Mechanisms of Pharmaceutical and Personal Care Product Removal in Algae-Based Wastewater Treatment

by

Christian William Talbot Larsen

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

Department of Civil & Mineral Engineering University of Toronto

© Copyright by Christian William Talbot Larsen, 2018

ii

Mechanisms of Pharmaceutical and Personal Care Product

Removal in Algae-Based Wastewater Treatment

Christian Larsen

Master of Applied Science

Department of Civil & Mineral Engineering

University of Toronto

2018

Abstract

Algae-based wastewater treatment is a form of passive wastewater treatment used to treat

municipal and agricultural wastewaters. While pharmaceutical and personal care product (PPCP)

treatment has been observed, the removal mechanisms in these systems are poorly understood.

In this study, lab-scale algal bioreactors were used to simulate algae-based wastewater treatment.

Concentrations of carbamazepine, ibuprofen, gemfibrozil, and triclosan were monitored in these

reactors alongside controls designed to isolate and elucidate removal processes. Ibuprofen was

primarily treated by biotransformation, which was dependent on interactions of algae with the

bacteria and media. Triclosan was rapidly phototransformed, though there was evidence of

biodegradation or sorption. There was no evidence of carbamazepine and gemfibrozil treatment

in algae-based wastewater systems.

Based on the results of these experiments, algae can facilitate PPCP removal in passive water

treatment systems. Further research on PPCP removal in these systems should be focused on the

interactions between bacteria, algae, and media.

iii

Acknowledgments

First and foremost, I would like to thank Jesus Christ, my beautiful Lord and Saviour. To you be

the glory.

Thank you to Elodie Passeport for her constant presence throughout this journey. You have

always exceeded expectations with your willingness to give your time and energy to your

students and I am extremely thankful for that. My degree wouldn’t have been what it was if it

were not for your support, drive, and hard work throughout the process. And of course, thank

you for the hours and hours of editing.

Thank you to those in Biozone and the Department of Ecology & Evolutionary Biology for all

the technical support and resources they have provided. In particular, thanks to Susie, Sam,

Mitchell, Megan, and Jason.

Thank you to Leandra, Suchana, Shirley, Ceren, Kelsey, Melisa, Antoine, and the remainder of

my lab group. For every long day in the lab, there was always someone to give encouragement

(and often snacks) to a very tired Christian. Thank you all for of your emotional and technical

support throughout this journey.

Thank you to Wei Cheng Hoi for assisting me in the lab this summer and for doing many, many

hours of algae counting.

Thank you to my wonderful parents for supporting me financially and morally throughout my

education, this would not be possible without you. Thank you to my friends and church for

encouraging me and lifting my spirits during my most stressed out moments.

Finally, this project was provided by both the Department of Civil & Mineral Engineering at the

University of Toronto and a Queen Elizabeth II Graduate Scholarship in Science & Technology.

iv

Table of Contents

Acknowledgments.......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

List of Appendices ....................................................................................................................... viii

Chapter 1 Literature Review ............................................................................................................1

Introduction .................................................................................................................................1

1.1 Literature Review.................................................................................................................2

1.1.1 Biotransformation ....................................................................................................3

1.1.2 Phototransformation .................................................................................................5

1.1.3 Sorption ....................................................................................................................7

1.1.4 Volatilization............................................................................................................9

1.2 Compound Selection ..........................................................................................................10

Chapter 2 Methods .........................................................................................................................12

Methods .....................................................................................................................................12

2.1 Chemicals ...........................................................................................................................12

2.2 Lagoon Water and Algae Inoculum ...................................................................................12

2.3 Experimental Setup ............................................................................................................12

2.4 Sampling ............................................................................................................................15

2.5 Analytical Methods ............................................................................................................16

Chapter 3 Results ...........................................................................................................................17

Results .......................................................................................................................................17

3.1 Algae Cell Density .............................................................................................................17

3.1.1 pH ...........................................................................................................................18

3.1.2 Ibuprofen ................................................................................................................18

v

3.1.3 Triclosan ................................................................................................................20

3.1.4 Carbamazepine and Gemfibrozil ...........................................................................23

Chapter 4 Discussion .....................................................................................................................26

Discussion .................................................................................................................................26

4.1 Ibuprofen ............................................................................................................................26

4.2 Triclosan ............................................................................................................................28

4.3 Carbamazepine and Gemfibrozil .......................................................................................30

Chapter 5 Conclusions and Recommendations ..............................................................................31

Conclusions and Recommendations .........................................................................................31

References ......................................................................................................................................33

Appendices .....................................................................................................................................42

vi

List of Tables

Table 1: Structures and properties of studied PPCPs.................................................................... 11

Table 2: Concentrations of Chlorella vulgaris cells in Experiment 1 ........................................... 17

Table 3: Pseudo first-order degradation rate coefficients for triclosan ......................................... 22

vii

List of Figures

Figure 1: Reactor conditions investigated in Experiments 1 and 2 .............................................. 13

Figure 2: Ibuprofen concentrations in experimental reactors of Experiments 1 and 2 ................. 19

Figure 3: Triclosan concentrations in experimental reactors of Experiments 1 and 2 .................. 21

Figure 4: Carbamazepine concentrations in experimental reactors of Experiments 1 and 2 ........ 24

Figure 5: Gemfibrozil concentrations in experimental reactors of Experiments 1 and 2 ............. 25

viii

List of Appendices

Appendix 1: Reactor Conditions ................................................................................................... 42

Appendix 2: SPE Recovery Rates ................................................................................................. 46

Appendix 3: Sample Chromatograms ........................................................................................... 47

Appendix 4: Calibration Curves ................................................................................................... 49

Appendix 5: PPCP Limits of Detection and Quantification ......................................................... 52

Appendix 6: Algae Counting Limits of Detection and Quantification ......................................... 55

Appendix 7: pH Data .................................................................................................................... 57

Appendix 8: Ibuprofen Variability in Experiment 2 ..................................................................... 57

Appendix 9: Triclosan Removal Kinetics ..................................................................................... 60

1

Chapter 1 Literature Review

Introduction

Municipal wastewater is a complex mixture of household wastewater, industrial

wastewater, and stormwater runoff. As a result, conventional wastewater treatment has many

treatment goals, including the removal of wastewater suspended solids, biological oxygen

demand (BOD), pathogens, and nutrients. However, the treatment of pharmaceuticals and

personal care products (PPCPs) in municipal wastewater has been ignored until relatively

recently, and as a result hormones, stimulants, antimicrobials, synthetic fragrances, pain

relievers, and antidepressants are now found on a ng/L to µg/L scale in surface water,1,2

groundwater,3,4 and drinking water5 in Canada and worldwide.

The presence of these compounds in the environment poses an environmental and human

health risk. Aquatic species downstream from WWTPs have shown evidence of stimulation of

the immune response6 and reproductive impairment, including increased presence of intersex

fish.7 Additionally, antibiotics in the environment have been shown to alter microbial ecology

and can encourage antibiotic resistance.8 While the very low concentrations of individual

pharmaceuticals in drinking water are well below the therapeutic doses, little is known about the

synergistic effects or the effects of inevitable long-term exposure.9 Improving municipal

wastewater treatment to remove these compounds is critical to prevent the discharge of these

compounds into the environment and to ensure the long-term health of water resources.

There is a variety of advanced water treatment processes that can treat PPCPs in

municipal wastewater. For instance, some PPCPs can be broken down by advanced oxidation

processes, adsorbed on activated carbon, or phototransformed by UV disinfection processes.10,11

However, the high operating costs and level of technical knowledge required to operate these

technologies is often prohibitive.

Passive water treatment is an alternative approach to wastewater treatment that does not

require external energy or chemicals. It includes constructed wetlands, lagoons, and bioretention

cells. These systems are designed to optimize natural processes that treat wastewater, leading to

low installation and operating costs, minimal maintenance, and a lower risk of damaged

2

components. This use of natural processes also facilitates the removal of PPCPs and other

organic contaminants via natural physical, chemical, and biological processes that treat

wastewater, including biotransformation, phototransformation, volatilization, and sorption.

Algae are a diverse group of photosynthetic organisms that exist in almost every aquatic

environment. While the use of the term “algae” is somewhat contentious, in this thesis it will

refer to green microalgae, notably excluding prokaryotic cyanobacteria and large brown algae

such as giant kelp. Because of the ubiquity of algae in natural ecosystems and their affinity for

shallow, nutrient-rich waters, green microalgae also appear to some extent in almost all passive

wastewater treatment systems that have permanent ponding water.

However, several passive wastewater treatment processes, such as facultative lagoons,

free surface flow constructed wetlands, and high rate algal ponds, use algal biological processes

to improve water quality. As they grow, algae photosynthesize, releasing oxygen and consuming

carbon dioxide; they consume organic compounds, thus reducing biological oxygen demand;

they uptake nutrients, thus reducing dissolved phosphorus and nitrogen; and they increase the

pH, thus inactivating pathogens. Algae-based wastewater treatment is a low-cost and low-energy

treatment option that can reduce the risk of eutrophication in receiving water bodies.

Algae-based wastewater treatment also has the potential to remove PPCPs from

wastewater. The long retention times, direct exposure to sunlight, and high concentrations of

algal biomass present in these systems could also facilitate PPCP-removal processes that do not

occur in conventional wastewater treatment. Algae-based wastewater treatment has been

demonstrated to be effective at removing conventional wastewater contaminants such as BOD,

nutrients, and pathogens,12–14 but its ability to treat PPCPs is not well-studied.15

1.1 Literature Review

Understanding the mechanisms involved in PPCP removal during passive wastewater treatment

is challenging because of the complexity of these systems. In any treatment system, PPCPs

interact with countless bacteria, algae, organic compounds, particulates, and dissolved ions under

variable temperature, light, and pH conditions. Furthermore, treatment occurs at the microbial

and molecular level, necessitating sophisticated techniques to observe these components. This

3

complex and dynamic reaction environment is difficult to characterize, making it difficult to

unravel the mechanisms governing PPCP removal in algal-based wastewater treatment systems.

To shed a new light on these reaction mechanisms, lab-scale reactors can be used to simulate

full-scale treatment systems, making it possible to isolate removal mechanisms and control

environmental conditions. The results of such studies can then be used to better help manage and

design field-scale algal ponds.

Based on the behavior of PPCPs in other biological water treatment systems, hypotheses can be

formulated about the removal mechanisms in algae-based water treatment. Matamoros et al.

(2015) suggested four main processes that could result in PPCP removal in HRAPs:

biotransformation, phototransformation, algal sorption, and volatilization.16 The relevance of

these processes to PPCPs in algae-based water treatment is discussed below.

1.1.1 Biotransformation

In wastewater treatment, biotransformation is almost synonymous with the aerobic respiration of

organic compounds by bacteria, the primary removal mechanism of BOD removal in almost

every wastewater treatment process. Aerobic respiration can be thought of as two

complementary processes: catabolism, which breaks down organic molecules to simpler ones

and releases energy, and anabolism, which uses this energy to construct components of cells.

Because many PPCPs are susceptible to catabolism, and microbes can be cultured using several

ECs as their sole carbon source, including pesticides,17 biologically-active wastewater treatment

processes facilitate the catabolism of select PPCPs.

Biotransformation is a complex mechanism that can vary dramatically depending on the

conditions in the treatment environment. For instance, while sometimes transformation rates can

be markedly similar across species,18 several studies have observed differences between the

capacity of different algae species to treat ECs.19,20 Several studies have also observed

interactions between species of microbes. Xiong et al. (2017) observed that a consortium of algae

species was as effective at removing enrofloxacin from wastewater as the most effective

microalgal species, suggesting that interaction effects promoted transformation.19 Matamoros et

4

al. (2016) observed that the addition of algae to wastewater improved biotransformation rates

and eliminated the lag phase for treatment of both ibuprofen and caffeine.21

Furthermore, even within the same species in the same environment, antecedent conditions can

affect the interactions of a microbe with a contaminant. Chlorella vulgaris pre-exposed to 200

mg/L of levofloxacin for 11 days exhibited consistently improved removal of levofloxacin in

subsequent exposures.22 However, this adaptation effect likely only occurs at concentrations

higher than are relevant to municipal wastewater treatment for most contaminants: Spain and

Van Veld (1983) observed similar adaptation to a number of xenobiotic compounds, but only

above a threshold concentration of 10 µg/L.23

The aqueous chemistry of the environment in which the microbial community acts in also can

also dramatically influence removal rates. Xiong et al. (2017) demonstrated that the addition of

glucose and sodium formate to algae growth media can decrease or completely suppress

ciprofloxacin removal by Chlamydomonas mexicana, likely by replacing ciprofloxacin as the

preferred source of organic carbon in a process called catabolic repression. However, in the same

study, the addition of sodium acetate and methanol increased removal rates from 12% to almost

60%, likely because of the algal co-metabolism of these compounds with ciprofloxacin.24

Interactions between microbes, organic contaminants, and the other organic matter has been

observed in other studies as well. Estrone transformation by biomass cultured in synthetic

wastewater varied dramatically depending on the quality of the wastewater organic matter, which

had been modified by aging it 0, 2, or 8 days.25 The relevance of these processes to municipal

wastewater treatment is obvious, as municipal wastewater consistently contains a diverse set of

organic compounds with which microbes interact, in turn affecting their tendencies to degrade

PPCPs. This also suggests that preceding treatment steps also affect PPCP removal in subsequent

biological treatment, as one of the major goals of municipal wastewater treatment is the

elimination of these dissolved organic molecules and ions.

