Aerobic and Anaerobic Biotransformation of Chloroanilines, Chlorobenzenes, and Dichlonitrobenzenes at a Complex Industrial Site in

Brazil and Analysis of Associated Microbial Communities

by

Suzana de Paula Queiroz Kraus

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

Department of Chemical Engineering and Applied Chemistry University of Toronto

© Copyright by Suzana de Paula Queiroz Kraus 2018

ii

Aerobic and Anaerobic Biotransformation of Chloroanilines, Chlorobenzenes, and Dichlonitrobenzenes at a Complex Industrial Site in

Brazil and Analysis of Associated Microbial Communities

Suzana de Paula Queiroz Kraus

Master of Applied Science

Department of Chemical Engineering and Applied Chemistry

University of Toronto

2018

Abstract

Environmental contamination is a widespread problem and many industrial sites need

significant attention. The potential for using bioremediation as a low cost environmentally-

friendly restoration approach was evaluated at a contaminated site in Brazil. Aerobic and

anaerobic biotransformation of chloroanilines, chlorobenzenes, and dichloronitrobenzenes were

studied in long-term microcosm experiment, where novel reactions were observed, such as

anaerobic biotransformation of dichloronitrobenzenes. Further microbial community analysis

was performed based on multiple samples with the objective to recommend a course of action at

the site. To evaluate the identity and distribution of microorganisms in microcosms and field

samples, amplicon sequencing of the 16S rRNA gene was performed. Cupriavidus was found to

degrade dichlorobenzenes, Diaphorobacter degrades dichloronitrobenzenes aerobically, among

others. Statistical analysis was done to interpret the data and identify significant factors that

drive a microbial community. The conclusion is that this site has a high potential for being

bioremediated, by promoting aerobic biodegradation processes.

iii

Acknowledgments

This thesis and work would not have been possible without help and support from really

important people in my academic, professional, and personal life.

Firstly, I would like to thank my supervisor, Dr. Elizabeth Edwards, who has always shown

passion and excitement about research and has inspired me during the past two years! I extend

my thanks to my committee members, Dr. Elodie Passeport and Dr. Gary Wealthall, for their

availability to help and provide guidance in this important step of my academic life.

I would like to thank DuPont for proving the funding for this research. Special thanks to James

K. Henderson, Paloma Carvalho, and Elizabeth Erin Mack for being so supportive during these

years and for teaching me so much. I really appreciate this opportunity!

Thank you Line Lomheim for training me since the beginning of my program, for being so

patient and kind everyday, and for doing such an amazing work with the Camaçari microcosms.

Thank you Camilla Nesbø for helping me with bioinformatics and being patient enough to teach

me a complete new world of command lines and scripts. Thank you to Amy Li for helping in the

laboratory over the summers. You all have contributed a lot to this research!

Thank you to Susie Susilawati for saving me every time I needed to be and for always being

“awesome!”. Thank you Vinthiya Paramananthasivam and Katrina Chu for the administrative

support and for your kindness.

I would like to thank Savia Gavazza for giving me best hugs and smiles ever! I felt like part of

your family while you were in Toronto and I could not be more thankful for this. When I think

about the moments I spent with your lovely daughters, Mari and Lili, I can only think about

love, happiness, and peace.

Thank you to my friends from Ed Lab who were always ready to help with anything I needed. I

learned a lot during these years and all of you were part of it! Thank you for all the fun moments

we had together. I would like to thank Nadia Morson for not only baking every week with me,

but for all the talks, advices, and great moments we spent together – you’re my best miga and it

has been great to have you for these two years! Special thanks to Lais Mazullo and Peter Lee for

iv

also being so amazing to me! Charlie Chen, Ivy Yang, Fei Luo, Olivia Molenda, Courtney Toth,

Luz Puentes, Shen Guo, Zahra Choolaei, and Mabel Wong, thank you for your support and

friendship.

Speaking of friends, I have to thank my friends from Brazil who are my second family! Even

from far away, I always had their support, we were always close, and this was really important

to help me through tough times here in Toronto. We have known each other for over 20 years

now, so you probably know I’m writing this and crying like a baby. I miss you guys so much!

A special thank you for my boyfriend Jovi for his endless love, support, and patience. Thank

you for being there every time I needed you and for believing in me even more than I did. This

is just the beginning of a beautiful history we are writing together! I love you.

And most importantly my family: my mom Mariangela, my dad Euclydes, and my sister Marina

for being the best human beings I have even known. Thank you for always believing in me, for

being supportive in my decisions, and for your love. I miss you everyday since I got here and

many times I wish to be there with you. This thesis is for you!

v

Table of Contents

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

Table of Contents ...................................................................................................................... v

List of Tables ...........................................................................................................................ix

List of Figures ........................................................................................................................... x

List of Appendices ................................................................................................................. xii

List of Abbreviations .............................................................................................................. xv

Chapter 1 Literature review and objectives .............................................................................. 1

Literature review ........................................................................................................... 1

1.1.1 Remediation and bioremediation ...................................................................... 1

1.1.2 The presence of pesticides in soil and groundwater ......................................... 1

1.1.3 Site history ........................................................................................................ 2

1.1.4 Compounds of interest (COIs) for this study .................................................... 3

Research objectives ...................................................................................................... 6

Thesis structure ............................................................................................................. 6

Chapter 2 Materials and Methods ............................................................................................. 8

Preparation of Solutions ............................................................................................... 8

2.1.1 COI stock, sodium sulfate, sodium nitrate, and sodium lactate solutions ........ 8

2.1.2 Anaerobic mineral medium .............................................................................. 9

Microcosm study ........................................................................................................ 11

2.2.1 Microcosm study set up .................................................................................. 11

2.2.2 VOC monitoring using GC ............................................................................. 14

2.2.3 SVOC monitoring using HPLC ...................................................................... 14

2.2.4 Other measurements: oxygen, pH, and sulfate and nitrate concentrations ..... 15

vi

Preparation of samples for DNA extraction ............................................................... 16

2.3.1 Groundwater samples ..................................................................................... 16

2.3.2 Soil samples .................................................................................................... 17

DNA extraction, amplicon sequencing, and qPCR .................................................... 17

Statistical analysis: MetaAmp and RStudio ............................................................... 18

Road map of experiments conducted during this research ......................................... 19

Chapter 3 Background: Microcosm study #1 ......................................................................... 21

Motivation and sample location.................................................................................. 21

Methodology and COIs............................................................................................... 21

Results and discussion ................................................................................................ 23

3.3.1 Aerobic microcosms ....................................................................................... 23

3.3.2 Anaerobic microcosms ................................................................................... 26

Chapter 4 Laboratory activity tests ......................................................................................... 30

Aniline and chloroaniline anaerobic microcosm study #2 ......................................... 30

4.1.1 Motivation and sample collection ................................................................... 30

4.1.2 Methodology ................................................................................................... 30

4.1.3 Results and discussion .................................................................................... 31

Aerobic degradation experiments of multiple COIs in Camaçari laboratory, Brazil . 31

4.2.1 Motivation and sample collection ................................................................... 31

4.2.2 Methodology ................................................................................................... 32

4.2.3 Results and discussion .................................................................................... 33

Highly enriched cultures from collaborating laboratories .......................................... 34

4.3.1 Motivation and samples .................................................................................. 34

4.3.2 Methodology ................................................................................................... 35

4.3.3 Results ............................................................................................................ 36

vii

Influence of pH in microcosms................................................................................... 38

4.4.1 Motivation and samples .................................................................................. 38

4.4.2 Methodology ................................................................................................... 39

4.4.3 Results ............................................................................................................ 40

Chapter 5 Microbial community analysis ............................................................................... 41

Motivation and samples .............................................................................................. 41

Results and discussion ................................................................................................ 44

5.2.1 qPCR of samples from microcosms, enriched cultures, soil, groundwater, and

groundwater from Cetrel................................................................................. 44

5.2.2 Main operational taxonomic units (OTUs) in groundwater and soil samples

from the site .................................................................................................... 46

5.2.3 Changes in microbial community over time in microcosms samples ............ 51

5.2.4 Comparison between external cultures, microcosms samples, and

environmental samples ................................................................................... 59

5.2.5 NMDS analyses in multiple groups of samples .............................................. 62

Chapter 6 Conclusions and future work ................................................................................. 72

Conclusions ................................................................................................................ 72

6.1.1 Aerobic and anaerobic reactions observed during microcosms study ............ 72

6.1.2 Impact of pH in different degradation laboratory tests ................................... 72

6.1.3 Microbial community analysis ....................................................................... 73

6.1.4 Potential microorganisms responsible for biodegrading COIs in this study .. 73

Recommendations and future work ............................................................................ 75

References............................................................................................................................... 77

Appendix A. Supplementary information for Chapter 1 ........................................................ 83

Appendix B. Supplementary information for Chapter 2 ........................................................ 94

Appendix C. Supplementary Information for Chapter 3 ........................................................ 98

viii

Appendix D. Supplementary information for Chapter 4 ...................................................... 106

Appendix E. Supplementary information for Chapter 5 ....................................................... 123

Appendix F. Supplementary information for statistical analyses ......................................... 145

Appendix G. Electronic files available as supplementary data ............................................ 161

ix

List of Tables

Table 2.1 Treatment table for microcosms study #1……………………………………………13

Table 3.1 Summary of aerobic biodegradation observed during main microcosm study............24

Table 3.2 Summary of anaerobic reactions observed during main microcosm study…………..26

Table 6.1 Summary of aerobic degradation and anaerobic biotransformation observed in the

microcosm study #1….………………………………………………………………………….72

x

List of Figures

Figure 2.1 Microcosm study set up……………………………………………………………...12

Figure 2.2 Groundwater filtration set up………………………………………………………..17

Figure 2.3 Road map for experiments in this research………………………………………….20

Figure 3.1 Sample location for microcosm study #1………………………………………...….22

Figure 3.2 Concentration versus time in an aerobic active control microcosm from site 1A…..25

Figure 3.3 Concentration of SVOCs versus time in anaerobic electron donor amended

microcosm from site 2A ………………………………………………………………………..29

Figure 4.1 Impact of pH and bioaugmentation on aerobic microcosms from site 2A…………..37

Figure 4.2 Impact of pH and bioaugmentation on aerobic microcosms from site 1B…………..38

Figure 5.1 qPCR results (copies/mL) for microcosms, soil, groundwater, and pure culture

samples………………………………………………………………………………………….45

Figure 5.2 Relative abundance (> 0.5%) in soil samples collected from the site……………….47

Figure 5.3 Dendrogram and relative abundance (> 1%) in groundwater samples collected from

the site…………………………………………………………………………………………...48

Figure 5.4 Relative abundance (> 0.5%) in groundwater samples from Cetrel and from hydraulic

barrier……………………………………………………………………………….…………...50

Figure 5.5 Relative abundance (%) in aerobic microcosms samples from Site 1A and Site 1B..52

Figure 5.6 Relative abundance (%) in aerobic microcosms samples from Site 2A and Site 2B..53

Figure 5.7 Relative abundance (%) of microorganisms in anaerobic microcosms samples from

site 1A…………………………………………………………………………………………...56

xi

Figure 5.8 Relative abundance (%) of microorganisms in anaerobic microcosms samples from

site 1B…………………………………………………………………………………………...57

Figure 5.9 Relative abundance (%) of microorganisms in anaerobic microcosms samples from

site 2A………………………………………………………………………………………...…58

Figure 5.10 Most abundant OTUs in external laboratory highly enrichment cultures used for the

experiments in UofT…………………………………………………………………………...…………59

Figure 5.11 Relative abundance (%) of Pandoraea (OTU10) in all the samples……………….61

Figure 5.12 NMDS plots for all the samples……………………………………………………………..64

Figure 5.13 NMDS plots for all microcosms, aerobic microcosms, and anaerobic microcosms………...65

Figure 5.14 NMDS plots for aerobic microcosms and significant OTUs………………………………..66

Figure 5.15 NMDS plots for anaerobic microcosms and significant OTUs…………………………….67

Figure 5.16 NMDS plots for aerobic and anaerobic microcosms from Site 1A and significant

OTUs…………………………………………………………………………………………….68

Figure 5.17 NMDS plots for aerobic and anaerobic microcosms from Site 1B and significant

OTUs…………………………………………………………………………………………….69

Figure 5.18 NMDS plots for aerobic and anaerobic microcosms from Site 2A and significant

OTUs…………………………………………………………………………………………….70

Figure 5.19 NMDS plots for aerobic and anaerobic microcosms from Site 2B and significant

OTUs…………………………………………………………………………………………….71

xii

List of Appendices

Appendix A Supplementary information for Chapter 1

Table A.1 Concentrations (mg/kg) of specific COIs in soil samples from the site.. ................... 83

Table A.2 Concentrations (mg/L) of specific COIs in groundwater samples from the site. ....... 84

Table A.3 Physical characteristics of COIs.. ............................................................................... 85

Table A.4 Aerobic degradation of aniline and chloroanilines reported in the literature. ............ 86

Table A.5 Anaerobic transformation of aniline and chloroanilines reported in the literature ..... 90

Table A.6 Aerobic degradation of chlorobenzenes reported in the literature. ............................. 91

Table A.7 Anaerobic transformation of chlorobenzenes reported in the literature ..................... 92

Table A.8 Aerobic degradation of dichloronitrobenzenes reported in the literature ................... 93

Appendix B Supplementary information for Chapter 2

Table B.1 Chemical compounds and solvents used……………………………………………..91

Figure B.1 Calibration curve for methane, benzene, and DCBs in GC…………………………92

Figure B.2 Calibration curves for anilines, chloroanilines, dichloroanilines, and

dichloronitrobenzenes in HPLC………………………………………………………………...93

Appendix C Supplementary information for Chapter 3

Figure C.1 Soil samples used for microcosms study #1…….…………………………………..95

Table C.1 Average aerobic degradation rates (mg/L/day) per site in the microcosms………….96

Figure C.1 Concentration versus time in an aerobic active control microcosm from site 2B…..97

Table C.2 Average anaerobic transformation rates (mg/L/day) per site in the microcosms……98

Figure C.3 Anaerobic nitrate amended microcosm……………………………………………..99

Figure C.4 Concentration of SVOCs versus time in anaerobic electron donor amended

microcosm from site 1A……………………………………………………………………….100

xiii

Figure C.5 Concentration of VOCs versus time in anaerobic electron donor amended microcosm

from site 1A……………………………………………………………………………………101

Figure C.6 Concentration of VOCs versus time in anaerobic electron donor amended microcosm

from site 2A……………………………………………………………………………………102

Appendix D Supplementary information for Chapter 4

Figure D.1 Sample location for anaerobic microcosms study #2, assessing aniline and

chloroanilines degradation………………………………………………………..……..….….103

Table D.1 Treatment table for anaerobic microcosms study #2………………………….……105

Figure D.2 Anaerobic transformation graphs for microcosms study, assessing aniline and

chloroanilines……………………………………………………………………………….….106

Table D.2 Treatment table for aerobic test performed in Camaçari laboratory with puddle

water…………………………………………………………………………………………...108

Figure D.3 Aerobic degradation from test conducted in Camaçari with puddle water………..109

Figure D.4 Process of growing aerobic cultures in the laboratory…………………………….113

Table D.1 Experiment set up to test if aerobic microcosms from sites 2A and 1B inoculated with

culture mix would show more degradation……..……………………………………………..115

Table D.4 Influence of pH in microcosms and treatments in the bottles……………………....116

Figure D.5 Results of pH adjustment in aerobic vitamin amended microcosms from site 1A..117

Figure D.6 Results of pH adjustment in aerobic vitamin amended microcosms from site 2A..118

Figure D.7 Results of pH adjustment in aerobic active control microcosms from site 2B……119

Appendix E Supplementary information for Chapter 5

Table E.1 Summary of samples used for microbial community analysis……………………...120

Figure E.1 Groundwater sample location at the site…………………………………………...126

Table E.2 Details of the standard curves generated for qPCR…………………………………127

Table E.3 Raw qPCR results…………………………………………………………………..128

xiv

Figure E.2 qPCR results (copies/mL) for microcosms, soil, groundwater, and pure culture

samples.………….…………………………………………………………………………….133

Figure E.3 Relative abundance (%) of microorganisms in anaerobic microcosms samples from

site 2B………….……………………………………………………………..………………..134

Table E.1 Most abundant OTUs found in pure and enrichment cultures from external

laboratories and tested in UofT………………………………………………………………...135

Figure E.4 Relative abundance (%) of Rhodanobacter (OTU72) in all the

samples……………………………………………………………………………………..….136

Figure E.5 Relative abundance (%) of Pelosinus (OTU43), Desulfotomaculum (OTU89), and

Propionicicella (OTU108) in all the samples……………………………………...…………..137

Figure E.6 Relative abundance (%) of Diaphorobacter (OTU9) in all the samples……..……138

Figure E.7 Relative abundance (%) of Rhodococcus (OTU23) in all the samples………….....139

Figure E.8 Relative abundance (%) of Alcaligenaceae (OTU11) in all the samples………….140

Figure E.9 Relative abundance (%) of Cupriavidus (OTU24) in all the samples…………..…141

Appendix F Supplementary information for statistical analyses

R Markdown script used to analyze all the samples………………..………………………….142

Appendix G Electronic files available as supplementary data

List of files available in Syntrophy folder……………………………………………………..158

xv

List of Abbreviations

CA Chloroaniline

CB Chlorobenzene

COI Compounds of interest

DCA Chloroaniline

DCB Dichlorobenzene

DCNB Dichloronitrobenzene

DNA Deoxyribonucleic acid

GC Gas chromatography

HPLC High pressure liquid chromatography

IC Ion chromatography

MCB Monochlorobenzene

NMDS Non-metric multidimensional scaling

OTU Operational taxonomic unit

qPCR Quantitative polymerase chain reaction

SVOC Semi-volatile organic compounds

VOC Volatile organic compounds

1

Chapter 1 Literature review and objectives

Literature review

1.1.1 Remediation and bioremediation

Industrial activities have exposed soil and groundwater over time to different toxic compounds which are

harmful for humans and ecosystems. Pollutants can enter the environment as a result of spills, leakage

from product storage, and leakage from waste disposal (Khan, et al., 2004). Therefore, remediation is

required to reduce the contamination and risk of exposure, and to restore soil and groundwater functions

(O’Brien, et al., 2017). To achieve a desirable and safe concentration of contaminants in the environment,

different remediation techniques can be applied. Bioremediation is one of these techniques that will be

described further throughout this thesis.

Bioremediation has been studied since the 1940s (Zobell, 1946), but became known and more broadly

studied in the 1980s as an alternative for cleaning up shorelines contaminated with oil spills (Hoff, 1993).

When compared to chemical or physical remediation techniques, bioremediation is a low cost and

environmentally friendly approach that can be widely used, depending on the nature of pollutant and

characteristics of the site (Azubuike, et al., 2016).

It is important to define bioremediation within the context of biodegradation. Biodegradation is a

naturally occurring process in which microorganisms alter and break down contaminant compounds into

other substances or products (Hoff, 1993). Whereas bioremediation is the acceleration of this process,

wherein microorganisms break down the molecules, reducing the abundance of these compounds in the

environment by degradation, detoxification, stabilization, or transformation (Azubuike, et al., 2016). The

process of changing environmental conditions during the biodegradation process, such as temperature or

pH, and providing the microorganisms with better conditions to degrade the contaminants is called

bioremediation.

1.1.2 The presence of pesticides in soil and groundwater

Persistent organic pollutants are of global concern because they are human toxins, harmful for the

environment, and they can be widely transported by air and water. Scientific studies have shown that they

are the most dangerous substances released in the environment by human activities (Gavrilescu, 2005).

These substances can be introduced into the environment by industrial activities, use of fossil fuels, and

2

the use of pesticides in the agricultural industry. Moreover, such substances can persist in the

environment after their use, accumulating in soil, sediments, water, and in the atmosphere (Kordel, et al.,

1997).

1.1.3 Site history

The subject of this thesis is a heavily contaminated industrial site in Brazil, located in the Industrial

Complex of Camaçari, in the state of Bahia. This Industrial Complex contains more than 90 chemical and

petrochemical companies and is responsible for an annual revenue of approximately $15 billions USD.

The site used to produce and store raw materials for the production of pesticides and herbicides since

1987 to shut down in 2014.

Between October 2012 and March 2014, an investigation project was conducted on the site to assess the

environmental contamination of soil and groundwater. For 17 months, more than 200 soil and

groundwater samples were collected and analyzed, and a diverse range of contaminants were found in the

shallow and deep layers of this site. The compounds were divided into the following categories: anilines

and chloroanilines, chlorobenzenes, chloronitrobenzenes, BTEX (Benzene, Toluene, Ethylbenzene and

Xylenes), phthalates, pesticides, and others. The focus of this studies were the first three groups of

compounds and the average concentrations found in the site for some of the contaminants are presented in

Table A.1 for soil and Table A.2 for groundwater.

To plan a remediation project for the site, the management team started a series of treatability studies that

aimed to identify different techniques that could be applied to the site, and in the future, reduce the

contamination to acceptable and non-harmful levels, according to local and international standards.

Different universities and consulting firms in Brazil, the U.S., and Canada are involved in this project, but

with different objectives, according to each of their research capabilities.

In 2015, the University of Toronto received soil and groundwater samples from the site to conduct a

microcosm study in which the following compounds of interest (COIs) were analyzed: aniline, 2-

Chloroaniline (2-CA), 3-Chloroaniline (3-CA), 4-Chloroaniline (4-CA), 3,4-Dichloroaniline (3,4-DCA),

2,3-Dichloroaniline (2,3-DCA), 2,5-Dichloroaniline (2,5-DCA), 1,2-Dichlorobenzene (1,2-DCB), 1,3-

Dichlorobenzene (1,3-DCB), 1,4-Dichlorobenzene (1,4-DCB), 2,3-Dichloronitrobenzene (2,3-DCNB),

2,5-Dichloronitrobenzene (2,5-DCNB), and 3,4-Dichloronitrobenzene (3,4-DCNB). More details about

how the study was set up and other tests conducted since 2015 are described later in this thesis. Physical

characteristics of these compounds are shown in Table A.3.

3

All of the aforementioned COIs have been studied in the literature independently as single compounds,

but no research has been done about the interactions between them and their inhibitory effects and/or

synergetic effects when combined.

1.1.4 Compounds of interest (COIs) for this study

While many chlorinated organic compounds can be aerobically biodegraded to CO2, the dense non-

aqueous phase liquids compounds (DNAPLs) migrate down to deep zones that are often anaerobic

(Nelson, et al., 2014), and this is the reason why this study was conducted under both conditions. As

previously stated, the COIs are divided in three groups: aniline and chloroanilines, chlorobenzenes, and

chloronitrobenzenes.

Aniline and Chloroanilines

Aromatic amines, such as aniline and chloroanilines (2-, 3-, 4-CA), are raw materials for different

industrial processes and are frequently found in industrial effluents (Orge, et al., 2015). For example, they

are important intermediates in the production of dyes, pharmaceuticals, pesticides, and herbicides

(Latorre, et al., 1984; Zeyer, et al., 1985). These compounds can persist in nature for a long time, and

accumulate in living organisms (Zhu, et al., 2012). Accumulation of dichloroanilines (DCAs) in the

environment can also be caused by the complete reduction of the nitro-group during anoxic

transformation of chlorinated nitroaromatic compounds, such as dichloronitrobenzenes (Tas, et al., 2006).

These aromatic pesticides with amino groups such as diuron, linuron, propanil, and triclocarban can be

transformed into CAs in the environment (Silar, et al., 2011). Besides this, chlorinated nitrobenzenes can

be reduced to the corresponding CA, which can lead the CA accumulation in groundwater, soil, crops,

and sludge (Zhang, et al., 2017).

Other than distribution due to industrial use, 3-CA is one of the primary intermediates generated by

microbial transformation of phenylurea, acylanilide and phenylcarbamate herbicides (Haggblom, 1992;

Zeyer and Kearney, 1982). Another source of accumulation of these compounds in the environment is

from the degradation of other chemicals, such as propanil, a widely used anilide herbicide. Due to its

instability when it is photodegraded, it produces 3,4-DCA, which is more toxic than the herbicide itself

and can persist in the environment for up to 10 years (Bartha, 1971; Herrera-Gonzalez, et al., 2013).

However, under anaerobic conditions, slow degradation to monochloroaniline can occur (Hund-Rinke and

Simon, 2004)

4

As previously stated, bioremediation is the use of microorganisms to degrade specific contaminants in the

environment. A compilation of the literature describing biodegradation of aniline and chloroanilines is

provided in Table A.4 (aerobic) and Table A.5 (anaerobic). This literature review reveals that

Pseudomonas and Delftia are the most common microorganisms related to aerobic degradation of aniline

and chloroanilines. Anaerobically, Desulfobacterium was reported to degrade aniline completely (Schnell and

Schink, 1991), and microorganisms like Desulfobacterium anilini, Ignavibacterium album, and

Dehalococcoides mccartyi were involved in the biotransformation of these compounds.

Chlorobenzenes

Monochlorobenzene (MCB) and dichlorobenzenes (1,2-, 1,3-, 1,4-DCB) have been used industrially as

solvents, surface cleansers, and feedstocks, which has made them common groundwater and soil

contaminants (Fung, et al., 2009). They are also used to produce pesticides and dyes (Chakraborty and

Coates, 2004), and have therefore been extensively released in the environment. These are toxic

compounds and are considered harmful contaminants to human and animal health, as they have been

reported as toxic compounds by the US EPA.

Aerobic chlorobenzene degradation has been studied extensively, and these compounds can be

metabolized aerobically to CO2 by well characterized pathways (Haigler, et al., 1988; Haigler, et al.,

1992; Leahy, et al., 2003). When in contact with nanomolar concentrations of DCBs, Burkholderia sp.

strain PS14 has been shown to degrade highly polychlorinated compounds, such as and 1,2,3-

trichlorobenzene and 1,2,4,5-tetrachlorobenzene (Rapp and Timmis, 1999). Table A.6 shows different

studies conducted under aerobic conditions, showing Pseudomonas as the main microorganism

responsible for the degradation of chlorobenzenes.

But MCB and DCBs can also form dense nonaqueous phase liquid (DNAPLs) which can migrate to deep

and anaerobic regions of the soil, inhibiting aerobic degradation. In these anaerobic environments,

halogenated compounds can serve as an electron acceptor for their degradation by different

microorganisms, such as Dehalococcoides, Desulfobacterium, Dehalobacter, and Sulforospirillum

(Holliger, et al., 1997; Smidt and de Vos, 2004). A microcosm study using sediments from a

contaminated industrial site demonstrated anaerobic degradation of DCBs and MCB, where all the DCBs

isomers were dehalogenated to MCB and then further dehalogenated to benzene (Fung, et al., 2009).

Dehalobacter sp. was involved in the dichlorination of all the isomers, however 1,2-DCB was degraded at

the fastest rate compared to the other isomers (Nelson, et al., 2011). Other studies have also shown that

1,2-DCB was the fastest to be degraded in soil sediments (Elango, et al., 2010; Quistorff, 1999).

5

In the literature, previous studies have reported that Dehalococcoides mccartyi CBDB1 can degrade

chlorinated benzenes, but only when the number of chlorine is higher than three, which means MCB and

DCBs cannot be degraded by this organism (Holscher, et al., 2003; Jayachandran, et al., 2003). More

details about anaerobic biotransformation of chlorobenzenes are provided in Table A.7.

Since the research reported in this thesis focuses on the mixture of different mono- or dichlorinated

compounds and analyzes environmental samples with a rich microbial community, it is important to

understand how mixed cultures interact with the COIs as well, instead of just exploring singular

microorganisms in pure cultures.

Chloronitrobenzene

From the chloronitrobenzene group of contaminants, three dichloronitrobenzenes (DCNBs) were

investigated in this study: 2,3-, 2,5-, 3,4-DCNB. Chloronitrobenzenes are toxic and carcinogenic

compounds that have been used in the production of dyes, herbicides, pesticides, and other chemical

substances for the past decades (Zhu, et al., 2015). Although these compounds are intentionally applied to

the environment for agricultural use, the improper handling and poor storage of these compounds might

result in severe soil and groundwater contamination (Ju and Parales, 2010).

Biodegradation of nitrobenzenes has been reported to occur aerobically through an oxidative, meta

cleavage pathway by Comamonas sp. strain JS765, which uses this compound as source or energy

(Nishino and Spain, 1995). Chloronitrobenzene can also be used as source of energy for microbes, such as

Pseudomonas stutzeri ZWLR2-1 that degrades 2-chloronitrobenzene aerobically (Liu, et al., 2005). Xiao

et al. (2006) demonstrated that Pseudomonas putida ZWL73 can transform 4-chloronitrobenzene to 2-

amino-5-chlorophenol.

Palatucci (2017) used soil and groundwater samples from the Camaçari site in Brazil and was able to

identify bacteria responsible for aerobic 2,3- and 3,4-DCNBs degradation. These samples were collected

at the same time as the soil and groundwater samples used for the microcosms study described in Chapter

3. When these DCNBs were degraded, nitrite was released as an end-product of degradation. Isolates

affiliated with Acidovorax, Diaphorobacter and Pseudomonas were found to be responsible for the

observed degradation (Table A.8). Prior to the Palatucci (2017) thesis, aerobic biodegradation of

dichloronitrobenzenes has not been reported in the literature.

Anaerobic biodegradation of 2,5-dichloronitrobenzene was reported in a thesis from Zhang (2016), where

the following carbon sources were used: pyruvate, formate, acetate, and lactate. Results show that bottles

6

amended with pyruvate were able to biotransform 2,5-DCNB after 4 hours of experiment. Some

microorganisms were identified during this work as potential responsible for the biodegradation:

Clostridium sp., Eubacterium sp., Streptomyces sp., and Propionibacterium sp. This work was not fully

available and the information above was taken from the thesis abstracts, available at the website Globe

Thesis, ID number 2271330461978280, available at https://www.globethesis.com/?t=2271330461978280.

To date, this is the only work that has analyzed anaerobic biodegradation of dichloronitrobenzenes.

Research objectives

Aerobic biodegradation and anaerobic biotransformation of aniline, chloroaniline, dichloroaniline,

chlorobenzene, and dichloronitrobenzene has been reported in the literature. However, the mixture of

these compounds has never been studied and this research aims to explore the conditions at which these

compounds degrade and to identify the microbes responsible for degradation at the Camaçari site. To

achieve this goal, laboratory tests, microbial community analyses and statistical analyses were conducted

on groundwater, soil, and enrichment culture samples to address the following objectives:

1) Interpret degradation and transformation data from an extensive microcosms study initiated by

Line Lomheim;

2) Assess effect of pH on degradation and develop a biodegradation activity assay that could be

easily performed on samples on site;

3) Perform a comprehensive microbial community analysis using DNA amplicon sequencing data

from microcosms samples, activity assays, and field samples; and

4) Compile data and interpret results to recommend best course of action for remediation at the site

of interest.

Thesis structure

Chapter 1 contains the literature review and a brief introduction on the topics that will be discussed

throughout this thesis, as well the objectives and thesis structure.

Chapter 2 describes general materials and methods used during this research, including analytical

methods, sampling procedures, maintenance of microcosms, and bioinformatic techniques to analyze

microbial data results.

Chapter 3 explains the background for this study, which is a microcosm study that was set up first in

2015 with environmental material from the field site by Line Lomheim in the Edwards laboratory and has

7

been periodically monitored since that time. Degradation and transformation graphs are shown in this

chapter, in support of Objective 1.

Chapter 4 describes the laboratory activity tests conducted between 2016 and 2018, including results

from trainings performed at the site in Brazil, and experiments conducted at the University of Toronto

using enrichment cultures and pure cultures from collaborating universities. The influence of pH on

aerobic and anaerobic degradation and biotransformation are also shown in this chapter, in support of

Objective 2.

