The Effect of Anaerobic Treatment of Pulp Mill Effluents on Reactor Performance and Granular Sludge

THE EFFECT OF ANAEROBIC TREATMENT OF PULP MILL EFFLUENTS ON REACTOR PERFORMANCE AND GRANULAR SLUDGE

MINQING IVY YANG

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Chemical Engineering and Applied Chemistry University of Toronto

Minqing Ivy Yang

Doctor of Philosophy

Department of Chemical Engineering and Applied Chemistry University of Toronto

2015

ABSTRACT

Pulp mill wastewaters contain high concentrations of organic compounds that can be partially converted into methane. Granulation of sludge is the key to successful treatment of pulp mill effluents in high rate anaerobic reactors. This research focused on the anaerobic treatment of a high strength pulp mill alkaline effluent from sulphite pulping (AE) of softwood chips, which was characterized by rich COD content and relatively high concentrations of resin acids and long-chain fatty acids (RFAs) known to be compounds inhibitory to methanogens.

Two continuous reactor experiments were conducted to study the treatment of this softwood AE.

In the first experiment, different concentrations of AE were treated for one month in four reactors. In the second experiment, increasing loadings of AE were added to the test reactor over a nine-month period.

The negative impact of the addition of AE on reactor performance and granulation was confirmed, shown as poorer organic removals, lower biogas production, and smaller and weaker granules in the AE sludge. Larger amounts of RFAs (>50mg RFAs / g TSS sludge) were found to associate with the sludge solids receiving a high AE loading, with palmitic acid being the most dominant RFA. RFA loadings were found to be significantly negatively correlated to the ii biogas production. Therefore, RFAs were proposed to play an important role in the negative impact of AE. Microbial community analysis using pyrotag sequencing of amplified 16S rRNA genes from various samples collected from the two studies revealed that the communities of the sludge treating AE were very different than those of the AE-free sludge. In terms of the organisms affected by the addition of AE, Oscillospira was significantly positively correlated to

AE loadings. The sludge treating AE also contained significantly lower percentages of methanogens. Furthermore, no clear sign of sludge acclimating to AE with enhanced biogas production was found. It is recommended that AE should be pretreated to reduce its toxic effect and to achieve greater COD removals toward higher biogas production.

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ACKNOWLEDGEMENTS I am using this opportunity to express my gratitude to everyone who supported me throughout the course of this Ph.D. project. I would like to express my special appreciation and thanks to:

• Professor D. Grant Allen and Professor Elizabeth A. Edwards, my tremendous mentors, for their supervision, advice, guidance, encouragement and patience.

• Professor Bradley A. Saville and Professor Emma Master, my committee members, for their helpful advice and suggestions

• Professor Ramin Farnood for offering being my additional faculty member in my departmental oral examination

• Endang Susilawati, Weijun Gao, Christina Heidorn, Joan Chen, Mary Butera, Leticia

Gutierrez, Pauline Martini, Julie Mendonca, Phil Milczarek, Gorette Silva, Arlene Smith,

Kathleen Weishar and Daniel Tomchyshyn, the staffs in the Department and in Biozone, for their administrative and technical help

• My research partner, Torsten Meyer, for helping me to run the UofT continuous study and giving me suggestions

• the past and current colleagues Yi, Nalina, Elena, June, Scott, Sofia, Peter, Sam, Tim,

Ariel, Cheryl, Laura, Shuiquan, Fei, Olivia, Sarah, Luz, Line, Mel, Nigel and other members in

Edlab and Allen lab for their training, help and solidarity

• Weijun Wang, Jon Obnamia, Kayla Nemr and Andrei Starostine for their technical support and advice

• My previous thesis students and summer students Liqun Zheng, Jennifer Leung and

Angie Tse for their help in experiments and sample analysis

• Tembec Temiscaming Mill and FPInnovations personnel for sample collection and sharing of analysis data

• The Environmental Consortium members of the Pulp and Paper Centre at the University of Toronto, Genome Canada, the Ontario Genomics Institute, Ontario Government Scholarship program (OGS) and The Natural Sciences and Engineering Research Council of Canada for funding my research

• My parents, brother, grandparents, parents in-law and sister in-law for their endless love, support and encouragement

I would like give my deepest appreciation and thanks to my husband Zhicheng Ryan Liang, for his unconditional understanding, support and love in the past 17 years. There have been ups and downs during my Master and Ph.D. studies. When I was upset and stressful, Ryan gave me advice and courage to overcome the challenges.

