been shown to be effectively

removed in MWWE by UV-irradiation (Ryan et al. 2011). Trimethoprim is usually

prescribed in combination with the antibiotic, sulfamethoxazole, so it is difficult to

explain why the latter compound was not detected downstream of the WWTP (Table 4b).

The artificial sweetener, sucralose was detected upstream and downstream of the

WWTP, with estimated concentrations of sucralose 280% higher at the downstream

location (Table 4b). Several studies have shown that artificial sweeteners are present in

high concentrations in wastewater, are very poorly removed during the wastewater

treatment process and may be persistent in the environment for months or years

(Mawhinney et al. 2011; Buerge et al. 2009; Scheurer et al 2009); all of which makes

artificial sweeteners such as sucralose ideal tracers of wastewater contamination.

Spoelstra et al. (2013) observed concentrations of sucralose in the Grand River watershed

at concentrations as high as 21 µg/L. The elevated concentrations of sucralose

throughout the watershed may be due to differences in seasonal sampling periods,

sampling technologies, or low flow conditions. It is instructive that this persistent

compound was not detected at the rural sites, unlike the presence of carbamazepine and

naproxen at these upstream sites.

During the deployment of the POCIS, a significant precipitation event occurred

across southern Ontario. This led to an increase in flow rates in the Grand River during

the deployment. The increased flow at each of the sites may have led to the increased

42

dilution of target analytes. However, the precipitation event may also have carried

pesticides from agricultural land into the watershed. This increase in flow may be

responsible for several compounds not being detected. In situ sampling rates for POCIS

vary due to fluctuations in temperature, flow, water chemistry and fouling of the sampler

membranes (Harman et al. 2009). One approach to correcting for these environmental

factors is to use Performance Reference Compounds (Mazzella et al. 2010) but this

approach was not used in the present study due to the short monitoring period.

Table 4a: Mean estimated TWA concentrations (ng/L) ±S.D. of current use pesticides at

each of the five study sites.

Site

Gordonville Petherton FMCDS Horse Ranch Pioneer Tower

Fungicides

Tebuconazole 3.61 ± 0.98 2.07 ± 0.44 2.29 ± 0.69 <LOD <LOD

Propiconazole 0.40 ± 0.28 1.22 ± 0.34 1.42 ± 0.44 <LOD <LOD

Ketoconazole <LOD <LOD <LOD <LOD <LOD

Climbazole <LOD <LOD <LOD <LOD 0.24 ± 0.04

Fluconazole <LOD <LOD <LOD <LOD 2.42 ± 0.44

Carbendazim <LOD <LOD <LOD <LOD 2.65 ± 0.72

Azoxystrobin <LOD <LOD <LOD <LOD <LOD

Myclobutanil <LOD <LOD <LOD <LOD <LOD

Iprodione 1.62 0.28 ± 0.29 0.78 ± 0.54 <LOD <LOD

Herbicides

Mecoprop <LOD <LOD <LOD <LOD <LOD

Atrazine 396.65 ± 31.23 398.46 ± 13.40 418.38 ± 33.38 71.44 ± 5.59 129.66 ± 14.07

Dicamba <LOD <LOD <LOD <LOD 31.14 ± 18.68

2,4-D <LOD <LOD <LOD <LOD 38.57 ± 19.75

Biocides

Irgarol 1051 <LOD <LOD <LOD <LOD <LOD

Terbutryn <LOD <LOD <LOD <LOD <LOD

43

Table 4b: Mean estimated TWA concentrations (ng/L) ±S.D. of contaminants of steroid

hormones, pharmaceuticals and sucralose at each of the five study sites.

Site

Gordonville Petherton FMCDS Horse Ranch Pioneer Tower

Hormone

Estrone <LOD <LOD <LOD <LOD <LOD

Androstenedione <LOD <LOD <LOD <LOD <LOD

Painkiller

Acetaminophen <LOD <LOD <LOD 0.19 ± 0.06 0.30 ± 0.32

Anti-inflammatory

Naproxen 0.38 ± 0.11 0.13 ± 0.23 0.33 ± 0.57 1.81 ± 1.12 2.56 ± 1.00

Ibuprofen <LOD <LOD <LOD 5.72 ± 1.15 4.88 ± 1.22

Artificial

Sweetener

Sucralose <LOD <LOD <LOD 40.87 ± 10.25 156.54± 30.12

Antibiotic

Sulfamethoxazole <LOD <LOD <LOD <LOD <LOD

Trimethoprim <LOD <LOD <LOD 0.48 ± 0.20 2.42 ± 0.17

Cholesterol

reducer

Gemfibrozil <LOD <LOD <LOD 0.21 ± 0.06 0.62 ± 0.03

Anti-convulsant

Carbamazepine 0.1 ± 0.04 0.14 ± 0.01 0.15 ± 0.03 1.63 ± 0.12 4.00 ± 0.17

44

3.3. Fish somatic index data

Table 5 summarizes the data on the average total length and weight of the

rainbow darters before sacrifice (Table 6). On average, both length and weight did not