The effects of water chemistry on biotransformation extend beyond organic carbon presence and

quality. One of the primary benefits of using algae in wastewater treatment is the oxygenation of

wastewater, facilitating algal or bacterial aerobic degradation of organic contaminants. This

property has been used to promote the removal of salicylate in algal-bacterial microcosms.26,27

Salinity of the growth media can be a dominant factor in biotransformation of select

5

pharmaceuticals. For instance, levofloxacin removal after 11 days was increased by more than

80% by the addition of 10 g/L sodium chloride to both Chlorella vulgaris and Scenedesmus

obliquus cultures.22,28

1.1.2 Phototransformation

Activated sludge systems recycle active biomass to increase the rate of biodegradation of organic

molecules that create BOD. This is supplemented by mechanical aeration of the water, providing

rapid transfer of atmospheric oxygen to the wastewater and facilitating aerobic biodegradation.

This allows treatment to occur within an hydraulic retention time (HRT) of 1.5-3 hours and a

relatively small land use requirement.29 Instead of mechanical oxygenation, algae-based

wastewater treatment uses algal photosynthesis to oxygenate the water, eliminating the need for

energy-intensive mechanical oxygenation. However, this is a much slower process, and HRTs

for HRAPs typically vary between 3 and 8 days.30

The longer HRT also allows for more time for slower processes to treat contaminants, such as

phototransformation. In activated sludge treatment, water is exposed to at most several hours of

light before discharge, while water treated using a passive system is exposed to several days

worth of sunlight before discharge, providing a larger and much more consistent treatment of

photodegradable compounds. This advantage is emphasized in the use of select algae-dominated

systems such as high rate algal ponds, which are often shallow enough (10-50 cm15) to allow

light penetration to the bottom of the water column, facilitating algae growth and

phototransformation throughout. Ruhmland et al. (2015) have observed that phototransformation

can be limited to the top 10-20 cm of a water column,31 dramatically limiting its effectiveness in

lagoons and activated sludge treatment processes, which can be up to 2 and 6 m deep,

respectively.15

Phototransformation in passive wastewater treatment can be either direct or indirect. Direct

phototransformation is a change in chemical structure caused by the impact of a photon directly

striking the contaminant and inducing a bond breaking, photoionization, or a transformation into

a reactive excited state.32 In order for this to occur, a compound must absorb photons at the

wavelengths that it is exposed to, which is typically sunlight for algae-based wastewater

6

treatment. Direct phototransformation of a PPCP is therefore affected by the quantity of photons

that are available at the relevant (absorptive) wavelengths.

The degree to which relevant photons are available for direct phototransformation is dependent

on variables surrounding the quantity and quality of light, including duration, intensity, and

spectrum. This means that phototransformation can be affected by anything that changes the light

source, such as seasonal varations.33 However, photons can also be absorbed by other

compounds or particles in the water before coming into contact with target compounds. Because

of this, the wastewater matrix also affects direct phototransformation. Suspended solids and

microorganisms can block light penetration and inhibit phototransformation. In reactors with

very high concentrations of algae, transformation of PPCPs is inhibited at very high

concentrations of algal biomass.34 However, dissolved constituents that absorb light, such as

humic acids,35 and nitrates36 can also inhibit direct phototransformation. These compounds are

all common in natural waters and municipal wastewater, and because of this, compounds that

undergo direct photodegradation demonstrate higher phototransformation rates in purer

water.32,37

However, direct phototransformation is also sensitive to factors that alter a contaminant’s

absorption spectrum, including temperature, dissolved ions in the water matrix, and pH.15 For

PPCPs with acid dissociation constants (typically referenced as its logarithmic, the pKa value) at

environmentally relevant values, pH can control whether a contaminant is in its ionic or

molecular form during wastewater treatment. Because the ionic and molecular forms of a

contaminant often absorb different wavelengths of light, pH also controls whether a contaminant

will absorb photons during treatment and whether it will degrade.33

Triclosan is a pertinent and well-studied example of this. Its anionic form absorbs light at higher

wavelengths than the molecular form, creating much more overlap with the sunlight intensity

spectrum. As a result, the pseudo-first-order rate constants for direct phototransformation of

triclosan are more than 100 times higher for its anionic form compared to its uncharged form.33

Triclosan’s pKa is 7.9,38 making these transformations relevant to triclosan’s behavior in natural

water bodies and in wastewater treatment. Specifically, algae are capable of inducing large

increases in pH during algae-based wastewater treatment,13,39 suggesting that they could also

7

induce the transformation of contaminants such as triclosan by deprotonating triclosan it and

making it more prone to direct phototransformation.

The second component of phototransformation is indirect phototransformation This occurs when

a photon strikes a secondary compound called a photosensitizer, causing it to become a reactive

transient species, often a radical or a reactive excited state much like direct photodegradation.

These transient species then cause indirect phototransformation when they react with the

compound of interest. The group of compounds that act as photosensitizers are extremely

diverse, and include nitrites and nitrates,40 dissolved chloride ions41, humic substances40, fulvic

acids42, iron35, and dissolved oxygen.37

All of the photosensitizers listed are ubiquitous in both wastewaters and surface waters, meaning

that indirect phototransformation is possible in all passive wastewater treatment processes where

direct phototransformation occurs. However, the coupling of a photosensitizer and the target

contaminant can be highly specific, and photosensitizers that are effective at the treatment of

some PPCPs are completely inactive on others.37 Furthermore, because the photosensitizers

necessary for indirect phototransformation absorb photons that could otherwise cause direct

phototransformation, conditions that lead to one type of phototransformation can prohibit the

other. The result is that phototransformation can produce different transformation products

depending on the aqueous environment that it occurs in.32,41 This raises the potential to

manipulate the transformation pathway produced by altering the aqueous matrix.

1.1.3 Sorption

Sorption is the transfer of a compound from an aqueous phase via adhesion onto (adsorption) or

incorporation into (absorption) a solid phase. This paper will consider sorption of PPCPs to algae

as it is the most relevant to algae-based wastewater treatment. In algae-based wastewater

treatment, PPCP sorption onto other solid surfaces (such as soil, suspended particles, or channel

walls) occurs as well, but those topics are reviewed extensively elsewhere and will not be

discussed here. Furthermore, adsorption (adhesion onto the outside of algal cells) and absorption

(uptake into the inside of algal cells) will not be distinguished in this analysis because of the

difficulties involved in distinguishing these processes. Sorptive effects depend on the properties

8

of compound, properties of the surface, the presence of competing other compounds, and

properties of aqueous phase (such as ionic strength, temperature, and pH).15 In algae-based

wastewater treatment, sorption can be affected by variations in algae species,43,44 amount of algal

exudates,45 and the state of the algae: alive or dead.43,46 Furthermore, variations in the wastewater

media47 as well as contaminant concentrations48 can affect sorption, suggesting that preceding

treatment steps can affect sorption in algae-based wastewater treatment. As a result, much like

biotransformation and phototransformation, sorption effects are challenging to characterize as a

group, varying based on the compound, algae state and species, the concentration of the

compound, and influent water quality. However, there are some unique properties of sorptive

removal that are worth discussing further.

Sorption to suspended algal biomass can occur very rapidly, removing contaminants from the

aqueous phase much faster than is typical of other removal mechanisms. For instance, in studies

spanning a variety of algal species, Wang et al. (2013) observed 50% decline in triclosan

concentration within the first hour,49 Shi et al. (2010) observed 20% decline in estradiol and

ethinylestradiol concentrations within two hours,50 and Tam et al. (2002) observed 85%

tributyltin removal in the first five minutes.46 The speed of this decrease in concentration can

likely be attributed to the large total surface area of the individual algae cells, providing a large

interface between the aqueous and algae phases. Passive wastewater treatment is typically reliant

on slower natural processes to treat wastewater, requiring long retention times and often large

areas of land, making the rapid treatment facilitated by algal sorption a unique mechanism in

passive wastewater treatment.

However, also unlike the other removal mechanisms discussed, sorption is an equilibrium

process and stops once it reaches equilibrium. Many studies that have investigated sorption have

observed a rapid decrease in concentration at the start of the experiment caused by sorption,

which is followed by relatively a much slower change in concentration caused by other processes

such as biotransformation or phototransformation.34,49–51 This incomplete removal is likely

because equilibrium is reached, preventing any further net transfer of contaminant to the algal

phase. Phototransformation and biotransformation typically lack a reverse transformation within

a specific environment, preventing an equilibrium state, and volatilization in algae-based

wastewater treatment is in equilibrium with the atmosphere, making residual contaminant at

equilibrium negligible.

9

Assessing which PPCPs are susceptible to algal sorption in algae-based wastewater treatment is

challenging. Algae in the environment have been shown to bioaccumulate lipophilic

compounds,52 suggesting that compounds with a high octanol-water partitioning coefficient

(KOW) would sorb well to algae. This does seem to be the case for compounds such as

triclosan,49,51 prometryne,53 bisphenol A,48 estrogens,50,54 and synthetic fragrances,55 with the

solid-phase concentrations of all of these contaminants regularly observed to be several hundred

times higher than the aqueous concentration.

However, sorption has also been observed for hydrophilic compounds such as caffeine55 and

tetracycline.56 These could be explained by active uptake by the algae or by other molecular

interactions; tetracycline has been shown to sorb extensively to algae, which could be due to of

its ionic or metal-complexing interactions.56 Furthermore, a contaminant’s charge at relevant pH

and the relative importance of other removal processes should be considered when investigating

sorption.

1.1.4 Volatilization

Volatilization is the transfer of compounds from an aqueous phase to a gaseous phase. For algae-

based wastewater treatment, this typically refers to transfer of a contaminant dissolved in the

water to the atmosphere. This can occur extensively in most wastewater treatment processes

because the water is often mechanically aerated, increasing the interface between water and the

air. However, the larger interface also gives volatile compounds in the wastewater the

opportunity to volatilize. This removes volatile compounds from the wastewater and improves

water quality.

While passive water treatment systems rarely use the energy-intensive mechanical aeration that

is common in conventional wastewater treatment, the long residence times and large air-water

interfaces of algae-based wastewater treatment systems can also facilitate the volatilization of

certain PPCPs. The potential for volatilization in algae-based water treatment has been

demonstrated for fragrances such as galaxolide, as well as for select plasticizers, such as

octylphenol, nonylphenol, bisphenol-A (BPA), and tributyl phosphate.57,58 These two groups of

10

contaminants volatilize more readily than most organic contaminants due to their non-polar

chemical structures, which produce weak intermolecular forces.

However, these properties also cause such compounds to accumulate in algal biomass, and it is

unclear whether the removal of bioaccumulative and volatile compounds from the aqueous phase

is due to sorption or volatilization. While Abargues et al. (2013) demonstrated low removal of

volatile plasticizers (less than 5% of the initial mass) through sorption to algal biomass,

Matamoros et al. (2015) observed many volatile synthetic fragrances in the particulate phase of a

pilot-scale HRAP.55,58 Understanding whether these compounds sorb to algae or evaporate into

the air is especially relevant to the management of algal biomass created during treatment and

the air quality around the treatment system.

1.2 Compound Selection

There are thousands of PPCPs in municipal wastewater, so a subset was chosen for study.

Compounds were selected based on the following criteria:

• Degree of previous study: A mix of well-studied and novel compounds was desired in

order to both prove consistency with previous literature and to broaden the knowledge of

PPCP treatment.

• Highest concentration and frequency of detection in wastewater: Higher concentration

contaminants would both be the most likely to be important environmentally but also

easier to examine at relevant concentrations.

• Quantifiable using existing analytical infrastructure: Contaminants would have to be

analyzed using high-performance chromatography with diode array detection (HPLC-

DAD).

• Other factors:

o While they consistently met other criteria, estrogens such as estrone, estriol, and

estradiol were ignored because several studies have observed that they

interconvert between each other in biological water treatment. Because studying

one would seemingly necessitate conducting a study on all estrogens, they were

ignored during compound selection.

11

o While one of the most prevalent wastewater contaminants, naproxen was

discarded due to its structural similarity to ibuprofen.

o Upon method development, it was discovered that acetaminophen and triclosan

were impossible to extract and analyze at the same time using existing analytical

equipment. As such, acetaminophen was discarded.

Table 1 lists the selected compounds and provides their chemical structures and properties.

Table 1: Structures and properties of studied PPCPs. All values were from Clarke’s

Analysis of Drugs and Poisons (2011) unless otherwise cited.

Compound and

Structure Use

Acid-Base

Dissociation

Constant

(pKa)

Octanol-

Water

Partitioning

Coefficient

(log kow)

Expected

Removal

Mechanism

Carbamazepine

Anti-

convulsant 13.959 2.5 Recalcitrant

Ibuprofen

Analgesic 5.2 4.0

Bio-

degradation57,60

or indirect

photo-

degradation47,61

Gemfibrozil

Lipid regulator 4.859 4.859 Previously

unstudied

Triclosan

Disinfectant 7.9 4.8

Sorption or

direct photo-

degradation51,62

12

Chapter 2 Methods

Methods

2.1 Chemicals

Carbamazepine (>98% purity), ibuprofen (>98% purity), gemfibrozil (>99% purity), and

triclosan (>97% purity) were obtained from Sigma-Aldrich (Oakville, Ontario). Methanol and

acetonitrile (HPLC-grade) were purchased from Fisher Scientific (Whitby, Ontario).