Chapter 5 explains the microbial community analysis, Illumina sequencing results and analysis, and the

statistical analysis that was performed on the samples. Changes in microbial community over time,

comparison between external pure cultures and microcosms samples, and other analysis are also in this

chapter, in support of Objectives 3 and 4.

Chapter 6 synthetizes the main findings and presents suggestions and recommendations for future work,

in support of Objective 4.

The appendices support the text and show raw data from different experiments. They also present graphs

and table with results and more technical information mentioned in the thesis.

8

Chapter 2 Materials and Methods

Preparation of Solutions

2.1.1 COI stock, sodium sulfate, sodium nitrate, and sodium lactate solutions

Contaminants stock solution

Multiple stock solutions were prepared during this work, as different mixtures of contaminants were

tested in the microcosms. Table B.1 shows the chemical compounds and solvents used to prepare the

stock solutions, as well as their purity and brand.

There were two types of stock solutions prepared with the COIs during this research: neat and dissolved

in solvent. The neat stock solutions were prepared by mixing the pure, undiluted compounds at a certain

concentration and then adding a small volume of this solution to the microcosm bottles. This technique

can be challenging when the volumes to be added are smaller than 3 µL, since it can be inaccurate to add

such small volumes to the bottles. Even though some compounds used for this research are solids, they

become liquid when combined. For example, after mixing the solids of 3,4-DCNB, 2,3-DCNB, and 3,4-

DCA, the mixture becomes a liquid and the neat stock solution can then be added to the bottles using a

syringe.

As these compounds are not very soluble in water (Table A.3), dissolving them in acetone is the best

option to increase the feeding volume, and therefore the accuracy of feeding. To avoid adding solvent in

the microcosms, some of the stock solutions were prepared in acetone but the acetone and COI solution

was not added to the microcosms directly. The desired volume of solution was dispensed onto a small

microscopy glass, left inside the fume hood until the acetone was completely evaporated (approximately 5

minutes), and then the glass was added to the microcosm bottles. After 24 hours, the contaminants were in

equilibrium with the liquid phase and therefore no solvent was added to the bottles. To add the glass slide

to the bottles anaerobically, it was necessary to open the microcosm bottles inside the glovebox, and any

gaseous losses during this process of opening and closing the bottles were assessed by collecting a sample

immediately after the 24-hour equilibrium period and checking if the VOC concentrations had decreased.

9

Sodium sulfate and sodium nitrate stock solutions

Some anaerobic microcosms were fed with either sodium sulfate or sodium nitrate stock solutions, that

would serve as electron acceptors. To achieve the concentration of 400 mM for both stock solutions, a

mass of 5.68 g and 3.39 g of Na2SO4 (MW=142.043 g/mol) and NaNO3 (MW=84.99 g/mol) were

measured, respectively. The compounds were added in a 100 mL glass bottle, topped with autoclaved

Mili-QTM water (Milipore Sigma, Oakville, ON, Canada) to 100 mL, and the solution was filtered using a

0.22 µm nylon syringe filter (Mandel Scientific, Guelph, ON, Canada) to another sterile glass bottle

capped with rubber stopper and aluminum crimped cap. The solutions were then purged for 20 minutes

with filtered N2 gas and stored anaerobically at room temperature.

Sodium lactate stock solution

To prepare a 0.7 M lactate stock solution, 6 g of sodium lactate (NaC3H5O3, MW = 112.06 g/mol) was

measured in a glass bottle. Mili-QTM water was used to complete the mass to 60 g, creating a 60% (w/w)

solution. The solution was then filtered using a 0.22 µm nylon syringe filter (Mandel, Canada) into

autoclaved bottles, sealed with rubber stoppers and aluminum crimped cap, and then purged for 20

minutes with filtered N2 gas and stored at room temperature.

2.1.2 Anaerobic mineral medium

Anaerobic mineral medium was used in some microcosms tests described in this thesis. To prepare 1L of

mineral medium, 10 mL of 2 mM phosphate buffer solution (27.2 g of KH2PO4 and 34.8 g K2HPO4 at pH

7.0 in 1 L of distilled water), 10 mL of salt solution (53.5 g of NH4Cl, 7.0 g of CaCl2×6H2O, and 2.0 g of

FeCl2×4H2O in 1 L of distilled water), 2 mL of 0.5 mM magnesium sulfate solution (62.5 g/L

MgSO4×7H2O), and 1 mL of redox indicator (1 g/L resazurin) were added into a glass screw cap bottle to

a volume of 970 mL. After the solution was autoclaved, the bottle was cooled in an ice bath and purged

with gas mix (80% N2, 20% CO2) for 30 minutes, and then transferred to the Vinyl Anaerobic Chamber

glove box (Coy Lab Products, Grass Lake, MI, USA). In the glove box, 1 mL of trace minerals solution

(500x), 10 mL of vitamins stock (100x), 10 mL of amorphous ferrous sulfide, and 10 mL of saturated

bicarbonate solution (100x) were added in that order to the bottle containing the autoclaved solution. The

protocols to prepare these medium stock solutions are described below. After adding all the medium stock

solutions, pH was measured, as described in Section 2.2.4, and the bottle was stored in the glove box to

allow for the settling of the amorphous ferrous sulfide.

10

Trace minerals solution

In a 160 mL serum bottle, the following were combined: 0.3 g of H3BO3, 0.1 g of ZnCl, 0.1 g of

Na2MoO4×2H2O, 0.75 g of NiCl2×6H2O, 1.0 g of MnCl2×4H2O, 0.1 g of CuCl2×2H2O, 1.5 g of

CoCl2×6H2O, 0.02 g of Na2SeO3, and 0.1 g of Al2(SO4)3×18H2O. Then, 1 mL of concentrated H2SO4 was

added to dissolve the compounds and distilled water was added to top up to 1 L. The bottle was sealed

with a rubber stopper and aluminum crimped cap and the solution was autoclaved and purged with N2 gas

for 20 minutes.

Vitamins stock solution

In a 1 L glass bottle, the following vitamins were added: 0.02 g of biotin, 0.02 g of folic acid, 0.1 g of

pyridoxine HCl, 0.05 g of riboflavin, 0.05 g of thiamine, 0.05 g of nicotinic acid, 0.05 g of pantothenic

acid, 0.05 g of para-aminobenzoic acid, 0.05 g of cyanocobalamin, 0.05 g of thioctic (lipoic) acid, and 1.0

g of coenzyme M. Deionized water was used to adjust the volume to 1 L and using 2 N NaOH, the

solution pH was adjusted to 7. This solution was then diluted 1:100 (v/v), filter sterilized into a 160 mL

serum bottle, sealed with a rubber stopper, crimped, and purged for 20 minutes with N2 gas.

Amorphous ferrous sulfide solution

Two initial solutions were prepared: FeSO4×7H2O (27.8 g/400 mL of anaerobic water) and Na2S×9H2O

(24 g/400 mL of anaerobic water). The anaerobic bottle was prepared by purging the desired volume of

water with N2 gas for 40 minutes. Inside the glove box, FeSO4×7H2O solution was added to the

Na2S×9H2O solution in a 1 L bottle, sealed immediately with a cap and septa, and shaken. The bottle was

removed from the glove box, purged for 40 minutes with N2 to remove the H2S gas formed during the

reaction, and returned to the glove box. The solution was divided into 4 Nalgene anaerobic centrifuge

tubes and centrifuged for 10 minutes at 4°C at 10,000 rpm. The supernatant was discarded, and the pellet

was resuspended with 200 mL of anaerobic water and centrifuged again. The resulting FeS suspension

from all 4 centrifuge tubes were combined into a 1 L bottle, sealed, and autoclaved.

Saturated bicarbonate solution

In a 160 mL serum bottle, 20 g of NaHCO3 was mixed with 100 mL of distilled water, covered with

aluminum foil and autoclaved. The bottle was capped with rubber stopped, crimp sealed, and purged for

15 minutes with N2 while cooling. This solution has a NaHCO3 precipitate that forms on the bottom of the

bottle which ensures saturation of NaHCO3 in the liquid phase.

11

Microcosm study

2.2.1 Microcosm study set up

The major microcosm study in this thesis was conducted using soil and groundwater from the industrial

site in Brazil. The experiment was set up in a flexible, inflatable polyethene glove bag (Aldrich-

AtmosBagTM) that allows the microcosms to be created anaerobically. Before setting up the glove bag, it

was necessary to wash, autoclave, and prepare the following materials: 250 mL clear Boston round glass

bottles (Scientific Instrument Services, Ringoes, NJ, USA), autoclavable bin covered with aluminum foil,

measuring spoons, funnel, mixing utensils, graduated cylinder, and glass beaker. MininertTM valves

(Chromatographic Specialties Inc., Brockville, ON, Canada) were washed and cleaned with alcohol wipes

or soaked in 70% ethanol bath for one hour before use.

The glove bag has two openings: an inlet and an outlet. Through the inlet, nitrogen gas and gas mix (80%

N2 and 20% CO2) would be alternated according to the need by switching the alternator between the two

gas cylinders. The outlet opening was connected to a vacuum pump. Anaerobic tape (3M™ Scotch-

Weld™ Anaerobic Adhesives) was used to seal the connections and to ensure no sure no gas was

escaping. The glove bag was set up inside the fume hood. The autoclaved items were placed inside the

glove bag while they were still warm, leaving them exposed to the laboratory atmosphere as little as

possible. The soil and groundwater samples were also placed inside the glove bag, along with the

MininertTM caps, and disposable material such as nitrile gloves, paper towels, autoclavable bags for waste,

permanent marker, scissors, and alcohol wipes. Once all the material was inside the glove bag, the main

opening was sealed with anaerobic tape.

The anaerobic gas composition inside the glove bag is 80% N2 and 20% CO2. To achieve this condition

inside the glove bag, it is necessary to flush the glove bag with N2 twice, by filling and deflating using the

N2 gas tank and the vacuum pump. The third fill is with the gas mix of 80% N2 and 20% CO2. A low flow

of the gas mix is maintained overnight to avoid bag deflation over the course of microcosms set-up.

Another gas mix deflation and inflation cycle was completed the following morning to maintain anaerobic

conditions. Figure 2.1 shows a scheme of the glove bag set-up in a fume hood.

12

All the bottles were set up by adding 4 spoons of soil (total of approximately 20 g) and 150 mL of

groundwater in Boston round bottles, caped with MininertsTM caps, leaving a headspace of approximately

100 mL. After the bottles were prepared and labeled, each received the respective treatment according to

the treatment table, Table 2.1. Both aerobic and anaerobic microcosms were prepared inside the glove

bag to avoid exposing the microcosms to the laboratory atmosphere, and oxygen was added to the aerobic

bottles when they were removed from the glove bag. In a microcosms study, groups of bottles receive

different amendments according to the objective of the project. For this study, they were set up in

triplicates in the following conditions:

Samples for pH, HPLC, and GC were taken after set-up of conditions, as described in the following

sections of this chapter. After the bottles received the assigned treatments, the anaerobic bottles were

transferred to the glove box (Coy Lab Products, USA) and the aerobic bottles were supplemented with

oxygen and were stored on a laboratory bench. All the microcosms were stored statically upside down to

avoid gas escaping through the MininertTM cap and covered with an opaque, black cloth to prevent

exposure to sunlight, to prevent photoreactions and photodegradation. The volumes of contaminants and

how they were added to the bottles is described in Chapter 3.

Figure 2.1 Microcosm study set up. Gas tanks (N2 and gas mix) are connected to the glove bag, which is

inside the fume hood, through plastic hose and alternating the gas cylinder as needed. Vacuum pump

deflates the glove bag to make sure the environment inside the bag is anaerobic. All the autoclaved

material, disposable material, and environmental samples are inside the glove bag and it is properly sealed

to start to set up.

13

Table 2.1 Treatment table for microcosms study #1

Treatment

Soil Ground-

water

Head-

space

Resazurin

(1g/L

stock)

HgCl2

(5%)

NaN3

(5%)

Vitamin

stock a

(N)

Salt

solu-

tiona

(P)

Phos-

phate

stocka

Lactate

stock b

(0.7M)

Ethanol

Sulfate

stock b

(400mM)

Nitrate

stock b

(400mM)

vol. in

mL* mL mL µL mL mL mL µL µL µL µL µL µL

Aer

ob

ic

Sterile controls

20 150 100 150 1.5 0.6

20 150 100 1.5 0.6

20 150 100 1.5 0.6

Active controls

20 150 100 150

20 150 100

20 150 100

Vitamin

amended

20 150 100 150 1.5 150 150

20 150 100 1.5 150 150

20 150 100 1.5 150 150

An

aero

bic

Sterile controls

20 150 100 150 1.5 0.6

20 150 100 1.5 0.6

20 150 100 1.5 0.6

Active controls

20 150 100 150

20 150 100

20 150 100

Electron donor

(ethanol &

lactate)

20 150 100 150 200 20

20 150 100 200 20

20 150 100 200 20

Sulfate amended

20 150 100 150 750

20 150 100 750

20 150 100 750

Nitrate amended

20 150 100 150 750

20 150 100 750

20 150 100 750 a Description of solutions in Section 2.1.2, in mineral medium preparation. b Description of stock solutions provided in Section 2.1.1

(*) soil from cores was added to the bottles, not slurry. Volume measured by spoons of soil.

14

2.2.2 VOC monitoring using GC

Dichlorobenzenes (1,2-, 1,3-, 1,4-DCB) and methane were analyzed using the Agilent 7890A gas

chromatograph (GC) with headspace autosampler G1888, equipped with a GSQ-Plot column (0.53 mm x

30 m) (both from Agilent Technologies, Santa Clara, CA, USA) and a flame ionization detector (FID).

The inlet is packed, at 200°C, and the carrier gas during the run was helium. The detector operates at

250°C, H2 flow was 40 mL/min, air flow was 400 mL/min, and total flow was 11 mL/min. The oven was

programed as follows: 35oC for 1.5 min, ramp 15oC/min to 100oC, ramp 5oC/min to 185oC hold 10 min,

ramp 20oC/min to 200oC, hold 10 min. The total run time is 43.6 min per sample vial and the retention

times are as follows: 0.92 min for methane, 15.16 min for benzene, 32.6 min for 1,3-DCB, 33.4 min for

1,4-DCB, and 34.4 min for 1,2-DCB. Standard curves these compounds are shown in Figure B.1.

The autosampler setting was as follows: oven at 70°C; loop at 80°C; transfer line at 90°C; vial

equilibration time 40 min; pressurization time: 0 min; loop fill time: 0.2 min; loop equilibration time: 0

min; inject time: 3 min; GC cycle time: 47 min; shaking: low.

Samples were collected using a 22G (0.7 mm x 40 mm) PrecisionGlideTM needle (BD, Franklin Lakes,

NJ, USA) attached to a Luer-Lock 2 mL Gastight® glass syringe (Hamilton Company, Reno, NV, USA),

and taken out of the glove box, for anaerobic samples. In the fume hood, clear glass flat bottom 10 mL

autosampler vials (Agilent Technologies, Santa Clara, CA, USA) were filled with 5 mL of acidified water

(2.4 mL of HCl 5 N topped up to 1 L with Mili-QTM water). The needle was placed into the acidified

water, the sample was rapidly dispensed, and the vial was crimped with an open top 20 mm aluminum

crimp seal with PTFE/silicone coated septum (200 mm, 130mil, white) (both from Chromatographic

Specialties Inc., Brockville, ON, Canada) by using a vial crimping tool.

2.2.3 SVOC monitoring using HPLC

Aniline, chloroanilines (2-, 3-, 4-CA), dichloroanilines (2,3-, 2,5-, 3,4-DCA), and dichloronitrobenzenes

(3,4-, 2,5-, 2,3-DCNB) were analyzed using a Hewlett-Packard/Agilent 1050 series high performance

liquid chromatograph (HPLC) system, combined with a quaternary pump and an autosampler (Agilent

Technologies, Santa Clara, CA, USA). The HPLC is equipped with an Acclaim™ 120 C18 column, 3 µm

particle size, 4.6 x 150 mm, with average pore diameter of 120 Å, attached to an AcclaimTM C18 guard

cartridge, with 5 µm particle size, 4.6 x 10 mm (both from Thermo Scientific, Waltham, MA, USA). The

UV detector is set for 254 nm, mobile phase contains 50% Acetonitrile and 50% Mili-QTM water

(Milipore Sigma) at a flow rate of 1 mL/min, isocratic flow.

15

Samples were collected using a 22G needle (BD) attached to a Luer-Lock 2 mL Gastight® glass syringe

and taken out of the glove box for anaerobic samples. In the fume hood, the needle was discarded, and an

ethanol pre-washed 0.22 µm Chromspec UV Syringe filter 13 mm (Chromatographic Specialties Inc.,

Brockville, ON, Canada) was attached to the syringe and a new 22G needle was attached. A 1 mL sample

was collected from the microcosm, for 0.5 mL of sample to be used to wash the filter and be discarded to

flush ethanol. The remaining 0.5 mL of sample was placed into a clear glass 350 µL flat bottom 6x31 mm

insert (Chromatographic Specialties Inc., Brockville, ON, Canada) until it was full, inside a clear 2 mL

autosampler glass vial (Agilent Technologies, Santa Clara, CA, USA). The remaining volume of sample

was trapped in the filter and was discarded. The vial was closed with a PTFE silicone coated cap (VWR,

Radnor, PA, USA). The HPLC run has a total time of 25 min per vial and the retention times are as

follows: 3.3 min for aniline, 4.9 min for 4-CA, 5.3 min for 3-CA, 5.38 min for 2-CA, 7.8 min for 3,4-

DCA, 9.0 min for 2,3-DCA, 10.0 for 2,5-DCA, 14.0 min for 2,3- and 2,5-DCNB (they elute in the same

peak in this method), and 16.5 min for 3,4-DCNB. Calibrations curves for these compounds are shown in

Figure B.2.

2.2.4 Other measurements: oxygen, pH, and sulfate and nitrate concentrations

Oxygen measurement

A Hewlett-Packard 5890 series gas chromatograph (GC) equipped with a thermal conductivity

detector (TCD) and an Alltech® CTR-I, 6’ x ½” column (Cole-Parmer, Montreal, QC, Canada), with

packed inlet was used to measure oxygen in the microcosms samples. The carrier gas used was helium,

oven temperature during the run is 50°C, and gas carrier flow was set for 180 kPa. Injector temperature

was 200°C during the run. 300 µL of aerobic microcosm headspace was sampled with a Pressure-Lok

gastight syringe (VICI Precision Sampling, Baton Rouge, LA, USA). Oxygen standards were run prior to

microcosm measurement.

pH measurement

A 22G needle was attached to a 1 mL Luer-LokTM tip syringe (BD, Franklin Lakes, NJ, USA) to

withdrawal 1 mL sample from the bottle for pH test. After collection, the sample was transferred into a

1.5 mL polypropylene microcentrifuge tube (Fisher Scientific Co., Markham, ON, Canada) and the pH

was measured by using a pH Spear meter (Oakton Instruments, Vernon Hills, IL, USA). Prior to

16

measuring samples, the pH meter was calibrated according to the manufacture’s protocol, using pH 4, pH

7, and pH 10 OrionTM buffer solutions (Thermo Scientific, Sunnyvale, CA, USA).

Sulfate and nitrate measurement in ion chromatography

Sulfate and nitrate concentrations in the microcosms were measured using a DionexTM ICS-2100 pump

(Ion Chromatography System), isocratic flow, starting column flow of 1 mL/min. The effluent used was

23 mM KOH. Suppressor type ASRS 4mm, current 57 mA. The pump is coupled with a DionexTM

IonPacTM AS18 analytical column (4x250mm) (Thermo Scientific, Sunnyvale, CA, USA). Standards were

prepared with the following concentrations: 0.005 mM, 0.01 mM, 0.05 mM, 0.2 mM, and 0.5 mM by

serial dilution. Retention times for the compounds measured in this method were: acetate 3.3 min,

chloride 4.5 min, nitrite 5.1 min, sulfate 6.8 min, nitrate 7.7 min, and phosphate 14 min.

Preparation of samples for DNA extraction

In addition to analysis of samples from microcosms bottles, samples were also collected directly from soil

and groundwater from the site. Groundwater and soil samples were collected from the site in Brazil and

shipped to Toronto for DNA extraction followed by Illumina Amplicon sequencing, as explained below.

2.3.1 Groundwater samples

Groundwater samples arrived at the University of Toronto in PTFE bottles and required preparation

before DNA extraction. These bottles were shipped in coolers with dry ice and remained under 4°C

during transit. The groundwater was filtered through a sterile SterivexTM 0.22 µm filter unit (EMD

Millipore Corporation, Billerica, MA, USA) until one liter was completely filtered or the filter got

clogged. After filtration, the filter was drained, sealed with parafilm and stored at -80°C until DNA

extraction. Figure 2.2 shows the filtration unit set up. When ready to extract the DNA, the SterivexTM

plastic casing was broken, the paper filter was cut and transferred to a microcentrifuge tube for DNA

extraction following DNeasy PowerSoil Kit (Qiagen, USA) protocol. DNA extracts from groundwater

were sent for amplicon sequencing at Genome Québec.

17

2.3.2 Soil samples

Soil samples arrived at the University of Toronto in plastic cores which were opened inside the glove bag

during microcosm study set up. At this point, approximately 0.25 g was collected in a 2 mL micro tube

PP (Mikro-Scharaubröhre, SARSTEDT AG & Co., Germany) for DNA extraction and then stored at -

80°C until DNA extraction. DNeasy PowerSoil Kit (Qiagen, USA) was used for soil samples according to

manufacturer’s protocol. DNA extracts from soil were sent for amplicon sequencing at Genome Québec.

DNA extraction, amplicon sequencing, and qPCR

Samples for DNA extraction were collected throughout the microcosm study, aiming to capture the

changes in the microbial communities. Over 100 samples were taken from the microcosms for DNA

extraction. To sample a microcosm for DNA extraction, 1 mL of groundwater was removed from the

bottle using a Luer-LokTM tip syringe coupled with a 22G needle, removed from the glove box, and

transferred to a 1.5 mL microcentrifuge tube to be centrifuged at 10,000 rpm for 25 minutes. The

supernatant was transferred to another microcentrifuge tube which was stored at -80°C for further

analysis. DNA extractions were done on the pellet using DNeasy PowerSoil Kit (Qiagen, USA) following

the manufacture’s protocol. DNA was eluted with UltraPureTM Distilled water (Invitrogen, Grand Island,

NY, USA) and quantified using a Thermo ScientificTM NanoDropTM Spectrophotometer (Thermo Fisher

Scientific, Waltham, MA, USA).

Vacuum pump

Sterivex fitter

Sample Fume hood

Loading unit

Figure 2.3 Groundwater filtration set up. Filtration unit was set up inside the fume hood by

connecting a loading unit, such as 60 mL syringe without the plunger, to the Sterivex filter and to the

vacuum pump.

18

After DNA extraction, the extracts were sent for Illumina MiSeq PE300 16S rRNA amplicon sequencing

at Genome Québec using forward primer 926f modified (5’-AAACTYAAAKGAATWGRCGG-3’) and

reverse primer 1392r modified (5’-ACGGGCGGTGWGTRC-3’). These primers amplify nearly 500 base

pairs of the 16S rRNA gene to target bacteria, archaea, and 18S rRNA gene of some eukaryotes. MiSeq

reagents kit produces about 25 million reads per run and depending on the number of samples that are

added to each plate, the final number of reads per sample will vary. Amplicon sequencing results were

processed in MetaAmp and will be explained in Section 2.5

The total copies of bacteria and archaea in each sample was quantified by quantitative polymerase chain

reaction (qPCR) using a CFX96TM real-time PCR detection system, with a C1000 thermocycler (Bio-Rad

Laboratories Inc., Hercules, CA, USA). Reactions had a total volume of 20 µL, being 10 µL of SsoFastTM

EvaGreen® SuperMix (Bio-Rad Laboratories Inc., USA), 0.5 µL of each forward and reverse primers

(concentration of 500 nM for both primers), 7 µL of UV treated UltraPureTM Distilled water (Invitrogen,

USA), and 2 µL of DNA extract diluted 1 in 10, using the same water to avoid and reduce any inhibitory

effects from the matrix. Each sample was run in triplicates and negative controls were run to identify any

contamination between the samples. R2 values were 0.99 or greater and efficiency values 87-100%.

For general bacteria runs, the cycles were as follows: 98°C for 2 min, 40 cycles of 98°C for 5 seconds and

55°C for 10 seconds, followed by an increase from 65°C to 95°C at 0.5°C increments over 10 seconds.

For general archaea, the cycles were as follows: 98°C for 2 min, 40 cycles of 98°C for 5 seconds and

60°C for 10 seconds, followed by an increase from 65°C to 95°C at 0.5°C increments over 10 seconds.

Standards were applied to each plate, using serial dilutions of target-containing plasmids between 101 and

108 gene copies/mL. Samples were analyzed using general bacteria 16S rRNA primers GenBac1055f (5’-

ATGGYTGTCGTCAGCT-3’) and GenBac1392r (5’-ACGGGCGGTGTGTAC-3’). To analyze general

archaea 16S rRNA, the primers were: GenArch787f (5’-ATTAGATACCCGBGTAGTCC-3’) and

GenArch1059r (5’-GCCATGCACCWCCTCT-3’). The final copy number per mL was calculated by

multiplying the total mean starting quantity values from qPCR in copies/µL, the dilution factor, and the

elution volume during DNA extraction (in µL), divided by the amount of culture filtered (mL).

Statistical analysis: MetaAmp and RStudio

To perform statistical analyses and identify relationships in the microbial community at the site, all of the

microcosms, groundwater, soil, and enrichment culture samples were sent for sequencing of 16S rRNA at

Genome Québec. After obtaining these results, the FASTA files were processed using MetaAmp version

19

2.0. MetaAmp is a pipeline for processing the small subunit (16S) of ribosomal ribonucleic acid (SSU

rRNA) genes and other amplicon sequencing data. This pipeline accepts both single-end or paired-end in

FASTA or FASTAQ files and uses UPARSE, Mothur, and SILVA databases for clustering, removal of

chimeric reads, taxonomic classification, and generation of diversity metrics (Dong, et al., 2017). This

pipeline is an online tool to manipulate the data computationally, similarly to other tools such as Mothur

or QIIME. The steps for using MetaAmp are: a) filter out poor quality sequences, b) trim off sequences

adapters and barcodes, c) merge each pair-ended read into a single sequence, d) assign sequences based

on barcodes, and e) cluster sequences using 97% similarity cut-off based on databases. The output files

from this program can be used as inputs for other software for further manipulation, such as RStudio and

Excel, which were used for these analyses.

To analyze sequencing data, the input parameters used in MetaAmp were: sequence format “fastq”,

sequencing type “paired-ended”, forward primer 5’-AAACTYAAAKGAATWGRCGG-3’, reverse

primer 5’-ACGGGCGGTGWGTRC-3’, marker gene type rRNA gene, similarity cutoff 0.97. For paired-

ended merging options the minimum length of overlap was 30 and maximum number of mismatches in

overlap region was 3. For quality filtering options, maximum number of differences to the primer

sequence was zero, maximum number of expected errors was 1, and trim amplicon was set to a fixed

length of 430. After selecting the parameters and uploading the result files, a link is generated, and the

results are available online after a few hours.

Once the sequencing data was processed in MetaAmp, the statistical analysis was conducted in RStudio,

using the Phyloseq and Vegan packages. The scripts used to run the samples can be found in Appendix

F. The statistical methods used to analyze the data was non-metric multidimensional scaling (NMDS) and

is described in Chapter 5.

Road map of experiments conducted during this research

Since multiple experiments were conducted during this research, in different locations, and with help

from other colleagues, Figure 2.3 below represents a road map to guide the reader through the next

chapters, including methodology and results from each of the experiments.

20

Figure 2.3 Road map for experiments in this research. The time line above shows all the experiments conducted during this research, as well as

their location in the thesis.

21

Chapter 3 Background: Microcosm study #1

Motivation and sample location

In 2015, the University of Toronto received soil and groundwater samples from the site in Brazil to

conduct a microcosm study. The objective of this study was to identify certain conditions under which

specific microorganisms would grow and possibly biodegrade contaminants of interest (COIs) and then,

in the future, apply such conditions in the site to have the contaminants degraded to acceptable national

and international levels, as shown in Table A.1 and A.2. These conditions could also affect the rate of

compounds degradation in contaminated ecosystems. This would provide guidance to better understand

optimal conditions for bioaugmentation in the site.

The samples were collected from two different locations in the site (Site 1 and Site 2), and two different

depths (A being shallow, B being deep), resulting in four different sites (1A, 1B, 2A, and 2B). Figure 3.1

shows the locations where groundwater and soil samples were collected from, Figure C.1a shows the soil

physical description when they arrived at the University of Toronto, and Figure C.1b shows their

respective depths and target COIs.

Methodology and COIs

The detailed methodology on how the microcosm study was set up, maintained, and sampled was

provided in Section 2.2. Please refer to Table 2.1 for the list of microcosm bottles and conditions. In this

section, specific characteristics of the microcosms study will be explained and discussed.

When the environmental samples arrived at UofT, groundwater samples were analyzed for semi volatiles

organic compounds (SVOCs) to assess initial concentration of COIs: PM19 did not show any

contamination and PM12 showed 0.55 mg/L of 2-CA, 0.29 mg/L of 2,3-DCA, 0.28 mg/L of 2,5-DCA,

and 4.67 mg/L of 3,4-DCNB, which were low compared to site historical information. The average for

these compounds in groundwater samples were: 9.68 mg/L for 2-CA, 14.4 mg/L for 2,3-DCA, and 3.5

mg/L of 2,5-DCA. Because of the low initial concentrations, 100 mL of natural groundwater was

removed and replaced with an equal volume of artificial groundwater containing COIs in order to reach a

target concentration of 5 mg/L of each specific COI per bottle.

22

Figure 3.1 Sample location for microcosm study #1. Soil and groundwater collected from NPAD (Site

1, borehole N083, groundwater well PM-19) and from UN11&12 (Site 2, borehole N082, groundwater well

PM-12). Samples collected in 2015 and shipped to University of Toronto under refrigerated conditions.

Figure prepared by CH2M.

Based on site historical information, it was decided that the COIs would be added to the microcosms

according to Figure C.1b: site 1A would receive dichlorobenzenes and dichloroanilines; 1B would

receive only dichlorobenzenes; site 2A would receive chloroanilines, dichloroanilines, 2,5-DCNB, and

3,4-DCNB; and site 2B would receive dichloroanilines, 3,4-DCNB, and 2-CA that was naturally present.

Site 1 has shown overall higher concentrations of COIs when compared to the other location: the average

of 1,2-DCB in soil was 111 mg/kg and in groundwater was 2025 µg/L, whereas in Site 2 the soil sample

showed 67 mg/kg and the groundwater, 1704 µg/L. Dichloroanilines were found in high concentrations in

both sites, whereas DCNBs were higher in Site 2, for both soil and groundwater matrixes, as shown

previously in Table A.1 and Table A.2. All the bottles were fed with neat stock solutions, using different

Luer-Lock Gastight® glass syringes. The concentrations of COIs ranged from 5 to 15 mg/L in the bottles,

depending on the site and historical information.

Analytical and molecular samples were routinely collected from microcosms to assess the COI

concentrations and microbial community compositions, as explained in Section 2.2.2 (VOC monitoring

using GC), Section 2.2.3 (SVOC monitoring using HPLC), and Section 2.4 (DNA samples). The

analytical results from HPLC and GC analyses are discussed in the next section and DNA results are

Figure prepared by CH2M

23

discussed in Chapter 5, where 16S rRNA sequences from microcosms samples were compared to other

types of samples.