differ across all sites significantly (p>0.05). However, the fish caught at the most

downstream rural site were significantly longer (p≤0.01) and heavier (p≤0.03) relative to

the other sites. Fantail darters sampled simultaneously across the rural sites did not vary

significantly in length (p≥0.75) and weight (p≥0.87).

The LSI data for the rainbow darters showed significant differences among sites

(p<0.001). The LSI was significantly lower in rainbow darters collected at the two

upstream sites located in the rural part of the watershed relative to fish collected at the

FMCDS site and the two urban sites (Table 6). The observed increase in LSI for the

rainbow darters collected at the urban sites suggests that contaminant inputs from

wastewater or other urban origins may be increasing energy storage within the liver.

Previous studies have shown a general elevation in fish LSI sampled in urban areas

(Tetreault et al. 2011; Hanson and Larsson 2009). As shown in Table 6, there was no

significant difference observed in the LSI of fantail darters collected at the 3 rural sites

(p≥0.39). Significant differences in LSI were observed between species of darters at

Petherton (p≤ 0.01) only. The observed LSI for female rainbow darters taken from the

urban sites in the present study were much lower than those reported by Fuzzen et al.

(2015) and Bahamonde et al. (2014) when they sampled female rainbow darters at the

same study sites. This may be primarily due to the sampling season, as the sampling

period in the above mentioned studies was during the spawning season, and the present

45

study sampled approximately one month after spawning. This suggests that the female

rainbow darters had lower energy reserves during the later sampling period.

The condition factor (k) relating length to weight calculated for rainbow darters

did not differ statistically between rural and urban sites (p≥0.11). Rainbow darters

collected previously at the urban study sites by Fuzzen et al. (2015) and Tanna et al.

(2013) had higher condition factors than those reported in the present study. The

differences in the observed condition factors may be due to the difference in seasonal

sampling (i.e. spring vs summer), but it cannot be ruled out that these changes are due to

technology upgrades made to the Doon WWTP in 2015. A trend of a decreasing

condition factor for fantail darters was observed from the most upstream to the most

downstream rural sites, but the only significant difference for this parameter was between

darters collected at Gordonville and Petherton (p<0.001). Fantail darters have been

shown to require warmer water temperatures to spawn, which typically results in a

spawning season roughly 1 month later than rainbow darters (Hubbs et al 1985). The end

of the fantail’s spawning season could have potentially coincided with the sampling

period and therefore sampled fantail darters may have had reduced condition factors due

to post-spawning exhaustion.

46

Table 5: Somatic index data for rainbow and fantail darters sampled at 5 locations in the

Grand River, including length, weight, liver somatic index (LSI) and condition factor (k).

Significant differences between sites are shown by a different letter code.

*LSI=(Wliver/Wfish) X 100

**k=(Wfish X 100) / (L3)

RBD=Rainbow darter

FTD=Fantail darter

3.4. EROD assay validation

Because of the low enzyme activity observed with S9 preparations of liver tissue

from darters, we conducted studies with rainbow trout exposed to a known CYP1A

inducer to validate our protocol for the EROD assay (Figure 2). The EROD activity

observed in the rainbow trout was low, but a >5-fold change in activity was observed

Site Species Length (cm) Weight (g) LSI * k **

Gordonville

RBD 5.18 ± 0.12 a 1.71 ± 0.14 a,b 0.99 ± 0.08 a 1.18 ± 0.02

FTD 5.35 ± 0.22 1.49 ± 0.19 1.03 ± 0.09 0.92 ± 0.01 a

Petherton

RBD 5.20 ± 0.10 a 1.62 ± 0.11 a 0.92 ± 0.04 a 1.12 ± 0.02

FTD 5.38 ± 0.03 1.31 ± 0.02 1.10 ± 0.06 0.84 ± 0.01 b

FMCDS

RBD 5.67 ± 0.09 b 2.12 ± 0.09 b 1.20 ± 0.08 a,c 1.15 ± 0.01

FTD 5.28 ± 0.04 1.34 ± 0.03 1.06 ± 0.08 0.88 ± 0.01 a,b

Horse Ranch

RBD 5.35 ± 0.12 a,b 1.91 ± 0.16 a,b 1.40 ± 0.05 b,c 1.19 ± 0.02

Pioneer Tower

RBD 5.39 ± 0.12 a,b 1.89 ± 0.15 a,b 1.45 ± 0.06 b,c 1.16 ± 0.02

47

0.0

0.5

1.0

1.5

2.0

2.5

Blank Solvent BNF

ERO

D A

ctiv

ity

(pm

ol/

min

/mg

pro

tein

)