2.2 Lagoon Water and Algae Inoculum

Lagoon water was sampled from the effluent of Omemee Wastewater Lagoon, located in

Kawartha Lakes, Ontario, Canada, on December 8, 2018. The lagoon receives an average of 790

m3/d of municipal wastewater from a primarily residential area of approximately 1,300 people.

To ensure a homogeneous media and to remove many indigenous algae species, the lagoon water

was filtered with 0.45-µm nylon filter prior to use. The water had a pH of 8.1 and was kept at 4

°C for 8 weeks before use in Experiment 1, and for 24 weeks before Experiment 2.

Axenic Chlorella vulgaris (strain CPCC 90) and Scenedesmus vulgaris (strain CPCC 5) were

obtained from the Canadian Phycological Culture Centre (Waterloo, Ontario, Canada). The algae

were cultivated in autoclaved (120 °C for 30 minutes) Bold’s Basal Medium with F/2 vitamin

solution (BBM) in a 1-L Erlenmeyer flask. The algae were pre-acclimatized to the experiment

lighting, temperature, and orbital shaking conditions for two weeks before the start of the

experiment.

2.3 Experimental Setup

Two experiments were conducted, each consisting of a series of reactors with specific conditions

as described in detail below, in Supporting Information (SI) Table S1, and graphically

represented in Figure 1. Each reactor consisted of a 1-L glass Erlenmeyer flask filled with 600

mL of media and placed on orbital shakers rotating at 120 rpm. Reactors were all fitted with a

sterilized foam stopper to let air permeate through while minimizing the risk for cross-

contamination between the reactors. The experiments took place in an environmental chamber at

13

Figure 1: Reactor conditions investigated in Experiments 1 and 2. To mitigate any spatial

differences in environmental conditions, reactors were positioned randomly and shuffled

every other day. This is represented in the figure by the random positioning of the reactors.

a consistent temperature of 22 °C and exposed to 90-160 photon m⁻2s⁻1 of light in a 16 hours

on/8 hours off cycle. The light source was a mixture of white incandescent and fluorescent bulbs.

Reactor positions were randomized every other day to account for any local differences in light

or temperature. The experimental reactors were spiked with a mixture of 4 PPCPs to reach an

initial concentration of 50 µg/L for ibuprofen and 10 µg/L for each gemfibrozil, triclosan, and

carbamazepine.

Specific reactor conditions were selected with the goal of isolating removal mechanisms.

Triplicate reactors were prepared for each experimental condition. Two experiments were

conducted successively for the sampling schedule of each experiment to be manageable. Both

experiments were conducted under the same lighting, temperature, shaking, and PPCP

concentrations described above. Experiment 1 started in February 2018 and used Chlorella

14

vulgaris; whereas, Experiment 2 started in June 2018 and focused on Scenedesmus obliquus. All

autoclaving of aqueous media was done at 120 °C for 30 min.

In Experiment 1, three factors were tested: (i) media, with three conditions: use of lagoon water,

lagoon water sterilized with 0.2 g/L of sodium azide (NaN3), or autoclaved BBM; (ii) algae, with

2 conditions: use of 30 mL of Chlorella vulgaris algae culture to reach an average initial

concentration of 1.4 × 105 cells/mL in each reactor, or 30 mL of ultrapure (18.2 Ω resistance)

water for algae-free reactors; and (iii) light, with 2 conditions: reactors exposed to environmental

chamber light conditions or wrapped in aluminum foil (i.e., dark condition). Note that not all of

the 12 possible combinations of these three factors were feasible; only 9 combinations were

realized, all in triplicates, for a total of 27 reactors with PPCPs (Figure 1 and SI Section S1).

Experiment 2 reactor conditions were chosen to accomplish three goals. The first goal was to test

the role of a different algae species, Scenedesmus obliquus, in PPCP removal from water. For

that, all the algae reactors of Experiment 1 were replicated using Scenedesmus obliquus instead

of Chlorella vulgaris. The second goal was to investigate the fate of PPCPs when algae were

grown in lagoon water without indigenous bacteria to isolate the effects of the indigenous

bacteria in this media from those of the algae. While NaN3 was used in Experiment 1, this

condition could not be tested as the NaN3 remaining in the solution would kill all

microorganisms, preventing algae growth. To remove indigenous bacteria while allowing

subsequent algae growth, lagoon water was autoclaved before algae and PPCPs were added. The

third goal of Experiment 2 was to evaluate and verify the consistency of the results between the

two experiments. For that, one series of reactors from Experiment 1, specifically those with algae

grown in lagoon water in the light with PPCPs, was replicated in Experiment 2 to provide a basis

of comparison between the two experiments. As such, in Experiment 2, three experimental

conditions were tested: (i) media, with 3 conditions: use of lagoon water, autoclaved lagoon

water, or autoclaved BBM; (ii) algae, with 2 conditions: use of 30 mL Chlorella vulgaris or

Scenedesmus obliquus algae culture to reach an average initial concentration of 1.7 × 105

cells/mL in each reactor; and (iii) light, with 2 conditions: reactors exposed to environmental

chamber light conditions or wrapped in aluminum foil (i.e., dark condition). In order to distribute

workload involved in conducting Experiment 2, the reactors were divided into two groups and

started 15 days apart. As such, these will be referred to as Experiments 2a and 2b. Reactor

15

replicates were divided evenly to account for any differences in conditions between the two

periods. Details are available in SI Section S1.

In addition to the experimental reactors described above, which all contained PPCPs, control

reactors without PPCPs were used in both experiments. The objectives of these control reactors

were to assess the impact of the PPCPs on the growth of the algae, to provide an estimate for

media and algae-induced noise in the HPLC chromatograms, and to verify the absence of PPCP

cross-contamination between the reactors. Control reactors were chosen to represent each

combination of algae and media used in each experiment. Two or three replicates were used for

combinations that were biologically active. Details are shown in Figure 1 and SI Table S2 and

S4.

2.4 Sampling

The reactors were sampled 0, 3, 7, 11, 17, and 25 days following the start of the experiment. The

start of the experiment was defined as the time when the reactors were spiked with the mixture of

the 4 PPCPs. On each sampling day, two samples were collected from each reactor. First, a 50-

mL sample was centrifuged in two 30-mL glass centrifuge tubes for 15 minutes at 5,000 rpm to

separate the algae from the aqueous phase. The supernatants and pellets were frozen in separate

vials at −20°C. Due to time constraints, the pellets were not processed by the time this thesis was

submitted. For the reactors that did not contain algae, the 50-mL aqueous sample was directly

frozen without prior centrifugation. Second, a 10-mL sample was taken to count microbial cells,

measure the pH, and quantify anion concentrations. For counting of microbial cells, a 20-µL

portion of the 10-mL sample was placed and let dry on a microscope slide. In the event that a

sample had a high concentration of algae, a dilution was first made with Milli-Q water, then the

dilution was loaded into the microscope slide in place of the direct sample. The slides were then

Gram stained after drying for determination of the algae cell density. With the remaining sample,

the pH was measured before 7 mL was filtered with a 0.2-µm nylon filter and frozen at −20 °C

for subsequent anion concentration analysis. Due to time constraints, the anion concentrations

were not available by the time this thesis was submitted.

16

The frozen aqueous phases of the 50-mL samples were thawed in a water-bath shaker rotating at

60 rpm at 22 °C before solid phase extraction (SPE). Hydrophobic C18 SPE cartridges (500 mg,

6 mL) purchased from Chromatographic Specialties (Brockville, Ontario) were preconditioned

with 6 mL of each methanol and ultrapure water. The thawed samples were then loaded onto the

cartridges at a rate of 1 mL/min. After loading, the cartridges were allowed to dry for at least 20

minutes. Elution was performed using 6 mL of HPLC-grade methanol no faster than at 3

mL/min. The extracts were then evaporated to dryness using a gentle stream of nitrogen and

reconstituted in 0.5 mL of HPLC-grade methanol with 0.5 mL of ultrapure water for a

concentration factor of 50. The recovery rates were 83% for carbamazepine and ibuprofen, 74%

for gemfibrozil, and 61% for triclosan. Full details are available in SI Section S2. Extracts were

filtered with a 0.2-µm PTFE filter from Chromatographic Specialties Inc. (Brockville, Ontario)

before analysis.

2.5 Analytical Methods

Fifty microliters of each SPE extract was analyzed in a Dionex UltiMate 3000 Series High

Performance Liquid Chromatography (HPLC) with diode array detection (DAD). The HPLC was

equipped with an Accucore C18 column (100 x 2.1 mm x 2.6 µm) purchased from Fisher

Scientific (Ottawa, ON). A constant flow rate of 0.3 mL/min was used for analysis. A buffer

solution made of 10 mM KH2PO4 in ultrapure water acidified to pH 2.5 with concentrated

hydrochloric acid constituted 20% of the flow. The remaining 80% was a gradient of acetonitrile

and ultrapure water: initially at 35% acetonitrile and 45% water, then ramped up to 60%

acetonitrile and 20% water 1 minute after injection, held at this composition until 7 minutes, then

returned to starting conditions and held at that composition for 6 minutes. Ibuprofen was

quantified at 207 nm, carbamazepine at 300 nm, and gemfibrozil and triclosan at 280 nm. The

limits of detection (LOD) and quantification (LOQ) were respectively 140 and 325 µg/L for

ibuprofen, 9.1 and 34 µg/L for carbamazepine, 26 and 66 µg/L for gemfibrozil, and 17 and 49

µg/L for triclosan. Additional information including sample chromatograms can be found in SI

S3-S5.

17

Chapter 3 Results

Results

3.1 Algae Cell Density

Due to time constraints, the cell densities were only determined for the algae reactors in

Experiment 1 (Table 2). The samples from days 7 and 17 were examined but could not be

counted due to heavy cell clumping on the slide wells. Similarly, the cell concentrations for the

lagoon water reactors in the light with PPCPs for day 3 were higher than could be quantified at

that dilution. The lagoon water reactors in the dark with PPCPs contained too few algae and

could not be accurately counted at the 100x dilution used on days 11 and 25. Methods for

determining quantification limits are presented in SI S6.

Table 2: Concentrations of Chlorella vulgaris cells in Experiment 1. Average value of the

reactor series is presented with the standard deviation of triplicates, or the standard

deviation of duplicates for the BBM control reactors in the light. Because one sample was

lost for the BBM Light Control on Day 3, there was only one replicate for this condition.

The unit of all values is cells/mL.

Day

Lagoon Water

Light

PPCPs

Lagoon Water

Dark

PPCPs

Lagoon Water

Light

Control

BBM

Light

PPCPs

BBM

Light

Control

0 8.6 ± 1.3 × 104 1.2 ± 0.9 × 105 8.0 ± 4.5 × 104 2.1 ± 1.1 × 105 2.3 ± 1.4 × 105

3 > 1.0 × 106 2.4 ± 0.1 × 105 1.0 ± 0.05 × 106 6.9 ± 5.4 × 105 4.2 × 105

11 1.8 ± 0.5 × 107 < 6.2 × 105 1.1 ± 0.4 × 107 8.9 ± 7.0 × 106 1.1 ± 0.3 × 107

25 2.1 ± 0.9 × 107 < 6.2 × 105 1.1 ± 0.2 × 107 4.5 ± 1.1 × 107 3.2 ± 0.5 × 107

Following the day 0 sample, the dark lagoon water reactors with PPCPs consistently had lower

concentrations of algae than either of the lagoon water reactor series exposed to light.

Illuminated reactors demonstrated similar algae concentrations up until after day 11. Between

days 11 and 25, the concentrations of algae in the lagoon water reactors did not demonstrate any

appreciable change, increasing by at most 40%, while the concentrations of algae in the BBM

reactors increased by at least 200%. As a result, on day 25, the BBM reactors consistently had

higher algae concentrations than their respective lagoon water counterparts.

18

3.1.1 pH

The initial pH of the lagoon water reactors varied between pH 7.9 and 8.3; whereas, that of the

BBM reactors varied between 6.5 and 6.6. Full pH data is available in the SI (Figure S9).

The only reactor series which demonstrated a net change in pH greater than 1 unit over the

experiment duration were the BBM reactors containing algae. The BBM reactors containing

Scenedesmus obliquus in Experiment 2 reached a pH value of 10.5 by day 17 and stabilized at

this value until day 25, while the equivalent Chlorella vulgaris reactors in Experiment 1 reached

a pH value of 10.3 on day 25.

While the BBM reactors with algae demonstrated a consistent pH increase over the duration of

the experiment, the illuminated lagoon water reactors with algae demonstrated a short-term pH

increase followed by a return to the initial pH value. The peak pH varied between reactor series,

but reactors with Scenedesmus obliquus had a higher peak pH at 10.2 ± 0.3 on day 7 compared to

equivalent reactors with Chlorella vulgaris with a peak pH value at 8.7 ± 0.1 on day 3.