Results and discussion

3.3.1 Aerobic microcosms

In this study, complete aerobic biodegradation of aniline, 2-CA, 3-CA, 4-CA, 2,3-DCA, 3,4-DCA, and

1,2-DCB occurred in specific sets from the site. Table 3.1 summarizes the aerobic biodegradation

observed in the bottles and in which treatment the degradation occurred. Active controls bottles have

shown degradation of aniline, all chloroaniline isomers, and 1,2-DCB in all the sites they were tested (1A,

2A, and 2B). DCNBs did not degrade aerobically, neither did 1,3- and 1,4-DCB. The reactions that

occurred in the active controls were the same as the vitamin amended bottles, which means that adding

vitamins had no apparent effect in inhibiting or enhancing any of the biodegradation processes observed.

Table C.1 summarizes average of aerobic degradation rates in active microcosms. The rates were

calculated using the concentration plot for each microcosm where X axis is time (in days) and Y axis is

concentration (mg/L) as follows: subtracting the compound concentration from a certain point B to point

A, and dividing this result by the subtraction of time B minus time A. The rate result is given in mg/L/day

and it was calculated when degradation was occurring in a specific bottle. In some microcosms, the

triplicates did not behave the same, so the rate was calculated in one microcosm (no deviation presented).

All these bottles received oxygen during incubation as explained in Section 2.2.4, so this was not a

limiting condition for the microorganisms to degrade these compounds. The pH was not adjusted during

this microcosm study, and the pH range in these bottles was 4.6 to 6.5. Because natural pH in the site is

low, a few tests adjusting the pH to neutral were conducted as described in Chapter 4. In some of these

experiments degradation of 3,4-DCNB only started after pH was adjusted to neutral, whereas

biodegradation of 1,2-DCB occurred either before and after pH adjustment (Section 4.4).

In general, acidic pH might affect the rate of aerobic and anaerobic biodegradation, since all

microorganisms are pH sensitive, and tend to have specific optima. As the site has low pH in general, it is

important to perform tests with both acidic and neutral pH to determine if neutral pH enhances

biodegradation.

Figure 3.2 illustrates the time trend for degradation in an aerobic active control bottle from site 1A. This

microcosm, which was amended with 2-CA, DCAs, and DCBs, was able to completely degrade 2-CA,

2,3-DCA, and 3,4-DCA completely after 160 days; partial degradation of 2,5-DCA was also observed. 2-

24

CA initial concentration was approximately 8 mg/L and was biodegraded in a rate of 0.78 mg/L/day

whereas 2,3-DCA had initial concentration less than 2 mg/L and had a degradation rate of 0.86 mg/L/day.

2,5-DCA concentrations started at 1.5 mg/L/day and it was re spiked in the bottles four times and it did

not show complete degradation in any of them, always stopping after approximately 50% was degraded.

For this site, the average pH was 5.6 and it did not change over time.

Table 3.1 Summary of aerobic biodegradation observed during main microcosm study. CA =

chloroaniline, DCA = dichloroaniline, DCNB = dichloronitrobenzene, DCB = dichlorobenzene

To make sure the degradation was occurring and to keep the bottles active, some of the compounds that

were degraded were re spiked in the microcosm on days 165, 197, 319, and 424. The compounds 2-CA,

2,5-DCA, and 2,3-DCA were added and the biodegradation occurred as the previous cycles, degrading

2,3-DCA and 2-CA completely, and slowly degrading 2,5-DCA.

Figure C.2 shows the time trend for degradation in aerobic microcosms from site 2B. Aniline, 1.2-DCB,

DCAs, and CAs are being degraded and were refed in order to keep the microcosms active.

In this study, the triplicates behave similarly, but not necessarily the same for all the treatments described

above. The complete data file and more degradation graphs for all the microcosms can be found in

Syntrophy folder in OwnCloud. More details about the files can be found in Appendix G.

1A 1B 2A 2B

Aniline ✔ nt nt nt AC/ Vit

2-CA ✔ AC/ Vit nt AC/ Vit AC/ Vit

3-CA ✔ nt nt AC/ Vit AC/ Vit

4-CA ✔ nt nt AC/ Vit AC/ Vit

2,3-DCA ✔ AC/ Vit nt AC/ Vit AC/ Vit

2,5-DCA* ✔ AC/ Vit nt AC/ Vit AC/ Vit

3,4-DCA ✔ AC/ Vit nt AC/ Vit AC/ Vit

2,3-DCNB ✖ - nt - -

2,5-DCNB ✖ - nt - -

3,4-DCNB ✖ - nt - -

1,2-DCB ✔ AC/Vit - AC/ Vit AC/ Vit

1,3-DCB ✖ - - nt nt

1,4-DCB ✖ - - nt nt

AC = active control / Vit = vitamins/ nt = not tested in this set

* = not complete degradation

✔ = degraded / ✖ = not degraded

Aniline and

chloroanilines

Dichloro-

nitrobenzene

Dichloro-

benzene

Site where reaction ocurredSummary of biodegradationContaminantGroup

25

Figure 3.2 Concentration versus time in an aerobic active control microcosm from site 1A. X axis show the elapsed time (in days)

since the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for each compound tested. Figure

prepared by Line Lomheim.

26

3.3.2 Anaerobic microcosms

There were five treatments in these bottles: sterile controls, active controls, electron donor amended,

sulfate amended, and nitrate amended. Under anaerobic conditions, the halogenated COIs could either be

electron acceptors (hence addition of donor), or electron donors (hence addition of acceptors like sulfate

or nitrate)

In this study, anaerobic biotransformation of several compounds occurred: 2,3-DCA, 2,5-DCA, 3,4-DCA,

2,5-DCNB, 3,4-DCNB, 1,2-DCB, 1,3-DCB, and 1,4-DCB in specific sets and treatments in the bottles.

Table 3.2 summarizes the anaerobic reactions observed during the study. Table C.2 shows the average

anaerobic transformation rates observed in these microcosms.

Table 3.2 Summary of anaerobic reactions observed during main microcosm study. CA =

chloroaniline, DCA = dichloroaniline, DCNB = dichloronitrobenzene, DCB = dichlorobenzene

Dichloronitrobenzenes (2,5-DCNB and 3,4-DCNB) were only tested in sites 2A and 2B and were

transformed to 2,5-DCA and 3,4-DCA respectively, in active control, electron donor amended, sulfate

amended, and nitrate amended microcosms from site 2A and only in active control from site 2B.

Dichloroanilines were tested in sites 1A, 2A, and 2B, and were only transformed in the shallow sites (1A

in electron donor amended bottles, and in site 2A in donor and sulfate amended bottles). It is important to

mention that 2,5-DCA did not degrade completely: in site 1A the concentration stabilizes at

approximately 2 mg/L and in site 2A, at 9 mg/L). 1,2-DCB degraded mostly in electron donor amended

bottles, and only after over 800 days of microcosms study, the other two isomers (1,3-DCB and 1,4-DCB)

showed degradation in nitrate amended bottles, in site 1B that was not tested for any other compound

anaerobically (Figure C.3).

1A 1B 2A 2B

Aniline ✖ nt nt - -

2-CA ✖ - nt - -

3-CA ✖ nt nt - nt

4-CA ✖ nt nt - nt

2,3-DCA ✔ Don nt Sulf -

2,5-DCA* ✔ Don nt - -

3,4-DCA ✔ Don nt Don, Sulf -

2,3-DCNB not tested nt nt nt nt

2,5-DCNB ✔ nt nt AC, Don, Sulf, Nit Don

3,4-DCNB ✔ nt nt AC, Don, Sulf, Nit Don

1,2-DCB ✔ Don Nit Don -

1,3-DCB ✔ - Nit nt nt

1,4-DCB ✔ - Nit nt nt

AC = active control / Don = donor amended / Sulf = sulfate amended / Nit = nitrate amended / nt = not tested in this set

* = not complete reaction

✔ = degraded / ✖ = not degraded

Aniline and

chloroanilines

Dichloro-

nitrobenzene

Dichloro-

benzene

Site where reaction ocurredSummary of biotransformationContaminantGroup

27

These results the difference between aerobic and anaerobic reactions in the environment. Aerobic

reactions tend to happen faster and have a shorter lag period at the beginning of the experiments, and

degradation does not produce detectable transformation products. In contrast, anaerobic reactions in this

study proceed via dichlorination or nitro-group reduction (in the case of the DCNBs). However, the

products of anaerobic reduction reaction were amenable to aerobic degradation. This indicates that natural

attenuation at the site might be occurring in both aerobic and anaerobic regions.

Figure C.4 shows the SVOCs in an electron donor amended anaerobic microcosm from site 1A where

degradation of 2,3-DCA, 3,4-DCA, and 1,2-DCB was observed. 3,4-DCA was dechlorinated to 4-CA

whereas 2,3-DCA transformation led to the transient production of 3-CA. 2,5-DCA was also being

transformed in this bottle, but not completely,

Figure C.5 shows VOCs from the same anaerobic electron donor amended microcosm as Figure C.4.

This bottle has shown 1,2-DCB transformation, but not 1,3-DCB and 1,4-DCB. While 1,2-DCB was

being dechlorinated to MCB, methane was also being produced from fermentation of excess lactate and

ethanol.

In site 2A, besides all the chloroanilines and dichloroanilines tested, dichlonitrobenzenes were also added.

While chloroanilines were not degraded, electron donor amended bottles have shown two different

reactions occurring in them. The first reactions occurred around day 233 and they were the transformation

of 2,5-DCNB to 2,5-DCA and 3,4-DCNB to 3,4-DCA. The DCNBs were re spiked in the bottle after 60

days of the first cycle and the same reactions occurred again, as shown in Figure 3.3. For over 500 days

of experiment, these were the only reactions taking place in these bottles, until 3,4-DCA was

dechlorinated to 3-CA. 2,3-DCA and 2,5-DCA concentrations also decreased at the same time as 2-CA

concentration increased. When looking at the GC graph for VOCs for these same bottles (Figure C.6),

1,2-DCB was naturally present in this bottle and its concentration also decreased while MCB

concentrations were increasing. The other two isomers of DCB were not added to the bottles and their

concentrations are under detection limits.

Both aerobic and anaerobic microcosms exhibit lag periods before the onset of degradation. This occurs

likely because the numbers of active microorganisms able to degrade the contaminants is very low

initially. Only after these microbes grow sufficiently is degradation detected. Overall, microcosms from

sites 2A and 2B were the most active, showing both aerobic and anaerobic transformations. Microcosms

from site 1A also exhibited significant aerobic degradation, as presented in Tables 3.1 and 3.2. Therefore,

28

the DNA samples collected from these sites are most likely to have the highest potential to yield

information on the microorganisms involved in these reactions. These results are discussed in Chapter 5.

29

Figure 3.3 Concentration of SVOCs versus time in anaerobic electron donor amended microcosm from site 2A. X axis show

the elapsed time (in days) since the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for

each compound tested. Figure prepared by Line Lomheim.

Figure 3.5 Concentration of SVOCs versus time in anaerobic electron donor amended microcosm from site 2A. X axis show

the elapsed time (in days) since the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for

30

Chapter 4 Laboratory activity tests

Four experiments were conducted in order to address specific questions about i) aniline and chloroaniline

transformation in anaerobic microcosms, ii) development of an activity test that could be performed on

site, iii) work with samples from collaborating universities, and iv) effect of pH on aerobic degradation

using a variety of different samples.

Aniline and chloroaniline anaerobic microcosm study #2

4.1.1 Motivation and sample collection

In February 2017, the University of Toronto received new soil and groundwater samples from the field

site to conduct more experiments. As anaerobic degradation of aniline, 2-, 3-, and 4-chloroaniline were

not extensively assessed during the study previously described in Chapter 3, it was decided that a new set

of microcosms would be prepared to study these compounds and their potential for bioremediation and/or

biotransformation under anaerobic conditions.

Soil and groundwater samples were collected from the DW-05 well, located downstream from NPAD, as

shown in Figure D.1, at approximately 48 meters below ground surface (mbgs). This is a very deep well

(the first sample at this depth in this study), and therefore possibly a better candidate for anaerobic

activity. Four days after collection, groundwater samples arrived in Toronto in 1L PTFE bottles with no

headspace and soil samples arrived in glass jars, with minimal headspace. Samples were kept at 4°C

during transit from Camaçari, Brazil to Toronto, Canada.

4.1.2 Methodology

When the site material arrived in Toronto, the groundwater was sampled for SVOC concentration as

described in Section 2.2.2, which were below detection limit for all the COIs analyzed. DNA samples

from soil and groundwater were also collected at this point, as described in Section 2.3. The methodology

to prepare this experiment is similar to that described in Section 2.2.1. More details about this specific

microcosm study is presented in Appendix D (page 104) and the treatment table presented on Table D.1.

31

4.1.3 Results and discussion

After sampling and maintaining the bottles, the concentrations were plotted, and the results are shown in

Figure D.2. None of the bottles showed degradation or transformation of anilines and chloroanilines, not

even when adjusting the pH to neutral. In these graphs, consistency, precise analytical procedures and

reproducibility of the measurements can be seen.

The DNA extracts showed low DNA concentrations, measured by the NanoDrop, of 2.90 ng/µL in soil

and 4.20 ng/µL in groundwater samples. Amplicon sequencing from Genome Québec failed due to low

DNA concentrations. As the samples were collected from deep surfaces in the site, it is possible that there

is a low abundance of microorganisms that can survive in such depths. Low abundance of

microorganisms could also be a reason why no degradation or transformation was observed in the bottles.

Aerobic degradation experiments of multiple COIs in Camaçari laboratory, Brazil

4.2.1 Motivation and sample collection

On location at the field site, there is an operating laboratory, capable of performing analytical and

biological analyzes in environmental samples. Carrying out activity tests on-site would preclude the need

to ship samples elsewhere, and thus would represent a huge advantage and cost saving, not to mention

empower local technical expertise to investigate the site. An aerobic degradation experiment with 5 COIs

(1,2-DCB, 2,3-DCA, 3,4-DCA, 2,3-DCNB, and 3,4-DCNB) was designed and conducted at the Camaçari

site in July 2017. I travelled to Camaçari to conduct this experiment with help from Olivia Molenda,

Isabela Camargo, and Ligia Carvalho.

The objectives of this experiment were to:

a. Train scientists and engineers on site on treatability study set up, and culture maintenance and

monitoring;

b. Prepare protocols for future degradation tests on site; and

c. Evaluate the potential for aerobic biodegradation of COIs that are present at the site.

As this was the first time that degradation experiments were performed on the site, the methodology and

procedures were tested. To do so, a water sample was collected from a puddle close to the NPAD, a

heavily contaminated area of the site. The sample was collected on the same day as the experiment set-up.

For further experiments, groundwater samples would be used to assess the potential for biodegradation.

32

No soil was added to the bottles in this experiment, since the purpose was to understand and train local

employees on the step by step protocol to carry out this kind of test and adapt the methodology to what

was available at the site.

4.2.2 Methodology

Since these experiments had never been conducted on site, the experiment was designed according to the

materials and equipment already available on site. To collect the water sample, a clean PTFE bottle was

used, and 1 L of puddle water was collected (puddle water was easily available and suitable for the

experiment objectives). The experiment was conducted inside the fume hood, in triplicates, in a total of 9

Boston round bottles (250 mL capacity each), with MininertTM caps, and the treatments were as follows

and are also shown in Table D.2:

a. Sterile controls: 100 mL of tap water was boiled for 15 minutes inside a beaker, on a hot plate (no

autoclave on site when the experiment was conducted), and no pH adjustment;

b. Active controls: 100 mL of puddle water without pH adjustment (pH of puddle water was 9.2);

c. pH-adjusted bottles: 100 mL of puddle water with pH adjustment using phosphate buffer solution

(0.2 M final concentration; prepared by adding 1 mL of 2.72 g KH2PO4 and 3.483 g K2HPO4 in

100 mL of MiliQ water, filter sterilized into a glass bottle). Final pH after adjustment was 7.14.

Since the puddle water did not contain any of the contaminants at the time of sampling, the COI stock

solution was added to this bottle. To prepare the COI stock solution, 3,4-DCNB, 2,3-DCNB, and 3,4-

DCA solids were added in that order, and when 3,4-DCA was added to the glass vial, the solids became a

brown, oily liquid. The glass vial was capped with a MininertTM cap, and then 2,3-DCA and 1,2-DCB

liquids were added by measuring the mass of the liquids inside syringes. The target concentrations were:

15 mg/L of 3,4-DCNB, 10 mg/L of 2,3-DCNB, and 5 mg/L of 1,2-DCB, 2,3-DCA, and 3,4-DCA. In each

Boston round bottle, 3 µL of the COI stock solution was added using a 5µL Luer-lock Gastight® glass

syringe.

Five samples were collected during the experiment on days 0, 5, 12, 19, 27, and they were analyzed by

GC and HPLC. An Agilent 5890 Series GC equipped with an Agilent Technologies capillary column

(DB-5MS, 60 m x 0.32 mm x 1µm), coupled with a flame ionization detector (FID), split mode 1:25 was

used to analyze 1,2-DCB concentrations.

33

To analyze 2,3-DCA, 3,4-DCA, 2,3-DCNB, and 3,4-DCNB, an Agilent Technologies HP-1100 Series

HPLC, coupled with a C8 column (4.6mm x 150mm x 3.5 Å particle size), UV-VIS detector using a 254

nm wavelength, flow rate of 1 mL/min in gradient mode, with a methanol-water mobile phase was used.

The gradient was as follows: (1) time zero: 55% H2O and 45% methanol, (2) 3 min: 55% H2O and 45%

methanol, (3) 15 minutes: 50% H20 and 50% methanol, and (4) 30 minutes: 100% methanol.

Samples for GC analysis were collected as follows: 1 mL of liquid sample was collected from the bottle

using a Luer-lock Gastight® glass syringe, dispensed into a 20 mL glass vial with 10 mL of NaCl

solution (pH=2), capped with silicon / PFTE cap, and left in the oven at 90°C for 15 minutes. The vial

was shaken and left for an additional 15 minutes in the oven, and then 1 mL of the headspace was

sampled and injected into the GC for analysis.

Samples for HPLC were collected as follows: 1 mL of liquid sample was collected from the bottle using a

Luer-lock Gastight® glass syringe, and then mixed with 1 mL of acetonitrile in a 2 mL glass vial. The

solution was then filtered through a 0.45 µm pore Millex HV 13mm filter (Millipore Industria e Comercio

Ltda., Barueri, Brazil) and the filtrate was placed in the HPLC glass vial for analysis.

Oxygen was not measured in the bottles during the experiment because the laboratory did not have an

oxygen meter at the time of the experiment. For further experiments, it was recommended that the bottles

could be opened inside the fume hood for about 5 minutes to ensure that oxygen was not a limiting factor

for aerobic biodegradation. Another possibility is to inject oxygen by using a syringe and needle through

the MininertTM cap.

4.2.3 Results and discussion

After 27 days of experiment, GC and HPLC results were plotted and the results are shown in Figure D.3.

As expected, the sterile controls did not show any discernable changes in concentrations of COIs, since

the water used in the experiment was boiled. These results illustrate reproducibility and consistency in

analytical methods.

In active controls without pH adjustment 3,4-DCA was completely degraded within the first 12 days of

the experiment, at a degradation rate of approximately 0.37 mg of 3,4-DCA/L/day. In addition, 2,3-DCA

was completely degraded after 19 days, at degradation rate of 0.16 mg of 2,3-DCA/L/day. Both 2,3- and

3,4-DCNB were not degraded completely over the 27 days of the experiment, however, the 3,4-DCNB

concentration was decreasing at a rate of 0.2 mg of 3,4-DCNB/L/day. Whereas the 2,3-DCNB

34

concentration had decreased only 16% of the initial concentration. It is likely that there would have been

further degradation observed in these bottles if the time course of the experiment was longer.

Overall, faster rates of COI degradation were observed in the pH-adjusted active controls. In these bottles,

the 3,4-DCA concentration was degraded at an estimated rate of 0.9 mg of 3,4-DCA/L/day. This is an

estimated rate because at the time of sampling, the concentration was already 0.0 mg of 3,4-DCA. The

second fastest COI that degraded completely was 2,3-DCA, with a degradation rate of 0.39 mg of 2,3-

DCA/L/day. While the other COIs were not completely degraded at the end of the experiment, the

concentration of these compounds was still decreasing, indicating biodegradation.

These results show a high potential for bioremediation at the site if oxygen is available. Adjusting the pH

to 7 may accelerate the degradation of COIs, but more experiments should be conducted with

groundwater samples from the site to assess the distribution of the microbial community responsible for

biodegradation. Furthermore, aerobic biodegradation of DCNBs has been reported by Palatucci (2017)

using samples from the site.

Highly enriched cultures from collaborating laboratories

4.3.1 Motivation and samples

As previously mentioned, collaborating universities and laboratories are involved in this remediation

project, studying different techniques using environmental samples from the Camaçari site. Two

laboratories in the United States, at the Clemson University and the University of West Florida, have

prepared enrichment cultures and pure cultures using water and sediment from the site, and from a water

treatment plant near the site, managed by Cetrel. The objective of this experiment was to compare the

microbial communities from enrichment cultures, which are known to degrade the COIs aerobically, to

the microbial communities found in the microcosms at the University of Toronto, in the Edwards

laboratory. The only anaerobic enrichment culture received for this experiment, from Clemson

University, was degrading 4-nitrotoluene, a compound that was not tested during this research.

Under the supervision of Dr. David Freedman, Quintero (2016) conducted a microcosm study using

environmental samples from the Camaçari site. Transfers were made from the microcosm bottles that

were degrading or transforming the COIs. Enrichment cultures were created that degraded chlorobenzene

and 1,2-DCB aerobically, and biotransformed 4-Nitrotoluene (4-NT) to benzylamide anaerobically, when

lactate was added as an electron donor. After the parent compounds were degraded in the microcosms,

and several re-amendments, transfers were made to new bottles with fresh groundwater to enrich activity.

35

The aerobic enrichment culture was fed, and pH was adjusted to neutral, then incubated on shaker at room

temperature. The anaerobic culture conditions were similar, except these bottles were incubated inside an

anaerobic chamber with a stir bar, at room temperature after pH adjustment. From these bottles, three

DNA extracts were sent to the University of Toronto in 2017: a) aerobic CB degrading, b) aerobic 1,2-

DCB degrading, and c) anaerobic 4-NT transforming. These DNA extracts were immediately stored at -

80°C until 16S rRNA sequencing.

A student in the research group of Dr. Jim Spain, at the University of West Florida, conducted a

microcosm study with samples from the site, and prepared enrichment cultures that aerobically degraded

2,3-DCNB, 3,4-DCNB, 1,2-DCB, and CB. The environmental samples were used as the inoculum of a

fluidized bed bioreactor (FBR). The enrichment cultures were maintained at neutral pH and incubated at

room temperature in a shaker. Pure cultures of DCNB isomer-degrading microorganisms were isolated as

described in Palatucci (2017). From these experiments, samples were sent to the University of Toronto in

2017: (a) agar plates containing both 3,4-DCNB- and 2,3-DCNB-degrading pure cultures, and (b) sand

samples from the FBR, containing chlorobenzene- and 1,2-DCB-degrading microorganisms. Before using

these various cultures in my experiments as test inocula and for DNA extraction, they were cultivated at

University of Toronto, as described in the Appendix D and shown in Figure D.4.

4.3.2 Methodology

After growing the external cultures from the University of West Florida, some of the Toronto microcosms

(from Chapter 3) that were not degrading specific COIs were inoculated with the culture mix to test if

bioaugmentation could work. The goal of this inoculation was to evaluate the potential for biodegradation

in these bottles if the appropriate microorganisms were present.

From site 2A, triplicates of active controls microcosms were combined, then split into 8 bottles, and each

pair of duplicates received treatments as shown in Table D.3. The treatments were: a) continuing with

maintenance as done in microcosms study #1, b) pH-adjusted to 7, c) inoculated without changing the pH,

and d) both inoculating and adjusting the pH to neutral. The pH was adjusted by adding bicarbonate

solution using a 1 mL plastic Luer-LokTM tip syringe (BD) coupled with a disposable 22 G needle. The

volume of bicarbonate solution added to each bottle varied from bottle to bottle, depending on initial pH.

If pH was too basic, the pH was adjusted by adding 5 N HCl. Chloroanilines (2-CA, 3-CA, and 4-CA)

and 1,2-DCB stock solutions were added for a final concentration of 13 mg/L in the liquid phase. The

chloroanilines were added as a positive control, since these compounds were degraded before in these

bottles, and re-spiked on day 60 of the experiment to keep the bottles active. Since 1,2-DCB is volatile it

was added to the bottles after sealing since it could have escaped during set-up. Other COIs, such as 3,4-

36

DCNB, 2,3-DCNB, and 2,5-DCA, were present in the bottles from the previous feedings during the

microcosm study.

From site 1B, the same procedure was carried out and the same conditions in duplicates were established.

In these bottles, a chloroaniline (2-CA, 3-CA, and 4-CA) stock solution was added for a final

concentration of 10 mg/L of each compound in the liquid phase. The chloroanilines were added as a

positive control, since these compounds were degraded before in these bottles, and re-spiked on day 60 of

the experiment to keep the bottles active. 1,2-DCB was also added to a final concentration of 10 mg/L in

the liquid phase. All bottles were sampled for GC and HPLC analysis, as described in Sections 2.2.2 and

2.2.3, for two months.

4.3.3 Results

The degradation graphs of the bottles from site 2A are shown in Figure 4.1. The chloroanilines were

rapidly degraded regardless of pH, but not other substrates (Fig. 4.1a and b). Addition of inoculum from

the University of West Florida resulted in biodegradation 2,3-DCNB and 3,4-DCNB at neutral pH

(Figure 4.1d) but not when pH was not adjusted (Figure 4.1c). The isomer 3,4-DCNB degraded at a rate

of 0.45 mg/L/day and was completely degraded after 48 days. Whereas the isomer 2,3-DCNB degraded

slightly slower and not completely but decreased in concentration from 6 mg/L to 2 mg/L after 30 days.

In the bottles from site 1B, as expected, all the chloroanilines were degraded after the feeding, and

degradation was complete after 30 days of experiment (Figure 4.2). The three CA isomers were re-spiked

on day 60 to determine if the bottles were still active. In general, the biodegradation of all three isomers

occurred at a similar rate of 0.26 mg/L/day, but 2-CA was slightly slower than the others. 1,2-DCB was

only degraded in the bottles inoculated with culture mix, regardless of pH adjustment (Figure 4.2c and

d). The average pH of the bottles in Figure 4.2c was 5, and complete degradation occurred after 48 days,

with the same rate of 0.2 mg/L/day when pH was adjusted to 7. It is possible that the microorganisms that

degrade 1,2-DCB are not affected by pH changes and can still degrade under acidic conditions. Whereas

the microorganisms that degrade DCNBs require neutral pH (Figure 4.1d).

In summary, this little experiment proved that inoculation with enriched cultures capable of DCNB or

1,2-DCB degradation enabled degradation of these compounds in microcosms that previously were

inactive against these compounds. The experiment also suggested that pH is more critical for DCNB

degradation than 1,2-DCB degradation. These results suggest that the microcosm bottles did not contain

microorganisms capable of aerobic degradation of these specific COIs. Another possibility is that the

37

microorganisms were present in the microcosms, but in such low abundance that was insufficient to

degrade the COIs.

Figure 4.1 Impact of pH and bioaugmentation on aerobic microcosms from site 2A. Average

biodegradation in the duplicate bottles observed with different treatments: A) continuing with

maintenance before combining; B) pH adjusted to neutral; C) bottles inoculated with culture mix and pH

was not adjusted; and D) bottles inoculated with culture mix and pH was adjusted to neutral. On day 60,

the chloroanilines were re-spiked. X axis represent time (days) and Y axis represent concentration of the

compound (mg/L). CA = chloroaniline, DCA = dichloroaniline, DCB = dichlorobenzene, DCNB =

dichloronitrobenzene.

38

Figure 4.2 Impact of pH and bioaugmentation on aerobic microcosms from site 1B. Average

biodegradation in the duplicate bottles observed with different treatments: A) continuing with

maintenance before combining; B) pH adjusted to neutral; C) bottles inoculated with culture mix and pH

was not adjusted; and D) bottles inoculated with culture mix and pH was adjusted to neutral. X axis

represent time (days) and Y axis represent concentration of the compound (mg/L). On day 60, the

chloroanilines were re-spiked. CA = chloroaniline, DCA = dichloroaniline, DCB = dichlorobenzene,

DCNB = dichloronitrobenzene.

Influence of pH in microcosms

4.4.1 Motivation and samples

As previously described in Chapter 3, the microcosm study was set-up to simulate the field site

conditions, where pH was not changed from what it was in the samples, as received. Since there was no

observable degradation in some of the bottles, while parallel studies done with pH adjustment showed

enhanced activity of microbes under neutral pH (Palatucci, 2017), it was decided to adjust the pH in some

of these bottles and monitor the effects on the degradation without adding an external culture and/or an

39

inoculum. Therefore, aerobic microcosms from 3 sampling locations (1A, 2A, and 2B) that showed

limited degradation were selected to determine if pH adjustment would alter biodegradation in the bottles.

Specifically, the bottles from sites 1A and 2A that were previously amended with vitamins were used for

this experiment, and the bottles from site 2B were previously designated as the active controls.

4.4.2 Methodology

For this experiment, bottles from the 3 sites were treated differently, as shown in Table D.4, and detailed

below. Sites 1A and 2A were kept in their original bottles, each in triplicates, and the site 2B bottles were

combined in a larger bottle and redistributed into 6 smaller bottles.

Aerobic microcosms from site 1A previously amended with vitamins

The triplicates from site 1A, previously amended with vitamins, had pH measurements between 5.5 and

5.8, and contained different DCNBs isomers. One of the replicate bottles had 3 mg/L of 2,3-DCNB, the

second had 6 mg/L of 2,5-DCNB, and the last one had 10 mg/L of 3,4-DCNB. These bottles had

previously biodegraded 2-CA, 3-CA, and 4-CA under natural pH. They all had been pH-adjusted to pH

6.9 by adding bicarbonate solution using a 1 mL plastic Luer-LokTM tip syringe (BD) coupled with a

disposable 22 G needle. The bottles were analyzed by HPLC for 160 days.

Aerobic microcosms from site 2A previously amended with vitamins

The triplicates from site 2A, previously amended with vitamins, had 2,3-DCNB, 3,4-DCNB, 2,5-DCA,

1,2-DCB that were not degraded during the microcosm study. Whereas 2-CA, 3-CA, 4-CA, 2,3-DCA, and

3,4-DCA were degraded in these bottles before this experiment. One of the triplicate bottles from this site

was maintained at pH 5.5. The two other replicates were pH-adjusted to pH 6.9 by adding bicarbonate

solution using a 1 mL plastic Luer-LokTM tip syringe (BD) coupled with a disposable 22 G needle. The

bottles were analyzed by GC and HPLC for 160 days.

Aerobic microcosms from site 2B previously treated as active controls

This test was conducted by Amy Li, a summer student who has worked on multiple studies in the

Edwards laboratory. The liquid phase of the triplicates from site 2B, previously known as active controls,

was removed from the original bottles using a glass pipettor and combined into an autoclaved glass bottle,

mixed, divided into six 150 mL amber glass bottles, with 35 mL of groundwater each and capped with

MininertTM caps. To recall, these bottles had degraded 2-CA, 3-CA, 4-CA, 2,3-DCA and 3,4-DCA during

the microcosm study, as described in Chapter 3. The 3 CA isomers were added as positive controls for

40

this experiment, and the DCA isomers were not added. No oxygen was added to the bottles during the

experiment, but oxygen measurements were performed in all the bottles and the oxygen content was

always between 17-19%. The following compounds remained in the bottles from the previous

experiment: 9 mg/L of 2,5-DCA, 3 mg/L of 2,3-DCNB, and 3mg/L of 3,4-DCNB.