Treatment

between the groups treated with BNF and the two control groups (p≤0.006). Comparable

trends were observed in the literature using BNF as an inducer of this CYP1A enzyme

(Hodson et al. 1996). The EROD activity generated from S9 preparations of rainbow

trout liver was comparable to activity observed for darters collected from the Grand River

(see data below). Greater activity would have been expected if microsomal preparations

had been tested for the fish species.

Figure 2: Mean EROD activity ± S.E. (pmol/min/mg protein) in livers of rainbow

trout exposed to BNF, and two control groups. Significance is noted by different

letter codes (p<0.05).

3.5. EROD

From the most upstream rural site to the most downstream rural site there was an

increase in EROD activity measured in the livers of rainbow darters (Figure 3). A

significant difference in EROD activity was observed between rainbow darters collected

at Gordonville and FMCDS (p≤0.007), and between this species collected at FMCDS and

Horse Ranch (p≤0.03). EROD activity is compared in Figure 4 among the rural study

sites for rainbow and fantail darters. A significant site effect was observed (p≤0.0006) on

a

a

a

a

a

a

b

a

a

48

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Gordonville Petherton FMCDS Horse Ranch Pioneer Tower

ERO

D A

ctiv

ity

(pm

ol/

min

/mg

pro

tein

)

Site

the EROD activity measured in both darter species. No significant species effects or

interactions between the different species with their sample sites were observed. The

high standard deviation in EROD activity may be explained by biological variability in

metabolism of xenobiotics. A review of the literature shows that site effects such as

water temperature and pH in combination with nutritional status, age and reproductive

state may influence EROD activity in fish as enzyme activity is increased to compensate

for lower reaction rates that result from environmental stress (Sole et al. 2015; Ribalta et

al. 2015).

Figure 3: Mean EROD activity ± S.E. (pmol/min/mg protein) in livers of rainbow

darters at each of the five sites. Significance noted by different letter code

(p<0.05)

a

a

a

ab

a

a

b

a

a

ac

a

a

abc

49

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Gordonville Petherton FMCDS

ERO

D A

ctiv

ity

(pm

ol/

min

/mg

pro

tein

)

Site

RBD

FTD

Figure 4: A comparison of mean EROD activity ± S.E. (pmol/min/mg protein) in

livers of rainbow (RBD) and fantail (FTD) darters at the rural sites.

The mean EROD activity measured in the livers of the darter species was

relatively low when compared to other studies. Hoeger et al. (2004) reports EROD

activity from 4-12 pmol/mg/min in the livers of rainbow trout exposed to a 10% MWWE.

Jasinka et al. (2015) exposed caged fathead minnows (Pimephales promelas) upstream

and downstream of a WWTP and measured EROD activity in liver tissue between 30-35

pmol/mg/min at the reference sites and between 40-70 pmol/mg/min immediately

downstream of the WWTP discharge, decreasing to below 20 pmol/mg/min at a site 10

km downstream of the discharge. However, in these other studies, microsomal

preparations from liver tissue were used in the EROD assays, as opposed to S-9

preparations used in the present study. Biological differences between species and the

sampling seasons may have also influence the level of EROD activity observed and

account for the different responses between the fish studied.

50

Exposure to endocrine disrupting compounds has been shown to influence the

hepatic metabolism of xenobiotics in fish (Guillette and Gunderson 2001). Several

studies conducted on darters in the Grand River watershed have documented gonadal

intersex related to the presence of endocrine disrupting compounds (Bahamonde et al.

2015; Tanna et al. 2013; Tetreault et al. 2011). Endocrine disrupting compounds have

been shown to reduce hepatic metabolism in female fish, thus reducing CYP450 activity

within the liver (Mortensen and Arukwe 2007; Guillette and Gunderson 2001).

Therefore, endocrine disrupting compounds at the five study sites may be down-

regulating hepatic EROD activity in the darters, resulting in relatively low EROD activity

in the liver tissue.