The BBM reactors without algae and the dark lagoon water reactors with algae demonstrated a

gradual increase in pH of 0.3-0.8 over the duration of the experiment. The pH in all other reactor

series was constant, changing by less than 0.2, over the experimental period.

3.1.2 Ibuprofen

Concentrations of ibuprofen are shown in Figure 2. In the BBM reactors either with Chlorella

vulgaris or without algae, no change in ibuprofen concentration was observed over the duration

of the experiment. However, in the BBM reactors with Scenedesmus obliquus, a steady decrease

in ibuprofen concentration from 54 ± 2 to 20 ± 10 µg/L was observed between days 0 and 11.

The concentration of ibuprofen in the Scenedesmus obliquus BBM reactors on days 17 and 25

were obscured by a co-eluting compound and therefore not quantified.

In the lagoon water reactors without algae, the ibuprofen concentration did not change,

remaining at an average of 52 ± 5 µg/L (n = 36) over the course of the experiment for both the

light and dark conditions. However, in Experiment 1, the equivalent reactors with Chlorella

19

vulgaris demonstrated a decrease in ibuprofen concentration by about 40% over the experimental

duration, primarily occurring after day 11.

In Experiment 2, a similar lag phase was observed for the first 11 days in the lagoon water

reactors. However, following this, the temporal trends in ibuprofen concentrations diverged

between the replicates of each series of lagoon water reactors. A rapid decline in ibuprofen

concentration was observed in some reactors, typically decreasing from approximately 80 to

100% of the initial concentration to below the detection limit over the span of a sampling

interval. However, at least one reactor in each series maintained its initial ibuprofen

concentration through to day 25. Ibuprofen concentration data for individual reactors in these

series are available in the SI S8.

Figure 2: Ibuprofen concentrations in experimental reactors of Experiment 1 (top panels)

and Experiment 2 (bottom panels). Points represent averages of triplicate reactors (except

for sample loss, then duplicate), and error bars represent one standard deviation of the

replicates. Solid lines represent illuminated reactors and dashed lines represent reactors

wrapped in aluminum foil. Crosses represent reactors without algae, dark green triangles

represent reactors with Scenedesmus obliquus and light green circles represent reactors

with Chlorella vulgaris. BBM stands for Bold’s Basal Medium.

20

These observed discrepancies occurred between the first and second groups of reactors which

started on June 11 and 26, 2018, respectively. In the first group of replicates (henceforth called

Experiment 2a), all illuminated lagoon water reactors had a final concentration of ibuprofen that

was below the LOD. However, the concentrations of ibuprofen in the equivalent replicates in the

second group of reactors (henceforth called Experiment 2b) remained essentially constant during

the experiment. This trend was reversed in the dark lagoon water reactors with Scenedesmus

obliquus: there was no change in ibuprofen concentration in Experiment 2a, but the two

replicates from Experiment 2bdemonstrated dramatic decreases in ibuprofen concentration, from

55 and 57 µg/L to below the detection limit for both replicates.

In the autoclaved lagoon water reactors, there was consistently no change in ibuprofen

concentration (< 20%) in the reactors with Scenedesmus obliquus. Similar results were observed

in two of the equivalent Chlorella vulgaris reactors, though the third demonstrated a 45%

decrease in ibuprofen concentration, from 54 to 30 µg/L. While there were discrepancies

between the ibuprofen concentrations of the illuminated lagoon water reactors in Experiments 2a

and 2b, within each group of reactors, the reactors with non-autoclaved lagoon water had lower

final ibuprofen concentrations (all below the detection limit in Experiment 2a, and at 43 and 46

µg/L in Experiment 2b) than their respective autoclaved lagoon water counterparts (30 and 55

µg/L in Experiment 2a and 64 ± 3 µg/L in Experiment 2b). Complete results for ibuprofen

concentrations in the individual reactors are presented in Appendix 8).

The ibuprofen concentration in the reactors containing lagoon water and sodium azide

consistently declined in both the light and dark conditions. Variation between replicates was

quite large, with coefficients of variation (COV) reaching as high as 140% and 110% in the light

and dark series respectively.

3.1.3 Triclosan

Concentrations of triclosan are shown in Figure 3. Triclosan concentrations decreased rapidly in

all illuminated reactors from the start of the experiment, declining by 56-88% in the first three

days and typically decreasing below quantification levels (< 1.6 µg/L) by day 7. The exception to

this trend was the illuminated BBM reactors without algae. This series also demonstrated a

21

decrease in triclosan concentration from 10 ± 1 to 7.9 ± 1.2 µg/L over the first 3 days, but the

concentration declined at a slower rate, from 10 ± 0.8 to 2.6 ± 0.03 µg/L over the first 17 days of

the experiment.

The concentration of triclosan in the BBM reactors containing Scenedesmus obliquus did not

decline below the method detection limit (0.57 µg/L), unlike most of the other illuminated

reactors. Instead, triclosan was still detected at levels below the LOQ on days 3, 7, and 11. On

day 17, triclosan concentrations increased to an average of 2.5 ± 1.5 µg/L before decreasing

below the LOQ again on day 25.

Figure 3: Triclosan concentrations in experimental reactors of Experiment 1 (top panels)

and Experiment 2 (bottom panels). Points represent averages of triplicate reactors (except

for sample loss, then duplicate), and error bars represent one standard deviation of the

replicates. Solid lines represent illuminated reactors and dashed lines represent reactors

wrapped in aluminum foil. Crosses represent reactors without algae, dark green triangles

represent reactors with Scenedesmus obliquus and light green circles represent reactors

with Chlorella vulgaris. BBM stands for Bold’s Basal Medium.

In the dark reactors without algae or with Chlorella vulgaris, triclosan concentrations

demonstrated no consistent trend over the duration of the experiment. Indeed, while

concentrations overall decreased over the course of the experiment, they increased by 30%

between days 7 and 11 in the dark BBM reactors, 26% between days 3 and 7 in the dark lagoon

22

water reactors, and 26% between days 7 and 11 in the dark lagoon water with sodium azide

reactors, all without algae. These increases occurred in all replicates of these series. However, in

dark reactors with Scenedesmus obliquus, the concentration decreased by an average of 64%

from 8.9 ± 0.7 µg/L on day 0 to 3.3 ± 1.2 µg/L by the end of the experiment. This occurred

primarily after day 11.

In order to compare the removal rates of triclosan, the kinetic parameters for triclosan removal in

all experiment were calculated. The pseudo first-order equation used in this analysis was:

ln[𝑇𝐶𝑆]𝑡 = −𝑘𝑡 + ln[𝑇𝐶𝑆]0

where [TCS]t is the concentration of triclosan in a reactor at time t, [TCS]0 is the initial

concentration of triclosan in the reactor, and k is the removal rate coefficient. Results are

presented in Table 3.

Table 3: Pseudo first-order degradation rate coefficients for triclosan, presented with the

standard error of the coefficient. (a) n is the number of triclosan concentrations used to

establish the model.

Reactor series k (day-1) n(a)

Pearson

correlation

coefficient

Half-life

(days)

Lagoon water

No algae

Light

0.33 ± 0.02 9 0.98 2.1

BBM

No algae

Light

0.092 ± 0.003 16 0.98 7.5

Autoclaved lagoon

Chlorella vulgaris

Light

0.27 ± 0.01 10 0.99 2.6

Autoclaved lagoon

Scenedesmus obliquus

Light

0.40 ± 0.02 9 0.97 1.7

Lagoon Water

Scenedesmus obliquus

Light

0.23 ± 0.02 9 0.97 3.0

Lagoon

Chlorella vulgaris

Light

0.40 ± 0.02 8 0.98 1.7

23

For almost all illuminated reactors, triclosan concentrations were well fitted with a pseudo first-

order model, which had coefficient of determination (r2) greater than 0.95 (Table S11).

Furthermore, several series of reactors demonstrated consistent removal rate coefficients

between replicates, with COV less than 15%. This included the lagoon water and BBM reactors

without algae from Experiment 1 and all illuminated lagoon water reactors with algae from

Experiment 2, both autoclaved and non-autoclaved, and both with Chlorella vulgaris and with

Scenedesmus obliquus. For these series of reactors, an overall removal rate coefficient was

determined. Full results and methodology for calculating triclosan first-order degradation rate

constants are available in Appendix 9.

3.1.4 Carbamazepine and Gemfibrozil

The concentrations of carbamazepine and gemfibrozil are shown in figures 4 and 5, respectively.

No change in carbamazepine or gemfibrozil concentration was observed in any of the reactor

series in either experiment. The COV for carbamazepine was 9% (n = 235) and for gemfibrozil it

was 10% (n = 237).

24

Figure 4: Carbamazepine concentrations in experimental reactors of Experiment 1 (top

panels) and Experiment 2 (bottom panels). Points represent averages of triplicate reactors

(except for sample loss, then duplicate), and error bars represent one standard deviation of

the replicates. Solid lines represent illuminated reactors and dashed lines represent

reactors wrapped in aluminum foil. Crosses represent reactors without algae, dark green

triangles represent reactors with Scenedesmus obliquus and light green circles represent

reactors with Chlorella vulgaris. BBM stands for Bold’s Basal Medium.

25

Figure 5: Gemfibrozil concentrations in experimental reactors of Experiment 1 (top panels)

and Experiment 2 (bottom panels). Points represent averages of triplicate reactors (except

for sample loss, then duplicate), and error bars represent one standard deviation of the

replicates. Solid lines represent illuminated reactors and dashed lines represent reactors

wrapped in aluminum foil. Crosses represent reactors without algae, dark green triangles

represent reactors with Scenedesmus obliquus and light green circles represent reactors

with Chlorella vulgaris. BBM stands for Bold’s Basal Medium.

26

Chapter 4 Discussion

Discussion

4.1 Ibuprofen

The results of this study showed that Scenedesmus obliquus, and not Chlorella vulgaris,

contributed to the observed decreases in ibuprofen concentrations in BBM. Indeed, neither the

reactors without algae nor those with Chlorella vulgaris resulted in observable changes in

ibuprofen concentrations. This indicates that none of the transfer and transformation mechanisms

possible in these reactors affected ibuprofen, such as hydrolysis, direct photodegradation,

indirect photodegradation via photosensitizers present in the BBM, and uptake or biodegradation

by Chlorella vulgaris. On the other hand, Scenedesmus obliquus might have led to ibuprofen

sorption, biotransformation, or indirect photodegradation. Distinguishing among these processes

was not possible in the present study. However, former research by Matamoros et al. (2016) with

synthetic wastewater reactors with an algae consortium containing Scenedesmus species

attributed a change in ibuprofen enantiomeric ratio over time to potential stereo-selective

biodegradation.57

Despite the observed biotransformation of ibuprofen by Scenedesmus obliquus in BBM, the

concentration of ibuprofen in the equivalent Scenedesmus obliquus reactors with autoclaved

lagoon water did not change over the experiment duration. Variations in the growth media have

previously been demonstrated to augment or completely inhibit biotransformation processes.

Estrone biotransformation by biomass cultured in synthetic wastewater has been shown to vary

depending on the age and organic matter quality of the wastewater.25 Xiong et al. (2017)

demonstrated that ciprofloxacin biotransformation by Chlamydomonas Mexicana can either be

suppressed completely or increased by more than three-fold depending on the addition of

competitive or co-metabolic organic substrates.24 Upon the introduction of 1% salinity,

biotransformation of increased of levofloxacin increased from 4.5% to 93% in Scenedesmus

obliquus bioreactors and from 12% to over 90% in Chlorella vulgaris bioreactors.22,63 While

further experimentation would be necessary to determine which water quality parameters

influenced the most the biotransformation of ibuprofen by Scenedesmus obliquus, it is apparent

that variations in growth media can induce dramatically different biotransformation behaviours.

27

The lagoon water reactors without algae maintained their initial ibuprofen concentration

throughout the experiment in both light and dark conditions, unlike several similar studies which

have reported ibuprofen biotransformation in reactors with other types of wastewater or media

with bacteria.47,57,60,64 However, it is possible that the lagoon water simply had relatively a small

degree of biological activity, potentially.

In contrast, the decrease in ibuprofen concentration in the lagoon water reactors with Chlorella

vulgaris was likely the result of biotransformation, as it occurred with the addition of Chlorella

vulgaris, a biological agent, to the lagoon water reactors, a biologically active media. This is

reinforced by the fact that the change in concentration occurred primarily after a 7- to 17-day lag

phase, which is characteristic of a biological agent adapting to a new environment or a new

substrate. Furthermore, several alternative explanations for the concentration decrease can be

ruled out by comparison with the relevant controls. For instance, it is unlikely that

photodegradation was the cause of the observed decrease in ibuprofen concentration in the

illuminated reactors because a comparable decrease occurred in the dark reactors, where

photodegradation would be impossible. It is also unlikely that sorption to algal biomass

significantly affected ibuprofen concentrations in the dark reactor. Indeed, even though the algae

cell concentration in the BBM reactors with Chlorella vulgaris was more than 70 times higher,

no change in ibuprofen concentration was observed.