The pH was adjusted to three different pH measurements: a) pH unadjusted at 4.6, b) pH adjusted from

4.7 to 5.8, and c) pH adjusted from 4.7 to 7. All the pH adjustments were done by adding bicarbonate

solution using a 1 mL plastic Luer-LokTM tip syringe (BD) coupled with a disposable 22 G needle. The

bottles were analyzed in HPLC for 51 days.

4.4.3 Results

The GC and HPLC results are shown in Figures D.5 to D.7. From the three sites, adjusting the pH to 7

did not lead to the degradation of the DCNBs. During the time of the experiment, no biodegradation of

DCNBs occurred in any of the bottles. The results suggest that either the microbial community in the

bottles were not capable of aerobic biodegradation of DCNBs, or the elapsed time (150 days for the first 2

experiments, and 51 days for the third experiment) was insufficient to enrich for a microbial community

that can degrade DCNBs. The bottles from site 2B, where chloroanilines were added as positive controls,

completely biodegraded, as expected (Figure D.7). This means that there were sufficient nutrients and

oxygen for biodegradation to occur, so the reason for the lack of DCNB degradation might be the lack of

microorganisms responsible for these reactions.

As presented in Section 4.3, when the enriched DCNB-degrading cultures from external laboratories were

used as inoculum in microcosms, degradation of 2,3-DCNB and 3,4-DCNB did indeed occur. Therefore,

bioaugmenting bottles with inoculum for certain more difficult to degrade compounds could help

degradation of other compounds that would use metabolites as part of their reactions.

In conclusion, the experiments described in Chapter 4 revealed that i) a deep sample collected for

exploring anaerobic processes in fact had very low biomass and limited activity; ii) aerobic

biodegradation of chloroanilines is robust while aerobic biodegradation of DCNBs and 1,2-DCB could be

observed in enrichment cultures and bioaugmented microcosms only. These results suggest uneven

distribution of biodegrading microbes at the site, highlighting the importance of further characterizing

microbial population distribution at the site (see Chapter 5). Increasing the pH to 7 can result in enhanced

rates of reactions, particularly for DCNBs. The influence of pH might be an important factor to be taken

into consideration when planning a pilot project in the field.

41

Chapter 5 Microbial community analysis

Motivation and samples

This chapter describes the process of microbial community analysis performed on samples from the site

as well as from microcosms and cultures that have been described in the previous chapters. The objectives

of these analyses are to:

1) Identify the most abundant microorganisms in groundwater and soil samples from the site;

2) Explore how the microbial community in microcosms samples behave over time when cultures

are enriched and exposed to different conditions (e.g. electron donor, vitamin, sulfate, or nitrate

amendments);

3) Compare microorganisms from enriched cultures generated by other labs to organisms found in

the microcosms and groundwater samples;

4) Identify which microorganisms are capable of biodegradation and biotransformation of COIs in

cultures and microcosms; and

5) Analyze the samples statistically and identify trends and clusters in the sample groups and among

different groups.

To achieve these objectives, samples were collected, prepared, and analyzed. A list of all the sample

names, sampling date, and additional information is shown in Table E.1 and a map showing their site

location in Figure E.1. Generally, the samples used for these analyses were:

a. Microcosm samples: Sample collected during the microcosm study started in 2015 and described

in Chapter 3. A total of 100 samples were collected and analyzed, from all the sites (1A, 1B, 2A,

and 2B, Figure C.1), under aerobic and anaerobic conditions, and focused on the bottles that were

active and biodegrading or bio-transforming some of the compounds. In most cases, the same

bottle was sampled over time, to achieve Objective 1 described above, demonstrated in Table

E.1. Even though some samples are from the same bottle, each was treated as a unique sample

during the analyses.

42

The following is a description of sample nomenclature, for example: A1_AC1_Day323a, where

the first descriptor is the site location of the sample (A1, B1, A2, or B2, Figure C.1); the second is

the treatment condition of the bottle (AC = active control, Don = electron donor, Nit = nitrate,

Sulf = sulfate, Vit = vitamins) with the number of the replicate in the series; the third descriptor is

the elapsed time of the microcosm study at the time of sample collection; and the last descriptor is

the aerobic (a) or anaerobic (an) conditions. Of note is that the site names are actually 1A, 1B,

2A, and 2B, however since it is not recommended that the sample names start with numbers, due

to programming and coding issues this might cause in RStudio, the order of letters and numbers

have been reversed.

b. Highly enriched cultures from collaborator laboratories: Samples were received from

Clemson University and the University of West Florida laboratories, as described in Section 4.3.

From Clemson University, the microbial communities of three enrichment cultures were

analyzed. These highly enrichment cultures were named to describe the compound degraded and

the condition under which degradation occurred. For example, DF2_DCBa, where DF stands for

Dr. David Freedman, principal investigator in this research at Clemson University, and the

number following is the sample number (1 to 3). The next descriptor is the compound degraded

by the culture, in this case 1,2-DCB. Lastly, the lower-case letter indicates if the culture was

grown aerobically (a) or anaerobically (an).

The microbial communities of five pure cultures from University of West Florida were also

analyzed, the details of these samples were previously described in Section 4.3. The pure cultures

names indicate many characteristics of the culture. For example, Jim_FBR_34DCNBa, where Jim

refers to Dr. Jim Spain, the principal investigator of this research at University of West Florida.

The next descriptor is the sample origin (FBR = fluidized bed bioreactor, 3050 = pure culture

degrading 3,4-DCNB, 3051 = pure culture degrading 2,3-DCNB). The third term is the

compound that was degrading, 3,4-DCNB, in this case. Lastly, the condition under which the

culture was grown (a = aerobically).

c. Soil samples: Soil samples were collected in 2015 from the Camaçari site. From the first batch of

sediment material sent to the University of Toronto to prepare the microcosm study, four soil

samples were collected, one from each site. These soil sample names explain its origin. For

example, one of these soil samples was named NO82_deep_2B, where the first descriptor is the

borehole where the sample was collected from at the site (NO82 in UN11&12, and NO83 in

NPAD, Figure C.1). The second descriptor refers to the depth of the sample (deep or shallow, as

43

shown in Figure C.2). The last descriptor is the same nomenclature for the site location as used in

the rest of this work (1A, 1B, 2A, or 2B).

d. Groundwater samples: Groundwater samples were collected from the site in 2016 and 2017.

From these samples, DNA was extracted in the on-site laboratory and DNA extracts were shipped

to the University of Toronto. These groundwater samples were collected using a low flow purge

method, in which a low flow submersible pump is placed in the monitoring well and the samples

are collected from within the well screen, which minimizes purging and improves sample quality.

This methodology is widely used in Brazil and follows the procedure published in 2010 in a

Brazilian legislation document, ABNT 15847, for groundwater sampling in monitoring wells

using different purging methods (in Portuguese, Amostragem de água subterrânea em poços de

monitoramento – métodos de purga). After the pump is introduced in the well and purging starts,

the field parameters become stable, and then sample collection can occur. The pumping flow rate

is between 0.1 to 0.5 L/min, and should not to exceed 1.0 L/min. For DNA sample collection, the

pump is connected to a plastic hose, which is then attached to a Sterivex filter 0.22 µm. The filter

will retain the microorganisms from filtering approximately 1 L of groundwater. The protocol for

DNA extraction from the Sterivex filter is described in Section 2.3.1.

The names of these DNA extracts includes the monitoring wells where they were collected from,

where DW stands for deep well, and PM stands for monitoring well (acronym for Portuguese

word). Additionally, there are two wells from the hydraulic barrier located at the site, so these

sample names include IHB (influent from hydraulic barrier) or EHB (effluent from hydraulic

barrier).

e. Groundwater samples from Cetrel: Cetrel is a company located in Camaçari and is responsible

for water supply, industrial effluent treatment, industrial waste treatment and disposal, water

reuse, and environmental monitoring of the Camaçari Industrial Complex since 1978. Cetrel’s

waste water treatment plant uses activated sludge to treat industrial effluent from multiple sites,

processing approximately 144,000 m3/day of sludge. The full process occurs in 5 steps: (a)

aerobic digestion, (b) aeration tanks, (c) landfarming, (d) settler, and (e) drying bed. The samples

for these analyses were collected from the three aeration tanks, TA01, TA02, TA04.

A total of 140 samples were sent for 16S rRNA Illumina amplicon sequencing at Genome Québec, in

2017, and the methodology is described in Section 2.4.

44

Results and discussion

5.2.1 qPCR of samples from microcosms, enriched cultures, soil, groundwater, and groundwater from Cetrel

All the samples that were sent for 16S rRNA amplicon sequencing were also analyzed by qPCR,

following the methodology described in Section 2.4. The samples were analyzed to quantify the bacteria

and archaea in all 140 samples analyzed in this study, except for 6 samples that did not have enough

volume to be analyzed. Table E.2 shows the slope, y-intercept, R2, and efficiency for all the qPCR runs

performed on these samples, and Table E.3 shows qPCR raw results.

When analyzing the results, there was a higher abundance of archaea in samples from anaerobic, electron

donor amended bottles, ranging from 1.04E+08 to 1.14E+09 copies/mL of sample. Groundwater samples

had the lowest number of archaea, ranging from 8.80E+03 to 4.06E+ 04 copies/mL, and the lowest

number of bacteria, ranging from 4.25E+05 to 4.26E+06 copies/L. The total number of bacteria was

higher than archaea in most of the samples. The highest abundance of bacteria was found in water

samples from the Cetrel aeration tanks, followed by the samples from the anaerobic, electron donor

amended bottles. These results make sense as these samples will have the highest substrate

concentrations.

The main objective of this analysis was to quantify the total amount of bacteria and archaea in all the

samples, to determine the abundance of biomass in different types of samples. As expected, enrichment

cultures and microcosm samples had more biomass when compared to environmental samples, such as

groundwater and soil samples. Samples from site 1 (NPAD) contained more bacteria and archaea than site

2 (UN11&12). To better visualize this information, the raw data is plotted in a bar chart in Figure 5.1,

ranked by Archaea and Figure E.2 shows the same graph ranked by Bacteria.

45

Figure 5.1 qPCR results (copies/mL) for microcosms, soil, groundwater, and pure culture samples. The lower graph is a continuation of the

upper graph for better visualization. The samples are ordinated from highest to lowest number of Archaea. X axis shows sample names and Y axis

show the concentration of bacteria or archaea in original sample (copies/mL).

46

5.2.2 Main operational taxonomic units (OTUs) in groundwater and soil samples from the site

A total of 32 site samples were analyzed, 28 groundwater samples and 4 soil samples that were used to set

up the microcosm study in 2015 (microcosm study #1). Other soil samples were collected and extracted

on site, but the shipment from Brazil to Canada lasted 5 days, which is longer than dry ice can last, and

DNA extracts did not survive shipment to Canada. From the analysis of these samples, three graphs were

generated: soil samples (Figure 5.2), groundwater samples (Figure 5.3), and other water samples (Figure

5.4), two samples from the hydraulic barrier and three samples from the aeration tanks from Cetrel.

Soil samples from sites 1A, 1B, and 2B contained Ktedonobacterales as the most abundant Order in this

type of sample, Figure 5.2. Environmental samples are typically diverse, unless the microorganisms are

enriched somehow in the environment. At the Camaçari site, the soil samples are from highly

contaminated areas where contamination has been present for decades, which explains why there are

dominant populations in these four soil samples.

The groundwater samples are much more diverse than the soil samples, but still enriched compared to

environmental samples that had not been exposed to contamination, shown in Figure 5.3 and collected

within the site as shown in Figure E.1. Of all the groundwater samples, the only wells sampled twice are

P073_19 and PM19, the first one collected in November 2016 and the second one in August 2017. Both

wells contain a high abundance of Betaproteobacteria. The phylogenetic tree ordinates samples according

to their microbial community similarity, which gives a better interpretation of the data. As the samples are

diverse, a phylogenetic tree is a good visualization of similar samples. Other important microorganisms

that might be able to degrade the COIs in this study are present in the groundwater samples, analysis in

Section 5.2.3. Some wells, such as DW03B, DW06, PM01, PM35, and PM45 have different

microorganisms that are highly enriched, as shown in Figure 5.3. Again, this shows high enrichment of

microorganisms at these sites due to environmental pollution over a long period of time.

Environmental samples tend to be more diverse than enriched samples, but when these samples are

exposed for long term soil and groundwater contaminations, they became enriched for specific organisms

that have the ideal metabolism to live in this type of environment.

47

Figure 5.2 Relative abundance (> 0.5%) in soil samples collected from the site. This is the resulting data from the 16S rRNA amplicon

sequencing results. The same color represents microorganisms belonging to the same taxonomy, phylogenetically assigned by MetaAmp, and

each horizontal line represents a different OTU of the same taxonomic classification. This graph shows data represented in OTU level.

48

Figure 5.3 Dendrogram and relative abundance (> 1%) in groundwater samples collected from the site. The dendrogram on the left ordinates

similar samples. As these samples are diverse, the main OTUs are plotted on the right, indicated by colors. Organisms with relative abundance lower

than 1% in the sample are not plotted. This graph shows data represented in OTU level.

49

The other water samples collected and analyzed in this study are from the hydraulic barrier (effluent and

influent) and from the aeration tanks from Cetrel, shown in Figure 5.4. Both influent and effluent water

samples contained a high abundance of the Family Comamonadaceae, at approximately 30% relative

abundance in each. The Comamonadaceae family is composed of aerobic Gram-negative

microorganisms. Diaphorobacter is also found in high abundance, around 15% relative abundance in

each, and is likely enriched due to the local contamination. In the influent sample, 47% of the sample is

composed of the Genus Burkholderia, which are obligatory aerobic, Gram-negative and rod-shaped

bacteria.

Three samples from Cetrel’s wastewater treatment plant (WWTP) were collected from the aeration tanks.

The material treated in the WWTP comes from different areas of the industrial complex and is combined

in these large capacity tanks. The genus Thauera is present in all the samples and is Gram-negative, rod-

shaped bacteria, previously found in wet soil and polluted freshwater. Aeration tank 2 contained

Phycisphaerae at 33% of the relative abundance, which is a strictly anaerobic and chemoheterotrophic

Class that had been identified in hypersaline sediments (Spring, et al., 2018).

The groundwater samples are more representative than soil samples, since the volume filtered through a

Sterivex filter is larger compared to the mass of soil used for a DNA extraction. Because of this,

groundwater samples tend to be more diverse and have more biomass since it is representing a larger

volume of the site subsurface.

50

Figure 5.4 Relative abundance (> 0.5%) in groundwater samples from Cetrel and from hydraulic barrier. This is the resulting data from the

16S rRNA amplicon sequencing results. The same color represents microorganisms belonging to the same taxonomy, phylogenetically assigned by

MetaAmp, and each horizontal line represents a different OTU of the same taxonomic classification. Names of the samples: EHB = effluent hydraulic

barrier; IHB = influent hydraulic barrier; TA = aeration tank from Cetrel. Results from 16S rRNA sequencing results. This graph shows data

represented in OTU level.

51

5.2.3 Changes in microbial community over time in microcosms samples

DNA samples were collected from the microcosms for the objective of capturing the changes in microbial

communities over time in active bottles. By doing this, it is possible to see which microorganisms were

enriched and identify community trends in the bottles.

Aerobic microcosms

Samples from active controls and vitamin-amended bottles were collected between days 70 and 333 of the

experiment. The 16S rRNA sequencing results of this experiment are presented in Figure 5.5 and Figure

5.6.

In site 1A, the Genus Pandoraea increased in relative abundance from the beginning of the experiment to

the end. This OTU is also present in one of the enrichment cultures from Clemson University, which was

reported to degrade 1,2-DCB (Quintero, 2016). Rhizomicrobium is another genus that increased in relative

abundance in site 1A vitamin-amended bottles, but not in the active control bottles. Adding vitamins to

the bottles did not drastically change the microbial communities and did not enhance or inhibit any

aerobic activity in the bottles. In site 1B, the active controls and vitamin-amended microcosms did not

show any aerobic degradation in the DCBs isomers tested. Some OTUs were enriched and contained a

high abundance of Clostridia and Ktedonobacterales, but these microorganisms are not related to the

aerobic biodegradation of dichlorobenzenes.

Similar to site 1, there was an increase in the relative abundance of Pandoraea in samples from site 2 that

degrade 1,2-DCB, specifically in site 2B. Burkholderia was the most abundant OTU in site 2, and site 2A

had a high abundance of Xanthomonadaceae, whereas site 2B had a high abundance of

Ktedonobacterales. Interestingly, the deep and shallow samples have different microbial composition

from each other, even though they were collected close to each other (Figure 5.1), which shows how

heterogeneous the soil is at this site.

52

Figure 5.5 Relative abundance (%) in aerobic microcosms samples from Site 1A and Site 1B. The same color represents microorganisms

belonging to the same taxonomy, phylogenetically assigned by MetaAmp, and each horizontal line represents a different OTU of the same taxonomic

classification. This graph shows data represented in OTU level.

53

Figure 5.6 Relative abundance (%) in aerobic microcosms samples from Site 2A and Site 2B. The same color represents microorganisms

belonging to the same taxonomy, phylogenetically assigned by MetaAmp, and each horizontal line represents a different OTU of the same taxonomic

classification. This graph shows data represented in OTU level.

54

Anaerobic microcosms

Samples from active controls, donor amended, nitrate amended, and sulfate amended bottles were

collected between days 70 and 333 of the experiment.

The microcosms from site 1A, of active controls, nitrate-amended, and sulfate-amended bottles did not

transform of any the tested compounds, as shown in Figure 5.7. The electron donor-amended bottles

transformed 2,3-DCA, 3,4-DCA, 2,5-DCA, and 1,2-DCB, and the DCAs were dechlorinated to CAs. In

these bottles, there is an increase in Dehalobacter that could be related to this anaerobic dechlorination

processes, since it is well known dechlorinators. There is also an increase in the relative abundance of

fermentative microbes in these bottles, such as Sporomusa, Anaerospora, and Thermoanaerobacteraceae,

due to the presence of lactate and ethanol in them.

The microcosms from site 1B were fed with dichlorobenzenes only, specifically 1,2-DCB, 1,3-DCB, and

1,4-DCB, as shown in Figure 5.8. None of these bottles degraded DCBs in the microcosm study. Only

after analyzing the DNA samples, it was possible to identify a dominant population of Cupriavidus in the

nitrate-amended bottles, which was reported to degrade 1,2-DCB in the cultures received from the

University of West Florida. Afterward, these bottles were re-analyzed by GC, and the concentrations

show that biotransformation of the three isomers occurred, as previously shown in Chapter 3. It is

possible that this same organism is also able to transform not only 1,2-DCB, as previously reported, but

also 1,3-DCB and 1,4-DCB. Further experiments should be done to confirm the hypothesis that this

microorganism is capable of this degradation, but the increase in abundance is a good indication. The

donor-amended bottles did degrade the DCBs tested, but the microbial community showed a significant

increase in relative abundance of Thermoanaerobacteraceae between the samples collected in days 151,

197, 241, and 323. As expected, bottles amended with electron donor have a significant effect on the

microbial community.

The microcosms from site 2A were fed with aniline, 2-CA, 3-DCA, 4-CA, 2,3-DCA, 2,5-DCA, 3,4-DCA,

2,3-DCNB, 3,4-DCNB, and 1,2-DCB, as shown in Figure 5.9. All the samples biotransformed both

isomers of DCNB and the electron donor-amended bottles also biotransformed 3,4-DCA and 1,2-DCB.

When comparing the background sample with the subsequent samples, there was an increase in

Chitinophagaceae in the active controls, sulfate-amended, and nitrate-amended bottles. Whereas the

electron donor-amended bottles are much more diverse than the background sample. The most active

microcosms, treated with electron donor, showed an increase of Leptolinea, Veillonellaceae, and

Syntrophomonas.

55

The microcosms from site 2B were fed with aniline, 2-CA, 2,3-DCA, 3,4-DCA, 2,5-DCA, 2,5-DCNB,

and 3,4-DCNB, as shown in Figure E.3. Only the electron donor-amended bottles biotransformed the

DCNBs, whereas the active controls, sulfate-amended, and nitrate-amended bottles did not. There was a

clear dominance of Ktedonobacterales in these bottles, since the beginning of the experiment, and in the

background sample. In the electron donor-amended bottles, the relative abundance of Desulfitobacterium

and Veillonellaceae slightly increased from zero to around 5% each, but not enough to be able to

hypothesize if these microorganisms can biotransform DCNBs to DCAs.

In general, it is possible to determine which populations increased over time in the microcosm samples,

according to what compounds were fed, and which were degraded and transformed. This technique can be

used as a tool to monitor the microbial population of the site and infer whether the microorganisms that

are present are capable of biodegrading and biotransforming the contaminants or, if not present, it can be

used a decision-maker for bioaugmentation at the site. In order to determine which microorganisms are

responsible for each reaction, a specific study needs to be done to isolate the microorganisms and feed

only one compound to identify biodegradation or biotransformation.

56

Figure 5.7 Relative abundance (%) of microorganisms in anaerobic microcosms samples from site 1A. Compounds tested in each site are shown

on the top of the graph in yellow box and the compound that were biotransformed are shown in the white boxes, if any. Only Bacteria is plotted in this

graph. X axis show samples names and Y axis show relative abundance (%) of each microorganism per sample. This graph shows data represented in

OTU level.

57

Figure 5.8 Relative abundance (%) of microorganisms in anaerobic microcosms samples from site 1B. Compounds tested in each site are shown

on the top of the graph in yellow box and the compound that were biotransformed are shown in the white boxes, if any. Only Bacteria is plotted in this

graph. X axis show samples names and Y axis show relative abundance (%) of each microorganism per sample. This graph shows data represented in

OTU level.

58

Figure 5.9 Relative abundance (%) of microorganisms in anaerobic microcosms samples from site 2A. Compounds tested in each site are shown

on the top of the graph in yellow box and the compound that were biotransformed are shown in the white boxes, if any. Only Bacteria is plotted in this

graph. X axis show samples names and Y axis show relative abundance (%) of each microorganism per sample. This graph shows data represented in

OTU level.

59

5.2.4 Comparison between external cultures, microcosms samples, and environmental samples

After sequencing the external enrichment cultures and obtaining the OTUs in these samples, they were

compared to all the other 140 samples sequenced in this work, to determine how abundant these

microorganisms were in the environmental samples, and where they are found throughout the site.

The eight samples from external highly enriched cultures were sequenced and the result with the 12 most

abundant OTUs are shown in Figure 5.10 and in Table E.4.

Figure 5.10 Most abundant OTUs in external laboratory highly enrichment cultures used for the

experiments in UofT. X axis are samples and Y axis are relative abundance (%). In the legend, g =

genus, f= family. The first 3 columns are the samples from Clemson University and the other samples are

from University of West Florida. CB = chlorobenzene, DCB = dichlorobenzene, NT = nitrotoluene,

DCNB = dichloronitrobenzene. FBR = fluidized bed reactor. DF = David Freedman’s samples. J = Jim

Spain’s samples. This graph shows data represented in OTU level.

In the aerobic cultures received from Clemson University (DF1 and DF2, Figure 5.10), the most

abundant OTU was Pandoraea at a 77% relative abundance in the chlorobenzene-degrading culture, and

87% in the 1,2-DCB-degrading culture. These microorganisms are Gram-negative from the family

Burkholderiaceae and were identified in crude oil contaminated sites, in soil and groundwater samples

(Tirado-Torres, et al., 2017). When compared to all the other samples in this study, this exact OTU was

found in 109 samples, and in 33 of these samples at more than 1% of relative abundance. At this relative

abundance, this OTU can be considered high in environmental samples. The higher abundant

microorganisms were mostly found in aerobic, active control microcosms, which were degrading 1,2-

DCB during the study. Therefore, the microorganism capable of degrading 1,2-DCB is present in these

bottles. Even though Pandoraea was present in an aerobic culture, when comparing it to the rest of the

60

samples it is possible to see that anaerobic microcosms also contain this OTU, especially in the active

controls. It can also be found in some groundwater samples, which means these 1,2-DCB-degrading

microorganisms are present at the site. All the samples containing Pandoraea are shown in Figure 5.11.

Rhodanobacter was found at 14% relative abundance in the aerobic chlorobenzene-degrading culture and

when compared to all the other samples in the study, it was present in only 12 samples, and always lower

than 0.1%. Even though this culture was prepared under neutral pH, this genus has been reported to be

resistant to acidic environments and was identified in groundwater samples from long-term contaminated

sites (Green, et al., 2012). Figure E.4 shows the samples that contained this OTU.

The Clemson University anaerobic culture (the third column on Figure 5.10) contained Pelosinus (56%

relative abundance), Desulfotomaculum (16% relative abundance), and Propionicicella (11% relative

abundance) which may be capable of the reduction of 4-NT to benzylamine (C7H9N), since this sample

was taken from an anaerobic 4-nitrotoluene degrading culture. Pelosinus was found in 1 groundwater

sample, 2 anaerobic electron donor-amended microcosms, and 1 aerobic active control microcosm. It is

important to remember that 4-NT was not tested in any of the microcosms at the University of Toronto, so

this activity acted as a negative control for the samples, and because of this, it is expected that these

genera are not present in many samples. The other two OTUs, Desulfotomaculum and Propionicicella,

were also compared to all the samples and the three comparisons are shown in Figure E.5.

In the cultures received from the University of West Florida, four main OTUs were dominant. The pure

cultures, J1 and J5, contained mostly Diaphorobacter, at 76% relative abundance in the aerobic 2,3-

DCNB-degrading culture, and 97% relative abundance in the aerobic 3,4-DCNB-degrading culture. When

compared to other samples, this OTU was present in 36 of the 140 samples analyzed, mostly in the

groundwater samples, as shown in Figure E.6. In some wells, this OTU was highly abundant, higher than

10% in relative abundance in 3 samples: influent and effluent of the hydraulic barrier, and in PM01.

61

Figure 5.11 Relative abundance (%) of Pandoraea (OTU10) in all the samples. X axis represents relative abundance (%) of organism in

log scale, Y axis represents the sample name where the organism was found.

62

Besides Diaphorobacter, an OTU from the family Alcaligenaceae was dominant in two samples from the

FBR, one fed with 2,3-DCNB and the other with 3,4-DCNB, J3 and J7, respectively. The relative

abundance in J3 was 48% and in J7 was 43%, and in both cases, a second OTU was also highly abundant:

Rhodococcus, which was only found in one other sample besides the external culture, as shown in Figure

E.7. Alcaligenaceae was present in 80 samples, and mostly found in active control microcosms, under

both aerobic and anaerobic conditions. To identify which microorganism this OTU was most related to,

the 16S rRNA sequence was searched using the NCBI (National Center for Biotechnology Information)

basic local alignment search tool (BLAST), which uses an algorithm that compares biological sequence

information. BLAST results showed a similarity of 100% with Achromobacter and Bordetella, both

obligate aerobes. Since this organism is present in high abundance in both DCNB-degrading cultures, it is

possible that this microorganism contributes to the biodegradation pathway of 2,3- and 3,4-DCNB. The

samples that contain this microorganism are presented in Figure E.8.

The last sample analyzed from the University West Florida was J8, a sample prepared with sand from the

FBR and that was degrading 1,2-DCB aerobically in the bottles prepared in the Edwards lab. In this

sample, a relative abundance of 77% of the microbial community was Cupriavidus, which was found in

92 samples, mostly in groundwater from PM01 and DW04, and in anaerobic microcosms amended with

nitrate, as shown in Figure E.9. These microcosms degraded 1,2-DCB, 1,3-DCB, and 1,4-DCB, and it is

possible that this organism is using the oxygen from the nitrate for aerobic respiration, since these bottles

are anaerobically maintained. This genus has been reported to biodegrade 2-chloro-4-nitrophenol in

temperatures varying from 20-40°C and pH values from 5 to 10 (Min, et al., 2018), which means it can

survive in the acidic sediments of the Camaçari site.

After comparing the highly abundant microorganisms from external enrichment cultures, most of these

microorganisms are present in site samples, and can be enriched when high concentrations of COIs are

present, either in the field as seen in the groundwater and soil samples, or in the laboratory as seen in

microcosms samples. This means there is a potential for bioaugmentation at the site, and if the proper

conditions are provided for the microorganisms to be enriched, they can certainly grow and biodegrade or

biotransform the COIs.

5.2.5 NMDS analyses in multiple groups of samples

The statistical analysis used for this study is called non-metric multidimensional scaling (NMDS) and the

objective of this analysis was to represent the samples in a low-dimensional space according to the

similarity.

63

NMDS is an analysis that identifies gradients and relationships between samples based on their similarity,

presented by sample distance on the generated graph (Ramette, 2007). The NMDS algorithm ranks

distances between objects and uses these ranks to map the objects in a non-linear and two-dimensional

way. As this method works with multidimensional data, it is important to define how many dimensions,

also known as K, which will be used for the analysis, and this number of K will define the stress in this

analysis, which can range from 0 to 1. A stress level higher than 0.3 indicates arbitrary ordination, higher

than 0.2 is suspect, equal to or below 0.1 is considered fair and might contain some distances misleading

the results, and equal to or less than 0.5 shows a good fit of the samples in the NMDS (Buttigieg and

Ramette, 2014).

In this study, NMDS was performed on all of the samples combined, and then for different groups, as

described later in this section. The software RStudio version 3.4.3 was used to perform these analyses and

different packages were used, such as Phyloseq and Vegan. The script used to process this data can be

found in Appendix F. The inputs used in this software to run these analyses were: a) an OTU table with

the respective taxonomy for the samples, b) sequencing results for all the samples with the number of

reads for each OTU, and c) a metadata table (electronic version available in Syntrophy folder) that

contains field and laboratory measurements, either quantitative (pH, temperature, amendments, etc.) or

qualitative (contaminant degraded, sample location in the site, treatment, etc.).

When plotting different types of samples in the same NMDS, is it expected that the stress level is high

when comparing the stress to a NMDS plot with similar samples. In this case, stress level was 0.18 when

plotting only two dimensions. Figure 5.12 shows all the samples combined in one NMDS plot, including

highly enrichment cultures samples from external laboratories, groundwater samples from the site,

groundwater from Cetrel samples, microcosms samples, and soil samples.

64

Figure 5.12 NMDS plots for all the samples. Enrichment culture (pure culture) samples, groundwater

samples, groundwater from Cetrel samples, microcosms samples, and soil samples in two different

NMDSs: A) different types of samples colored differently and clustering together according to their type;

B) samples colored by their pH when measured either in the field or during experiments (soil samples in

gray representing no pH measurement). Stress value 0.18, number of dimensions (k): 2.

The NMDS of aerobic and anaerobic microcosms in the same plot, the stress level was 0.17 when they

were combined. Aerobic microcosms NMDS showed a stress level of 0.07 and anaerobic microcosm, a

stress level of 0.2 (Figure 5.13). In this case, depth where the samples were collected from, pH, and

number of bacteria from qPCR were the three parameters that most drove the samples cluster.

65

Figure 5.13 NMDS plots for all microcosms, aerobic microcosms, and anaerobic microcosms. A)

NMDS plot for aerobic and anaerobic microcosms combined, colored by condition, shaped by site, stress

0.17, k=2. B) NMDS plot for aerobic microcosms showing the most significant metadata plotted in gray

arrows, stress 0.07, k=2. C) NMDS plot for anaerobic microcosms showing the most significant metadata

plotted in gray arrows, stress 0.2, k=2. Both graphs only show the significant metadata for these sets of

samples.

Figure 5.14 shows two NMDS plots for the aerobic microcosms, where plot A show the samples colored

by the degradation observed in each of them and plot B showing the most significant OTUs for these

samples. Figure 5.15 shows the same type of graph for the anaerobic microcosms. It is important to

mention that the OTUs that are plotted in these figures are not necessarily responsible for the degradation

or transformation, but they are somehow driving the sample clustering. In Figure 5.14B, for instance,

Alcaligenaceae is the most significant OTU for the samples degrading CAs and DCAs in site 2A. This

OTU is not necessarily related to the degradation, but as it is enriched in these samples, it may be

somehow involved in these reactions. In other words, this is a good initial analysis when looking for

OTUs that might be capable of degradation of COIs, but more specific experiments need to be conducted

in order to confirm this hypothesis.