3.6. TBARS

A general trend of an increase in the measured levels of TBARS was observed

heading downstream through the agriculturally impacted sites into the urban impacted

sites (Figure 5). The slight decrease in mean TBARS between fish collected at

Gordonville and Petherton was not significantly significant (p>0.05). Fish collected at

the most downstream rural site, FMCDS, had significantly higher TBARS compared to

the other two rural sites (p<0.001). An increase in TBARS was observed between the

urban sites at Horse Ranch and Pioneer Tower (p<0.001). Darters collected at Pioneer

Tower, which is below the Kitchener WWTP, exhibited an increase in TBARS of

approximately 50% compared to the site immediately upstream, and a range in increase

between 50-300% when compared to the rural sites.

Significant differences in biological responses related to the site (p<0.0001) and

species (p<0.0001) were observed among darters at Gordonville and FMCDS, but no

51

significant species-site interaction was observed. Fantail darters were found to have

significantly higher levels of TBARS compared to rainbow darters at each site (Figure 6).

Fantail darters sampled at the most upstream site, Gordonville, and at the most

downstream rural site, FMCDS, were observed to have more than two-fold the

concentration of measured TBARS compared to the rainbow darters sampled at the same

sites (p<0.001). Reasons for the significant difference between the species is unknown at

this time. However, it may be due to some intrinsic factor influencing anti-oxidant

defense systems in the two species, or differences in the sensitivity to reactive oxygen

species. Temperature and pH have been shown to influence TBARS in fish species,

making these possible confounding factors for the differences in responses observed

between rainbow and fantail darters (Maqsood and Sootawat 2011). Differences in the

reproductive cycles of the two fish species may also be responsible for the differences in

biological responses between species.

Typically TBARS are measured using liver tissue in fish (Oakes and Van der

Kraak 2003; Pedrajas et al. 1995). However, due to the small size of the darters and the

tissues required for other biomarker analyses, gill tissue was substituted in place of liver

for this assay. Although the liver is ideal due to its ability to express oxidative stress, the

collected gill tissue performed well to screen for differences in response of this particular

biomarker between sites. When compared to results in the literature, the observed

TBARS concentrations are within the range reported by Sanchez et al. (2007) who

measured responses of TBARS in stickleback (Gasterosteus aculeatus L.) liver at sites

contaminated with urban pollutants (i.e. MWWE) and sites that were relatively pristine.

Sanchez et al. (2007) reported TBARS ranges of 44-55 nmol/g for fish in the non-

52

contaminated sites, and 120-205 nmol/g for fish sampled from contaminated sites. By

comparison, rainbow darters exhibited TBARS within the lower range (i.e. pristine sites)

in the upper rural watershed, and exhibited TBARS within the higher range at the urban

sites, particularly downstream of the WWTP. Relative to the stickleback data, fantail

darters exhibited responses in the higher range at the most upstream and most

downstream rural sites, while falling within the lower range for the second most upstream

rural site.

Oakes and Van der Kraak (2003) measured TBARS in white sucker (Catostomus

commersoni) exposed to pulp mill effluent. The study reported that TBARS in the livers

of white suckers ranged from 250-350 nmol/g tissue, and 15-23 nmol/g tissue in the

gonad. The range in response reported in the present study is comparable. A comparison

between the results in the present study and those presented by Oakes and Van der Kraak

(2003) suggests that sampling the fish liver may result in higher TBARS concentrations

than gonad or gill tissue. The difference in response between TBARS in the liver and gill

must be considered when comparing the data from the present study to the values

reported by Sanchez et al. (2007) and by Oakes and Van der Kraak et al. (2003).

Studies have shown that the presence of certain PPCPs may elevate TBARS in

fish populations. Azole fungicides, carbamazepine, and atrazine have all been shown to

induce TBARS in fish (Ferreria et al. 2010; Nwani et al. 2010). In the present study, the

sites with the highest concentrations of these compounds detected in POCIS also

exhibited higher TBARS concentrations in the darters. Therefore, these compounds may

be influencing oxidative stress in rainbow and fantail darters.

53

0

50

100

150

200

250

300

350

Gordonville Petherton FMCDS

TBA

RS

(nm

ol/

g ti

ssu

e)

Site

RBD

FTD

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

Gordonville Petherton FMCDS Horse Ranch Pioneer Tower

TBA

RS

(nm

ol/

g ti

ssu

e)

Site

Figure 5: Mean levels of TBARS ± S.E. (nmol per g) in gill tissue collected from

rainbow darters sampled from each of the five sites. Significance is noted by

different letters (p<0.05).