Last but not least, I would like to thank my daughter Hayley Liang. Hayley is everything that keeps me motivated and inspired.

This thesis is dedicated to the bright memory of my grandfather,

Jutian Lei (1928-2014).

TABLE OF CONTENTS ABSTRACT ...... ii ACKNOWLEDGEMENTS ...... iv LIST OF FIGURES...... xi LIST OF TABLES ...... xiv LIST OF APPENDICES ...... xv NOMENCLATURE ...... xvi CHAPTER1. INTRODUCTION ...... 1 1.1 Anaerobic Treatment of Pulp Mill Effluents and Anaerobic Granulation ...... 1 1.2 Problem Definition ...... 2 1.3 Objectives ...... 3 1.4 Hypotheses ...... 3 1.5 Research Approach ...... 4 1.6 Thesis Outline ...... 5 1.7 Authorships and Contributors to this Research ...... 5 1.8 Publications and Academic Achievements ...... 6 CHAPTER 2. LITERATURE REVIEW ...... 8 2.1 Anaerobic Degradation and Treatment ...... 8 2.1.1 Pathway and Microorganisms ...... 8 2.1.2 High Rate Anaerobic Reactors ...... 10 2.2 Anaerobic Granulation ...... 11 2.2.1 Different Models of Granulation ...... 11 2.2.2 Granule Disintegration and Floatation, and Agents to Enhance Granulation ...... 12 2.3 Anaerobic Treatment of Pulp Mill Effluents ...... 13 2.3.1 Anaerobic Treatability and Toxicity of Pulp Mill Effluents and Constituents ...... 14 2.3.2 Studies of Granular Sludge Treating Pulp Mill Effluents and Constituents...... 15 2.4 Resin Acids and Long-Chain Fatty Acids (RFAs) ...... 16 2.4.1 General Introduction to RFAs ...... 17 2.4.2 RFAs in Anaerobic Treatment ...... 18 2.5 Methods to Characterize Anaerobic Granules...... 22 2.5.1 Physical Examinations of Anaerobic Granules ...... 23 2.5.2 Microbial Examinations of Anaerobic Sludge ...... 25 2.6 Concluding Remarks ...... 31 CHAPTER 3. CHARACTERISTICS OF PULP MILL EFFLUENTS...... 33 3.1 Introduction ...... 33 3.2 Process Overview, Effluent Sources and Various Grades of Sulphite Pulp ...... 34 3.2.1 BCTMP and Sulphite Pulping Processes ...... 34 3.2.2 Wood Species Used to Produce Various Types of Sulphite Pulp ...... 37 vii

3.2.3 General Operation of the Full Scale Internal Circulation (IC) Reactors in the Mill ...... 38 3.3 Data Sources, Analytical Methods and Stream Samples ...... 38 3.4 Results of the Characteristics of BCTMP Effluent ...... 40 3.5 Results of the Characteristics of Acid Condensate (AC) ...... 41 3.6 Results of the Characteristics of Pulp Washer Effluent (AE) ...... 44 3.6.1 Comparison of Various Types of AE ...... 44 3.6.2 Results of the HPLC Analysis of SW1 AE ...... 47 3.6.3 Total Carbohydrates and Total Proteins in SW1 and SW2 AEs ...... 49 3.6.4 Summary of the AE Characteristics ...... 50 3.7 Summary of the Characteristics of BCTMP Effluent, AC and AE ...... 50 CHAPTER 4. DEVELOPMENT OF METHODS TO STUDY THE PHYSICAL PROPERTIES AND THE MICROBIAL COMMUNITIES OF GRANULAR SLUDGE ...... 53 4.1 Introduction ...... 53 4.2 Development of Methods to Test Particle Size Distribution ...... 53 4.2.1 Sludge Samples and Methods ...... 53 4.2.2 Results of the Method Development for Particle Size Distribution Analysis ...... 55 4.3 Development of Granule Weakness Test ...... 58 4.3.1 Sludge Samples and Methods ...... 58 4.3.2 Results of Method Development of the Granule Weakness Test ...... 60 4.4 Evaluations of Various Molecular Methods of Microbial Community Analysis ...... 62 4.4.1 Sludge Samples and Methods ...... 62 4.4.2 Results: Evaluation of Different Molecular Methods of Microbial Community Analysis ...... 66 4.5. Summary of Method Development ...... 72 CHAPTER 5. THE FP CONCENTRATION STUDY: THE EFFECT OF DIFFERENT CONCENTRATIONS OF AE ...... 74 5.1 Introduction ...... 74 5.2 Materials and Methods ...... 75 5.2.1 Reactor Setup and Seed Sludge ...... 75 5.2.2 Feeds: Pulp Mill Effluents and Additional Nutrients ...... 76 5.2.3 Analysis of Feeds and Effluents ...... 78 5.2.4 Sampling, Storage, and Physical and Chemical Analyses of Sludge ...... 79 5.2.5 Microbial Examinations of the FP Sludge ...... 80 5.3 Effect of the Addition of AE on Reactor Performance ...... 83 5.3.1 Removal of Total Suspended Solids (TSS) ...... 83 5.3.2 Removal of Soluble COD (sCOD) ...... 84 5.3.3 Biogas Production ...... 86 viii