66

Figure 5.14 NMDS plots for aerobic microcosms and significant OTUs. A) NMDS plot for aerobic

microcosms with samples colored by degradation observed in each sample. The color legend shows all

the compounds being degraded in these bottles, and the shapes mean the treatment each bottle received.

B) NMDS plot for the same samples and plotting the most significant OTUs, with p value 0.001, and r2 >

0.7. Stress level 0.07, k=2 for both plots.

67

Figure 5.15 NMDS plots for anaerobic microcosms and significant OTUs. A) NMDS plot for

anaerobic microcosms with samples colored by transformation observed in each sample. The color legend

shows all the compounds being transformed in these bottles, and the shapes mean the treatment each

bottle received. B) NMDS plot for the same samples and plotting the most significant OTUs, with p value

0.001, and r2 > 0.4. Stress level 0.13, k=2 for both plots.

Similarly, the four sites were analyzed separately, with aerobic and anaerobic microcosms from each of

them in the figures. Figure 5.16 shows the NMDS plots for site 1A, stress level 0.07, showing the

samples clustering by degradation and transformation, and the main OTU in these samples: Methanocella,

Acetobacteraceae, Deinococci, Bradyrhizobium, and Burkholderia.

68

Figure 5.16 NMDS plots for aerobic and anaerobic microcosms from Site 1A and significant OTUs.

A) NMDS plot for all microcosms from site 1A with samples colored by aerobic degradation (in cold

colors) and anaerobic transformation (in warm colors) observed in each sample. The color legend shows

all the compounds being degraded or transformed in these bottles, and the shapes mean the treatment each

bottle received. In the legend, A= aerobic and AN = anaerobic, followed by the compounds being

degraded. B) NMDS plot for the same samples and plotting the most significant OTUs, with p value

0.001, and r2 > 0.75. Stress level 0.07, k=2 for both plots.

NMDS plot for site 1B, that only degraded 1,2-DCB, shows Miscellaneous-Crenarchaeotic-Group

(Archaea), Candidatus-Koribacter, and Rhizomicrobium as the main OTUs for this site, as shown in

Figure 5.17. Even though Cupriavidus is highly abundant in the anaerobic microcosms from site 1B

(around 35% of relative abundance), the aerobic microcosms are also plotted in the NMDS and can

69

influence the main OTUs to be plotted. When looking at Figure 5.5, Candidatus-Koribacter is highly

abundant (around 30%) in the aerobic microcosms from site 1B.

Figure 5.17 NMDS plots for aerobic and anaerobic microcosms from Site 1B and significant OTUs.

A) NMDS plot for all microcosms from site 1B with samples colored by aerobic degradation (in cold

colors) and anaerobic transformation (in warm colors) observed in each sample. The color legend shows

all the compounds being degraded or transformed in these bottles, and the shapes mean the treatment each

bottle received. In the legend, AN = anaerobic, followed by the compound being degraded. B) NMDS

plot for the same samples and plotting the most significant OTUs, with p value 0.001, and r2 > 0.75.

Stress level 0.08, k=2 for both plots.

Degradation and transformation from site 2A, as well as the main OTUs are shown in Figure 5.18. In this

case, the main OTUs presented are Burkholderia, Cupriavidus, and Leptolinea, Thermincola.

70

Figure 5.18 NMDS plots for aerobic and anaerobic microcosms from Site 2A and significant OTUs.

A) NMDS plot for all microcosms from site 2A with samples colored by aerobic degradation (in cold

colors) and anaerobic transformation (in warm colors) observed in each sample. The color legend shows

all the compounds being degraded or transformed in these bottles, and the shapes mean the treatment each

bottle received. In the legend, AN = anaerobic, followed by the compound being degraded. B) NMDS

plot for the same samples and plotting the most significant OTUs, with p value 0.001, and r2 > 0.6. Stress

level 0.1, k=2 for both plots.

In samples from site 2B, the main OTUs were Alcaligenaceae, Ktedonobacterales, and

Xanthomonadaceae, which are shown in Figure 5.19.

71

Figure 5.19 NMDS plots for aerobic and anaerobic microcosms from Site 2B and significant OTUs.

A) NMDS plot for all microcosms from site 2B with samples colored by aerobic degradation (in cold

colors) and anaerobic transformation (in warm colors) observed in each sample. The color legend shows

all the compounds being degraded or transformed in these bottles, and the shapes mean the treatment each

bottle received. In the legend, AN = anaerobic, followed by the compound being degraded. B) NMDS

plot for the same samples and plotting the most significant OTUs, with p value 0.001, and r2 > 0.75.

Stress level 0.09, k=2 for both plots.

These molecular techniques are great tools to initially assess the samples in a large study like this, since it

provides an understanding of the big picture of the microbial communities of the site and which

conditions impact the microbial community in situ. It also suggests the next experiments to be performed

in order to narrow down the possibilities and understand which microorganisms are capable of the

biodegradation and biotransformation, if they are at the site, and how they can be enriched.

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Chapter 6 Conclusions and future work

Conclusions

As previously presented in Chapter 1, this research’s objectives aimed to:

1) Interpret degradation and transformation data from microcosms studies;

2) Assess impact of pH on degradation in different samples;

3) Perform a comprehensive microbial community analysis in multiple groups of samples;

4) Compile data and interpret results to recommend best course of action for remediation at the site.

6.1.1 Aerobic and anaerobic reactions observed during microcosms study

The table below shows all the reactions observed during the initial microcosm study and the different

bottles were the reactions occurred. These results fill some gaps in the literature review, such as anaerobic

transformation of dichloronitrobenzenes and the analysis of a mixture of these compounds. To date,

studies have not been undertaken that involve mixing these compounds and assessing their inhibitory

effects to each other.

Table 6.1 Summary of aerobic degradation and anaerobic biotransformation observed in the

microcosm study #1

6.1.2 Impact of pH in different degradation laboratory tests

Different laboratory tests were conducted during this work with the objective of assessing the impact of

pH in aerobic and anaerobic reactions in microcosms. For some aerobic processes, neutral pH seems to

determine if the reactions will occur, as shown in Figure 4.1, or if they will be slightly accelerated, as

1A 1B 2A 2B 1A 1B 2A 2B

Aniline nt nt nt AC/ Vit nt nt - -

2-CA AC/ Vit nt AC/ Vit AC/ Vit - nt - -

3-CA nt nt AC/ Vit AC/ Vit nt nt - nt

4-CA nt nt AC/ Vit AC/ Vit nt nt - nt

2,3-DCA AC/ Vit nt AC/ Vit AC/ Vit Don nt Sulf -

2,5-DCA* AC/ Vit nt AC/ Vit AC/ Vit Don nt - -

3,4-DCA AC/ Vit nt AC/ Vit AC/ Vit Don nt Don, Sulf -

2,3-DCNB - nt - - nt nt nt nt

2,5-DCNB - nt - - nt nt AC, Don, Sulf, Nit Don

3,4-DCNB - nt - - nt nt AC, Don, Sulf, Nit Don

1,2-DCB AC/Vit - AC/ Vit AC/ Vit Don Nit Don -

1,3-DCB - - nt nt - Nit nt nt

1,4-DCB - - nt nt - Nit nt nt

AC = active control / Vit = vitamins/ Don = donor amended / Sulf = sulfate amended / Nit = nitrate amended / nt = not tested in this set

* = slow or not complete degradation

Dichloro-

benzene

ANAEROBICGroup Contaminant

AEROBIC

Aniline and

chloroanilines

Dichloro-

nitrobenzene

73

shown in Figure 4.2. For anaerobic samples, all the reactions observed were under acidic conditions and

when pH was adjusted to neutral, it did not seem to change the reactions in these bottles. With that, it is

possible to infer that environmental conditions can influence in degradation rate if the microorganisms

responsible for these reactions are present in the sample and if they are abundant enough to perform these

reactions. Some microorganisms have an optimum pH to live in and some have the capacity to adapt to a

broader range of pH.

6.1.3 Microbial community analysis

Different analyses were performed using amplicon sequencing for different types of samples: microcosms

samples, soil samples, groundwater samples, and pure cultures samples. These results were analyzed by

the NMDS statistical method to understand what are the major factors that influence the microbial

community in these samples. The treatment each bottle received, and the reactions observed in them are

the two factors that most determine the microbial abundance in environmental samples for this study.

The microbial community in microcosms samples change over time according to the reactions observed

in them and tend to enrich for the OTUs that might be responsible for the degradation reactions observed.

In samples that did not show degradation, and have more than one DNA sample, it is possible to see the

reproducibility of the method, since the relative abundance of the main organisms almost does not change

(Figure 5.5, site 1B).

When looking at environmental samples (groundwater and soil), it is possible to see that soil samples

represent a smaller (more discrete) portion of the subsurface, whereas groundwater represents a larger

sampling volume. This can be seen in the bar charts (Figures 5.2 and 5.3), where the soil is highly

enriched, and groundwater is more diverse.

When comparing the highly enriched cultures samples grown repeatedly on a single substrate to

environmental and microcosms samples, it was possible to see that the main organisms known to degrade

some compounds are present in high abundance in groundwater samples from the site (Figure E.5) and

enriched in microcosms (Figure 5.11).

6.1.4 Potential microorganisms responsible for biodegrading COIs in this study

When combining the amplicon sequencing results and analyzing the degradation and transformation

reactions in the samples, it is possible to identify microorganisms that might be responsible, or at least

very involved somehow, in these processes. In aerobic microcosms, Burkholderia was the most abundant

74

organism overall, Betaproteobacteria was also dominant in site 1A, Candidatus-Koribacter in site 1B,

Xanthomonadaceae in site 2A, and Ktedonobacterales in site 2B. In anaerobic microcosms, the most

abundant Bacteria were: Ktedonobacterales in site 1A, Cupriavidus in active bottles from 1B,

Burkholderia in site 2A, and Ktedonobacterales in site 2B.

From these results, it is possible to categorize the microbial assignments related to biodegradation from

microcosms and cultures in three types: 1) indisputable, 2) probable, and 3) potential. In 1) indisputable

degraders, it is possible to conclude that Pandoraea degrades 1,2-DCB and CB aerobically (Figure 5.10

and Figure 5.11), Cupriavidus degrades 1,2-DCB (Figure 5.10 and 5.8), and Diaphorobacter degrades

2,3- and 3,4-DCNB (Figure 5.10 and E.5). The genus Pandoraea belongs to Burkholderiaceae family

and class betaproteobacteria. Pandoraea sp. has been reported to biodegrade lindane (Okeke, et al., 2002),

phenol (Amer, 2008), and also lignin (Kumar, et al., 2018).

Different studies have reported Cupriavidus as responsible microorganisms for aerobic degradation of the

pesticide 2,4-dichlorophenoxyacetic acid. Cupriavidus campinensis BJ71 was isolated from contaminated

soil samples from Beijing (Han, et al., 2015), Cupriavidus sp. CY-1 has demonstrated to degrade the

same compound in samples from Japan (Chang, et al., 2015), and Cupriavidus gilardii T-1 has been

reported to have optimal conditions of pH 7 – 9, and temperature between 37°C and 42°C to degrade the

same compound (Wu, et al., 2017). Even though all the studies have demonstrated aerobic activity from

Cupriavidus, this genus was found in high abundance in samples collected from an anaerobic microcosm

fed with nitrate (Figure C.3 for the degradation plot and Figure 5.8 for the bar charts with amplicon

sequencing data). This might suggest that Cupriavidus is not a strict aerobe and can perhaps use nitrate as

an alternative electron acceptor to oxygen to degrade dichlorobenzenes.

The genus Diaphorobacter belongs to the family of Comamonadaceae and class of betaproteobacteria.

This genus has been reported to degrade 3-nitrotoluene aerobically, after being isolated from industrial

wastewater from a facility treatment plant in India (Singh and Ramanathan, 2013).

In 2) probable degraders, it is possible that Dehalobacter is dechlorinating DCAs anaerobically (Figure

5.7) and Burkholderia and Betaproteobacteria are involved in aerobic degradation of CAs and DCAs

(Figures 5.5 and 5.6). As there were many different numbers of OTUs for these organisms in the

sequencing data from this work, it is important to match these OTU sequences to those in well-known

databases to try to identify more specifically the roles of the microorganism in the bottles. Further work

to characterize and sequence the dominant strains is required.

75

In 3) potential degraders, it is possible to say that Alcaligenaceae (OTU11) can be involved in the aerobic

degradation of 2,3- and 3,4-DCNB (Figure 5.10), degradation of CAs and DCAs (Figure 5.14B) and is

highly present (over 10%) in some anaerobic active control microcosms transforming DCNBs (Figure

E.7). It is possible to say that this microorganism can be involved in reactions degrading DCAs and

DCNBs and bio transforming anaerobically DCNBs.

The NMDS graphs also show different OTUs that are significant to certain groups of samples that might

also be involved in the degradation and transformation of compounds. To better assess and confirm these

hypotheses, it is important to further isolate specific bacteria and identify which OTUs can degrade these

compounds. Moreover, it is important to understand specific conditions where these organisms can live

and perform their reactions properly.

Recommendations and future work

Based on the outcomes of this study, a number of recommendations should be considered, including:

a) Continue to monitor and re-amend microcosms that are showing degradation and transformation

for further DNA analysis and assess new changes in microbial community. Active bottles can

also be used for further experiment using different techniques, such as compound specific isotope

analysis;

b) From active microcosms, try to isolate specific microorganisms by making transfers and feeding

them specific compounds to enrich for relevant microbial population. Perform further DNA

analysis and amplicon sequencing to identify this organism for each compound analyzed;

c) Compare significant OTUs present in pure cultures and microcosms to new groundwater and soil

samples from the site. This will allow a better understanding of the microbial distribution in the

site and potential hot spots where an active community might be present at the site;

d) Sequence and analyze more groundwater and soil samples from the site to monitor microbial

community abundance and composition, aiming to identify zones where natural attenuation is

occurring or can be enhanced. It can also help identifying areas that need more intervention to

mitigate the risk of spreading the contamination even more in the site. All this information can be

integrated into the conceptual site model that is being developed for this study site.

Analyzing the microbial community at a contaminated site can be a powerful tool to identify hot spots of

biodegradation and biotransformation of compounds of interest. It can also guide the decision making in a

case of a pilot project, regarding which conditions should be changed or maintained in the field to

stimulate these reactions to occur. When combining the results from this works with historical data from

76

the site, field parameters, and other tools being used in this site, it can lead to an optimized remediation

strategy.

From this study, it is possible to conclude that the site has a potential for biostimulation, where specific

conditions can be altered in the field to stimulate the growth and enrichment of certain organisms.

77

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83

Appendix A. Supplementary information for Chapter 1

Table A.1 Concentrations (mg/kg) of specific COIs in soil samples from the site. Values taken from the Real Time Investigation Technical

Memorandum, conducted by CH2MHill in 2015.

Group Compound

Soil Concentration (mg/kg - dry weight)

Site 1 (NPAD) Site 2 (UN11&12) CONAMA

420 a USEPA b

Maximum Average Low Maximu

m Average Low Brazil U.S.

Aniline and

chloroanilines

Aniline 16 11.6 7.09 2.1 2.1 2.1 410

m-Chloroaniline (3-CA) 117165 15825 0.66 4478.9 1126.6 8.4

o-Chloroaniline (2-CA) 87 28.3 1.01 4 2.5 0.9

p-Chloroaniline (4-CA) 17892 7142 4.28 ND ND ND 12

2,3-Dichloroaniline (2,3-DCA) 14897 1663 0.44 633.2 129.4 0.4

3,4-Dichloroaniline (3,4-DCA) 1812 270.9 0.93 406.9 77.4 1

2,5-Dichloroaniline (2,5-DCA) 1315 304.7 2.58 37.3 23.3 9.5

Chloro-

benzenes

o-Dichlorobenzene (1,2-DCB) 1847 111.3 0.01 2053.4 67.6 0 400 930

p-Dichlorobenzene (1,4-DCB) 1908 130.6 0.02 464.8 30.8 0 150 11

m-Dichlorobenzene (1,3-DCB) 315 33.2 0.03 261.1 28.1 0 NA NA

Chloronitro-

benzene

3,4-Dichloronitrobenzene

(3,4-DCNB) 3537 679 19.48 7573.9 951.5 1.5

2,3-Dichloronitrobenzene

(2,3-DCNB) 47 13.2 0.69 3392.5 652.6 0.4

2,5-Dichloronitrobenzene

(2,5-DCNB) 97 41.6 2.23 41.5 41.5 41.5

a Environmental agency from Brazil, CONAMA 420 (2009). Maximum acceptable values for soil in industrial area.

b Environmental agency from the U.S. November 2017

84

Table A.2 Concentrations (mg/L) of specific COIs in groundwater samples from the site. Values taken from the Real Time Investigation

Technical Memorandum, conducted by CH2MHill in 2015.

Group Compound

Groundwater Concentration (mg/L)

Site 1 (NPAD) Site 2 (UN11&12) CONAMA 420 a USEPA

b

Maximum Average Low Maximum Average Low Brazil U.S.

Aniline and

chloroanilines

Aniline 1.874 1.204 0.316 25.657 5.69 0.127 0.013

m-Chloroaniline (3-CA) 13.077 5.626 1.232 - - -

o-Chloroaniline (2-CA) 27.522 9.689 2.638 40.638 8.485 0.094

p-Chloroaniline (4-CA) 4.383 1.526 0.474 17.207 5.546 0.116 0.00036

2,3-Dichloroaniline (2,3-DCA) 33.919 14.409 1.371 100.571 19.187 0.124

3,4-Dichloroaniline (3,4-DCA) 60.419 16.626 0.081 122.762 42.923 0.055

2,5-Dichloroaniline (2,5-DCA) 8.188 3.568 0.213 3.252 1.454 0.559

Chloro-

benzenes

o-Dichlorobenzene (1,2-DCB) 5.556 2.025 0.057 3.943 1.704 0.053 1

p-Dichlorobenzene (1,4-DCB) 4.177 1.068 0.042 1.517 0.371 0.041 0.3

m-Dichlorobenzene (1,3-DCB) 1.239 0.495 0.025 0.326 0.141 0.029

Chloronitro-

benzenes

3,4-Dichloronitrobenzene

(3,4-DCNB) 0.847 0.847 0.847 25.879 11.766 0.493

2,3-Dichloronitrobenzene

(2,3-DCNB) ND ND ND 3.195 1.318 0.113

2,5-Dichloronitrobenzene

(2,5-DCNB) ND ND ND 0.144 0.144 0.144 1 0.03

a Environmental agency from Brazil, CONAMA 420 (2009). Maximum acceptable values for soil in industrial area.

b Environmental agency from the U.S. Resident soil to groundwater RSL November 2017

ND = not detected

85

Table A.3 Physical characteristics of COIs. Information obtained from PubChem Compound Database and National Center for Biotechnology

Information.

Group Compound Molecular

formula

CAS

number

Molecular

weight

(g/mol)

Phase (at

room

temperature)

Color / Odor

Boiling

point

(°C)

Melting

point

(°C)

Solubility

in water

(mg/L) /

at X °C

Anil

ine

and c

hlo

roan

ilin

es

Aniline C6H7N or

C6H5NH2 62-53-3 93.129 liquid

colorless to brown when

oxidized. Fish odor 184 -6

36000 /

25

2-CA C6H6ClN or

(C6H4) Cl(NH2) 95-51-2 127.571 liquid

Colorless to amber. Sweet

odor 209 -2 8165 / 25

3-CA C6H6ClN or

(C6H4) Cl(NH2) 108-42-9 127.571 liquid

Colorless to amber. Sweet

odor 230 -10 5400 / 20

4-CA C6H6ClN or

(C6H4) Cl(NH2) 106-47-8 127.571 solid

White or pale yellow.

Sweet odor 232 72 3900 / 25

2,3-DCA C6H5Cl2N or

(C6H3) Cl2(NH2) 27134-27-6 162.013 solid

Ambar to brown crystalline

solid / crystals. 252 24 none

2,5-DCA C6H5Cl2N or

(C6H3) Cl2(NH2) 95-82-9 162.013 solid Brown crystalline solid. 251 50 none

3,4-DCA C6H5Cl2N or

(C6H3) Cl2(NH2) 95-76-1 162.013 solid

Light brown crystals with

characteristic odor. 272 72 none

Ch

loro

ben

zen

es

1,2-DCB C6H4Cl2 95-50-1 146.998 liquid Colorless to pale yellow.

Pleasant aromatic odor 180 -17 156 / 25

1,3-DCB C6H4Cl2 541-73-1 146.998 liquid Colorless 173 -24.8 125 / 25

1,4-DCB C6H4Cl2 106-46-7 146.998 solid

Colorless or white

crystalline. Mothball-like

odor

174 53 79 / 25

Chlo

ron

itro

-

ben

zenes

2,3-DCNB C6H3Cl2NO2 3209-22-1 191.995 solid Colorless to yellow. 257 61 62.4 / 20

2,5-DCNB C6H3Cl2NO2 89-61-2 191.995 solid Colorless to light beige 255 41 121 / 20

3,4-DCNB C6H3Cl2NO2 99-54-7 191.995 solid Yellow flakes 261 55 92.1 / 20

86

Table A.4 Aerobic degradation of aniline and chloroanilines reported in the literature.

Substrate Organism reported to

degrade / transform Comments Reference

Aniline <16mM

Pseudomonas multivorans

strain An1

Isolated from forest soil. Optimal pH was 6.5. Concentrations

higher than 16mM of aniline were toxic for the organism. (Helm and Reber, 1979)

Aniline

Rhodococcus erythropolis

AN-13

Aniline concentrations from 0.65 to 2.6 mg/L. Aniline was

metabolized through catechol. (Aoki, et al., 1983)

Aniline Pseudomonas sp. B13

Chloroanilines were used to genetically select different organisms

that would degrade aniline. (Latorre, et al., 1984)

Aniline, 2-CA, 3-

CA, 4-CA, 4-

fluoroaniline, 4-

bromoaniline

Moraxella sp. strain G 4-CA generation time was 6h. This organism did not degrade 3,4-

DCA. (Zeyer, et al., 1985)

Aniline as low as

50nM

Pseudomonas sp. strain K1 Generation time in 1mM of aniline was 2h, and in 8mM of aniline

was 2.2 h. (Konopka, et al., 1989)

Aniline, 3-CA, 4-

CA, 2-CA

Pseudomonas acidovorans

CA26, CA28, CA37, CA45

CA26 and CA45 showed low rates of 2-CA.

CA28 showed generation time of 3 hours and complete

mineralization at 2.25mmol aniline g-1 of biomass/hour, and 7.7

hours for 3-CA with rate of 1.63mmol 3-CA g-1 of biomass/hour.

(Loidl, et al., 1990)

2-CA, 3-CA, 4-

CA, 3,4-DCA

(mixed and

separated)

Pseudomonas acidovorans

strain BN3.1

Pseudomonas ruhlandii

strain FRB2

Pseudomonas cepacia strain

JH230

Pseudomonas aeruginosa

strain RHO1

Organisms from soil slurry. Both studies analyze the mixture of

organisms in a mixture of contaminants.

(Brunsbach and

Reineke, 1993;

Brunsbach and Reineke,

1995)

87

Substrate Organism reported to

degrade / transform Comments Reference

3-CA

Comamonas testosterone

strain I2gfp

Isolated from activate sludge. During mineralization, a yellow

intermediate was generated because of the meta-cleavage of

chlorocatechol.

(Boon, et al., 2000)

Aniline up to

53.8mM

Delftia sp. AN3

Uses aniline or acetanilide as sole carbon source.

Aniline degradation rate 5000 mg/L, at 30°C and pH7. First study

to report Delftia as responsible for aniline degradation.

(Liu, et al., 2002)

3,4-DCA, 3-CA

Pseudomonas sp.

Acidovorax sp.

Delftia sp.

Achromobacter sp.

Comamonas sp.

Organisms isolated from soil samples, testes in both 3-CA and

3,4-DCA. (Dejonghe, et al., 2002)

Aniline up to

34.4mM

Delftia tsuruhatensis 14S

Organism isolated from activated sludge of sewage disposal

plants. Aniline degradation at concentrations up to 3200 mg/L in

less than 20 days.

(Sheludchenko, et al.,

2005)

4-CA (main

compound tested),

but also 2-CA, 3-

CA, and aniline

Acinetobacter baumannii

CA2

Pseudomonas putida CA16

Klebsiella sp. CA17

Degradation occurred through ortho-cleavage pathway. 4CA

concentrations from 0.2 – 1.2mM. Also grew on 2-CA, 3-CA, and

aniline.

(Vangnai and

Petchkroh, 2007)

88

Substrate Organism reported to

degrade / transform Comments Reference

Aniline Rhodococcus

Aniline concentration from 3 to 4mM, at 30°C, with optimal pH

6.4. Concentrations higher than 10mM of aniline were toxic for

the organism.

(Obinna, et al., 2008)

Aniline Delftia sp. XYJ6

Best conditions for growth: pH 7 and 30°C. Aniline initial

concentration of 2000 mg/L removed after 22h.

Degradation pathway from aniline being converted to catechol and

then biodegraded to smaller products.

(Xiao, et al., 2009)

2-CA, 3-CA, 4-CA

(Concentrations

from 100 – 400

mg/L) and some

DCAs

Delftia tsuruhatensis H1

The presence of aniline inhibited the degradation of

chloroanilines. Addition of yeast extract, citrate or succinate

appeared to accelerate CA degradation. Some dichloroanilines

were also degraded by the organism (2,3-, 2,4-, and 3,4-DCA)

possibly through ortho-cleavage pathway.

(Zhang, et al., 2010)

3,4-DCA,

dichloroanilines, 4-

CA

Acinetobacter baylyi strain

GFJ2

Organism isolated from soil contaminated with herbicides. Aniline

present as first intermediate when degrading 4-CA and 4-

chlorocatechol. 4-CA was present as the first intermediate in 3,4-

DCA degradation.

(Hongsawat and

Vangnai, 2011)

89

Substrate Organism reported to

degrade / transform Comments Reference

Propanil and its

main product: 3,4-

DCA

Xanthomonas sp.

Acinetobacter calcoaceticus

Rhodococcus sp.

Pseudomonas sp.

Kocuria

Study conducted in a biofilm reactor. Complete removal of

contaminants at propanil loading rates up to 24.9 mg/L/h. When

loading rate was higher than that, removal efficiency decayed.

First study to report Kocuria as 3,4-DCA degrader.

(Herrera-Gonzalez, et

al., 2013)

Aniline, 3-CA

Comamonas testosterone

strain A

Delftia acidovorans strain B

(from WWTP)

Delftia acidovorans strain C

(from a linuron treated soil)

When aniline was present, degradation of 3-CA was completed

within 14 to 24 hours, faster than when 3-CA was the only carbon

source. Initial concentrations of 3-CA between 100-200 mg/L.

D. acidovorans B was not able to use 3-CA as sole carbon source.

(Shah, 2014)

Aniline, 4-CA

Pseudomonas stutzeri

Comamonas testosterone

Pseudomonas putida

Stenotrophomonas

maltophilia

Incomplete dichlorination of 4-CA and accumulation of 4-

chlorocatechol, which was further degraded via ortho-cleavage

pathway.

Interspecies interactions responsible for both degradations.

(Kalam, 2016)

90

Table A.5 Anaerobic transformation of aniline and chloroanilines reported in the literature

Substrate Organism reported to

degrade / transform Comments Reference

Aniline and

dihydroxybenzenes

Desulfobacterium anilini

Ani1 (sulfate reducing

organism)

Isolated from marine sediment. Degraded aniline completely to

CO2 and NH3. Grew on sulfide-reduced mineral medium. The

identified intermediate was 4-ainobenzoate.

(Schnell, et al., 1989;

Schnell and Schink,

1991)

Aniline

Strain HY99 (96% overall

similarity to Delftia

acidovorans)

Strain HY99 was similar to Delftia acidovorans that degraded

aniline aerobically, but HY99 degraded also anaerobically with

nitrate reduction. Aniline concentration was 1000 µM: aerobically

it degraded in 30 hours and anaerobically, in more than 7 days.

(Kahng, et al., 2000)

3,4-DCA Rhodococcus sp. strain 2

Degradation of 3,4-DCA under nitrate reducing conditions.

Formation of 1,2-DCB as one intermediate in this reaction.

Cultures incubated at 28°C, with 3,4-DCA concentration of

0.6mM.

(Travkin, et al., 2002)

2,3-DCA Dehalococcoides mccartyi

strain CBDB1

2,3-DCA → 3-CA growth yield of 5.7 - 8.7 × 1013 cells/mol

halogen released. (Cooper, et al., 2015)

Aniline

Ignavibacterium album

Acidovorax spp.

Anaerolineaceae

Initial concentration of 100 µM in phase I and 1500 µM in phase

II. Aniline loss was observed in both nitrate and sulfate amended

microcosms.

(Sun, et al., 2015)

91

Table A.6 Aerobic degradation of chlorobenzenes reported in the literature.

Substrate Organism reported to

degrade / transform Comment Reference

1,2-DCB Pseudomonas sp. JS100 Organism isolated from activated sludge. Minimal doubling time of

growth was 5.5h. Nitrate was the preferred nitrogen source. (Haigler, et al., 1987)

1,4-DCB Pseudomonas sp. JS6

Organism isolated from activated sludge. Concentrations of 1,4-DCB

varied from 0.5 – 5 mg/L over a period of 6 weeks. A yellow substrate

was released in the medium into the medium during growth of JS6 on

benzene. Doubling time on benzene was 5h.

(Spain and Nishino,

1987)

1,2-, 1,4-DCB Not reported

Material: soil and groundwater from contaminated site. Removal of

54% of 1,2-DCB within 7 days, with no accumulation of 3-

chlorocatechol. DCB was transformed by ortho pathway. Groundwater

concentrations approximately 40 to 50 mg/L.

(Nishino, et al., 1992)

1,2-, 1,3-DCB Pseudomonas sp. strain

JS150

For 1,2-, and 1,3-DCB metabolism, no products were identified in

HPLC, which might indicate complete metabolism of these

compounds.

(Haigler, et al., 1992)

1,4-DCB, CB

Pseudomonas sp. strain

JS1474, JS1344, JS700,

JS701, JS150

Study comparing JS150 with indigenous population from the site. Site

with historical CB contamination showed transformation of CB and

DCBs, proving that inoculating the site with specific CB-degrading

culture is not necessary, if oxygen is widely present.

(Nishino, et al., 1993)

DCBs Burkholderia sp. strain

PS14

The three DCBs isomers were metabolized within 1 hour from initial

concentration of 500 nM.

(Rapp and Timmis,

1999)

92

Table A.7 Anaerobic transformation of chlorobenzenes reported in the literature

Substrate Organism reported to

degrade / transform Comment Reference

Trichlorobenzene

and

dichlorobenzene

Not reported

Experiment done in anaerobic sediment column testing

trichlorobenzenes and its products. Dichlorination observed until

MCB.

(Bosma, et al., 1988)

DCBs Not reported

Microcosm study done with 1,2-, 1,3-, 1,4-DCB mixed and separated.

1,2-DCB showed the highest dehalogenation rate whereas 1,4-DCB

showed was the slowest. Benzene was accumulated over 5000 µmol/l

in the bottles. Material used for microcosms from Chambers Works

sediments.