Figure 6: Comparison of mean levels of TBARS ± S.E. (nmol per g) in gill tissue

collected from rainbow (RBD) and fantail (FTD) darters sampled from each of the

three rural sites.

a

a

a

a

a

a

b

a

a

b

a

a

c

a

a

54

Non-steroidal anti-inflammatory drugs (NSAIDs) have been shown to influence

TBARS in two different ways. Naproxen has been shown to act as a strong inducer of

TBARS within fish (Nunes et al. 2015; Gagne et al. 2006). Increases in naproxen

concentrations align with TBARS responses between sites, suggesting possible influence

on the oxidative stress response in the darters. On the contrary, ibuprofen has been

shown to reduce the impacts of reactive oxygen species as it acts as a cellular protection

mechanism (Bartoskova et al. 2013). The presence of ibuprofen at the urban sites may be

counteracting the TBARS induction caused by exposure to other contaminants at these

sites.

3.7. AChE

As shown in Figure 7, AChE activity in the brain tissue of rainbow darters

decreased in rainbow darters with distance downstream (p≤0.0002). Significant

differences in AChE activity were observed between fish collected at Petherton and

Horse Ranch (p≤0.038), and between fish collected at Petherton and Pioneer Tower

(p≤0.007). A significant species-site interaction (p≤0.0006) was observed in AChE

responses, but no significant differences between species collected at the three rural sites

were observed (Fig 8). It is possible that differences in individual health related to age,

nutrition and habitat may be responsible for variability that obscures the significance of

differences between species. The fantail darters sampled at the agriculturally impacted

sites experienced a trend of increasing AChE activity between Petherton and FMCDS

(p≤0.002).

The AChE activity in brain tissue from both species of fish observed in this study

are generally within the ranges reported for the brain tissue of fish in several other

55

studies. Sumith et al. (2012) reported AChE activity in the brain tissue of G. ceyonensis,

D. malabaricus, and R. daniconius collected from agriculturally impacted sites to be

within the ranges of 0.6-1.4 µmol/min/mg protein, which is similar to the range of 0.4-

2.75 µmol/min/mg protein reported in this study. There is no obvious explanation for

why AChE activity is different between rainbow and fantail darters. While activity in the

brains of the rainbow darters gradually decreases downstream, there is an increase for the

fantail darters. It is possible that there are differences in the rates of metabolism of

pesticides between species, but this requires further study.

The significant decrease in AChE activity in rainbow darters from the

agriculturally impacted watershed into the urban watershed is consistent with the

presence of 2,4-D and dicamba at both urban sites. Both of these herbicides have been

shown to inhibit AChE in fish when present at concentrations similar to those mentioned

in the present study (da Fonseca et al. 2008; Cattaneo et al. 2008). Atrazine has also been

shown to inhibit AChE over extended periods of exposure similar to those measured in

this study (Santos and Martinez 2012). Therefore it is possible that AChE activity in

darters collected at both agriculturally impacted and urban impacted sites is being

influenced by the presence of these herbicides. The presence of other contaminants

including neonicotinoids, and organophosphate pesticides that were not measured in this

study may also be influencing AChE in darters as these insecticides are commonly used

across agriculture in southern Ontario (Cutler and Scott-Dupree 2014).

56

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Gordonville Petherton FMCDS

AC

hE

Act

ivit

y (u

mo

l/m

in/m

g p

rote

in)

Site

RBD

FTD

0.0

0.5

1.0

1.5

2.0

2.5

Gordonville Petherton FMCDS Horse Ranch Pioneer Tower

AC

hE

Act

ivit

y (u

mo

l/m

in/m

g p

rote

in)

Site

Figure 7: Mean AChE activity ± S.E. (µmol/min/mg protein) in brain tissue of

rainbow darters at all five sites. Significance noted by different letter (p<0.05).

Figure 8: A comparison of the mean AChE activity ± S.E. (µmol/min/mg protein) in

brain tissue of rainbow darters and fantail darters collected at the rural sites.

a

a

a

a

a

a

a

ab

a

a

b

a

a

b

a

a

57

4.0 Conclusions

The use of in situ biomarkers to determine biological impacts from CECs,

including PPCPs and CUPs is a powerful tool for environmental assessment when used in

combination with contaminant monitoring. Biomarkers altered in rainbow and fantail

darters included elevated levels of oxidative stress (TBARS), an induction of EROD

metabolism, which is associated with CYP1A activity, and the inhibition of the

neurotransmitter AChE. Fantail darters exhibited elevated concentrations of TBARS

compared to rainbow darters, as well as greater inhibition of AChE at the most

downstream agriculturally impacted site. Rainbow darters exhibited higher EROD

activity compared to fantail darters at the most upstream agricultural site only.