While biotransformation was the most likely contributor to the decreases in ibuprofen

concentration in the lagoon water reactors with algae, from the results in Experiment 1 alone, it

is unclear whether biotransformation was caused by the lagoon water bacteria, the added

Chlorella vulgaris, or both. However, in Experiment 2, ibuprofen concentrations consistently

decreased less in the autoclaved lagoon water reactors with algae compared to the non-

autoclaved lagoon water reactors with algae. This suggests that the change in ibuprofen

concentration observed in Experiment 1 was primarily caused by the bacteria indigenous to the

lagoon water.

However, because there was no concentration change in the lagoon water reactors without

Chlorella vulgaris, it is likely that Chlorella vulgaris or possibly Scenedesmus obliquus were

needed to support ibuprofen bacterial biotransformation in the lagoon water reactors. There are

several different ways by which algae can support contaminant biotransformation performed by

28

bacteria. Algae have long been used in wastewater treatment processes because they support of

aerobic biodegradation by bacteria through the provision of oxygen,65,66 including for the

treatment of antibiotics in animal wastewaters.14 It has also been suggested that pH fluctuations

induced by algal metabolic processes can help support biodegradation.15 However, both of these

processes are dependent on algal photosynthesis, which did not take place in the dark reactors

and as such cannot be used to explain the reduction in ibuprofen concentration in the dark

reactors. While algal exudates may support bacterial biodegradation, though these are also likely

modified by light intensity.67 As such, the nature of this interaction remains unclear.

Finally, the rapid decrease of ibuprofen concentration in the lagoon water reactors with sodium

azide compared to the stable concentration in the equivalent reactors with non-sterile lagoon

water suggests that a reaction occurred involving both ibuprofen and sodium azide. This reaction

did not occur in the BBM reactors, which also contained ibuprofen and sodium azide, suggesting

that the reaction was dependent on a dissolved molecule, ion, or environmental condition

specific to the lagoon water. Sodium azide has been demonstrated as an effective preservative of

ibuprofen in lake water for up to 25 days at 25 °C,68 but several other studies using sodium

azide-sterilized lake and wastewater controls have observed lower ibuprofen and other PPCP

concentrations in azide-sterilized controls compared to their biologically active counterparts.47,69

The details of this reaction were beyond the scope of this study.

4.2 Triclosan

The rapid decrease in triclosan concentration in all illuminated reactors and the much slower

concentration decrease in all corresponding dark reactors demonstrate that phototransformation

was likely the dominant mechanism affecting triclosan.

Triclosan is a weak acid with a pKa at 7.9,38 whose ionized form, at pH > pKa, is the most

susceptible to direct phototransformation.33 The lower pH of the BBM (6.6) compared to the

lagoon water (8.1), corresponding to 5% and 67% of triclosan present in its anionic form,

respectively, was likely the cause of the slower decrease in triclosan concentration in the BBM

compared to the lagoon water. However, the BBM was also mineral medium with fewer organic

compounds, therefore resulting in a lower amount of potential photosensitizers that would have

limited the indirect phototransformation of triclosan. However, several studies have observed

that triclosan transforms more slowly in wastewater than in ultrapure water, suggesting that

29

direct phototransformation is more important than indirect photodegradation, even when

photosensitizers are available, and that the lower pH of the BBM was the likely cause of the

slower phototransformation.70,71

The decrease in triclosan concentration in the dark lagoon water reactors with Scenedesmus

obliquus cannot be attributed to photodegradation. The 11-day lag phase preceding the decrease

in triclosan concentration in the dark lagoon water reactors with Scenedesmus obliquus was also

observed for ibuprofen and points to biotransformation. Both bacteria found in wastewater

treatment and algae can induce biotransformation of triclosan, producing a variety of chlorinated

and hydroxylated diphenyl ethers.72,73 While sorption is another possible algae-induced removal

mechanism in dark conditions, several studies have demonstrated that sorption typically occurs

within one hour of coming into contact with the algae, making sorption unlikely to significantly

affect triclosan concentration after 11 days.51,62,73

The fact that biotransformation likely explained the triclosan concentration decrease in the dark

reactors with algae suggests that it might have also contributed to the results observed in the

illuminated reactors with algae, along with phototransformation as discussed above. However,

whereas biotransformation started after an 11-day lag phase in the dark reactors, in the

illuminated lagoon water reactors without algae, over 85% of the triclosan had phototransformed

within 7 days. Therefore, rapid phototransformation in the illuminated lagoon water reactors with

algae likely prevented biotransformation from affecting triclosan concentrations. Conversely, in

the illuminated BBM reactors with algae, two lines of evidence suggest that biotransformation

was more competitive. First, no significant lag phase was observed as the algae were cultured in

BBM, thus requiring less time to adapt to the growth conditions in the BBM reactors, unlike the

algae in the lagoon water reactors. Second, slower phototransformation was found as a result of

the low pH of the media maintaining triclosan in its protonated form. This suggests that

biotransformation contributed to the quicker decline in triclosan concentration in the BBM

reactors with algae compared to those without. However, protonated triclosan has a stronger

potential for adsorption, and triclosan adsorption is a very rapid process, so it is possible that it

partly explained these results.

30

4.3 Carbamazepine and Gemfibrozil

Carbamazepine has been demonstrated to be recalcitrant in several other studies examining

PPCP treatment in algae-based wastewater treatment.57,59,62,64 However, duckweed and other

macrophytes have been shown to be very effective at removing carbamazepine from wastewater

via plant uptake,61,74,75 and aerobic degradation of carbamazepine has been performed using

white-rot fungus.76

Unlike carbamazepine, gemfibrozil is poorly studied in algal bioreactors and in passive

wastewater treatment in general. Kang et al. (2018) observed less than a 20% decrease in

concentrations of gemfibrozil and carbamazepine in a plug-flow periphyton reactor, very similar

to what was observed in this study.59 However, the concentration of gemfibrozil decreased by

95% in a hybrid lagoon/marsh treatment system while the carbamazepine concentration only

decreased by 51%,77 implying that there was a selective treatment process in that system. Further

research is needed to better understand which removal processes govern these contaminants to

better facilitate their treatment.

31

Chapter 5 Conclusions and Recommendations

Conclusions and Recommendations

While algae-based wastewater treatment is effective in the treatment of BOD, pathogens, and

nutrients from wastewater, further understanding is needed its capacity to treat PPCPs. This

thesis attempted to evaluate the capacity of algae-based wastewater treatment to remove PPCPs

from wastewater, to investigate the removal mechanisms responsible for treatment, and to

compare the effects of two different common algae species.

Carbamazepine and gemfibrozil were not treated under any test condition. Subsequent studies

investigating their treatment in passive wastewater treatment systems should examine the

potential for treatment in other passive systems, such as duckweed or fungal bioreactors.

Triclosan is rapidly removed in all reactors exposed to light, making it well suited to algae-based

wastewater treatment. However, a number of removal mechanisms can be used to treat triclosan,

including direct photodegradation, indirect photodegradation, sorption, and biodegradation.

Manipulation of environmental conditions can likely alter the treatment path. Thorough

characterization of degradation products and partitioning to the biomass phase will allow safe

management of all waste streams.

Algae-based wastewater treatment is a good candidate for ibuprofen removal. While no change

in concentration was observed in the lagoon water without algae, the addition of Chlorella

vulgaris and seemingly the addition of Scenedesmus obliquus to lagoon water seemed to

facilitate bacterial biotransformation. Furthermore, Scenedesmus obliquus appeared to

biotransform ibuprofen in BBM and not in lagoon water, suggesting that the media altered the

behavior of the algae species. Further research should focus on the impact of influent water

quality on PPCP treatment efficacy of algae-based wastewater treatment.

Chlorella vulgaris and Scenedesmus obliquus were similar in their capacities to treat the

examined PPCPs. However, Scenedesmus obliquus was uniquely able to removal ibuprofen in

BBM, while Chlorella vulgaris was not, suggesting that there are environmental conditions

where certain algae species outperform others. Further research should investigate the conditions

32

which regulate biotransformation of PPCPs and the species of algae that perform best in field-

relevant conditions.

33

References

(1) Metcalfe, C. D.; Chu, S.; Judt, C.; Li, H.; Oakes, K. D.; Servos, M. R.; Andrews, D. M.

Antidepressants and Their Metabolites in Municipal Wastewater, and Downstream

Exposure in an Urban Watershed. Environ. Toxicol. Chem. 2010, 29 (1), 79–89.

(2) Couperus, N. P.; Pagsuyoin, S. A.; Bragg, L. M.; Servos, M. R. Occurrence, Distribution,

and Sources of Antimicrobials in a Mixed-Use Watershed. Sci. Total Environ. 2016, 541,

1581–1591.

(3) Lapworth, D. J.; Baran, N.; Stuart, M. E.; Ward, R. S. Emerging Organic Contaminants in

Groundwater: A Review of Sources, Fate and Occurrence. Environ. Pollut. 2012, 163,

287–303.

(4) Carrara, C.; Ptacek, C. J.; Robertson, W. D.; Blowes, D. W.; Moncur, M. C.; Sverko, E.;

Backus, S. Fate of Pharmaceutical and Trace Organic Compounds in Three Septic System

Plumes, Ontario, Canada. Environ. Sci. Technol. 2008, 42 (8), 2805–2811.

(5) Metcalfe, C.; Hoque, M. E.; Sultana, T.; Murray, C.; Helm, P.; Kleywegt, S. Monitoring

for Contaminants of Emerging Concern in Drinking Water Using POCIS Passive

Samplers. Environ. Sci. Process. Impacts 2014, 16 (3), 473–481.

(6) Gillis, P. L.; Gagné, F.; Mcinnis, R.; Hooey, T. M.; Choy, E. S.; André, C.; Hoque, M. E.;

Metcalfe, C. D. The Impact of Municipal Wastewater Effluent on Field-Deployed

Freshwater Mussels in the Grand River (Ontario, Canada). Environ. Toxicol. Chem. 2014,

33 (1), 134–143.

(7) Tetreault, G. R.; Bennett, C. J.; Shires, K.; Knight, B.; Servos, M. R.; Mcmaster, M. E.

Intersex and Reproductive Impairment of Wild Fish Exposed to Multiple Municipal

Wastewater Discharges. 2011, 104, 278–290.

(8) Aminov, R. I. Minireview The Role of Antibiotics and Antibiotic Resistance. 2009, 11,

2970–2988.

(9) World Health Organization (WHO). Pharmaceuticals in Drinking-Water. 2012.

34

(10) Snyder, S. A.; Adham, S.; Redding, A. M.; Cannon, F. S.; DeCarolis, J.; Oppenheimer, J.;

Wert, E. C.; Yoon, Y. Role of Membranes and Activated Carbon in the Removal of

Endocrine Disruptors and Pharmaceuticals. Desalination 2007, 202 (1–3), 156–181.

(11) Rivera-Utrilla, J.; Sánchez-Polo, M.; Ferro-García, M. Á.; Prados-Joya, G.; Ocampo-

Pérez, R. Pharmaceuticals as Emerging Contaminants and Their Removal from Water. A

Review. Chemosphere 2013, 93 (7), 1268–1287.

(12) Craggs, R.; Sutherland, D.; Campbell, H. Hectare-Scale Demonstration of High Rate

Algal Ponds for Enhanced Wastewater Treatment and Biofuel Production. J. Appl. Phycol.

2012, 24 (3), 329–337.

(13) Park, J. B. K.; Craggs, R. J. Wastewater Treatment and Algal Production in High Rate

Algal Ponds with Carbon Dioxide Addition. Water Sci. Technol. 2010, 61 (3), 633–639.

(14) Godos, I. de; Blanco, S.; García-Encina, P. A.; Becares, E.; Muñoz, R. Long-Term

Operation of High Rate Algal Ponds for the Bioremediation of Piggery Wastewaters at

High Loading Rates. Bioresour. Technol. 2009, 100 (19), 4332–4339.

(15) Norvill, Z. N.; Shilton, A.; Guieysse, B. Emerging Contaminant Degradation and Removal

in Algal Wastewater Treatment Ponds: Identifying the Research Gaps. J. Hazard. Mater.

2016, 313, 291–309.

(16) Matamoros, V.; Gutiérrez, R.; Ferrer, I.; García, J.; Bayona, J. M. Capability of

Microalgae-Based Wastewater Treatment Systems to Remove Emerging Organic

Contaminants: A Pilot-Scale Study. J. Hazard. Mater. 2015, 288 (May), 34–42.

(17) Cycoń, M.; Wójcik, M.; Piotrowska-Seget, Z. Biodegradation of the Organophosphorus

Insecticide Diazinon by Serratia Sp. and Pseudomonas Sp. and Their Use in

Bioremediation of Contaminated Soil. Chemosphere 2009, 76 (4), 494–501.

(18) Ji, M. K.; Kabra, A. N.; Choi, J.; Hwang, J. H.; Kim, J. R.; Abou-Shanab, R. A. I.; Oh, Y.

K.; Jeon, B. H. Biodegradation of Bisphenol A by the Freshwater Microalgae

Chlamydomonas Mexicana and Chlorella Vulgaris. Ecol. Eng. 2014, 73, 260–269.

(19) Xiong, J.; Kurade, M. B.; Jeon, B. Ecotoxicological Effects of Enrofloxacin and Its

35

Removal by Monoculture of Microalgal Species and Their Consortium *. Environ. Pollut.