(Fung, et al., 2009)

1,2-, 1,3, -1,4-

DCB, MCB Dehalobacter spp. Responsible for reductive dehalogenation. (Nelson, et al., 2011)

MCB and benzene Not reported Chlorobenzene produced benzene that was degraded transformed to

CO2 and CH4. MCB concentration of approximately 700 µM. (Liang, et al., 2013)

1,2-, 1,3-, 1,4-

DCB and other

chlorinated

benzenes

Dehalobacter spp. strains

12DCB1, 13DCB1,

14DCB1

1,2-DCB was dehalogenated to MCB by 12DCB1.

13DCB1 strain used 1,2- and 1,3-DCB as substrates. 14DCB1 used

1,2- and 1,4-DCB as substrates.

(Nelson, et al., 2014)

93

Table A.8 Aerobic degradation of dichloronitrobenzenes reported in the literature

Substrate Condition Organism reported to degrade / transform Comment Reference

3,4-DCNB Aerobic

Acidovorax sp. strain JS 3050 (99% similarity to

Acidovorax sp. JS42)

Diaphorobacter sp. strain JS 3052

Experiments done at 30°C, pH around 7.

Initial concentrations of 0.3mM. Fluidized

bed reactor experiments were conducted

with the samples and isolates. This study

was conducted with soil and groundwater

samples collected from the same field site

in Brazil. These samples were collected at

the same time as the soil and groundwater

samples used to prepare the microcosm

study described in Chapter 3 of this thesis.

(Palatucci, 2017)

2,3-DCNB Aerobic Pseudomonas sp. strain JS 3051

94

Appendix B. Supplementary information for Chapter 2

Table B.1 Chemical compounds and solvents used

Group Compound Purity Brand

Aniline and

chloroanilines

Aniline 99.50% Sigma Aldrich

2-CA 99% Sigma Aldrich

3-CA 99% Sigma Aldrich

4-CA 98% Sigma Aldrich

2,3-DCA 99% Sigma Aldrich

2,5-DCA 99% Sigma Aldrich

3,4-DCA 99.90% Sigma Aldrich

Chlorobenzenes

1,2-DCB 99% Sigma Aldrich

1,3-DCB 98% Sigma Aldrich

1,4-DCB 99% Sigma Aldrich

Chloronitrobenzenes

2,3-DCNB 99.90% Sigma Aldrich

2,5-DCNB 99% Sigma Aldrich

3,4-DCNB 99% Sigma Aldrich

Solvents

Acetonitrile - BDH

Acetone - Caledon

Methanol - BDH

95

Figure B.1 Calibration curve for methane, benzene, and DCBs in GC. X axis represent the area in the

graph and Y axis represent concentration (mg/L) of each compound.

96

7

Figure B.2 Calibration curves for anilines, chloroanilines, dichloroanilines, and

dichloronitrobenzenes in HPLC. X axis represent the area in the graph and Y axis represent

concentration (mg/L) of each compound.

97

Figure B.2 (cont.) Calibration curves for anilines, chloroanilines, dichloroanilines, and

dichloronitrobenzenes in HPLC. X axis represent the area in the graph and Y axis represent

concentration (mg/L) of each compound.

98

Appendix C. Supplementary Information for Chapter 3

Figure C.1 Soil samples used for microcosms study #1. Both sets divided into shallow (A) and deep

(B) portions. A) sediment description when samples arrived in Toronto in 2015; B) target COIs in four

sets of samples. NO82 and NO83 represent different locations where the soil samples were collected

from; mbgs = meters below ground surface. DCB = dichlorobenzene, DCA = dichloroaniline, DCNB=

dichloronitrobenzene, CA = chloroaniline.

A.

B.

99

Table C.1 Average aerobic degradation rates (mg/L/day) per site in the microcosms. CA =

chloroaniline, DCA = dichloroaniline, DCB = dichlorobenzene, DCNB = dichloronitrobenzene. Values in

red are the fastest degradation rates (>0.2 mg/L/day).

1A 1B 2A 2B

AC nt nt nt 1.1

Vit nt nt nt NA

AC 0.78 nt 0.4 0.21

Vit 0.13 nt NA 0.36

AC nt nt 0.2 0.28

Vit nt nt NA NA

AC nt nt 0.15 0.2

Vit nt nt NA NA

AC 0.86 nt 0.11 0.12

Vit 0.09 nt NA NA

AC 0.55 nt 0.09 0.09

Vit 0.53 nt NA NA

AC 0.1 nt 0.2 0.14

Vit 0.08 nt NA NA

AC - nt - -

Vit - nt - -

AC - nt - -

Vit - nt - -

AC - nt - -

Vit - nt - -

AC NA - 0.11 0.18

Vit 0.19 - NA NA

AC - - nt nt

Vit - - nt nt

AC - - nt nt

Vit - - nt nt

AC = active control / Vit = vitamins/ nt = not tested in this set / "-" means no degradation occurred

* = not complete degradation

NA = not applicable due to not continuos measurement

CA = chloroaniline / DCA = dichloroaniline / DCB = dichlorobenzene / DCNB = dichloronitrobenzene

Dichloro-

benzene

Dichloro-

nitrobenzene

3,4-DCNB

1,2-DCB

1,3-DCB

1,4-DCB

2,3-DCNB

2,5-DCNB

Average aerobic degradation rate (mg/L/day) per site

Aniline

Treatment in

microcosm

Aniline and

chloroanilines

2-CA

3-CA

4-CA

2,3-DCA

2,5-DCA*

3,4-DCA

Group Contaminant

100

Figure C.1 Concentration versus time in an aerobic active control microcosm from site 2B. X axis show the elapsed time (in days) since

the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for each compound tested. Figure prepared by

Line Lomheim.

101

Table C.2 Average anaerobic transformation rates (mg/L/day) per site in the microcosms. CA =

chloroaniline, DCA = dichloroaniline, DCB = dichlorobenzene, DCNB = dichloronitrobenzene. Values in

red are the fastest degradation rates (>0.2 mg/L/day).

1A 1B 2A 2B

AC nt nt - -

Don nt nt - -

Sulf nt nt - -

Nit nt nt - -

AC - nt - -

Don - nt - -

Sulf - nt - -

Nit - nt - -

AC nt nt - nt

Don nt nt - nt

Sulf nt nt - nt

Nit nt nt - nt

AC nt nt - nt

Don nt nt - nt

Sulf nt nt - nt

Nit nt nt - nt

AC - nt - -

Don 0.08 nt - -

Sulf - nt 0.02 -

Nit - nt - -

AC - nt - -

Don 0.07* nt - -

Sulf - nt - -

Nit - nt - -

AC - nt - -

Don 0.1 nt 0.34 -

Sulf - nt 0.03 -

Nit - nt - -

2,3-DCNB nt nt nt nt nt

AC nt nt 0.02 -

Don nt nt 0.37 0.38

Sulf nt nt 0.03 -

Nit nt nt 0.01 -

AC nt nt 0.02 -

Don nt nt 0.31 0.43

Sulf nt nt 0.03 -

Nit nt nt 0.03 -

AC - - - -

Don 0.27 - 0.04 -

Sulf - - - -

Nit - NA - -

AC - - nt nt

Don - - nt nt

Sulf - - nt nt

Nit - NA nt nt

AC - - nt nt

Don - - nt nt

Sulf - - nt nt

Nit - NA nt nt

* = not complete reaction

NA = not applicable due to not continuos measurement

CA = chloroaniline / DCA = dichloroaniline / DCB = dichlorobenzene / DCNB = dichloronitrobenzene

Dichloro-

benzene

AC = active control / Don = donor amended / Sulf = sulfate amended / Nit = nitrate amended / nt = not tested in this

set / "-" means no degradation occurred

1,2-DCB

1,3-DCB

1,4-DCB

3,4-DCA

Aniline and

chloroanilines

2,5-DCNB

3,4-DCNB

Dichloro-

nitrobenzene

Group ContaminantAverage anaerobic biotransformation rate (mg/L/day) per site

2,3-DCA

2,5-DCA

Treatment in

microcosm

Aniline

2-CA

3-CA

4-CA

102

Figure C.2 Anaerobic nitrate amended microcosm. Biotransformation of the three dichlorobenzene isomers after 900 days of experiment. As the

bottle was not sampled for a long period (~700 days), the dashed lines indicate a long period with incertanty. X axis represent time (days) and Y axis

represent concentration of the compound (mg/L). CA = chloroaniline, DCA = dichloroaniline, DCB = dichlorobenzene, DCNB =

dichloronitrobenzene.

0

1

2

3

4

5

6

7

8

9

10

0

1

2

3

4

5

6

7

8

9

10

0 200 400 600 800 1000

Co

nce

ntr

atio

n m

eth

ane

(m

g/L

)

Co

nce

ntr

atio

n D

CB

s an

d M

CB

(m

g/L)

Time (days)

Biotransformation overtime in anaerobic nitrate amended microcosm from site 1B

MCB

1,4-DCB

1,3-DCB

1,2-DCB

pH site 1B: 4.6 - 5.6 (not changing)

Methane

Time points for DNA sampling

Time points for nitrate amended

103

Figure C.4 Concentration of SVOCs versus time in anaerobic electron donor amended microcosm from site 1A. X axis show

the elapsed time (in days) since the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for

each compound tested. Figure prepared by Line Lomheim.

104

Figure C.5 Concentration of VOCs versus time in anaerobic electron donor amended microcosm from site 1A. X axis show

the elapsed time (in days) since the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for

each compound tested. Figure prepared by Line Lomheim.

Figure 6.4 Concentration of VOCs versus time in anaerobic electron donor amended microcosm from site 1A. X axis show

the elapsed time (in days) since the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for

each compound tested. Figure prepared by Line Lomheim.

105

Figure C.6 Concentration of VOCs versus time in anaerobic electron donor amended microcosm from site 2A. X axis show

the elapsed time (in days) since the beginning of the experiment and Y axis represent the contaminants concentration (mg/L) for

each compound tested. Figure prepared by Line Lomheim.

106

Appendix D. Supplementary information for Chapter 4

Figure D.1 Sample location for anaerobic microcosms study #2, assessing aniline and

chloroanilines degradation. Well sampled: DW-05. Samples were collected at 48 mbgs (meters below

ground surface). Groundwater flows towards north. Figure prepared by CH2M.

107

Methodology: Aniline and chloroanilines anaerobic microcosm study #2

New microcosms bottles were set up according to the procedure described in Section 2.2, except for the

sample amount that was different for these bottles. In this case, 140 mL of groundwater and 15 - 20 g of

soil was mixed in 250 mL Boston round bottles, leaving a headspace of approximately 100 mL. These

bottles received the treatments below, in duplicates:

a. Sterile control: 1.4 mL of HgCl2 (5% stock, final concentration of 0.05%) and 0.56 mL of NaN3

(5% stock, final concentration of 0.02%), to inhibit biological activity;

b. Active control: soil and groundwater from the site;

c. Electron donor amended: 170 µL of sodium lactate stock (0.7 M stock, final concentration 100

mg/L), and 17.7µL of neat ethanol (final concentration of 100 mg/L);

d. Sulfate amended: 700 µL of sodium sulfate stock solution (400 mM, final concentration of 2

mM);

e. Nitrate amended: 700 µL of sodium nitrate stock solution (400 mM, final concentration of 2

mM).

A concentration of 1 mg/L of resazurin, a redox indicator, was added in two of the bottles to indicate

redox potential in the bottles: an electron donor and a sulfate amended. The pH was adjusted to 7.0 by

adding the previously described bicarbonate solution, and pH was monitoring throughout the first few

months to ensure the pH remained neutral. Mineral medium (described in Section 2.1.2) was added in two

bottles fed with aniline and chloroaniline to determine if it would stimulate the degradation when

compared to the microcosms prepared with groundwater from the site. The complete treatment table for

this study is presented in Table D.1.

Once the bottles were prepared, a final concentration of 10 mg/L of aniline and chloroaniline were added.

The aniline stock solution was prepared by mixing 78.3 µL of neat aniline in an 8 mL glass vial capped

with MininertTM cap with 7920 mg of MiliQ water. The final stock solution concentration was 10,000

mg/L. The chloroaniline stock solution was prepared by mixing 0.5 g of each isomer (2-CA, 3-CA, and 4-

CA) in a glass vial capped with MininertTM cap. The final stock solution concentration was 224,502 mg/L.

For six months, these bottles were sampled and analyzed by HPLC to assess SVOC concentrations,

following procedures described in Section 2.2.3.

108

Table D.2 Treatment table for anaerobic microcosms study #2. All the bottles were set up in duplicated, in a total of 18 bottles. The solutions

mentioned in the table were prepared according to Chapter 2.

COI Treatment Soil

Ground-

water

Head-

space Resazurin

HgCl2

(5%)

NaN3

(5%)

Lactate

stock

(0.7M)

Ethanol

(neat)

Sulfate

stock

(400mM)

Nitrate

stock

(400mM)

vol. in mL mL mL µL mL mL µL µL µL µL

Aniline

+ CAs Sterile controls

15 140 100 1.4 0.56

15 140 100 1.4 0.56

Aniline

Active controls 15 140 100

15 140 100

Electron donor

(ethanol and lactate)

15 140 100 140 170 17.7

15 140 100 170 17.7

Sulfate amended 15 140 100 140 700

15 140 100 700

Nitrate amended 15 140 100 700

15 140 100 700

CAs

(2-CA,

3CA, 4-

CA)

Active controls 15 140 100

15 140 100

Electron donor

(ethanol and lactate)

15 140 100 170 17.7

15 140 100 170 17.7

Electron donor and

pH7

15 140 100 170 17.7

15 140 100 170 17.7

Aniline

and

CAs

Soil + medium: pH7 &

electron donor

amended

15 140 100 170 17.7

15 140 100 170 17.7

109

Figure D.2 Anaerobic transformation graphs for microcosms study #2, assessing aniline and

chloroanilines. All the graphs show average of the duplicates after HPLC analysis. X axis represent

time (days) and Y axis represent concentration of the compound (mg/L). CA = chloroaniline, DCA =

dichloroaniline, DCB = dichlorobenzene, DCNB = dichloronitrobenzene.

110

Figure D.2 (cont.) Anaerobic transformation graphs for microcosms study #2, assessing aniline

and chloroanilines. All the graphs show average of the duplicates after HPLC analysis. X axis

represent time (days) and Y axis represent concentration of the compound (mg/L). CA = chloroaniline,

DCA = dichloroaniline, DCB = dichlorobenzene, DCNB = dichloronitrobenzene.

111

Table D.3 Treatment table for aerobic test performed in Camaçari laboratory with puddle water.

ASC = aerobic sterile control, AAC = aerobic active control, APH = aerobic pH. A

ero

bic

Treatment Name

Ground-

water

Boiled tap

water Headspace

pH

adjusted

(pH = 7)

number of

bottles

mL mL mL

Sterile controls

ASC-1 100 150 no 1

ASC-2 100 150 no 1

ASC-3 100 150 no 1

Active controls

AAC-1 100 150 no 1

AAC-2 100 150 no 1

AAC-3 100 150 no 1

pH adjustment

APH-1 100 150 yes 1

APH-2 100 150 yes 1

APH-3 100 150 yes 1

Total 600 300 9

112

Figure D.3 Aerobic degradation from test conducted in Camaçari with puddle water. A) Sterile

controls with natural pH; B) Active controls with natural pH; C) pH adjusted to 7. X axis represent time

(days) and Y axis represent concentration of the compound (mg/L). CA = chloroaniline, DCA =

dichloroaniline, DCB = dichlorobenzene, DCNB = dichloronitrobenzene.

A

A

B

B

C

C

113

Methodology for Section 4.3: Cultivating pure and highly enrichment cultures from

collaborating laboratories

Plates containing pure cultures and sand from the bioreactor from the University of West Florida

were sent to the University of Toronto. Before receiving the samples, mineral medium was

prepared, as described in Section 2.1.2, but resazurin, vitamin stock and FeS were not added to

the medium. This batch of mineral medium was also prepared using half the concentration of the

medium stock solutions as described before. The microorganisms from the agar plates and from

FBR sand were grown using the procedure described below. Figure D.4a and Figure D.4b show

a diagram of the culturing process for both pure cultures and the sand sample from the

bioreactor.

In Figure D.4a, the steps were:

a. Preparation of flasks: Two feeding stock solutions were prepared in acetone with

final concentrations of 39.8 g/L of 2,3-DCNB and 36.3g/L of 3,4- DCNB. In 5

autoclaved Erlenmeyer flasks (represented by F1 to F5 in the figure), the COIs were

added, for a final concentration of 15 mg/L in the liquid phase of each compound as

follows: only 2,3-DCNB was added to F1, only 3,4-DCNB was added to F2 and F3,

and both stock solutions were added to F4 and F5. Inside the fume hood, with a gas

tight glass syringe, the stock solution was added to each flask, the acetone evaporated

from the flask for 5 minutes, and 230 mL of mineral medium was added using a glass

pipettor. A similar procedure was done for an additional four 250 mL Boston round

bottles (represented by B1 to B4 in the figure), by adding both stock solutions in the

empty bottles, allowing the acetone to evaporate, and adding 50 mL of mineral

medium in each bottle. DCNB-containing flasks and bottles were incubated for 1.5

hours at 30°C, shaking at 200 rpm, to allow the COIs that had been dried on the glass

surface dissolve in the liquid phase. During this incubation period, the flasks and

bottles were covered with aluminum foil.

b. Inoculum: Each flask was prepared with 230 mL of medium and COIs. From four of the

flasks (F1 to F4), 10 mL was transferred to each of the 3 autoclaved glass tubes (T1 to

114

T4) using a glass pipettor. The remaining volume in each of these four flasks was 200

mL. In the fifth flask, 230 mL remained, and each bottle still had 50 mL of solution.

Pieces of the agar plates containing the pure cultures (orange, green, and blue, Figure

D.4a) were used to inoculate three flasks (F1, F2, and F3, Figure D.4a) and three tubes

(T1, T2, and T3, Figure D.4a), using an aseptic metal stem loop that was alcohol

sterilized and flamed between each flask inoculation. The sand from the FBR (yellow,

Figure D.4a) was transferred from the falcon tubes to two flasks (F4 and F5, Figure

D.4a), a tube (T4, Figure D.4a), and the four bottles (B1 to B4, Figure D.4a) by pouring

in the same volume in each of them. A 50 mL glass pipettor was also used to transfer the

sand to the tubes. The bottles were capped with MininertTM caps and a final concentration

of 10 mg/L of 1,2-DCB was added to each bottle.

c. Incubation: All 5 flasks, 12 tubes, and 4 bottles were incubated in a 30°C shaker at 200

rpm. After 2 days of incubation, the flasks and tubes with sand were yellow and the COI

concentrations in all the flasks and tubes were zero.

d. Culture combination: To ensure the COIs were not evaporated during incubation, the

contents of the flasks were completely transferred to autoclaved Boston round bottles. In

Figure D.4a, each flask is now represented by a bottle with the original name, and the

tubes were combined into one bottle, also with the original name.

As the COI concentrations were zero in all the flaks and tubes after incubating over the

weekend, the same COI in acetone evaporation method was performed again for each of

the newly transferred bottles, to a final concentration of 15 mg/L. The bottles were

incubated on a 30°C shaker at 200 rpm. Over a period of 9 days, these bottles were fed

twice, and incubated under the same conditions.

e. Growth/degradation: After incubating and refeeding the bottles, the contents of the

bottles were transferred again into 10 bottles (J1 to J10, Figure D.4a) and the bottles were

fed for the last time. In Figure D.4a, green check marks represent bottles that degraded

COIs in this last step (J1, J3, J5, J7, and J8) and red crosses are bottles did not (J2, J4, J6,

J9, J10).

115

Continuing in Figure D.4b, the steps were:

f. Bottles that showed degradation: The bottles described in the previous section that

degraded COIs in the last feeding (J1, J3, J5, J7, and J8, Figure D.4b).

g. Sample collection: From each of the active bottles, three types of samples were

collected:

DNA samples: 2 mL of culture was pipetted from each bottle, placed into a 2 mL

screw cap microtube and centrifuged for 25 min at 10,000 rpm. The supernatant

was removed using a glass Pasteur pipette. The pellet was stored in the freezer at

-80°C until DNA extraction, followed by 16S rRNA amplicon sequencing and

qPCR, as described in Section 2.5.5. The results of this analysis will be

described in Chapter 5.

Frozen active cultures: 40 mL from each bottle was pipetted to 50 mL falcon

tubes, centrifuged at 6,000xg for 30 min. The supernatant was removed from

tubes and the pellet was transferred to a 2 mL screw cap microcentrifuge tube.

The tubes were stored at -80°C. These pellets can be used in the future to culture

for further experimentation.

Sample for Culture Mix preparation: 3 mL from each bottle was collected using

a glass pipettor and combined into a 40 mL glass vial with MininertTM cap. This

combination of culture will be hereby referred to as the culture mix. The culture

mix acted as an active inoculum in inactive microcosms, explained in the next

section.

116

Figure D.4 Process of growing aerobic cultures in the laboratory.

117

Figure D.4 (cont.) Process of growing aerobic cultures in the laboratory.

118

Table D.4 Experiment set up to test if aerobic microcosms from sites 2A and 1B inoculated with culture mix would show more degradation.

CA = chloroaniline, DCA = dichloroaniline, DCB = dichlorobenzene, DCNB = dichloronitrobenzene.

Inactive

microcosm name

Microcosm

description

# of

bottles What was done

Treatment and average pH of

duplicated

Average pH

of duplicates

after

treatment

Contaminants in

bottles

Cam-2A-AAC-1

AAC =

aerobic active

control from

set 2A

2 Combine content from 3

bottles in a larger glass bottle

and split in 8 autoclaved

Boston round bottles (150 mL

capacity) using a glass

pipettor (approximately 35

mL of groundwater and 2

scoops of soil).

Continuing with maintenance

before combining 5.4

2,3-DCNB

3,4-DCNB

2,5-DCA

1,2-DCB

2-CA, 3-CA,

4-CA

Cam-2A-AAC-2 2 Adjust pH to neutral 7.1

Cam-2A-AAC-3

2 Inoculate with culture mix, do

not change pH 5.3

2 Inoculate with culture mix

and adjust pH to neutral 7.2

Cam-1B-AAC-1

AAC =

aerobic active

control from

set 1B

2 Combine content from 3

bottles in a larger glass bottle

and split in 8 autoclaved

Boston round bottles (150 mL

capacity) using a glass

pipettor (approximately 50

mL of groundwater and 2

scoops of soil)

Continuing with maintenance

before combining 5.0

1,2-DCB

2-CA

3-CA

4-CA

Cam-1B-AAC-2 2 Adjust pH to neutral 6.9

Cam-1B-AAC-3

2 Inoculate with culture mix, do

not change pH 5.1

2 Inoculate with culture mix

and adjust pH to neutral 6.8

119

Table D.5 Influence of pH in microcosms and treatments in the bottles. CA = chloroaniline, DCA = dichloroaniline, DCB = dichlorobenzene,

DCNB = dichloronitrobenzene.

Inactive

microcosm

name

Microcosm

description # of bottles

Bottle

capacity

(mL)

Treatment Original

pH

pH after

adjustment

Contaminants

in the bottle

when test was

set up

Compounds

previously

degraded

Cam-1A-AAM-1 AAM =

aerobic

vitamins

amended

(site 1A)

original

bottle 250

Adjust pH to neutral

5.8 6.9 2,3DCNB

(3 mg/L) 2-CA

3-CA

4-CA

Cam-1A-AAM-2 original

bottle 250 5.8 6.9

2,5DCNB

(6 mg/L)

Cam-1A-AAM-3 original

bottle 250 5.5 6.9

3,4DCNB

(10 mg/L)

Cam-2A-AAM-1 AAM =

Aerobic

vitamins

amended

(site 2A)

original

bottle 250 Keep bottle as is 5.5 -

3,4-DCNB

2,5-DCNB

2,5-DCA

1,2-DCB

2-CA

3-CA

4-CA

3,4-DCA

2,3-DCA

1-2DCB (only

in the second

replicate)

Cam-2A-AAM-2 original

bottle 250

Adjust pH to neutral

5.5 6.9

Cam-2A-AAM-3 original

bottle 250 5.5 6.9

Cam-2B-AAC-1 AAC =

aerobic

active

controls

(site 2B)

2* 150 Keep bottle as is 4.6 -

2,5-CA

2,3-DCNB*

2-CA

3-CA

4-CA

3,4-DCA

2,3-DCA

Cam-2B-AAC-2 2* 150 Change pH to an

intermediate value 4.7 5.8

Cam-2B-AAC-3 2* 150 Adjust pH to neutral 4.7 7

* This test was done by combining the content from 3 microcosms and splitting into 6 bottles. Details in the text, Section 4.4.2.

120

Figure D.5 Results of pH adjustment in aerobic vitamin amended microcosms from site 1A. Each

graph represents one of the triplicates, as mentioned in Table D.4. X axis represent time (days) and Y axis

represent concentration of the compound (mg/L). CA = chloroaniline, DCA = dichloroaniline, DCB =

dichlorobenzene, DCNB = dichloronitrobenzene.

0

2

4

6

8

10

12

0 50 100 150 200

Conce

ntr

atio

n (

mg/L

)

Time (days)

Cam-1A-AAM-1 (pH adjusted to neutral)pH = 6.9

2,3-DCNB

3,4-DCNB

0

2

4

6

8

10

12

0 50 100 150 200

Co

nce

ntr

atio

n (

mg/L

)

Time (days)

Cam-1A-AAM-2 (pH adjusted to neutral)pH = 6.9

2,5-DCNB

3,4-DCNB

0

2

4

6

8

10

12

0 50 100 150 200

Co

nce

ntr

atio

n (

mg/L

)

Time (days)

Cam-1A-AAM-3 (pH adjusted to neutral)pH = 6.9

3,4-DCNB

121

Figure D.6 Results of pH adjustment in aerobic vitamin amended microcosms from site 2A. A)

microcosms kept as is. B and C) pH adjusted to 7. X axis represent time (days) and Y axis represent

concentration of the compound (mg/L). CA = chloroaniline, DCA = dichloroaniline, DCB =

dichlorobenzene, DCNB = dichloronitrobenzene.

0

5

10

15

20

0 50 100 150 200

Co

nce

ntr

atio

n (

mg/L

)

Time (days)

Cam-2A-AAM-2 (pH adjusted to neutral)pH = 6.9

2,5-DCA

2,5-DCNB

3,4-DCNB

0

2

4

6

8

10

12

0 50 100 150 200

Conce

ntr

atio

n (

mg/L

)

Time (days)

Cam-2A-AAM-3 (pH adjusted to neutral)pH = 6.9

2,5-DCA

2,5-DCNB

3,4-DCNB

1,2-DCB

0

2

4

6

8

10

12

0 50 100 150 200

Conce

ntr

atio

n (

mg/L

)

Time (days)

Cam-2A-AAM-1 (bottle kept as is) pH = 5.5

2,5-DCA

2,5-DCNB

3,4-DCNB

1,2-DCB

A

B

C

122

Figure D.7 Results of pH adjustment in aerobic active control microcosms from site 2B. A) pH was

kept as natural. B) pH was adjusted to an intermediate value (5.8). C) pH was adjusted to neutral. X axis

represent time (days) and Y axis represent concentration of the compound (mg/L). CA = chloroaniline,

DCA = dichloroaniline, DCB = dichlorobenzene, DCNB = dichloronitrobenzene.