This study found that in some instances, the intensity of biomarker responses

reflected the presence of specific chemical contaminants. Biomarker responses in the

fantail darters sampled at the most downstream agricultural site were variable from one

biomarker to the next and did not fit the trends observed with biomarkers at the other two

agricultural sites. Only AChE inhibition exhibited a significant species-site interaction,

but a species-site trend that was close to significance was also observed with the TBARS

assay. Significant site and species effects were observed with the TBARS assay. There

was a trend for differences between species for AChE inhibition that was close to

significance. Further investigation is required to determine the mechanisms for the

differences in species responses, as these data are insufficient to identify the origin of

these differences.

58

Chapter 3: Conclusions and Future Steps

1.0. Major Findings

The primary goal of this study was to evaluate and compare biological responses

in darter species collected in sections of the Grand River watershed that are primarily

impacted by agricultural or urban activities. The first major finding of this study was that

there is a trend of increasing biological responses in darters collected from the Grand

River with distance downstream from areas dominated by agricultural land uses to urban

sites. Secondly, there was evidence of differences in biological response between the two

species of darters that were collected.

While biomarkers in fish collected at each site did not always show significant

differences, there were several significant differences in biomarker responses observed

across the study area. The changes in biological responses between the study sites

indicate that there are cumulative effects from contaminants as they are carried

downstream. Some of the biological responses observed in the present study indicate that

fish sampled in the urban watershed may be experiencing higher levels of stress due to

exposure to contaminants from urban sources (i.e. municipal wastewater effluent, runoff)

in combination with upstream agricultural contaminants. The rural watershed does

experience inputs from several small WWTPs and potentially from septic leakage, but it

is expected that fish will be primarily affected by chemicals of agricultural origin, such as

pesticides and runoff from land amended with biosolids. In this case, exposures will be

influenced by the timing of applications, and meteorological events that affect runoff, as

59

well as Best Management Practices, such as the size and location of buffer strips and

riparian zones.

Fantail darters were collected simultaneously with rainbow darters at the three

rural study sites. Differences in biomarker responses were observed between the two

species at all sites. However, the trend was not always consistent. The fantail darters did

not seem to always follow similar patterns of response. AChE activity measured in the

fantail darters was particularly interesting as they showed a significant increase in AChE

activity at the most downstream rural site compared to the other two upstream sites.

Rainbow darters showed a trend of greater AChE inhibition at downstream sites, which

would be expected from cumulative exposure to contaminants of agricultural origin. No

obvious relationship in responses between the species of darter and the study sites were

observed. Only AChE was found to have a significant species-site interaction. A more

detailed study is required to validate the differences in response between the two species

of darters and to determine the mechanisms for these responses.

2.0.Project Objectives and Hypotheses

This study aimed to achieve three objectives: 1) to monitor for the presence and

estimated TWA concentrations of targeted pharmaceuticals and personal care products

(PPCPs) and current use pesticides (CUPs) at the five study sites, 2) to monitor for

differences in the biological responses between rainbow darters between the five study

sites, and 3) to monitor for differences in biological responses between two species of

darters at each of the three rural sites. Using POCIS at each of the five study sites,

several PPCPs and CUPs were detected and successfully analyzed. Although some

60

compounds were not detected or quantifiable at all sites, many were quantified at either

all of the sites, or at all sites within either the rural or urban watershed.

The hypothesis H1 set out for this study predicted that biological responses

indicative of exposure to chemical contaminants would be observed in both darter species

at each of the study sites. This hypothesis was accepted as there was typically a

significant increase in the response of TBARS, EROD, and AChE trending downstream,

with some of the most intense responses at the urban study sites. As discussed in Section

1.0 of this chapter, biological responses were observed in darters collected at the five

study sites. The differences in responses observed in darters between several of the sites

were found to be statistically significant. However, for reasons that are difficult to

explain at this time, the AChE response in the fantail darters followed an inverse trend

compared to what was observed in the rainbow darters. When compared to the literature,

the observed responses in rainbow darters are similar to those found in fish collected at

sites impacted by chemical contaminants. The trends of response noticed in fantail

darters were comparable to the literature in terms of induction of TBARS and EROD, but

followed an inverse trend for AChE. The observed trends indicate that the changes in

biological response between darters collected in the Grand River may be due to

contaminants discharged through point and non-point sources that are specific to the rural

and urban study sites.

Hypothesis H2 was rejected, as there were significantly different responses in

TBARS, EROD, and AChE between rainbow and fantail darters collected at the three

rural study sites. There was no obvious trend for which species exhibited more intense

responses, as the fantail darters exhibited significantly higher TBARS compared to

61

rainbow darters, but typically exhibited less EROD activity and a reverse trend in AChE

inhibition. Reasons for differences in biomarker responses may be due to differences in

health and condition (e.g. exhaustion post-spawning, nutrition) or age. Fish in poor

health or with poor nutritional status may have been more susceptible to exhibiting

biomarker responses. Differences in tolerance to water temperature, pH, dissolved

oxygen concentrations, and ammonia concentrations (not measured in this study) may

have also influenced biological response. A more intensive study is required to

investigate the origins of the differences in biological response between rainbow and

fantail darters.