2017, 226, 486–493.

(20) Peng, F. Q.; Ying, G. G.; Yang, B.; Liu, S.; Lai, H. J.; Liu, Y. S.; Chen, Z. F.; Zhou, G. J.

Biotransformation of Progesterone and Norgestrel by Two Freshwater Microalgae

(Scenedesmus Obliquus and Chlorella Pyrenoidosa): Transformation Kinetics and

Products Identification. Chemosphere 2014, 95, 581–588.

(21) Matamoros, V.; Uggetti, E.; García, J.; Bayona, J. M. Assessment of the Mechanisms

Involved in the Removal of Emerging Contaminants by Microalgae from Wastewater: A

Laboratory Scale Study. J. Hazard. Mater. 2016, 301 (June 2016), 197–205.

(22) Xiong, J.; Kurade, M. B.; Jeon, B. Biodegradation of Levofloxacin by an Acclimated

Freshwater Microalga , Chlorella Vulgaris. Chem. Eng. J. 2017, 313, 1251–1257.

(23) Spain, J. C.; Veld, P. a Van. Adaptation of Natural Microbial Communities to Degradation

of Xenobiotic Compounds: Effects of Concentration, ExposureTime, Inoculum, and

Chemical Structure. Appl. Environ. Microbiol. 1983, 45 (2), 428–435.

(24) Xiong, J. Q.; Kurade, M. B.; Kim, J. R.; Roh, H. S.; Jeon, B. H. Ciprofloxacin Toxicity

and Its Co-Metabolic Removal by a Freshwater Microalga Chlamydomonas Mexicana. J.

Hazard. Mater. 2017, 323, 212–219.

(25) Tan, D. T.; Temme, H. R.; Arnold, W. A.; Novak, P. J. Estrone Degradation: Does

Organic Matter (Quality), Matter? Environ. Sci. Technol. 2015, 49 (1), 498–503.

(26) Guieysse, B.; Borde, X.; Muñoz, R.; Hatti-Kaul, R.; Nugier-Chauvin, C.; Patin, H.;

Mattiasson, B. Influence of the Initial Composition of Algal-Bacterial Microcosms on the

Degradation of Salicylate in a Fed-Batch Culture. Biotechnol. Lett. 2002, 24 (7), 531–538.

(27) Muñoz, R.; Köllner, C.; Guieysse, B.; Mattiasson, B. Photosynthetically Oxygenated

Salicylate Biodegradation in a Continuous Stirred Tank Photobioreactor. Biotechnol.

Bioeng. 2004, 87 (6), 797–803.

(28) Xiong, J. Q.; Kurade, M. B.; Patil, D. V.; Jang, M.; Paeng, K. J.; Jeon, B. H.

Biodegradation and Metabolic Fate of Levofloxacin via a Freshwater Green Alga,

36

Scenedesmus Obliquus in Synthetic Saline Wastewater. Algal Res. 2017, 25 (December

2016), 54–61.

(29) Metcalf, E.; Eddy, H. Wastewater Engineering: Treatment and Reuse. Tata McGraw-Hill

Publishing Company Limited, 4th edition. Boston. 2003, p 1819.

(30) Young, P.; Taylor, M.; Fallowfield, H. J. Mini-Review: High Rate Algal Ponds, Flexible

Systems for Sustainable Wastewater Treatment. World J. Microbiol. Biotechnol. 2017, 33

(6), 117.

(31) Rühmland, S.; Wick, A.; Ternes, T. A.; Barjenbruch, M. Fate of Pharmaceuticals in a

Subsurface Flow Constructed Wetland and Two Ponds. Ecol. Eng. 2015, 80, 1–15.

(32) Bianco, A.; Fabbri, D.; Minella, M.; Brigante, M.; Mailhot, G.; Maurino, V.; Minero, C.;

Vione, D. New Insights into the Environmental Photochemistry of 5-Chloro-2-(2,4-

Dichlorophenoxy)Phenol (Triclosan): Reconsidering the Importance of Indirect

Photoreactions. Water Res. 2015, 72, 271–280.

(33) Tixier, C.; Singer, H. P.; Canonica, S.; Müller, S. R. Phototransformation of Triclosan in

Surface Waters: A Relevant Elimination Process for This Widely Used Biocide -

Laboratory Studies, Field Measurements, and Modeling. Environ. Sci. Technol. 2002, 36

(16), 3482–3489.

(34) de Godos, I.; Muñoz, R.; Guieysse, B. Tetracycline Removal during Wastewater

Treatment in High-Rate Algal Ponds. J. Hazard. Mater. 2012, 229–230, 446–449.

(35) Martínez-Zapata, M.; Aristizábal, C.; Peñuela, G. Photodegradation of the Endocrine-

Disrupting Chemicals 4n-Nonylphenol and Triclosan by Simulated Solar UV Irradiation

in Aqueous Solutions with Fe(III) and in the Absence/Presence of Humic Acids. J.

Photochem. Photobiol. A Chem. 2013, 251, 41–49.

(36) Zhang, N.; Liu, G.; Liu, H.; Wang, Y.; He, Z.; Wang, G. Diclofenac Photodegradation

under Simulated Sunlight: Effect of Different Forms of Nitrogen and Kinetics. J. Hazard.

Mater. 2011, 192 (1), 411–418.

(37) Lin, A. Y.-C.; Reinhard, M. Photodegradation of Common Environmental

37

Pharmaceuticals and Estrogens in River Water. Environ. Toxicol. Chem. 2005, 24 (6),

1303.

(38) Moffat, A. C.; Osselton, M. D.; Widdop, B. Clarke’s Analysis of Drugs and Poisons.

Pharm. Press. London 2011, 2.

(39) Gentili, F. G.; Fick, J. Algal Cultivation in Urban Wastewater: An Efficient Way to

Reduce Pharmaceutical Pollutants. J. Appl. Phycol. 2016, 29 (1), 255–262.

(40) Kwon, J. W.; Armbrust, K. L. Degradation of Citalopram by Simulated Sunlight. Environ.

Toxicol. Chem. 2005, 24 (7), 1618–1623.

(41) Chiron, S.; Minero, C.; Vione, D. Photodegradation Processes of the Antiepileptic Drug

Carbamazepine, Relevant to Estuarine Waters. Environ. Sci. Technol. 2006, 40 (19),

5977–5983.

(42) Werner, J. J.; McNeill, K.; Arnold, W. A. Environmental Photodegradation of Mefenamic

Acid. Chemosphere 2005, 58 (10), 1339–1346.

(43) Luo, L.; Wang, P.; Lin, L.; Luan, T.; Ke, L.; Tam, N. F. Y. Removal and Transformation

of High Molecular Weight Polycyclic Aromatic Hydrocarbons in Water by Live and Dead

Microalgae. Process Biochem. 2014, 49 (10), 1723–1732.

(44) Hom-Diaz, A.; Llorca, M.; Rodríguez-Mozaz, S.; Vicent, T.; Barceló, D.; Blánquez, P.

Microalgae Cultivation on Wastewater Digestate: β-Estradiol and 17α-Ethynylestradiol

Degradation and Transformation Products Identification. J. Environ. Manage. 2015, 155,

106–113.

(45) Sijm, D. T. H. M.; Broersen, K. W.; De Roode, D. F.; Mayer, P. Bioconcentration

Kinetics of Hydrophobic Chemicals in Different Densities of Chlorella Pyrenoidosa.

Environ. Toxicol. Chem. 1998, 17 (9), 1695–1704.

(46) Tam, N. F. Y.; Chong, A. M. Y.; Wong, Y. S. Removal of Tributyltin (TBT) by Live and

Dead Microalgal Cells. Mar. Pollut. Bull. 2002, 45 (1–12), 362–371.

(47) de Wilt, A.; Butkovskyi, A.; Tuantet, K.; Leal, L. H.; Fernandes, T. V.; Langenhoff, A.;

38

Zeeman, G. Micropollutant Removal in an Algal Treatment System Fed with Source

Separated Wastewater Streams. J. Hazard. Mater. 2016, 304, 84–92.

(48) Li, R.; Chen, G. Z.; Tam, N. F. Y.; Luan, T. G.; Shin, P. K. S.; Cheung, S. G.; Liu, Y.

Toxicity of Bisphenol A and Its Bioaccumulation and Removal by a Marine Microalga

Stephanodiscus Hantzschii. Ecotoxicol. Environ. Saf. 2009, 72 (2), 321–328.

(49) Wang, S.; Wang, X.; Poon, K.; Wang, Y.; Li, S.; Liu, H.; Lin, S.; Cai, Z. Removal and

Reductive Dechlorination of Triclosan by Chlorella Pyrenoidosa. Chemosphere 2013, 92

(11), 1498–1505.

(50) Shi, W.; Wang, L.; Rousseau, D. P. L.; Lens, P. N. L. Removal of Estrone, 17a-

Ethinylestradiol, and 17B-Estradiol in Algae and Duckweed-Based Wastewater Treatment

Systems. Environ. Sci. Pollut. Res. 2010, 17 (4), 824–833.

(51) Bai, X.; Acharya, K. Removal of Trimethoprim, Sulfamethoxazole, and Triclosan by the

Green Alga Nannochloris Sp. J. Hazard. Mater. 2016, 315, 70–75.

(52) Coogan, M. A.; Edziyie, R. E.; La Point, T. W.; Venables, B. J. Algal Bioaccumulation of

Triclocarban, Triclosan, and Methyl-Triclosan in a North Texas Wastewater Treatment

Plant Receiving Stream. Chemosphere 2007, 67 (10), 1911–1918.

(53) Jin, Z. P.; Luo, K.; Zhang, S.; Zheng, Q.; Yang, H. Bioaccumulation and Catabolism of

Prometryne in Green Algae. Chemosphere 2012, 87 (3), 278–284.

(54) Lai, K. M.; Scrimshaw, M. D.; Lester, J. N. Biotransformation and Bioconcentration of

Steroid Estrogens by Chlorella Vulgaris Biotransformation and Bioconcentration of

Steroid Estrogens by Chlorella Vulgaris. 2002, 68 (2), 859–864.

(55) Matamoros, V.; Gutiérrez, R.; Ferrer, I.; García, J.; Bayona, J. M. Capability of

Microalgae-Based Wastewater Treatment Systems to Remove Emerging Organic

Contaminants: A Pilot-Scale Study. J. Hazard. Mater. 2015, 288, 34–42.

(56) Kim, S.; Eichhorn, P.; Jensen, J. N.; Weber, A. S.; Aga, D. S. Removal of Antibiotics in

Wastewater: Effect of Hydraulic and Solid Retention Times on the Fate of Tetracycline in

the Activated Sludge Process. Environ. Sci. Technol. 2005, 39 (15), 5816–5823.

39

(57) Matamoros, V.; Uggetti, E.; García, J.; Bayona, J. M. Assessment of the Mechanisms

Involved in the Removal of Emerging Contaminants by Microalgae from Wastewater: A

Laboratory Scale Study. J. Hazard. Mater. 2016, 301, 197–205.

(58) Abargues, M. R.; Ferrer, J.; Bouzas, A.; Seco, A. Removal and Fate of Endocrine

Disruptors Chemicals under Lab-Scale Postreatment Stage. Removal Assessment Using

Light, Oxygen and Microalgae. Bioresour. Technol. 2013, 149, 142–148.

(59) Kang, D.; Zhao, Q.; Wu, Y.; Wu, C.; Xiang, W. Removal of Nutrients and

Pharmaceuticals and Personal Care Products from Wastewater Using Periphyton

Photobioreactors. Bioresour. Technol. 2018, 248, 113–119.

(60) Reinhold, D.; Vishwanathan, S.; Park, J. J.; Oh, D.; Michael Saunders, F. Assessment of

Plant-Driven Removal of Emerging Organic Pollutants by Duckweed. Chemosphere 2010,

80 (7), 687–692.

(61) Garcia-Rodríguez, A.; Matamoros, V.; Fontas, C.; Salvado, V. The Influence of Lemna

Sp. and Spirogyra Sp. on the Removal of Pharmaceuticals and Endocrine Disruptors in

Treated Wastewaters. Int. J. Environ. Sci. Technol. 2014, 12 (7), 2327–2338.

(62) Bai, X.; Acharya, K. Algae-Mediated Removal of Selected Pharmaceutical and Personal

Care Products (PPCPs) from Lake Mead Water. Sci. Total Environ. 2017, 581–582, 734–

740.

(63) Xiong, J.-Q.; Kurade, M. B.; Patil, D. V.; Jang, M.; Paeng, K.-J.; Jeon, B.-H.

Biodegradation and Metabolic Fate of Levofloxacin via a Freshwater Green Alga,

Scenedesmus Obliquus in Synthetic Saline Wastewater. Algal Res. 2017, 25 (December

2016), 54–61.

(64) Garcia-Rodríguez, A.; Matamoros, V.; Fontàs, C.; Salvadó, V. The Influence of Lemna

Sp. and Spirogyra Sp. on the Removal of Pharmaceuticals and Endocrine Disruptors in

Treated Wastewaters. Int. J. Environ. Sci. Technol. 2015, 12 (7), 2327–2338.

(65) Muñoz, R.; Guieysse, B. Algal-Bacterial Processes for the Treatment of Hazardous

Contaminants: A Review. Water Res. 2006, 40 (15), 2799–2815.