A

B

C

123

Appendix E. Supplementary information for Chapter 5

Table E.2 Summary of samples used for microbial community analysis. Samples divided in type, sampling date, sample name, site (for

microcosms only), conditions (aerobic or anaerobic), location at the site, groundwater origin, soil origin, and depth (meters below ground surface)

# Type of sample Sampling

date Sample name

Site (for

microcosms

and soil

only)

Condition

a = aerobic

an=

anaerobic

Specific

location at

site

Ground-

water

origin

Soil

Origin

Depth

(mbgs)

1 microcosm 22-Feb-16 A1_AC_Day070an 1A an NPAD PM19 N083 5.5

2 microcosm 13-May-16 A1_AC_Day151an 1A an NPAD PM19 N083 5.5

3 microcosm 2-Jun-16 A1_AC_Day171an 1A an NPAD PM19 N083 5.5

4 microcosm 28-Jun-16 A1_AC_Day197an 1A an NPAD PM19 N083 5.5

5 microcosm 11-Aug-16 A1_AC_Day241an 1A an NPAD PM19 N083 5.5

6 microcosm 1-Nov-16 A1_AC_Day323an 1A an NPAD PM19 N083 5.5

7 microcosm 1-Nov-16 A1_AC1_Day323a 1A a NPAD PM19 N083 5.5

8 microcosm 22-Feb-16 A1_AC2_Day070a 1A a NPAD PM19 N083 5.5

9 microcosm 13-May-16 A1_AC2_Day151a 1A a NPAD PM19 N083 5.5

10 microcosm 28-Jun-16 A1_AC2_Day197a 1A a NPAD PM19 N083 5.5

11 microcosm 11-Aug-16 A1_AC2_Day241a 1A a NPAD PM19 N083 5.5

12 microcosm 1-Nov-16 A1_AC2_Day323a 1A a NPAD PM19 N083 5.5

13 microcosm 1-Nov-16 A1_AC3_Day323a 1A a NPAD PM19 N083 5.5

14 microcosm 11-Aug-16 A1_Don1_Day241an 1A an NPAD PM19 N083 5.5

15 microcosm 1-Nov-16 A1_Don1_Day323an 1A an NPAD PM19 N083 5.5

16 microcosm 13-May-16 A1_Don2_Day151an 1A an NPAD PM19 N083 5.5

17 microcosm 2-Jun-16 A1_Don2_Day171an 1A an NPAD PM19 N083 5.5

18 microcosm 28-Jun-16 A1_Don2_Day197an 1A an NPAD PM19 N083 5.5

19 microcosm 11-Aug-16 A1_Don2_Day241an 1A an NPAD PM19 N083 5.5

20 microcosm 1-Nov-16 A1_Don2_Day323an 1A an NPAD PM19 N083 5.5

21 microcosm 2-Jun-16 A1_Don3_Day171an 1A an NPAD PM19 N083 5.5

22 microcosm 28-Jun-16 A1_Don3_Day197an 1A an NPAD PM19 N083 5.5

23 microcosm 11-Aug-16 A1_Don3_Day241an 1A an NPAD PM19 N083 5.5

124

# Type of sample Sampling

date Sample name

Site (for

microcosms

and soil

only)

Condition

a = aerobic

an=

anaerobic

Specific

location at

site

Ground-

water

origin

Soil

Origin

Depth

(mbgs)

24 microcosm 1-Nov-16 A1_Don3_Day323an 1A an NPAD PM19 N083 5.5

25 microcosm 11-Aug-16 A1_Nit2_Day241an 1A an NPAD PM19 N083 5.5

26 microcosm 1-Nov-16 A1_Nit2_Day323an 1A an NPAD PM19 N083 5.5

27 microcosm 1-Nov-16 A1_Sulf2_Day323an 1A an NPAD PM19 N083 5.5

28 microcosm 1-Nov-16 A1_Vit1_Day323a 1A a NPAD PM19 N083 5.5

29 microcosm 1-Nov-16 A1_Vit2_Day323a 1A a NPAD PM19 N083 5.5

30 microcosm 1-Nov-16 A1_Vit3_Day323a 1A a NPAD PM19 N083 5.5

31 microcosm 22-Feb-16 A2_AC_Day041an 2A an UN11&12 PM12 N082 5.1

32 microcosm 13-May-16 A2_AC_Day122an 2A an UN11&12 PM12 N082 5.1

33 microcosm 2-Jun-16 A2_AC_Day142an 2A an UN11&12 PM12 N082 5.1

34 microcosm 28-Jun-16 A2_AC_Day168an 2A an UN11&12 PM12 N082 5.1

35 microcosm 11-Aug-16 A2_AC_Day212an 2A an UN11&12 PM12 N082 5.1

36 microcosm 8-Nov-16 A2_AC_Day301an 2A an UN11&12 PM12 N082 5.1

37 microcosm 1-Nov-16 A2_AC1_Day316a 2A a UN11&12 PM12 N082 5.1

38 microcosm 22-Feb-16 A2_AC2_Day063a 2A a UN11&12 PM12 N082 5.1

39 microcosm 13-May-16 A2_AC2_Day144a 2A a UN11&12 PM12 N082 5.1

40 microcosm 28-Jun-16 A2_AC2_Day190a 2A a UN11&12 PM12 N082 5.1

41 microcosm 1-Nov-16 A2_AC2_Day316a 2A a UN11&12 PM12 N082 5.1

42 microcosm 1-Nov-16 A2_AC3_Day316a 2A a UN11&12 PM12 N082 5.1

43 microcosm 11-Aug-16 A2_Don1_Day212an 2A an UN11&12 PM12 N082 5.1

44 microcosm 8-Nov-16 A2_Don1_Day301an 2A an UN11&12 PM12 N082 5.1

45 microcosm 13-May-16 A2_Don2_Day122an 2A an UN11&12 PM12 N082 5.1

46 microcosm 28-Jun-16 A2_Don2_Day168an 2A an UN11&12 PM12 N082 5.1

47 microcosm 11-Aug-16 A2_Don2_Day212an 2A an UN11&12 PM12 N082 5.1

48 microcosm 8-Nov-16 A2_Don2_Day301an 2A an UN11&12 PM12 N082 5.1

49 microcosm 2-Jun-16 A2_Don3_Day142an 2A an UN11&12 PM12 N082 5.1

50 microcosm 8-Nov-16 A2_Don3_Day301an 2A an UN11&12 PM12 N082 5.1

51 microcosm 11-Aug-16 A2_Nit2_Day212an 2A an UN11&12 PM12 N082 5.1

125

# Type of sample Sampling

date Sample name

Site (for

microcosms

and soil

only)

Condition

a = aerobic

an=

anaerobic

Specific

location at

site

Ground-

water

origin

Soil

Origin

Depth

(mbgs)

52 microcosm 8-Nov-16 A2_Nit2_Day301an 2A an UN11&12 PM12 N082 5.1

53 microcosm 8-Nov-16 A2_Sulf2_Day301an 2A an UN11&12 PM12 N082 5.1

54 microcosm 1-Nov-16 A2_Vit2_Day316a 2A a UN11&12 PM12 N082 5.1

55 microcosm 22-Feb-16 B1_AC_Day070an 1B an NPAD PM19 N083 6

56 microcosm 13-May-16 B1_AC_Day151an 1B an NPAD PM19 N083 6

57 microcosm 28-Jun-16 B1_AC_Day197an 1B an NPAD PM19 N083 6

58 microcosm 11-Aug-16 B1_AC_Day241an 1B an NPAD PM19 N083 6

59 microcosm 1-Nov-16 B1_AC_Day323an 1B an NPAD PM19 N083 6

60 microcosm 22-Feb-16 B1_AC2_Day080a 1B a NPAD PM19 N083 6

61 microcosm 13-May-16 B1_AC2_Day161a 1B a NPAD PM19 N083 6

62 microcosm 28-Jun-16 B1_AC2_Day207a 1B a NPAD PM19 N083 6

63 microcosm 11-Aug-16 B1_AC2_Day251a 1B a NPAD PM19 N083 6

64 microcosm 1-Nov-16 B1_AC2_Day333a 1B a NPAD PM19 N083 6

65 microcosm 13-May-16 B1_Don2_Day151an 1B an NPAD PM19 N083 6

66 microcosm 28-Jun-16 B1_Don2_Day197an 1B an NPAD PM19 N083 6

67 microcosm 11-Aug-16 B1_Don2_Day241an 1B an NPAD PM19 N083 6

68 microcosm 1-Nov-16 B1_Don2_Day323an 1B an NPAD PM19 N083 6

69 microcosm 11-Aug-16 B1_Nit2_Day241an 1B an NPAD PM19 N083 6

70 microcosm 1-Nov-16 B1_Nit2_Day323an 1B an NPAD PM19 N083 6

71 microcosm 11-Aug-16 B1_Sulf2_Day241an 1B an NPAD PM19 N083 6

72 microcosm 1-Nov-16 B1_Sulf2_Day323an 1B an NPAD PM19 N083 6

73 microcosm 1-Nov-16 B1_Vit_Day333a 1B a NPAD PM19 N083 6

74 microcosm 22-Feb-16 B2_AC_Day047an 2B an UN11&12 PM12 N082 5.6

75 microcosm 13-May-16 B2_AC_Day128an 2B an UN11&12 PM12 N082 5.6

76 microcosm 2-Jun-16 B2_AC_Day148an 2B an UN11&12 PM12 N082 5.6

77 microcosm 28-Jun-16 B2_AC_Day174an 2B an UN11&12 PM12 N082 5.6

78 microcosm 11-Aug-16 B2_AC_Day218an 2B an UN11&12 PM12 N082 5.6

79 microcosm 8-Nov-16 B2_AC_Day307an 2B an UN11&12 PM12 N082 5.6

126

# Type of sample Sampling

date Sample name

Site (for

microcosms

and soil

only)

Condition

a = aerobic

an=

anaerobic

Specific

location at

site

Ground-

water

origin

Soil

Origin

Depth

(mbgs)

80 microcosm 13-May-16 B2_AC1_Day144a 2B a UN11&12 PM12 N082 5.6

81 microcosm 1-Nov-16 B2_AC1_Day316a 2B a UN11&12 PM12 N082 5.6

82 microcosm 22-Feb-16 B2_AC2_Day063a 2B a UN11&12 PM12 N082 5.6

83 microcosm 13-May-16 B2_AC2_Day144a 2B a UN11&12 PM12 N082 5.6

84 microcosm 28-Jun-16 B2_AC2_Day190a 2B a UN11&12 PM12 N082 5.6

85 microcosm 11-Aug-16 B2_AC2_Day234a 2B a UN11&12 PM12 N082 5.6

86 microcosm 1-Nov-16 B2_AC2_Day316a 2B a UN11&12 PM12 N082 5.6

87 microcosm 13-May-16 B2_AC3_Day144a 2B a UN11&12 PM12 N082 5.6

88 microcosm 1-Nov-16 B2_AC3_Day316a 2B a UN11&12 PM12 N082 5.6

89 microcosm 8-Nov-16 B2_Don1_Day307an 2B an UN11&12 PM12 N082 5.6

90 microcosm 13-May-16 B2_Don2_Day128an 2B an UN11&12 PM12 N082 5.6

91 microcosm 2-Jun-16 B2_Don2_Day148an 2B an UN11&12 PM12 N082 5.6

92 microcosm 28-Jun-16 B2_Don2_Day174an 2B an UN11&12 PM12 N082 5.6

93 microcosm 11-Aug-16 B2_Don2_Day218an 2B an UN11&12 PM12 N082 5.6

94 microcosm 8-Nov-16 B2_Don2_Day307an 2B an UN11&12 PM12 N082 5.6

95 microcosm 8-Nov-16 B2_Don3_Day307an 2B an UN11&12 PM12 N082 5.6

96 microcosm 11-Aug-16 B2_Nit2_Day218an 2B an UN11&12 PM12 N082 5.6

97 microcosm 8-Nov-16 B2_Nit2_Day307an 2B an UN11&12 PM12 N082 5.6

98 microcosm 11-Aug-16 B2_Sulf2_Day218an 2B an UN11&12 PM12 N082 5.6

99 microcosm 8-Nov-16 B2_Sulf2_Day307an 2B an UN11&12 PM12 N082 5.6

100 microcosm 1-Nov-16 B2_Vit2_Day316a 2B a UN11&12 PM12 N082 5.6

101 external culture 04-May-17 DF1_CBa - a - - - -

102 external culture 04-May-17 DF2_DCBa - a - - - -

103 external culture 17-Jun-17 DF3_4NTan - an - - - -

110 external culture 28-Jun-17 Jim1_3051_23DCNBa - a - - - -

111 external culture 28-Jun-17 Jim3_FBR_23DCNBa - a - - - -

112 external culture 28-Jun-17 Jim5_3050_34DCNBa - a - - - -

113 external culture 28-Jun-17 Jim7_FBR_34DCNBa - a - - - -

127

# Type of sample Sampling

date Sample name

Site (for

microcosms

and soil

only)

Condition

a = aerobic

an=

anaerobic

Specific

location at

site

Ground-

water

origin

Soil

Origin

Depth

(mbgs)

114 external culture 28-Jun-17 Jim8_FBR_12DCBa - a - - - -

115 soil spring-15 N082_deep_2B 2B - ** - N082 -

116 soil spring-15 N082_shallow_2A 2A - ** - N082 -

117 soil spring-15 N083_deep_1B 1B - ** - N083 -

118 soil spring-15 N083_shallow_1A 1A - ** - N083 -

104 groundwater 08-Aug-17 DW03B - - * * - -

105 groundwater 18-Jul-17 DW04 - - * * - -

106 groundwater 05-Jul-17 DW06 - - * * - -

107 groundwater 03-Aug-17 DW13 - - * * - -

108 groundwater 25-Nov-16 EHB - - - - - -

109 groundwater 25-Nov-16 IHB - - - - - -

119 groundwater 04-Aug-17 PM01 - - * * - -

120 groundwater 25-Nov-16 PM073_19 - - * * - -

121 groundwater 4-Jul-17 PM11 - - * * - -

122 groundwater 10-Aug-17 PM128_06 - - * * - -

123 groundwater 14-Aug-17 PM128_11 - - * * - -

124 groundwater 10-Jul-17 PM13 - - * * - -

125 groundwater 11-Jul-17 PM15 - - * * - -

126 groundwater 20-Jul-17 PM18 - - * * - -

127 groundwater 01-Aug-17 PM19 - - * * - -

128 groundwater 7-Jul-17 PM21 - - * * - -

129 groundwater 7-Jul-17 PM27 - - * * - -

130 groundwater 6-Jul-17 PM29 - - * * - -

131 groundwater 10-Jul-17 PM30 - - * * - -

132 groundwater 21-Jul-17 PM35 - - * * - -

133 groundwater 03-Aug-17 PM36 - - * * - -

134 groundwater 21-Jul-17 PM38 - - * * - -

135 groundwater 12-Jul-17 PM39 - - * * - -

128

# Type of sample Sampling

date Sample name

Site (for

microcosms

and soil

only)

Condition

a = aerobic

an=

anaerobic

Specific

location at

site

Ground-

water

origin

Soil

Origin

Depth

(mbgs)

136 groundwater 20-Jul-17 PM45 - - * * - -

137 groundwater 12-Jul-17 PMTW - - * * - -

138 groundwater (Cetrel) 25-Nov-16 TA01 - a - - - -

139 groundwater (Cetrel) 25-Nov-16 TA02 - a - - - -

140 groundwater (Cetrel) 25-Nov-16 TA04 - a - - - -

mbgs = meters below ground surface

*sample location on the map, Figure E.1.

** location for soil and groundwater used to prepare the microcosms shown in Figure 3.1.

129

Figure E.1 Groundwater sample location at the site. Black circles indicate where groundwater samples for DNA analysis

were collected from. Figure prepared by CH2M and modified for this thesis.

130

Table E.3 Details of the standard curves generated for qPCR. Summary includes average slopes, Y-intercepts, R2 and their corresponding standard

deviations for general Bacteria and general Archaea.

Legend: Eff = efficiency; R = coefficient of determination; N = number of standard curves used for the calculations; stdev = standard deviation.

Target Date slope y intercept R2 Eff N

Gen arch February 9, 2018 -3.413 36.356 0.999 96.3 3

Gen arch February 12, 2018 -3.49 36.54 0.999 93.4 3

Gen arch February 13, 2018 -3.497 33.267 0.999 93.2 3

Gen arch February 13, 2018 -3.529 36.954 0.999 92 3

Gen arch February 22, 2018 -3.313 31.852 0.998 100.4 3

Gen arch AVERAGE -3.45 34.99 0.999 95.06 3 STDEV 0.09 2.29 0.000 3.38

Gen bac January 31, 2018 -3.548 34.773 0.998 91.4 3

Gen bac February 5, 2018 -3.448 34.911 0.998 95 3

Gen bac February 6, 2018 -3.66 37.139 0.983 87.6 3

Gen bac February 7, 2018 -3.391 33.973 0.998 97.2 3

Gen bac February 8, 2018 -3.553 51.259 0.999 91.2 3

Gen bac February 8, 2018 -3.322 33.521 0.998 100 3

Gen bac February 15, 2018 -3.429 35.596 0.997 95.7 3

Gen bac February 16, 2018 -3.47 34.133 0.999 94.2 3

Gen bac February 22, 2018 -3.317 33.817 0.999 100.2 3

Gen bac AVERAGE -3.46 36.57 0.997 94.72 3 STDEV 0.11 5.62 0.005 4.18

131

Table E.4 Raw qPCR results. Results for microcosms samples (copies/mL), groundwater samples (copies/mL), soil samples (copies/g), and pure

culture samples (copies/mL).

# Sample name

Total bacteria

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

Total archaea

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

1 TA01 4.91E+09 1.91E+05 1.47E+07 6.73E+04

2 TA02 4.15E+09 1.91E+05 7.32E+06 6.73E+04

3 TA04 3.15E+09 1.91E+05 9.45E+06 6.73E+04

4 A1_Don1_Day323an 2.47E+09 1.46E+05 9.98E+08 4.57E+05

5 A1_Don3_Day323an 2.07E+09 1.46E+05 7.27E+08 7.05E+05

6 A1_Don3_Day197an 1.69E+09 1.46E+05 5.26E+08 4.57E+05

7 A1_Don1_Day241an 1.66E+09 1.46E+05 4.88E+08 4.57E+05

8 A1_Don2_Day323an 1.64E+09 1.46E+05 3.85E+08 4.57E+05

9 A1_Don2_Day197an 1.49E+09 1.46E+05 2.73E+08 4.57E+05

10 A1_Don2_Day151an 1.34E+09 1.46E+05 1.64E+08 4.57E+05

11 A1_Don3_Day241an 1.15E+09 1.46E+05 7.30E+08 7.05E+05

12 A1_Vit3_Day323a 1.14E+09 1.46E+05 3.24E+06 7.05E+05

13 A1_AC2_Day197a 8.12E+08 1.02E+05 4.40E+06 4.66E+04

14 A1_Sulf2_Day323an 8.06E+08 1.46E+05 1.16E+07 4.57E+05

15 A1_Vit2_Day323a 8.05E+08 1.02E+05 5.77E+06 4.66E+04

16 Jim3_FBR_23DCNBa 6.61E+08 5.10E+04 1.19E+05 2.02E+04

17 A1_AC_Day197an 6.51E+08 1.46E+05 2.35E+07 4.57E+05

18 A1_AC2_Day323a 6.27E+08 1.02E+05 5.84E+06 4.66E+04

19 A1_AC_Day323an 5.98E+08 1.46E+05 2.38E+07 4.57E+05

20 Jim7_FBR_34DCNBa 5.98E+08 5.10E+04 1.14E+05 2.02E+04

21 A1_Vit1_Day323a 5.96E+08 1.18E+05 5.56E+06 7.05E+05

22 A1_AC2_Day151a 5.76E+08 1.02E+05 3.96E+06 4.66E+04

23 A1_AC2_Day241a 5.73E+08 1.02E+05 2.70E+06 4.66E+04

24 Jim8_FBR_12DCBa 5.63E+08 5.10E+04 3.97E+04 2.02E+04

25 A1_AC_Day241an 5.24E+08 1.46E+05 2.76E+07 4.57E+05

26 A1_Don2_Day241an 5.23E+08 1.46E+05 1.40E+08 4.57E+05

27 A1_AC3_Day323a 4.76E+08 1.02E+05 2.92E+06 4.66E+04

28 A1_AC_Day151an 3.93E+08 1.46E+05 1.86E+07 7.05E+05

29 A1_AC2_Day070a 3.43E+08 1.02E+05 4.06E+06 4.66E+04

30 A1_AC1_Day323a 3.32E+08 1.02E+05 1.79E+06 4.66E+04

31 B1_Sulf2_Day323an 3.29E+08 1.45E+05 8.16E+04 4.66E+04

32 B1_Don2_Day323an 3.29E+08 1.45E+05 2.86E+08 4.66E+04

132

# Sample name

Total bacteria

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

Total archaea

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

33 A1_Nit2_Day241an 3.28E+08 1.45E+05 4.05E+06 4.57E+05

34 B1_Nit2_Day323an 2.75E+08 1.45E+05 2.34E+06 4.66E+04

35 A1_Don2_Day171an 2.67E+08 1.09E+05 1.40E+08 3.10E+05

36 A1_Nit2_Day323an 2.64E+08 1.45E+05 3.24E+06 4.57E+05

37 B1_AC2_Day333a 2.31E+08 1.18E+05 2.76E+06 7.05E+05

38 Jim5_3050_34DCNBa 2.10E+08 5.10E+04 1.27E+05 2.02E+04

39 A1_Don3_Day171an 2.08E+08 1.09E+05 1.73E+08 3.10E+05

40 B2_AC2_Day190a 1.94E+08 9.45E+04 5.86E+06 4.66E+04

41 B1_Don2_Day241an 1.89E+08 1.45E+05 3.19E+08 4.66E+04

42 B2_AC1_Day316a 1.86E+08 9.45E+04 3.89E+06 4.66E+04

43 B1_Sulf2_Day241an 1.86E+08 1.45E+05 7.51E+04 4.66E+04

44 A1_AC_Day070an 1.82E+08 1.09E+05 8.10E+06 3.10E+05

45 B2_AC2_Day234a 1.76E+08 9.45E+04 5.65E+06 4.66E+04

46 B1_AC2_Day207a 1.56E+08 1.45E+05 3.42E+06 4.57E+05

47 A2_Don2_Day168an 1.52E+08 1.45E+05 1.02E+07 4.66E+04

48 A1_AC_Day171an 1.50E+08 1.09E+05 2.23E+07 3.10E+05

49 B2_Don1_Day307an (*) 1.48E+08 9.45E+04 1.14E+09 4.66E+04

50 B1_Don2_Day197an 1.43E+08 1.45E+05 1.90E+08 4.66E+04

51 A2_Don3_Day301an 1.43E+08 1.45E+05 4.19E+07 4.66E+04

52 B1_Nit2_Day241an 1.41E+08 1.45E+05 9.71E+05 4.66E+04

53 B1_AC2_Day161a 1.38E+08 1.45E+05 2.55E+06 4.57E+05

54 B2_AC1_Day144a 1.36E+08 9.45E+04 9.56E+06 4.66E+04

55 B2_AC2_Day316a 1.34E+08 9.45E+04 3.18E+06 4.66E+04

56 B2_AC3_Day316a 1.25E+08 9.45E+04 2.27E+06 4.66E+04

57 B2_Vit2_Day316a 1.24E+08 9.45E+04 6.75E+06 4.66E+04

58 A2_AC1_Day316a 1.22E+08 1.46E+05 9.54E+05 7.05E+05

59 B2_AC2_Day063a 1.20E+08 9.45E+04 4.23E+06 4.66E+04

60 B2_AC3_Day144a 1.13E+08 9.45E+04 3.28E+06 4.66E+04

61 A2_Don2_Day122an 1.12E+08 1.46E+05 1.53E+06 7.05E+05

62 B2_AC_Day148an 1.11E+08 1.09E+05 4.16E+06 3.10E+05

63 B2_AC2_Day144a 1.06E+08 9.45E+04 5.14E+06 4.66E+04

64 A2_Don1_Day212an 1.05E+08 1.46E+05 3.20E+06 7.05E+05

65 B2_AC_Day047an 9.95E+07 1.09E+05 1.45E+07 3.10E+05

66 B2_Sulf2_Day307an 9.56E+07 1.02E+05 1.45E+07 4.66E+04

67 B1_Vit_Day333a 9.41E+07 1.45E+05 1.36E+06 4.57E+05

133

# Sample name

Total bacteria

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

Total archaea

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

68 B1_AC2_Day080a 9.40E+07 1.45E+05 2.27E+06 4.57E+05

69 B2_Don2_Day148an 9.20E+07 1.09E+05 1.82E+07 3.10E+05

70 N082_deep_2B 9.13E+07 1.06E+06 1.23E+08 3.43E+05

71 B2_Nit2_Day307an 8.73E+07 2.04E+05 1.33E+07 9.31E+04

72 B1_AC_Day241an 8.26E+07 1.45E+05 2.69E+07 4.57E+05

73 B2_Nit2_Day218an 8.10E+07 1.02E+05 1.40E+07 4.66E+04

74 DF1_CBa 8.04E+07 1.02E+03 0.00E+00 4.04E+02

75 A2_AC2_Day316a 7.78E+07 1.46E+05 1.86E+06 7.05E+05

76 B2_Don2_Day218an 7.66E+07 9.45E+04 3.10E+07 4.66E+04

77 B1_AC_Day151an 7.54E+07 1.45E+05 1.95E+07 4.57E+05

78 B1_Don2_Day151an 7.49E+07 1.45E+05 3.38E+07 4.66E+04

79 A2_Don1_Day301an 7.45E+07 1.46E+05 1.66E+06 7.05E+05

80 B1_AC2_Day251a 7.33E+07 1.45E+05 2.83E+05 4.57E+05

81 A2_Don2_Day301an 7.30E+07 1.45E+05 2.34E+07 4.66E+04

82 B2_Sulf2_Day218an 7.29E+07 9.45E+04 9.77E+06 4.66E+04

83 A2_AC2_Day144a 7.27E+07 1.46E+05 5.76E+06 7.05E+05

84 A2_Don2_Day212an 7.25E+07 1.45E+05 1.40E+07 4.66E+04

85 B2_AC_Day218an 7.24E+07 9.45E+04 1.14E+07 4.66E+04

86 B2_AC_Day174an 7.20E+07 9.45E+04 1.41E+07 4.66E+04

87 B2_Don2_Day174an 7.10E+07 9.45E+04 1.54E+07 4.66E+04

88 B1_AC_Day323an 6.93E+07 1.45E+05 1.86E+07 4.57E+05

89 A2_AC2_Day063a 6.78E+07 1.46E+05 1.88E+06 7.05E+05

90 A2_AC2_Day190a 6.75E+07 1.46E+05 3.59E+06 7.05E+05

91 B1_AC_Day197an 6.66E+07 1.45E+05 1.99E+07 4.57E+05

92 B2_Don3_Day307an 6.63E+07 9.45E+04 7.77E+07 4.66E+04

93 N083_shallow_1A 6.49E+07 8.45E+05 1.38E+07 2.98E+05

94 A2_Vit2_Day316a 6.03E+07 1.46E+05 1.72E+06 7.05E+05

95 B1_AC_Day070an 5.93E+07 1.45E+05 8.45E+06 4.57E+05

96 B2_Don2_Day307an 5.79E+07 9.45E+04 3.71E+07 4.66E+04

97 A2_Don3_Day142an 5.16E+07 1.09E+05 1.76E+07 3.10E+05

98 Jim1_3051_23DCNBa 4.37E+07 5.10E+04 0.00E+00 2.02E+04

99 B2_AC_Day128an 4.31E+07 1.18E+05 9.72E+06 7.05E+05

100 B2_AC_Day307an 3.96E+07 9.45E+04 6.11E+06 4.66E+04

101 B2_Don2_Day128an 3.78E+07 9.45E+04 5.78E+06 4.66E+04

102 A2_Nit2_Day301an 3.67E+07 9.45E+04 1.41E+06 4.66E+04

134

# Sample name

Total bacteria

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

Total archaea

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

103 A2_AC3_Day316a 3.20E+07 1.46E+05 4.61E+05 7.05E+05

104 N082_shallow_2A 2.82E+07 9.38E+05 2.18E+07 3.04E+05

105 A2_Nit2_Day212an 2.18E+07 9.45E+04 6.23E+05 4.66E+04

106 A2_AC_Day212an 2.02E+07 1.46E+05 1.78E+06 7.05E+05

107 N083_deep_1B 1.93E+07 7.86E+05 1.95E+07 2.77E+05

108 A2_AC_Day142an 1.78E+07 1.46E+05 2.63E+06 7.05E+05

109 A2_AC_Day168an 1.69E+07 1.46E+05 1.14E+06 4.66E+04

110 A2_AC_Day301an 1.16E+07 1.46E+05 1.33E+06 7.05E+05

111 EHB 1.14E+07 4.74E+02 1.55E+04 1.88E+02

112 A2_AC_Day122an 9.95E+06 1.46E+05 1.29E+06 7.05E+05

113 IHB 7.41E+06 1.67E+02 1.92E+03 6.62E+01

114 DF2_DCBa 7.19E+06 1.02E+03 5.86E+02 4.04E+02

115 A2_AC_Day041an 5.90E+06 1.09E+05 2.79E+06 3.10E+05

116 A2_Sulf2_Day301an 4.35E+06 9.45E+04 6.18E+05 4.66E+04

117 PM19 4.24E+06 3.90E+02 1.53E+04 1.38E+02

118 PM21 1.65E+06 3.32E+02 2.12E+04 1.17E+02

119 DW06 1.34E+06 1.46E+02 1.23E+04 5.16E+01

120 PM18 6.53E+05 2.37E+02 3.05E+04 8.36E+01

121 PM35 5.88E+05 1.68E+02 8.41E+02 5.93E+01

122 PM073_19 4.25E+05 1.04E+02 4.23E+03 4.11E+01

123 PM01 3.72E+05 1.25E+02 9.87E+03 4.39E+01

124 PM13 2.87E+05 1.25E+02 1.71E+04 4.39E+01

125 PM45 2.28E+05 2.02E+02 1.96E+04 7.14E+01

126 PMTW 2.17E+05 1.25E+02 8.80E+03 4.39E+01

127 PM38 1.88E+05 1.25E+02 4.61E+04 4.39E+01

128 PM29 9.59E+04 1.25E+02 2.62E+04 4.39E+01

129 DW03B 5.82E+04 3.07E+02 6.33E+02 1.08E+02

130 PM39 4.49E+04 1.25E+02 3.60E+03 4.39E+01

131 DW04 6.18E+03 1.25E+02 1.83E+02 4.39E+01

132 PM27 6.14E+03 1.25E+02 4.80E+02 4.39E+01

133 PM15 1.88E+03 1.25E+02 2.87E+02 4.39E+01

134 PM11 1.02E+03 1.25E+02 3.95E+01 4.39E+01

135 DF3_4NTan - - - -

136 DW13 - - - -

137 PM128_06 - - - -

135

# Sample name

Total bacteria

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

Total archaea

concentration in original

sample (copies/mL)

Detection limit

(copies/mL)

138 PM128_11 - - - -

139 PM30 - - - -

140 PM36 - - - -

Legend: Samples with “-“ were not quantified due to low amount of sample left.

(*) 16S rRNA results from this sample were different from other similar samples, which might indicate an error. This sample was not considered in

any of the analysis.

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Figure E.2 qPCR results (copies/mL) for microcosms, soil, groundwater, and pure culture samples. The lower graph is a continuation of the

upper graph for better visualization. The samples are ordinated from highest to lowest number of Bacteria. X axis shows sample names and Y axis

show the concentration of bacteria or archaea in original sample (copies/mL).

137

Figure E.3 Relative abundance (%) of microorganisms in anaerobic microcosms samples from site 2B. Compounds tested in each site are

shown on the top of the graph in yellow box and the compound that were biotransformed are shown in the white boxes, if any. Only Bacteria is plotted

in this graph. X axis show samples names and Y axis show relative abundance (%) of each microorganism per sample. This graph shows data

represented in OTU level.

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Table E.5 Most abundant OTUs found in pure and enrichment cultures from external laboratories and tested in UofT. Cells highlighted in

bright yellow are the most dominant taxa in the sample, and light yellow are high abundance organisms that can be also responsible for biodegradation

of the compounds of interest. CB = chlorobenzene, DCB = dichlorobenzene, NT = nitrotoluene, DCNB = dichloronitrobenzene. FBR = fluidized bed

reactor. DF = David Freedman’s samples. J = Jim Spain’s samples.

Sample DF1 DF2 DF3 J1 J5 J3_FBR J7_FBR J8_FBR

Substrate CB 1,2-DCB 4-NT 2,3-DCNB 3,4-DCNB 2,3-DCNB 3,4-DCNB 1,2-DCB

Condition Aerobic Aerobic Anaerobic Aerobic Aerobic Aerobic Aerobic Aerobic

Pelosinus (OTU 43) 0 0 56 0 0 0 0 0

Desulfotomaculum (OTU 89) 0 0 16 0 0 0 0 0

Propionicicella (OTU 108) 0 0 11 0 0 0 0 0

Pandoraea (OTU 10) 77 87 0 0 0 0 0 0

Rhodanobacter (OTU 72) 14 0.013 0 0 0 0 0 0

Diaphorobacter (OTU 09) 0 0 0 76 97 5.5 0.36 0.012

Alcaligenaceae (OTU 11) 0 0 0 2.6 0.004 48 43 0.17

Rhodococcus (OTU 23) 0 0 0 0 0 32 27 8.9

Luteimonas (OTU 51) 0 0 0 6.8 0 5.6 12 0.24

Cupriavidus (OTU 24) 3.4 3.5 0 0 0 0 0 77

Comamonadaceae (OTU 177) 0 0 0 3.8 0 0.61 0.14 1.8

Flavobacterium (OTU 56) 0 0 0 3.8 0 1.0 7.3 7.0

Other OTUs in lower abundances 6 9.6 17 7 3 7 10 5

Total (%) 100 100 100 100 100 100 100 100

139

Figure E.4 Relative abundance (%) of Rhodanobacter (OTU72) in all the samples. X axis represents relative

abundance (%) of organism in log scale, Y axis represents the sample name where the organism was found.

140

Figure E.5 Relative abundance (%) of Pelosinus (OTU43), Desulfotomaculum (OTU89), and Propionicicella (OTU108) in all the

samples. X axis of the graph show relative abundance of organism in log scale, Y axis show the sample name where the organism was found.

141

Figure E.6 Relative abundance (%) of Diaphorobacter (OTU9) in all the samples. X axis of the graph show relative abundance of

organism in log scale, Y axis show the sample name where the organism was found.

142

Figure E.7 Relative abundance (%) of Rhodococcus (OTU23) in all the samples. X axis of the graph show relative

abundance of organism in log scale, Y axis show the sample name where the organism was found.

143

Figure E.8 Relative abundance (%) of Alcaligenaceae (OTU11) in all the samples. X axis of the graph show

relative abundance of organism in log scale, Y axis show the sample name where the organism was found.

144

Figure E.9 Relative abundance (%) of Cupriavidus (OTU24) in all the samples. X axis of the graph show relative

abundance of organism in log scale, Y axis show the sample name where the organism was found.