Typically, compounds of urban wastewater origin were found at the urban sites

and those of agricultural origin were found at the rural sites. However, there were some

exceptions. Atrazine, was found at between 70-140 ng/L at the urban sites, indicating

that there may have been urban inputs. Discharges of atrazine from WWTPs have been

observed before and attributed to the deposition of contaminated septic waste or

contaminated storm water overflow (Kolpin et al. 2006), but it is possible that this

herbicide originates from agricultural applications at upstream locations in the watershed.

The herbicides 2,4-D and dicamba, and the fungicide, carbendazim were also detected at

the site downstream of the Doon WWTP, indicating that the WWTP or another point

source (e.g. golf course) may be the origin of these compounds. Some pharmaceuticals

were detected in the rural watershed, which may have been from biosolids runoff, septic

leakage or through discharges from small, rural WWTPs. There are insufficient data to

accept or reject hypothesis H3 which focused on the distribution of contaminants between

the study sites. Some of target compounds were detected throughout the watershed (e.g.

62

atrazine, carbamazepine), but others were only detected in the rural section of the

watershed (e.g. tebuconazole, propiconazole, iprodione), or in the urban areas (e.g.

carbendazim, ibuprofen, trimethoprim, sucralose). The presence of some chemical

contaminants could be related to specific biomarker responses in the darters (e.g.

naproxen and TBARS), but other biological responses, such as induction of EROD could

be caused by a whole range of contaminants that were not monitored in this study.

In the future it may be beneficial to select a greater number of sample sites within

rural and urban sections of the watershed. This would allow for a more robust data set in

terms of differences in biomarker responses across a gradient of rural and urban sites. It

would also be beneficial to include proper reference sites for both rural and urban study

areas to have an appropriate baseline for the different somatic indices and biomarker

responses.

When sampling at both rural and urban sites there is a potential for biological

responses induced by confounding chemical and physical factors such as changes in

water temperature, flow, pH, nutrient levels, and dissolved oxygen. For future work it

would be beneficial to find reference sites of similar habitat to control for these

influences. This study would have also benefited from sampling species of darters that

are present at all of the sampling sites to create a more complete data set. The darters

collected may have been at different points in their reproductive cycle, which may have

influenced biomarker responses due to post-spawning exhaustion. Sampling fish at

different trophic levels instead of similar species may have offered more instructive

results as to how different fish species respond to the chemical stressors.

63

3.0. Future Work

Several observations in this study open up potential for future research. Female

darters were targeting in this study to maintain consistency and to reduce variability in

the ability of each sex to metabolize contaminants. The trends observed in the present

study indicates that a more extensive project may be beneficial. Five study sites were

used in this study, and only polar contaminants were considered. Future work may look

at expanding the total number of study sites to monitor across an exposure gradient, while

monitoring for both polar and non-polar organic contaminants.

An investigation into why fantail darters exhibited significantly different

responses compared to rainbow darters may also be of value. Both species share a

similar life history, making mechanistic studies of interest. It may be beneficial to

explore potential differences in responses to known inducers of targeted biomarkers (i.e.

β-napthoflavone for EROD induction) in a controlled setting. Laboratory trials may

indicate whether the species has an intrinsic buffering capacity for xenobiotic exposure,

or if the reasons for differences in their responses are more closely linked to differences

in habitat selection and life history.

Sucralose was detected at high concentrations downstream of the Doon WWTP.

However, this persistent compound was not detected at any of the rural sites. It may be

beneficial to investigate the removal of artificial sweeteners, including sucralose, at

several different WWTPs utilizing different treatment technologies and with services for

different population sizes. Sucralose is primarily discharged through WWTP effluent and

is persistent, only showing signs of degradation in water at pH 10 (Tollefsen et al. 2012).

64

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Appendix 1: POCIS Sampling Rates for PPCPs

Table A1: Mean (±SD) sampling rates (Rs) in litres per day determined for the target

compounds in POCIS in static experiments at 15oC (n=3). Sampling rates were

determined by Li et al. (2010b).

1) Rs determined by Metcalfe et al. (2014).