40

(66) Muñoz, R.; Köllner, C.; Guieysse, B.; Mattiasson, B. Salicylate Biodegradation by

Various Algal-Bacterial Consortia under Photosynthetic Oxygenation. Biotechnol. Lett.

2003, 25 (22), 1905–1911.

(67) Barranguet, C.; Veuger, B.; Van Beusekom, S. A. M.; Marvan, P.; Sinke, J. J.; Admiraal,

W. Divergent Composition of Algal-Bacterial Biofilms Developing under Various

External Factors. Eur. J. Phycol. 2005, 40 (1), 1–8.

(68) Vanderford, B. J.; Mawhinney, D. B.; Trenholm, R. A.; Zeigler-Holady, J. C.; Snyder, S.

A. Assessment of Sample Preservation Techniques for Pharmaceuticals, Personal Care

Products, and Steroids in Surface and Drinking Water. Anal. Bioanal. Chem. 2011, 399

(6), 2227–2234.

(69) Kunkel, U.; Radke, M. Fate of Pharmaceuticals in Rivers: Deriving a Benchmark Dataset

at Favorable Attenuation Conditions. Water Res. 2012, 46 (17), 5551–5565.

(70) Wang, Y.; Roddick, F. A.; Fan, L. Direct and Indirect Photolysis of Seven Micropollutants

in Secondary Effluent from a Wastewater Lagoon. Chemosphere 2017, 185, 297–308.

(71) Sanchez-Prado, L.; Llompart, M.; Lores, M.; García-Jares, C.; Bayona, J. M.; Cela, R.

Monitoring the Photochemical Degradation of Triclosan in Wastewater by UV Light and

Sunlight Using Solid-Phase Microextraction. Chemosphere 2006, 65 (8), 1338–1347.

(72) Wang, S.; Yin, Y.; Wang, J. Microbial Degradation of Triclosan by a Novel Strain of

Dyella Sp. Appl. Microbiol. Biotechnol. 2018, No. 2017, 1–10.

(73) Wang, S.; Wang, X.; Poon, K.; Wang, Y.; Li, S.; Liu, H.; Lin, S.; Cai, Z. Removal and

Reductive Dechlorination of Triclosan by Chlorella Pyrenoidosa. Chemosphere 2013, 92

(11), 1498–1505.

(74) Dordio, A. V.; Belo, M.; Martins Teixeira, D.; Palace Carvalho, A. J.; Dias, C. M. B.;

Picó, Y.; Pinto, A. P. Evaluation of Carbamazepine Uptake and Metabolization by Typha

Spp., a Plant with Potential Use in Phytotreatment. Bioresour. Technol. 2011, 102 (17),

7827–7834.

(75) Dordio, A.; Carvalho, A. J. P.; Teixeira, D. M.; Dias, C. B.; Pinto, A. P. Removal of

41

Pharmaceuticals in Microcosm Constructed Wetlands Using Typha Spp. and LECA.

Bioresour. Technol. 2010, 101 (3), 886–892.

(76) Jelic, A.; Cruz-Morató, C.; Marco-Urrea, E.; Sarrà, M.; Perez, S.; Vicent, T.; Petrović, M.;

Barcelo, D. Degradation of Carbamazepine by Trametes Versicolor in an Air Pulsed

Fluidized Bed Bioreactor and Identification of Intermediates. Water Res. 2012, 46 (4),

955–964.

(77) Conkle, J. L.; White, J. R.; Metcalfe, C. D. Reduction of Pharmaceutically Active

Compounds by a Lagoon Wetland Wastewater Treatment System in Southeast Louisiana.

Chemosphere 2008, 73 (11), 1741–1748.

42

Appendices

Appendix 1: Reactor conditions

Experiment 1 was designed to provide a broad basis of understanding for what factors influence

the removal of the four studied PPCPs. Lagoon water was chosen as a partially-treated

wastewater matrix, sodium azide-sterilized lagoon water was chosen as a biodegradation control,

and BBM was chosen as an ideal algae growth media that is similar to many previous studies on

PPCP removal by algae. Light and dark conditions were chosen to isolate for photodegradation

and photosynthetic algae processes, and a “no algae” condition was used to isolate any algae

effects.

While 12 combinations of these factors was possible, three conditions were omitted (BBM with

Chlorella vulgaris in the dark, sterile lagoon water with chlorella vulgaris in the dark, and sterile

lagoon water with chlorella vulgaris in the light) because they were not representative of field

conditions and were not helpful for isolating PPCP removal mechanisms. Selected combinations

of experimental reactors are provided in Table S1.

Table S1: Combinations of experimental reactor conditions investigated in Experiment 1.

BBM Lagoon Water Sterile lagoon water

(NaN3)

Dark Chlorella

vulgaris

3

Light 3 3

No algae 3 3 3

Dark 3 3 3

In Experiment 2, reactors were chosen to augment the conditions investigated in experiment 1.

Three goals were identified for Experiment 2 and reactors were chosen to accomplish each of

these goals. The goals and the corresponding reactors are:

1. To recreate all Chlorella vulgaris reactors from Experiment 1 with Scenedesmus

obliquus, to facilitate comparisons between the two microalgae species.

• BBM reactors with Scenedesmus obliquus in light

• Lagoon water reactors with Scenedesmus obliquus in light

• Lagoon water reactors with Scenedesmus obliquus in dark

2. To assess removal rates in autoclaved lagoon water reactors

43

• Autoclaved lagoon water reactors with Scenedesmus obliquus in light

• Autoclaved lagoon water reactors with Chlorella vulgaris in light

3. To provide a basis of comparison between Experiment 1 and Experiment 2

• Lagoon water reactors with Chlorella vulgaris

Selected combinations of experimental reactors are provided in Table S2.

Table S2: Combinations of experimental conditions investigated in Experiment 2.

BBM Lagoon Water Sterile lagoon water

(autoclaved)

Dark Scenedesmus

obliquus

3

Light

3 3 3

Chlorella

vulgaris 3 3

Control reactors were used to provide indications of HPLC-background noise in the different

types of reactors. For each experiment, control reactors were chosen to represent each

combination of algae and media. The number of replicates used for each control was based on

the expected amount of uncertainty for each media and algae condition; controls with algae and

with unsterilized lagoon water were thought to be more variable than those without algae and

those with sterile media.

Experiment 1 control reactors are provided in Table S3.

Table S3: Combinations of control reactors used in Experiment 1.

BBM Lagoon Water Sterile lagoon water

(NaN3)

Dark Chlorella

vulgaris

Light 2 3

No algae

Dark 1 2 1

Experiment 2 control reactors are provided in Table S4.

44

Table S4: Combinations of control reactors used in Experiment 2.

BBM Lagoon Water Sterile lagoon water

(autoclaved)

Dark Scenedesmus

obliquus

Light

2 3

Chlorella

vulgaris 3

In Experiment 2, reactors were dividend into two groups. These groups of reactors were started

15 days apart in order to ease sampling and analytical requirements. The reactors were split in

order to ensure that at least one reactor from each series was in each group, and that each

experimental reactor had a corresponding control in the same group, wherever possible.

45

Table S5: Reactor split in Experiment 2.

Series Description and Name

Experiment 2a

Started June

26

Experiment

2b

Started June

11

# of

Replicates

Sterile lagoon

water

Chlorella vulgaris

Light

SCL SCL1 SCL2

SCL3 3

Sterile lagoon

water

Scenedesmus

obliquus

Light

SSL SSL1

SSL2 SSL3 3

Lagoon water

Scenedesmus

obliquus

Dark

LSD LSD1 LSD2

LSD3 3

Lagoon water

Scenedesmus

obliquus

Light

LSL LSL1 LSL2

LSL3 3

BBM

Scenedesmus

obliquus

Light

BSL BSL1 BSL2

BSL3 3

Lagoon water

Scenedesmus

obliquus

Control

LSC LSC1 LSC2

LSC3 3

BBM

Scenedesmus

obliquus

Control

BSC BSC1 BSC2 2

Lagoon water

Chlorella vulgaris

Light

LCL LCL1

LCL2 LCL3 3

Lagoon water

Chlorella vulgaris

Light

LCC LCC1

LCC2 LCL3 3

Total: 12 14 26

46

Appendix 2: SPE Recovery Rates

To assess SPE recovery rates, a PPCP-contaminated water was made by spiking 300 mL of

Milli-Q water with PPCPs to 10 µg/L for carbamazepine (CBZ), gemfibrozil (GFB), and

triclosan (TCS) and to 50 µg/L for ibuprofen (IBU). Of this sample, 4x50 mL sub-samples were

extracted according to the previously specified extraction method, then analyzed by HPLC.

Recovery rates were calculated using the following formula:

𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑅𝑎𝑡𝑒 =𝑚𝑎𝑠𝑠 𝑖𝑛 𝑒𝑥𝑡𝑟𝑎𝑐𝑡

𝑚𝑎𝑠𝑠 𝑖𝑛 𝑠𝑎𝑚𝑝𝑙𝑒∗ 100%

Recovery rates were averaged to determine the recovery rate of the analyte in the extraction

process.

Table S6a: Recovery rates of PPCPs, extract and sample masses.

Replicate

Extract Concentration Extract

Volume

Extract PPCP Mass

CBZ IBU GFB TCS CBZ IBU GFB TCS

ug/L, ng/mL mL ng

A 435 2132 390 317 1.00 437 2142 392 319

B 501 2503 431 351 0.84 421 2100 361 295

C 451 2276 418 336 0.90 407 2057 377 303

D 473 2394 419 349 0.86 405 2047 358 299

Replicate

Sample Concentration Sample

Volume

Sample PPCP Mass

CBZ IBU GFB TCS CBZ IBU GFB TCS

ug/L, ng/mL mL ng

A 10 50 10 10 50 500 2500 500 500

B 10 50 10 10 50 500 2500 500 500

C 10 50 10 10 50 500 2500 500 500

D 10 50 10 10 50 500 2500 500 500

47

Table S6b: Recovery rates of PPCPs.

Recovery Rate

CBZ IBU GFB TCS

A 87% 86% 78% 64%

B 84% 84% 72% 59%

C 81% 82% 75% 61%

D 81% 82% 72% 60%

Average 83% 83% 74% 61%

StDev 3% 2% 3% 2%

COV 0.04 0.02 0.04 0.03

Appendix 3: Sample Chromatograms

The following is a chromatogram at 207 nm (the wavelength for ibuprofen quantification) of 300

µg/L standard solution. This is representative of starting concentration of all compounds.

Figure S1: 300 µg/L calibration standard.

The following is are a series of chromatograms of a 25 µg/L standard, which is below the

quantification limit of all compounds. The chromatograms demonstrate absorbance at 207 nm

(ibuprofen quantification wavelength), 280 nm (triclosan and gemfibrozil quantification

wavelength), and 300 nm (carbamazepine quantification wavelength) respectively.

48

Figure S2: 25 µg/L calibration standard, 207 nm.

Figure S3: 25 ug/L calibration standard, 280 nm.

49

Figure S4: 25 µg/L calibration standard, 300 nm.

Appendix 4: Calibration Curves

The following calibration curve data and curves represent one sample of the HPLC-DAD data

that was used to generate concentration information from peak area in the detector for

carbamazepine (CBZ), ibuprofen (IBU), gemfibrozil (GFB), and triclosan (TCS).

50

Table S7: Calibration curve data table.

Concentration (µg/L) Peak Area (mAU*min)

CBZ, GFB,

TCS IBU

CBZ IBU GFB TCS

300 nm 207 nm 280 nm 280 nm

0 0 0.000 0.034 0.010 0.044

25 125 0.153 0.884 0.032 0.094

50 250 0.287 1.616 0.052 0.171

100 500 0.580 3.231 0.103 0.346

200 1000 1.158 6.484 0.204 0.682

300 1500 1.769 10.035 0.324 1.068

400 2000 2.396 13.440 0.420 1.443

500 2500 2.989 16.785 0.528 1.807

slope 167 149 955 279

intercept 2 8 -3 0

R^2 0.9998 0.9998 0.9992 0.9988

Figure S5: Sample carbamazepine calibration curve.

y = 167.11x + 1.9408

R² = 0.9998

0

100

200

300

400

500

600

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Conce

ntr

atio

n (

µg/L

)

Signal (mAU*min)

Carbamazepine

51

Figure S6: Sample ibuprofen calibration curve.

Figure S7: Sample gemfibrozil calibration curve.

y = 148.82x + 7.5878

R² = 0.9998

0

500

1000

1500

2000

2500

3000

0 5 10 15 20

Conce

ntr

atio

n (

µg/L

)

Signal (mAU*min)

Ibuprofen

y = 954.88x - 2.8134

R² = 0.9992

0

100

200

300

400

500

600

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Con

centr

atio

n (

µg/L

)

Signal (mAU*min)

Gemfibrozil

52

Figure S8: Sample triclosan calibration curve

Appendix 5: PPCP Limits of Detection and Quantification

Limits of detection and quantification were calculated twice: once for injection of a clean extract,

made in 50% Milli-Q water (18.2 Ω resistance, henceforth refered to as MQ water) and 50%

HPLC grade methanol, and the second was one that incorporated noise from the SPE and the

lagoon water.