5.3.4 Specific Biogas Yields ...... 87 5.3.5 Summary of Reactor Performance ...... 88 5.4 The Effect of the Addition of AE on Granulation of Sludge ...... 88

5.4.1 TSS and VSS Contained in Particles Larger than 200 µm (%TSS >200 µm and %VSS >200 µm) ...... 88 5.4.2 Particle Size Distribution for Granules (>200µm) ...... 90 5.4.3 Weakness of Granular Sludge ...... 91 5.4.4 Summary of the Physical Properties of Sludge ...... 92 5.5 Effect of the Addition of AE on the Microbial Communities of Anaerobic Sludge ...... 92 5.5.1Reproducibility of Sampling and Sequencing ...... 93 5.5.2 Microbial Diversity ...... 94 5.5.3 Microbial Composition ...... 94 5.5.4 Similarity and Variations among Different Sludge Samples ...... 96 5.5.5 Identification of Microorganisms Affected by Operational Parameters ...... 97 5.6 RFAs in Anaerobic Treatment of Pulp Mill Effluents ...... 100 5.6.1 RFAs in Reactor Feeds and Effluents ...... 101 5.6.2 RFA Concentrations in Sludge Samples ...... 103 5.6.3 Summary of the RFA Analysis ...... 104 5.7 Summary of the FP Concentration Study ...... 104 CHAPTER 6. THE UOFT LONG-TERM STUDY: THE EFFECT OF CONTINUOUS TREATMENT OF AE ...... 107 6.1 Introduction ...... 107 6.2 Materials and Methods ...... 108 6.2.1 Reactor Setup ...... 108 6.2.2 Synthetic Feed, AE and Additional Nutrients ...... 109 6.2.3 Organic Loading Rates and Feeding Schedule ...... 111 6.2.4 Sample Collection and Routine Measurements...... 112 6.2.5 Batch Assay Setup ...... 113 6.3 Effect of the Addition of AE on Reactor Performance...... 115 6.3.1 %sCOD Removal ...... 115 6.3.2 Daily Biogas Production ...... 116 6.3.3 TSS in Reactor Effluents ...... 117 6.4 Effect of the Addition of AE on the Physical Properties of Granular Sludge ...... 118

6.4.1 Percentage of Total Suspended Solids Contained in Sludge > 200 µm (%TSS >200 µm) ...... 119 6.4.2 Particle Size Distribution of Sludge Based on Image Analysis ...... 119 6.4.3 Granule Weakness ...... 121