COMPOUND Rs

Hormone

Estrone 0.636 ± 0.068

Androstenedione 0.410

Painkiller

Ibuprofen 0.254 ± 0.019

Acetominophen 0.111 ± 0.016

Anti-inflammatory

Naproxen 0.298 ± 0.016

Artificial Sweetener

Sucralose1 0.160

Antibiotic

Sulfamethoxazole 0.348 ± 0.049

Trimethoprim 0.411 ± 0.073

Cholesterol reducer

Gemfibrozil 0.306 ± 0.031

Anti-convulsant

Carbamazepine 0.397 ± 0.018

79

Appendix 2: POCIS Sampling Rates - CUPS

Table A2: Mean (±SD) sampling rates (Rs) in litres per day determined for the target

compounds in POCIS in static experiments at 20oC (n=3). Sampling rates were

determined by Metcalfe et al. (submitted).

1) Determined from amounts accumulated on POCIS sorbent

COMPOUND Rs

Fungicides

Propiconazole 0.469 ± 0.049

Tebuconazole 0.440 ± 0.047

Ketoconazole 0.474 ± 0.037

Climbazole Rs?

Fluconazole 0.379 ± 0.048

Clotrimazole 0.564 ± 0.065

Azoxystrobin 0.318 ± 0.008

Myclobutanil 0.293 ± 0.032

Carbendazim 0.341 ± 0.032

Iprodione 0.492 ± 0.013

Thiophanate-methyl1 0.092

Herbicides/Biocides

Atrazine 0.214 ± 0.069

Diuron 0.765 ± 0.066

Irgarol 1051 0.396 ± 0.019

Glyphosate Not determined

Terbutryn 0.461 ± 0.031

Dicamba1 0.0312

2,4-D1 0.0292

Mecoprop1 0.0672

80

Appendix 3: Fungicide LC-MS/MS Parameters

Table A3.1: Ionization parameters for the pesticides targeted in this study.

Analyte Q1 Q3 Polarity DP EP CE CXP

Azoxystrobin 404.145 85.5 + 146 10 33 18

Fluconazole 306.99 238 + 121 10 23 22

Irgarol

254.076 198 + 76 10 25 20

Climbazole 293.006 197 + 131 10 23 18

Myclobutanil 289.008 69.9 + 121 10 23 10

Propiconazole 342.122 158.9 + 136 10 37 18

Tebuconazole 308.117 69.9 + 126 10 45 8

Carbendazim 192.097 159.9 + 116 10 23 18

Atrazine

216.189 174 + 101 10 23 16

Ketoconazole 531.233 489 + 166 10 43 30

Terbutryn 242.133 186 + 121 10 25 22

Dicamba

218.867 160.8 - -55 -10 -18 -17

2,4-D

219.906 161.9 - -50 -10 -18 -13

Iprodione 328.007 140.8 - -80 -10 -16 -17

Mecoprop 212.948 140.9 - -90 -10 -20 -19

81

Table A3.2: Ionization parameters for all pesticide surrogates used in this study.

Internal Standard Q1 Q3 Polarity DP EP CE CXP

Ketoconazole-d4 535.041 81.1 + 211 10 107 10

Carbendazim-d4 196.05 164 + 56 10 7 18

Fluconazole-d4 311.021 70.1 + 131 10 51 12

Propiconazole-d5 347.023 279.1 + 66 10 13 28

Atrazine-d5 220.996 72.9 + 176 10 29 10

Terbutryn-d5 247.045 172.9 + 151 10 23 20

Tebuconazole-d6 313.3 91.2 + 14 10 28 4

2,4-D-d3

221.722 163.9 - -25 -10 -18 -19

2,4-C-d3

216 143.8 - -25 -10 -17 -10

3,6-D-d3

221.816 164 - -60 -10 -18 -7

Iprodione-d5 333.03 96.9 - -40 -10 -38 -11

82

Table A3.3: Pesticide analytes with their corresponding internal standards (IS) used in

the present study.

Analyte MW (g/mol) I.S.

Azoxystrobin 404.145 Atrazine-d5

Fluconazole 306.99 Fluconazole-d4

Irgarol 254.076 Atrazine-d5

Climbazole 293.006 Atrazine-d5

Myclobutanil 289.008 Atrazine-d5

Propiconazole 342.122 Propiconazole-d5

Tebuconazole 308.117 Tebuconazole-d6

Carbendazim 192.097 Carbendazim-d4

Atrazine 216.189 Atrazine-d5

Ketoconazole 531.233 Ketoconazole-d4

Terbutryn 242.133 Terbutryn-d5

Dicamba 218.867 3,6-D-d3

2,4-D 219.906 2,4-D-d3

Iprodione 328.007 Iprodione-d5

Mecoprop 212.948 2,4-C-d3