145

Appendix F. Supplementary information for statistical analyses

R Markdown script used to analyze all the samples in RStudio

#Select the libraries you want to use for your analysis knitr::opts_chunk$set(echo = TRUE) rm(list=ls(all=TRUE)) library(readxl) library(ape) library(ggplot2) library(permute) library(dplyr) library(grid) library(reshape2) library(vegan) library(phyloseq) library(RColorBrewer) library(ampvis2) theme_set(theme_bw()) knitr::opts_chunk$set(echo = TRUE) #Select the directory where your working files are saved setwd("~/Suzana") sharedfile = "Suzana_140samples.0.03.subsample_newnames" #Input file from Mothur taxfile = "Taxonomyfile3.txt" #Text file with all the reads and taxonomy names mapfile = "env_SK.csv" #Excel spreadsheet with environmental data for all the samples mothur_data <- import_mothur(mothur_shared_file = sharedfile, mothur_constaxonomy_file = taxfile) map <- read.csv(mapfile) #To reformat the metadata file so that you can merge it with the phyloseq file mothur_data. In order to merge the files, the row names need to match the sample names. Here the Sample_name column in the metadata file contains the sample names. rownames(map) <- map$Sample_name map <- sample_data(map) moth_merge <- merge_phyloseq(mothur_data, map) #optional - renaming taxonomic levels from rank to something more meaningful colnames(tax_table(moth_merge))

colnames(tax_table(moth_merge)) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species") moth_merge

146

#relative abundance: moth_mergeP <- transform_sample_counts(moth_merge, function(x){100*x/sum(x)}) #Filter out low abundant taxa, more than 5 reads in at least 1% of samples wh0 = genefilter_sample(moth_merge, filterfun_sample(function(x) x > 5), A=0.01*nsamples(moth_merge)) moth_mergeF = prune_taxa(wh0, moth_merge) moth_mergeF

#Creating a subset for microcosms samples only Microcosms <- subset_samples(moth_merge, Matrix=="MC") wh0 = genefilter_sample(Microcosms, filterfun_sample(function(x) x > 5), A=0.01*nsamples(Microcosms)) MicrocosmsF = prune_taxa(wh0, Microcosms) MicrocosmsF

#odinate all the samples together AllSamples_NMDS <- ordinate(moth_mergeF, "NMDS", "bray", k=2)

#ordinate only microcosms samples Microcosms_NMDS <- ordinate(MicrocosmsF, "NMDS", "bray", k=2)

#Plot showing all the samples, coloring samples by matrix. SampleNMDS = plot_ordination(moth_mergeF, AllSamples_NMDS, type="samples", color="Matrix", label="Sample_name", title = "NMDS for all samples") + theme(legend.position = "right", legend.text = element_text(size = 11), legend.key.size = unit(0.5, "cm")) + scale_colour_brewer(palette = "Set1") + geom_hline(aes(yintercept = 0)) + geom_vline(aes(xintercept = 0)) SampleNMDS

#Plot showing all the samples and coloring them by pH. Shapes mean the matrix of each sample. SampleNMDS_MC = plot_ordination(moth_mergeF, AllSamples_NMDS, type="samples", color="pH", shape="Matrix", label="Sample_name", title="NMDS for all samples - pH") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))+ geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0)) + scale_color_gradient(low="blue", high="red") SampleNMDS_MC

#Plot showing all microcosms sample, including aerobic and anaerobic. The elipse has a confidence level of 95% by deafult. SampleNMDS_microcosms = plot_ordination(MicrocosmsF, Microcosms_NMDS, type="samples", color="Condition", title = "NMDS - Aerobic and Anaerobic Microcosms", shape= "Site") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm")) + stat_ellipse(aes(fill="Site"), taxa_names(20)) + scale_colour_brewer(palette = "Set1") + geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0))

SampleNMDS_microcosms

#Sub setting for aerobic microcosms Aerobic <- subset_samples(moth_merge, Condition=="Aerobic") wh0 = genefilter_sample(Aerobic, filterfun_sample(function(x) x > 5), A=0.01*

147

nsamples(Aerobic)) AF = prune_taxa(wh0, Aerobic) AF

#Sub setting for anaerobic microcosms Anaerobic <- subset_samples(moth_merge, Condition=="Anaerobic") wh0 = genefilter_sample(Anaerobic, filterfun_sample(function(x) x > 5), A=0.01*nsamples(Anaerobic)) ANF = prune_taxa(wh0, Anaerobic) ANF

#Sub setting for microcosms from site 1A (in the environmental file, the categories can not start with number so this why the sites are inverted, so instead of having site 1A, it is named A1 in the script. Same for all 4 sites) A1 <- subset_samples(moth_merge, Site=="A1") wh0 = genefilter_sample(A1, filterfun_sample(function(x) x > 5), A=0.01*nsamples(A1)) Site1AF = prune_taxa(wh0, A1)

#Sub setting for microcosms from site 2A A2 <- subset_samples(moth_merge, Site=="A2") wh0 = genefilter_sample(A2, filterfun_sample(function(x) x > 5), A=0.01*nsamples(A2)) Site2AF = prune_taxa(wh0, A2) Site2AF

#Sub setting for microcosms from site 1B B1 <- subset_samples(moth_merge, Site=="B1") wh0 = genefilter_sample(B1, filterfun_sample(function(x) x > 5), A=0.01*nsamples(B1)) Site1BF = prune_taxa(wh0, B1) Site1BF

#Sub setting for microcosms from site 2B B2 <- subset_samples(moth_merge, Site=="B2") wh0 = genefilter_sample(B2, filterfun_sample(function(x) x > 5), A=0.01*nsamples(B2)) Site2BF = prune_taxa(wh0, B2) Site2BF

#ordinate the aerobic microcosms samples AF_NMDS <- ordinate(AF, "NMDS", "bray", k=2)

#To choose the colors, use the hexadecimal code in this link http://www.sthda.com/english/wiki/colors-in-r #Graph showing the aerobic degradation in the microcosms ASamplesNMDS_deg2 = plot_ordination(AF, AF_NMDS, type="samples", color="Aerobic_Degradation", title = "NMDS samples-Aerobic Degradation in Microcosms", shape="Treatment") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm")) + scale_color_manual(values=c("#663300", "#0000FF" , "#6699CC", "#6600CC","#FF6600", "#99CC66", "#006600"

148

, "#FF99CC", "#FF3399", "#999999")) + geom_hline(aes(yintercept = 0)) + geom_vline(aes(xintercept = 0)) ASamplesNMDS_deg2

#Graph showing the aerobic microcosms colored by site. The elipse has a confidence level of 95% by deafult AerobicMicrocosmsSamplesNMDS = plot_ordination(AF, AF_NMDS, type="sample", color="Site", title="NMDS Samples - Aerobic Microcosms", shape="Soil_origin") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))+ stat_ellipse(aes(fill="Site"), species.names(20)) + scale_colour_brewer(palette = "Set1") + geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0))

AerobicMicrocosmsSamplesNMDS

#Adding the OTU data to the plot #First make the OTU table into a dataframe: Aotudata = data.frame(t(otu_table(AF))) #use the envfit function to fitvectors to our OTUs set.seed(152) plot(AF_NMDS, type="p") Afitotu <- envfit(AF_NMDS, Aotudata , permu = 999) plot(Afitotu, p.max=0.001, col = "red")

#you can change the p-value #print out OTU-correlation values Afitotu[["vectors"]]

#Function: select.envfit - Setting r2 cutoff values to display in an ordination.r.select<-0.3 # correlation threshold, see function below #__FUNCTION: select.envfit__# # function (select.envit) filters the resulting list of function (envfit) based on their p values. This allows to display only significant values in the final plot. # just run this select.envfit<-function(fit, r.select){ #needs two sorts of input: fit= result of envfit, r.select= numeric, correlation minimum threshold for (i in 1:length(fit$vectors$r)) { #run for-loop through the entire length of the column r in object fit$vectors$r starting at i=1 if (fit$vectors$r[i]<r.select) { #Check wether r<r.select, i.e. if the correlation is weaker than the threshold value. Change this Parameter for r-based selection fit$vectors$arrows[i,]=NA #If the above statement is TRUE, i.e. r is smaller than r.select, then the coordinates of the vectors are set to NA, so they cannot be displayed i=i+1 #increase the running parameter i from 1 to 2, i.e. check the next value in the column until every value has been checked } #close if-loop } #close for-loop

149

return(fit) #return fit as the result of the function } #close the function # Perform select.envfit on dataset with r cutoff at 0.7 - you can change the r cut of to get more or fewer OTUs added fit2<-select.envfit(Afitotu, r.select=0.7) #Get them plotted nicely to the NMDS plot #add environmental variables, With display="bp", arrows will be drawn. otuarrowmat <- vegan::scores(fit2, display = "bp") # Add labels, make a data.frame otuarrowdf <- data.frame(labels = rownames(otuarrowmat), otuarrowmat) # Define the arrow aesthetic mapping otuarrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL, label = labels) otulabel_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = labels) otuarrowhead = arrow(length = unit(0.02, "npc")) # Plot showing the aerobic microcosms and arrows representing the main OTUs ASamplesNMDS_deg2 + geom_segment(mapping = otuarrow_map, size = .5, data = otuarrowdf, color = "black", arrow = otuarrowhead) + geom_text(mapping = otulabel_map, size = 4, color= "black", data = otuarrowdf) + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))

keepvariables = which(sapply(sample_data(AF), is.numeric)) physeqsd = data.frame(sample_data(AF))[keepvariables] drop <- c( "Nitrite", "T", "Nitrate", "Chloride", "Sulfate") metadatanoMissing = physeqsd[,!(names(physeqsd) %in% drop)] set.seed(123) #not needed unless you want to reproduce a particular set of random numbers plot(AF_NMDS, type="p") efnm <- envfit(AF_NMDS, metadatanoMissing, permu = 999, na.rm = TRUE, p.max=0.001) #this will plot only arrows with a p value eauql to or less than 0.001: plot(efnm, p.max=0.001)

efnm[["vectors"]]

#making the same type of plot in ggplot efnm.df<-as.data.frame(efnm$vectors$arrows*sqrt(efnm$vectors$r)) efnm.df$metadata<-rownames(efnm.df) #plotting only metadata significant pvalues A <- as.list(efnm$vectors) #creating the dataframe

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pvals<-as.data.frame(A$pvals) arrows<-as.data.frame(A$arrows*sqrt(A$r)) Cnm<-cbind(arrows, pvals) #subset Cnmred <-subset(Cnm,pvals<0.002) Cnmred <- cbind(Cnmred, metadatanoMissing = rownames(Cnmred)) # Define the arrow aesthetic mapping arrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL) label_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = metadatanoMissing) arrowhead = arrow(length = unit(0.02, "npc")) AerobicMicrocosmsSamplesNMDS + geom_segment(mapping = arrow_map, size = 1.5, data = Cnmred, color = "gray", arrow = arrowhead) + geom_text(mapping = label_map, size = 4, data = Cnmred)

physeqsd = data.frame(sample_data(AF)) keep <- c( "Degradation_Transformation", "Depth_m", "pH") #you can add more variables that you want to keep metadataSelect = physeqsd[keep] set.seed(123) #not needed unless you want to reproduce a particular set of random numbers plot(AF_NMDS, type="p") efS <- envfit(AF_NMDS, metadataSelect, permu = 999, na.rm = TRUE, p.max=0.001) #this will plot only arrows with a p value eauql to or less than 0.001: plot(efS, p.max=0.001)

efS[["vectors"]] efS[["factors"]] #making the same type of plot in ggplot efS.df<-as.data.frame(efS$vectors$arrows*sqrt(efS$vectors$r)) efS.df$metadata<-rownames(efS.df) #plotting only metadata significant pvalues A <- as.list(efS$vectors) #creating the dataframe pvals<-as.data.frame(A$pvals) arrows<-as.data.frame(A$arrows*sqrt(A$r)) CS<-cbind(arrows, pvals) #subset CSred <-subset(CS,pvals<0.002) CSred <- cbind(CSred, metadataSelect = rownames(CSred)) # Define the arrow aesthetic mapping

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arrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL) label_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = metadataSelect) arrowhead = arrow(length = unit(0.02, "npc"))

AerobicMicrocosmsSamplesNMDS + geom_segment(mapping = arrow_map, size = 1.5, data = CSred, color = "gray", arrow = arrowhead) + geom_text(mapping = label_map, size = 4, data = CSred)

#ordinate anaerobic microcosms in NMDS ANF_NMDS <- ordinate(ANF, "NMDS", "bray",k=2)

#Plot showing the anaerobic samples and what there were degrading ANSamplesNMDS_deg = plot_ordination(ANF, ANF_NMDS, type="samples", color="Anaerobic_Transformation", title = "NMDS samples-Anaerobic Transformation in Microcosms", shape="Treatment") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm")) + scale_color_manual(values=c("#9900CC", "#FF3300" , "#339933", "#0000FF", "#999999")) + geom_hline(aes(yintercept = 0)) + geom_vline(aes(xintercept = 0)) ANSamplesNMDS_deg

#Microcosms separated by site and elipses showing 95% confidence ANSamplesNMDS = plot_ordination(ANF, ANF_NMDS, type="sample", color="Site", shape="Soil_origin", title="NMDS Samples - Anaerobic Microcosms") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))+ stat_ellipse(aes(fill="Site"), taxa_names(20))+scale_colour_brewer(palette = "Set1") + scale_colour_brewer(palette = "Set1") + geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0))

ANSamplesNMDS

#Adding only significant metadata #Adding the environmental data to the plot, using only numeric data. keepvariables = which(sapply(sample_data(ANF), is.numeric)) physeqsd = data.frame(sample_data(ANF))[keepvariables] set.seed(123) #not needed unless you want to reproduce a particular set of random numbers plot(ANF_NMDS, type="p") efAN <- envfit(ANF_NMDS, physeqsd , permu = 999, na.rm = TRUE, p.max=0.001)

#this will plot only arrows with a p value equal to or less than 0.001: plot(efAN, p.max=0.001)

efAN[["vectors"]] # This plots the values so we can check if the results look ok

#making the same type of plot in ggplot efAN.df<-as.data.frame(efAN$vectors$arrows*sqrt(efAN$vectors$r)) efAN.df$metadata<-rownames(efAN.df)

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#plotting only metadata significant pvalues AN <- as.list(efAN$vectors) #creating the dataframe pvals<-as.data.frame(AN$pvals) arrows<-as.data.frame(AN$arrows*sqrt(AN$r)) CAN<-cbind(arrows, pvals) #subset CANred<-subset(CAN,pvals<0.002) #I changd the p-cut off - you can change it back to 0.05 if you want less strict cut-off CANred <- cbind(CANred, metadata = rownames(CANred)) # Define the arrow aesthetic mapping arrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL) label_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = metadat)arrowhead = arrow(length = unit(0.02, "npc"))

ANSamplesNMDS + geom_segment(mapping = arrow_map, size = 1.5, data = CANred, color = "gray", arrow = arrowhead) + geom_text(mapping = label_map, size = 4, data = CANred)

#Adding the OTU data to the plot #First make the OTU table into a dataframe: ANotudata = data.frame(t(otu_table(ANF))) #use the envfit function to fitvectors to our OTUs set.seed(152) plot(ANF_NMDS, type="p") ANFotufit <- envfit(ANF_NMDS, ANotudata , permu = 999) plot(ANFotufit, p.max=0.001, col = "red")

#you can change the p-value #print out OTU-correlation values ANFotufit[["vectors"]]

#Function: select.envfit - Setting r2 cutoff values to display in an ordination.r.select<-0.3 # correlation threshold, see function below #__FUNCTION: select.envfit__# # function (select.envit) filters the resulting list of function (envfit) based on their p values. This allows to display only significant values in the final plot. # just run this select.envfit<-function(fit, r.select){ #needs two sorts of input: fit= result of envfit, r.select= numeric, correlation minimum threshold for (i in 1:length(fit$vectors$r)) { #run for-loop through the entire length of the column r in object fit$vectors$r starting at i=1 if (fit$vectors$r[i]<r.select) { #Check wether r<r.select, i.e. if the correlation is weaker than the threshold value. Change this Parameter for r-based selection fit$vectors$arrows[i,]=NA #If the above statement is TRUE, i.e. r is smaller than r.select, then the coordinates of the vectors are set to NA, so they can

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not be displayed i=i+1 #increase the running parameter i from 1 to 2, i.e. check the next value in the column until every value has been checked } #close if-loop } #close for-loop return(fit) #return fit as the result of the function } #close the function # Perform select.envfit on dataset with r cutoff at 0.3 - you can change the r cut of to get more or fewer OTUs added fit3<-select.envfit(ANFotufit, r.select=0.4) #Get them plotted nicely to the NMDS plot #add environmental variables, With display="bp", arrows will be drawn. otuarrowmat <- vegan::scores(fit3, display = "bp") # Add labels, make a data.frame otuarrowdf <- data.frame(labels = rownames(otuarrowmat), otuarrowmat) # Define the arrow aesthetic mapping otuarrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL, label = labels) otulabel_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = labels) otuarrowhead = arrow(length = unit(0.02, "npc")) # Plot showing the aerobic microcosms and arrows representing the main OTUs ANSamplesNMDS_deg + geom_segment(mapping = otuarrow_map, size = .5, data = otuarrowdf, color = "black", arrow = otuarrowhead) + geom_text(mapping = otulabel_map, size = 4, color= "black", data = otuarrowdf) + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))

#ordinate the samples in NMDS by site Site1A_NMDS <- ordinate(Site1AF, "NMDS", "bray",k=2) #Site 1A Site1B_NMDS <- ordinate(Site1BF, "NMDS", "bray",k=2) #Site 1B Site2A_NMDS <- ordinate(Site2AF, "NMDS", "bray",k=2) #Site 2A Site2B_NMDS <- ordinate(Site2BF, "NMDS", "bray",k=2) #Site 2B #NMDS plot for microcosms from site 1A Site1ASamplesNMDS_treat = plot_ordination(Site1AF, Site1A_NMDS, type="samples", shape="Treatment", color="Degradation_Transformation", title = "NMDS - Reactions observed in Microcosms from site 1A") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm") ) + scale_color_manual(values=c("#660099", "#FF00CC" , "#0066CC", "#FF0000", "#FF9900", "#999999"))+ geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0)) Site1ASamplesNMDS_treat

#NMDS plot for microcosms from site 1B Site1BSamplesNMDS_treat = plot_ordination(Site1BF, Site1B_NMDS, type="samples", shape="Treatment", color="Degradation_Transformation", title = "NMDS - Rea

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ctions observed in Microcosms from site 1B") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm") ) + scale_color_manual(values=c("#FF0000", "#999999"))+ geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0)) Site1BSamplesNMDS_treat

#NMDS plot for microcosms from site 2A Site2ASamplesNMDS_treat = plot_ordination(Site2AF, Site2A_NMDS, type="samples", shape="Treatment", color="Degradation_Transformation", title = "NMDS - Reactions observed in Microcosms from site 2A") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm") ) + scale_color_manual(values=c("#CC00CC", "#3399FF", "#009933", "#CC0033", "#FF6633"))+ geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0)) Site2ASamplesNMDS_treat

#NMDS plot for microcosms from site 2B Site2BSamplesNMDS_treat = plot_ordination(Site2BF, Site2B_NMDS, type="samples", shape="Treatment", color="Degradation_Transformation", title = "NMDS - Reactions observed in Microcosms from site 2B") + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm") ) + scale_color_manual(values=c("#FF66FF", "#3399FF", "#339933", "#CC0033", "#999999"))+ geom_hline(aes(yintercept = 0)) +geom_vline(aes(xintercept = 0)) Site2BSamplesNMDS_treat

#First make the OTU table into a dataframe: Site1AFotudata = data.frame(t(otu_table(Site1AF))) #use the envfit function to fitvectors to our OTUs set.seed(152) plot(Site1A_NMDS, type="p") Site1AFotufit <- envfit(Site1A_NMDS, Site1AFotudata , permu = 999) plot(Site1AFotufit, p.max=0.001, col = "red")

#you can change the p-value #print out OTU-correlation values Site1AFotufit[["vectors"]]

#Function: select.envfit - Setting r2 cutoff values to display in an ordination.r.select<-0.3 # correlation threshold, see function below #__FUNCTION: select.envfit__# # function (select.envit) filters the resulting list of function (envfit) based on their p values. This allows to display only significant values in the final plot. # just run this select.envfit<-function(fit, r.select){ #needs two sorts of input: fit= result of envfit, r.select= numeric, correlation minimum threshold for (i in 1:length(fit$vectors$r)) { #run for-loop through the entire length of the column r in object fit$vectors$r starting at i=1 if (fit$vectors$r[i]<r.select) { #Check wether r<r.select, i.e. if the correlation is weaker than the threshold value. Change this Parameter for r-based selection

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fit$vectors$arrows[i,]=NA #If the above statement is TRUE, i.e. r is smaller than r.select, then the coordinates of the vectors are set to NA, so they cannot be displayed i=i+1 #increase the running parameter i from 1 to 2, i.e. check the next value in the column until every value has been checked } #close if-loop } #close for-loop return(fit) #return fit as the result of the function } #close the function # Perform select.envfit on dataset with r cutoff at 0.3 - you can change the r cut of to get more or fewer OTUs added fit4<-select.envfit(Site1AFotufit, r.select=0.75) #Get them plotted nicely to the NMDS plot #add environmental variables, With display="bp", arrows will be drawn. otuarrowmat <- vegan::scores(fit4, display = "bp") # Add labels, make a data.frame otuarrowdf <- data.frame(labels = rownames(otuarrowmat), otuarrowmat) # Define the arrow aesthetic mapping otuarrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL, label = labels) otulabel_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = labels) otuarrowhead = arrow(length = unit(0.02, "npc")) # Plot showing the aerobic microcosms and arrows representing the main OTUs Site1ASamplesNMDS_treat + geom_segment(mapping = otuarrow_map, size = .5, data = otuarrowdf, color = "black", arrow = otuarrowhead) + geom_text(mapping = otulabel_map, size = 4, color= "black", data = otuarrowdf) + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))

#First make the OTU table into a dataframe: Site1BFotudata = data.frame(t(otu_table(Site1BF))) #use the envfit function to fitvectors to our OTUs set.seed(152) plot(Site1B_NMDS, type="p") Site1BFotufit <- envfit(Site1B_NMDS, Site1BFotudata , permu = 999) plot(Site1BFotufit, p.max=0.001, col = "red")

#you can change the p-value #print out OTU-correlation values Site1BFotufit[["vectors"]]

#Function: select.envfit - Setting r2 cutoff values to display in an ordination.r.select<-0.3 # correlation threshold, see function below

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#__FUNCTION: select.envfit__# # function (select.envit) filters the resulting list of function (envfit) based on their p values. This allows to display only significant values in the final plot. # just run this select.envfit<-function(fit, r.select){ #needs two sorts of input: fit= result of envfit, r.select= numeric, correlation minimum threshold for (i in 1:length(fit$vectors$r)) { #run for-loop through the entire length of the column r in object fit$vectors$r starting at i=1 if (fit$vectors$r[i]<r.select) { #Check wether r<r.select, i.e. if the correlation is weaker than the threshold value. Change this Parameter for r-based selection fit$vectors$arrows[i,]=NA #If the above statement is TRUE, i.e. r is smaller than r.select, then the coordinates of the vectors are set to NA, so they cannot be displayed i=i+1 #increase the running parameter i from 1 to 2, i.e. check the next value in the column until every value has been checked } #close if-loop } #close for-loop return(fit) #return fit as the result of the function } #close the function # Perform select.envfit on dataset with r cutoff at 0.3 - you can change the r cut of to get more or fewer OTUs added fit5<-select.envfit(Site1BFotufit, r.select=0.75) #Get them plotted nicely to the NMDS plot #add environmental variables, With display="bp", arrows will be drawn. otuarrowmat <- vegan::scores(fit5, display = "bp") # Add labels, make a data.frame otuarrowdf <- data.frame(labels = rownames(otuarrowmat), otuarrowmat) # Define the arrow aesthetic mapping otuarrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL, label = labels) otulabel_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = labels) otuarrowhead = arrow(length = unit(0.02, "npc")) # Plot showing the aerobic microcosms and arrows representing the main OTUs Site1BSamplesNMDS_treat + geom_segment(mapping = otuarrow_map, size = .5, data = otuarrowdf, color = "black", arrow = otuarrowhead) + geom_text(mapping = otulabel_map, size = 4, color= "black", data = otuarrowdf) + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))

#First make the OTU table into a dataframe: Site2AFotudata = data.frame(t(otu_table(Site2AF))) #use the envfit function to fitvectors to our OTUs

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set.seed(152) plot(Site2A_NMDS, type="p") Site2AFotufit <- envfit(Site2A_NMDS, Site2AFotudata , permu = 999) plot(Site2AFotufit, p.max=0.001, col = "red")

#you can change the p-value #print out OTU-correlation values Site2AFotufit[["vectors"]]

#Function: select.envfit - Setting r2 cutoff values to display in an ordination.r.select<-0.3 # correlation threshold, see function below #__FUNCTION: select.envfit__# # function (select.envit) filters the resulting list of function (envfit) based on their p values. This allows to display only significant values in the final plot. # just run this select.envfit<-function(fit, r.select){ #needs two sorts of input: fit= result of envfit, r.select= numeric, correlation minimum threshold for (i in 1:length(fit$vectors$r)) { #run for-loop through the entire length of the column r in object fit$vectors$r starting at i=1 if (fit$vectors$r[i]<r.select) { #Check wether r<r.select, i.e. if the correlation is weaker than the threshold value. Change this Parameter for r-based selection fit$vectors$arrows[i,]=NA #If the above statement is TRUE, i.e. r is smaller than r.select, then the coordinates of the vectors are set to NA, so they cannot be displayed i=i+1 #increase the running parameter i from 1 to 2, i.e. check the next value in the column until every value has been checked } #close if-loop } #close for-loop return(fit) #return fit as the result of the function } #close the function # Perform select.envfit on dataset with r cutoff at 0.3 - you can change the r cut of to get more or fewer OTUs added fit6<-select.envfit(Site2AFotufit, r.select=0.6) #Get them plotted nicely to the NMDS plot #add environmental variables, With display="bp", arrows will be drawn. otuarrowmat <- vegan::scores(fit6, display = "bp") # Add labels, make a data.frame otuarrowdf <- data.frame(labels = rownames(otuarrowmat), otuarrowmat) # Define the arrow aesthetic mapping otuarrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL, label = labels) otulabel_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = labels) otuarrowhead = arrow(length = unit(0.02, "npc"))

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# Plot showing the aerobic microcosms and arrows representing the main OTUs Site2ASamplesNMDS_treat + geom_segment(mapping = otuarrow_map, size = .5, data = otuarrowdf, color = "black", arrow = otuarrowhead) + geom_text(mapping = otulabel_map, size = 4, color= "black", data = otuarrowdf) + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))

#First make the OTU table into a dataframe: Site2BFotudata = data.frame(t(otu_table(Site2BF))) #use the envfit function to fitvectors to our OTUs set.seed(152) plot(Site2B_NMDS, type="p") Site2BFotufit <- envfit(Site2B_NMDS, Site2BFotudata , permu = 999) plot(Site2BFotufit, p.max=0.001, col = "red")

#you can change the p-value #print out OTU-correlation values Site2BFotufit[["vectors"]]

#Function: select.envfit - Setting r2 cutoff values to display in an ordination.r.select<-0.3 # correlation threshold, see function below #__FUNCTION: select.envfit__# # function (select.envit) filters the resulting list of function (envfit) based on their p values. This allows to display only significant values in the final plot. # just run this select.envfit<-function(fit, r.select){ #needs two sorts of input: fit= result of envfit, r.select= numeric, correlation minimum threshold for (i in 1:length(fit$vectors$r)) { #run for-loop through the entire length of the column r in object fit$vectors$r starting at i=1 if (fit$vectors$r[i]<r.select) { #Check wether r<r.select, i.e. if the correlation is weaker than the threshold value. Change this Parameter for r-based selection fit$vectors$arrows[i,]=NA #If the above statement is TRUE, i.e. r is smaller than r.select, then the coordinates of the vectors are set to NA, so they cannot be displayed i=i+1 #increase the running parameter i from 1 to 2, i.e. check the next value in the column until every value has been checked } #close if-loop } #close for-loop return(fit) #return fit as the result of the function } #close the function # Perform select.envfit on dataset with r cutoff at 0.3 - you can change the r cut of to get more or fewer OTUs added fit7<-select.envfit(Site2BFotufit, r.select=0.8) #Get them plotted nicely to the NMDS plot #add environmental variables, With display="bp", arrows will be drawn.

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otuarrowmat <- vegan::scores(fit7, display = "bp") # Add labels, make a data.frame otuarrowdf <- data.frame(labels = rownames(otuarrowmat), otuarrowmat) # Define the arrow aesthetic mapping otuarrow_map <- aes(xend = NMDS1, yend = NMDS2, x = 0, y = 0, shape = NULL, color = NULL, label = labels) otulabel_map <- aes(x = 1.3 * NMDS1, y = 1.3 * NMDS2, shape = NULL, color = NULL, label = labels) otuarrowhead = arrow(length = unit(0.02, "npc")) # Plot showing the aerobic microcosms and arrows representing the main OTUs Site2BSamplesNMDS_treat + geom_segment(mapping = otuarrow_map, size = .5, data = otuarrowdf, color = "black", arrow = otuarrowhead) + geom_text(mapping = otulabel_map, size = 4, color= "black", data = otuarrowdf) + theme(legend.position = "right", legend.text = element_text(size = 10), legend.key.size = unit(0.5, "cm"))

Making relative abundance files, and a file with low abundant taxa filtered out and plotting

a bar chart

#Load the libraries you need library(dendextend) library(ape) library(cowplot) library(tidyverse) #Sub setting groundwater samples from full metadata table Groundwater <- subset_samples(moth_merge, Matrix=="GW") wh0 = genefilter_sample(Groundwater, filterfun_sample(function(x) x > 5), A=0.01*nsamples(Groundwater)) GWF = prune_taxa(wh0, Groundwater) GWF

#relative abundance: GroundwaterP <- transform_sample_counts(Groundwater, function(x){100*x/sum(x)}) #1% in at least 1 sample require("genefilter") flist <- filterfun(kOverA(1, 1)) Groundwater1PS = filter_taxa(GroundwaterP, flist, TRUE) #making a genus color palette. "Paired" is a palette from RColorBrewer getPalette = colorRampPalette(brewer.pal(12, "Paired")) GenusList = unique(tax_table(Groundwater1PS)[,"Genus"]) GenusPalette = getPalette(length(GenusList)) GWFdist = distance(GWF, "jsd", type = "samples") GWF.hclust <- hclust(GWFdist, method = "average")

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#Dendrogram for groundwater samples dend = as.dendrogram(GWF.hclust) dend <- set(dend, "labels_cex", 0.6) p1 <- ggplot(dend, horiz = T) p1

#Bar chart showing relative abundance (%) in groundwater samples without legend p2 = plot_bar(Groundwater1PS, "Sample", fill="Genus")+ geom_bar(aes(x=Sample_name, fill=Genus, position = "fill"), stat="identity", position="stack") + theme(axis.text.x = element_text(size = 8), axis.text.y = element_text(size = 8)) + theme(text = element_text(size = 8), legend.position = "none") + scale_fill_manual(values= GenusPalette) + coord_flip()

p2$data$Sample <- factor(p2$data$Sample,levels= labels(dend)) p2

#Dendrogram and bar chart aligned plot_grid(p1, p2, align = "h", axis ="r", label_fontface = plain, label_size=2)

#Bar chart of groundwater samples with legend p3=plot_bar(Groundwater1PS, "Sample", fill="Genus") + geom_bar(aes(x=Sample_name, fill=Genus, position = "fill"), stat="identity", position="stack") + theme(axis.text.x = element_text(size = 8), axis.text.y = element_text(size = 8)) + theme(text = element_text(size = 8), legend.position = "bottom", legend.text = element_text(size = 3), legend.key.size = unit(0.1, "cm")) + scale_fill_manual(values= GenusPalette)

p3$data$Sample <- factor(p3$data$Sample,levels= labels(dend)) p3

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Appendix G. Electronic files available as supplementary data

The following documents were used during this research and are available online, in Syntrophy

folder, in OwnCloud. The folder location is Syntrophy/People/Students/Current/Suzana Kraus

2016/MASC Thesis additional data files.

Name of the file Type of file Content

Aerobic and anaerobic

degradation graphs

Excel spreadsheet Aerobic and anaerobic

microcosms degradation graphs

Raw results MetaAmp Excel spreadsheet Raw results generated by

MetaAmp

Metadata table Excel spreadsheet Metadata table used as input in

RStudio

Sequencing results bar

charts

Excel spreadsheet Amplicon sequencing results

processed. This file also contains

the bar charts.

qPCR results Excel spreadsheet qPCR raw results for all samples