6.5 Microbial Communities of Sludge in the Anaerobic Treatment of AE...... 121 6.5.1 Clustering of Samples, and Microbial Diversity of Sludge ...... 122 6.5.2 Microbial Compositions and Dynamics of the Sludge Samples ...... 124 6.5.3 Organisms Affected by the Addition of AE ...... 129 6.5.4 Summary of the Microbial Studies ...... 132 6.6 The Fate of Resin Acids and Long-Chain Fatty Acids (RFAs) ...... 133 6.7 Batch Assays to Evaluate Acclimation towards Better Treatment of AE ...... 134 6.8 Summary of the Chapter ...... 137 CHAPTER 7. OVERALL DISCUSSION OF THE ANAEROBIC TREATMENT OF PULP MILL EFFLUENTS...... 139 7.1 Summary of Characteristics of Pulp Mill Effluents and Synthetic Feed...... 140 7.2 Effect of the Addition of AE on Reactor Performance and Granulation ...... 142 7.3 Microbial Communities of Sludge in the Anaerobic Treatment of Pulp Mill Effluents ...... 147 7.3.1 Predominance of Microbial Groups in Sludge ...... 147 7.3.2 Microbial Groups Responding to AE ...... 152 7.3.3 Dynamics of the Microbial Communities ...... 154 7.3.4 Granulation of Anaerobic Sludge in the Treatment of Pulp Mill Effluents: the Microbial Perspectives ...... 155 7.3.5 Summary of the Microbial Studies ...... 157 7.4 Investigation of Possible Sludge Acclimation on AE and Feasible Strategies of Blending AE to the Reactor Feed...... 159 7.5 Discussion on the Developed Methods for Physical and Microbial Examinations ...... 161 CHAPTER 8. CONCLUSIONS, SIGNIFICANCE AND RECOMMENDATIONS ...... 164 8.1. Overall Conclusions of this Research ...... 164 8.2 Engineering and Scientific Significance ...... 167 8.3 Recommendations for Future Studies ...... 168 References ...... 170 References for Chapter 2 ...... 170 References for Chapter 3 ...... 176 References for Chapter 4 ...... 177 References for Chapter 5 ...... 178 References for Chapter 6 ...... 180 References for Chapter 7 ...... 181

LIST OF FIGURES Figure 2.1 Overall Anaerobic Degradation ...... 9 Figure 2.2 Configuration of the Internal Circulation Reactor (IC Reactor) ...... 11 Figure 2.3 Granule Floatation Mechanism ...... 13 Figure 2.4 Chemical Formulas of RFAs ...... 17 Figure 2.5 Process of Pyrotag Sequencing ...... 28 Figure 2.6 Distribution of Publications Using Pyrotag Sequencing to Study Anaerobic Communities .... 30

Figure 3.1 Simplified Diagram of the BCTMP Plant in the Mill ...... 35 Figure 3.2 Simplified Diagram of the Sulphite Pulp Plant in the Mill ...... 36 Figure 3.3 Simplified Diagram of the Chemical Recovery Process in the Sulphite Pulp Plant in Tembec ...... 37 Figure 3.4 Concentrations of Tannin-Lignin (Blue Bars) and %Total COD due to Tannin-Lignin (Red Bars) in Various Types of AC in the 2009 Effluent Campaign (Exova): not Significantly Different ...... 43 Figure 3.5 Concentrations of RFAs (Blue Bars) and %Total COD due to RFAs (Red Bars) in Various Types of AC in the 2009 Effluent Campaign (Exova): not Significantly Different ...... 43 Figure 3.6 Concentrations of Lignin-Tannin (Blue Bars) and %Total COD due to Lignin-Tannin (Red Bars) in Various Types of AE in the 2009 Effluent Campaign (Exova): Not Significantly Different ...... 45 Figure 3.7 Concentrations of Long-Chain Fatty Acids (Blue Bars) and their %Total COD due to LCFAs (Red Bars) in Various Types of AE in the 2009 Effluent Campaign (Exova): Significantly Higher in SW1 AE ...... 45 Figure 3.8 Concentrations of Resin Acids (Blue Bars) and % Total COD due to Resin Acids (Red Bars) in Various Types of AE in the 2009 Effluent Campaign (Exova): Significantly Lower in HW AE46 Figure 3.9 HPLC Chromatograms of SW1 AE (Oct25): RI Channel on the Left and UV Channel on the Right ...... 48 Figure 3.10 Results of Phenol-Sulphuric Acid Tests (Red) and Total Protein Tests (Blue): Lower in SW2 AE ...... 49

Figure 4.1 Food Sludge was Larger than Tembec Sludge ...... 54 Figure 4.2 Procedures in the Combined Method of Wet-Sieving and Image Analysis ...... 55