Clean Extract Detection and Quantification

Five different concentrations of PPCPs ranging from 10 to 50 µg/L were prepared in 50% MQ

water and 50% HPLC-grade methanol, the injection solvent combination used for analysis. Over

the course of three days over a range of 2 weeks, these extracts were each injected 9 times and

then quantified.

Conformity ratio was calculated as a measure of whether the injected concentration was the

correct concentration to be representative of a “low concentration.” Its formula is shown below.

y = 279.13x - 0.4385

R² = 0.9988

0

100

200

300

400

500

600

0.0 0.5 1.0 1.5 2.0

Conce

ntr

atio

n (

µg/L

)

Signal (mAU*min)

Triclosan

53

𝑐𝑜𝑛𝑓𝑜𝑟𝑚𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜 =𝑎𝑣𝑒𝑟𝑎𝑔𝑒

𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 ∗ 3

Once a conformity ratio between 4 and 10 was achieved, the following formulae were used to

calculate the LOD and LOQ:

𝐿𝑂𝐷 = 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 ∗ 3

𝐿𝑂𝑄 = 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 ∗ 10

Results from the selected injections are included in the table below. All units are µg/L.

Table S8: LOD and LOQ calculations and values for calibration standard.

CBZ IBU GFB TCS

Injected

Concentration 10 20 10 10

Observed

Concentrations

9.96 18.9 9.8 9.3

9.5 18.7 9.8 9.7

9.5 19.4 11.8 10.4

9.6 19.1 10.7 10.1

9.4 17.8 10.7 10.8

9.4 17.3 11.7 11.2

10.4 20.6 11.2 10.6

11.0 20.6 12.2 10.3

10.7 20.4 10.2 11.3

Average 9.9 19.2 10.9 10.4

Sample Standard

Deviation 0.60 1.2 0.87 0.67

n 9 9 9 9

COV 6% 6% 8% 6%

LOD 1.8 3.6 2.6 2.0

Conformity Ratio: 5.6 5.3 4.2 5.2

LOQ 6.0 12.1 8.7 6.7

54

Lagoon Water Limits of Detection and Quantification

The accurate detection and quantification of an analyte via HPLC-DAD is often affected by the

sample matrix and can be affected by the extraction process. The method used in this thesis

assessed PPCP concentrations in lagoon water via extraction in SPE. The previously calculated

LOD/LOQ would not be transferrable to this procedure because additional variability would be

introduced by variation in the SPE recovery rates as well as additional noise in the

chromatogram from the lagoon water and the extraction cartridge.

To accommodate these differences, six low concentration (1 ug/L) synthetic samples were

prepared in lagoon water. These samples were extracted using SPE to create low-concentration

samples that contained similar noise to samples being analyzed during subsequent experiments.

The variation in these extracts were used to assess the LOD and the LOQ using the same

formulae as in the MQ/methanol standards.

Table S9: LOD and LOQ calculations and values for SPE of lagoon water.

CBZ IBU GFB TCS

Injected

Concentration ~40 µg/L

~210

µg/L

~35

µg/L

~30

µg/L

Observed

Concentrations

39.9 284 51.4 26.1

42.4 267 57.6 32.1

34.1 221 46.5 23.6

40.4 255 58.8 31.5

37.6 269 57.6 29.7

42.0 289 58.8 33.9

Average 39.4 264 55.1 29.5

Sample

Standard

Deviation

3.1 24 5.1 3.9

n 6 6 6 6

COV 8% 9% 9% 13%

LOD 9.3 73 15 12

Conformity

Ratio: 4.2 3.6 3.6 2.5

LOQ 31 245 51 39

MDL 0.22 3.5 0.71 0.57

MQL 0.83 8.0 1.8 1.6

55

Method detection limit (MDL) and method quantification limit (MQL) were calculated as

estimations of the concentration that could be detected and quantified in the samples. They were

calculated as follows:

𝑀𝑒𝑡ℎ𝑜𝑑 𝑑𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛 𝑙𝑖𝑚𝑖𝑡 =𝐿𝑖𝑚𝑖𝑡 𝑜𝑓 𝑑𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛

𝑆𝑃𝐸 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟∗ 𝑆𝑃𝐸 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑟𝑎𝑡𝑒

𝑀𝑒𝑡ℎ𝑜𝑑 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑙𝑖𝑚𝑖𝑡 =𝐿𝑖𝑚𝑖𝑡 𝑜𝑓 𝑞𝑢𝑎𝑛𝑡𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛

𝑆𝑃𝐸 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟∗ 𝑆𝑃𝐸 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑟𝑎𝑡𝑒

Appendix 6: Algae Counting Limits of Quantification and Detection

Minimum

In order to assess the limits of quantification and detection for algae counting, an assessment of

the noise in a “blank” sample is necessary. To do this, the cell counts from the day 25 lagoon

water reactors were used. They were selected because they were the only biologically active

reactors without algae. As such, they were most likely to have particulate matter that could be

misinterpreted as algae.

The average and standard deviation of the algae density was determined. From this, the limits of

detection and quantification were determined using the following formulae:

𝐿𝑂𝐷 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑏𝑙𝑎𝑛𝑘 + 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 ∗ 3

𝐿𝑂𝑄 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑏𝑙𝑎𝑛𝑘 + 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 ∗ 10

Table S9 provides the levels of algae counted in the lagoon water reactors as well as the resulting

LOD and LOQ.

As a result, for this paper an LOD of 6.2 × 103 and an LOQ of 1.6 × 104 average cells/image are

derived. Noted that this is for an undiluted sample, and the LOD/LOQ for a diluted sample

would simply be multiplied by the dilution factor.

56

Table S10: Average number of Chlorella vulgaris counted in the lagoon water reactors.

Units are cells/image for all units.

Light 1 0.65

Light 2 0.25

Light 3 0.3

Dark 1 0.3

Dark 2 0.35

Dark 3 0.5

Control 1 0.05

Control 2 0.05

Maximum

When samples loaded onto the slide wells are too concentrated, the cells clump on the wells as

they dry. As a result, these cells cannot be counted and the cell density in that sample cannot be

quantified. To estimate the maximum number of cells that could have been counted, the sample

with the highest countable density of cells was used as the quantifiable upper limit.

57

Appendix 7: pH Data

pH values for all experimental reactors is provided in Figure S9 below.

Figure S9: pH values in experimental reactors of Experiment 1 (top panels) and

Experiment 2 (bottom panels). Points represent averages of triplicate reactors (except for

sample loss, then duplicate), and error bars represent one standard deviation of the

replicates. Solid lines represent illuminated reactors and dashed lines represent reactors

wrapped in aluminum foil. Crosses represent reactors without algae, dark green triangles

represent reactors with Scenedesmus obliquus and light green circles represent reactors

with Chlorella vulgaris. BBM stands for Bold’s Basal Medium.

Appendix 8: Ibuprofen Variability in Experiment 2

The following table provides full ibuprofen concentration data for all series in Experiment 2

where the standard deviation of the replicates was more than 10 µg/L.

Table S11: Ibuprofen concentrations for individual reactors. Dashes represent samples for

which the ibuprofen concentration could not be determined, either because of extract loss

or noise in the chromatogram.

58

Reactor Condition Ibuprofen Concentration (µg/L)

Day 0 Day 3 Day 7 Day 11 Day 17 Day 25

Autoclaved Lagoon Water

Chlorella vulgaris

Light

1 54 54 61 51 39 30

2 59 59 64 63 60 62

3 65 57 64 54 68 67

Lagoon Water

Scenedesmus obliquus

Dark

1 51 57 60 - 57 49

2 61 64 57 <LOD <LOD <LOD

3 76 56 62 55 <LOD <LOQ

Lagoon Water

Scenedesmus obliquus

Dark

1 53 55 57 56 60 <LOD

2 58 57 58 50 44 -

3 60 62 62 53 53 46

Lagoon Water

Chlorella vulgaris

Light

1 57 - - 43 <LOD <LOD

2 65 54 59 52 41 <LOD

3 55 60 52 46 52 44

These concentrations are presented graphically in Figure S10, below.

59

Figure S10: Ibuprofen concentrations in select individual experimental reactors of

Experiment 2. Solid lines represent illuminated reactors and dashed lines represent

reactors wrapped in aluminum foil. Crosses represent reactors without algae, dark green

triangles represent reactors with Scenedesmus obliquus and light green circles represent

reactors with Chlorella vulgaris. BBM stands for Bold’s Basal Medium

60

Appendix 9: Triclosan Removal Kinetics

To calculate a first-order degradation rate using the concentrations of triclosan found in this

experiment, the following procedure was performed on each experimental reactor:

1. Eliminate all concentration values <LOD.

2. Eliminate all concentration values <LOQ following the first sample that is below the LOQ.

3. For remaining values below the LOQ, use approximate concentrations estimated from

HPLC signal.

4. Assess the fit of a zeroth, first, and second-order degradation model to the concentration

data. This is done by calculating the r2 for sampling date with each of C/C0, ln(C/C0), and

1/(C/C0), representing the zeroth, first, and second-order models respectively. Because the

r2 was consistently highest for the first-order model, those models were used

5. For best fit (consistently the first-order rate), calculate the standard error of the intercept.

6. If the standard error of the intercept is larger than the intercept, then the intercept is not

significant in the model and should be forced to zero.

The equation describing pseudo-first order removal is presented below.

𝐶𝑡 = 𝐶0𝑒−𝑘𝑡

where Ct is the concentration at a measurement time, C0 is the initial concentration, k is the

removal rate coefficient, and t is the time (in days) elapsed since the start of the experiment. This

can be modelled by a linear regression according to the following formula:

ln (𝐶𝑡

𝐶0) = −𝑘𝑡 + 𝑏

where b is the fitted y-intercept. These values for each reactor are presented in Table S11, along

with the number of data points in that regression (n), their r2, and the average, standard deviation,

and covariance of the removal rate coefficients within a series of reactors.

61

Table S12: Pseudo-first order linear regression coefficients.

Reactor Series n k b

(r2)

Average

k

Stdev

k

COV

k E

xper

imen

t 1

Lagoon

No algae

Light

1 3 0.35 0 0.98

0.33 0.05 14% 2 3 0.36 0 0.98

3 3 0.28 0 1.00

Lagoon

No algae

Dark

1 6 0.02 0 0.57

0.02 0.01 50% 2 6 0.03 0 0.62

3 6 0.01 0.15 0.22

Lagoon

Chlorella vulgaris

Light

1 2 0.29 0 0.96

0.39 0.11 28% 2 2 0.50 0 1.00

3 2 0.37 0 1.00

Lagoon

Chlorella vulgaris

Dark

1 6 0.03 0 0.77

0.02 0.01 50% 2 6 0.01 -0.33 0.19

3 6 0.02 -0.11 0.62

BBM

Chlorella vulgaris

Light

1 2 0.71 0 0.98

0.49 0.19 39% 2 2 0.40 0 0.99

3 2 0.36 0 1.00

Lagoon with Azide

No algae

Light

1 3 0.28 0 0.98

0.32 0.05 17% 2 3 0.29 0 1.00

3 3 0.38 0 1.00

BBM

No algae

Light

1 5 0.09 -0.11 0.99

0.093 0.004 5% 2 6 0.09 0 0.99

3 5 0.10 0 0.96

Lagoon with Azide

No algae

Dark

1 5 0.01 0 0.59

0.013 0.003 25% 2 6 0.02 0 0.35

3 6 0.01 0 0.32

BBM

No algae

Dark

1 6 0.01 0 0.37

0.011 0.006 52% 2 6 0.01 0.10 0.18

3 5 0.02 0 0.43

Exper

imen

t 2

Autoclaved lagoon

Chlorella Vulgaris

Light

1 3 0.31 0 1.00

0.28 0.03 12% 2 4 0.25 0 1.00

3 3 0.26 0 1.00

Autoclaved lagoon

Scenedesmus obliquus

Light

1 3 0.39 0 1.00

0.38 0.02 6% 2 3 0.40 0 0.99

3 3 0.36 0 0.85

Lagoon

Scenedesmus obliquus

Dark

1 6 0.06 -0.33 0.81

0.04 0.02 37% 2 6 0.03 0 0.94

3 6 0.04 0 0.95

Lagoon

Scenedesmus obliquus

Light

1 3 0.25 0 0.99

0.25 0.02 10% 2 3 0.27 0 1.00

3 3 0.22 0 0.98

62

BBM

Scenedesmus obliquus

Light

1 3 0.35 0 0.87

0.24 0.10 43% 2 4 0.15 0.35 0.80

3 4 0.21 0.46 0.83

Lagoon

Chlorella Vulgaris

Light

1 2 0.41 0 1.00

0.41 0.06 14% 2 3 0.45 0 0.99

3 3 0.37 0 0.99

The following shows the plots of ln[TCS]t against time for the six reactors series which

accurately followed first-order degradation rate kinetics.

63

Lagoon water, no algae, light

BBM, no algae, light

Autoclaved lagoon water, Chlorella vulgaris,

light

Autoclaved lagoon water, Scenedesmus

obliquus, light

Lagoon water, Chlorella vulgaris, light Lagoon water, Scenedesmus obliquus, light

64

Figure S11: Plots of ln[TCS]t against time.