Figure 4.3 %TSS >500um and %VSS >500um : Intact Food Sludge > Intact Tembec Sludge; Intact Sludge > the Corresponding Vortexed Sludge ...... 57 Figure 4.4 Distribution for Particle > 500um from Image Analysis: Intact Food Sludge Larger than Intact Tembec Sludge; Intact Sludge Larger than the Corresponding Vortexed Sludge ...... 58 Figure 4.5 Effect of Vortex Duration, Vortex Frequency and Sample Volume on the Granule Weakness (Calculated as the Change in Absorbance Normalized to TSS) ...... 61 Figure 4.6 Food Sludge Stronger than Tembec Sludge Using the Established Granule Strength Test ...... 62 Figure 4.7 DGGE Image of the Bacterial Community of the FP Sludge: Good Reproducibility among Triplicates ...... 68 Figure 4.8 q-PCR Results: FP Sludge 1 Contained Higher %Archaea in the Population and Higher % Methanomethylovorans in the Archaeal Communities ...... 69 Figure 4.9 %Archaea in the Total Population and %Methanomethylovorans in the Archaeal Community: both Lower in FP Sludge 2 and 3 ...... 71

Figure 5.1 Schematic of the Upflow Anaerobic Digester Used in the FP Concentration Study ...... 76 Figure 5.2 Delta TSS in the FP Concentration Study: Severe Washout when Treating 100% AE ...... 84

Figure 5.3 %sCOD Removals in the FP Concentration Study: Lower %sCOD Removal When Receiving more AE ...... 85 Figure 5.4 Daily Biogas Production in the FP Concentration Study: Less Biogas Produced from Reactor Fed with a Higher Concentration of AE ...... 86

Figure 5.5 %TSS >200 µm and %VSS >200 µm at the End of Startup in the FP Concentration Study: Similar in all Reactors ...... 89

Figure 5.6 TSS >200 µm and %VSS >200 µm at the End of the FP Concentration Study: Significantly Higher in the Control Sludge ...... 90 Figure 5.7 Particle Size Distribution of Granules at the End of the Startup in FP Concentration Study: Similar in all Reactors ...... 90 Figure 5.8 Particle Size Distribution of Granules at the End of the FP Concentration Study: Control Sludge Contained the Lowest %Particles in the Smallest Tested Size Range (200-500µm) ...... 91 Figure 5.9 Granule Weakness of Sludge from the FP Concentration Study: Sludge Treating 64% AE was Weaker ...... 92 Figure 5.10 Jackknifed Tree based on the Relative Abundance and Phylogenetic Similarity of the OTUs: Samples within each Triplicate Set were Highly Reproducible ...... 93 Figure 5.11 Microbial Diversity of Sludge Collected from the FP Concentration Study: Sludge Treating AE was Less Diverse than the AE-Free Sludge ...... 94 Figure 5.12 Distribution of Major Organisms in Sludge Samples from the FP Concentration Study (based on Pyrotag Sequencing) ...... 95 Figure 5.13 PCoA Plot of the FP Sludge: Samples Clustering Affected by AE Loadings and Culture Time ...... 97 Figure 5.14 dbRDA for the FP Concentration Study: Effect of AE Loadings, Organic Loadings and Culture Time on Various Phyla ...... 98 Figure 5.15 dbRDA for the FP Concentration Study: Effect of AE Loadings, Organic Loadings and Culture Time on Various Major Organisms (>1.5% Total Population) at the Genus Level ...... 99 Figure 5.16 dbRDA for the FP Concentration Study: Effect of Lignins and RFAs on Various Major Organisms (>1.5% Total Population) at the Genus Level ...... 100 Figure 5.17 RFA Concentrations (Dissolved +Particulate Phase) in Feed (Red) and Effluents (Green) in the FP Concentration Study ...... 102 Figure 5.18 RFA Concentrations in FP Sludge: Lowest in the Control Sludge, Highest in the Sludge Treating 64% AE ...... 104

Figure 6.1 Schematic of the Continuous Reactors Used in the UofT Long-Term Study ...... 109 Figure 6.2 Basic Setup of a Biochemical Methane Potential (BMP) Assay ...... 113 Figure 6.3 %sCOD of Control Reactor (Red) and AE Reactor (Blue) in the UofT Long-Term Study: Poorer %sCOD Removal in AE Reactor during the 30% and 40% AE Test ...... 116 Figure 6.4 Biogas Production in Control Reactor (Red) and AE Reactor (Blue) in UofT Long-Term Study: Control Reactor Produced more Biogas than the Reactor Treating 30% and 40% AE (after Day 219) ...... 117 Figure 6.5 Effluent TSS from Control Reactor (Red) and AE Reactor (Blue) in UofT Long-Term Study: Reactor Treating 30% (after day 210) and 40% AE Showed Greater Washout than Control Reactor ...... 118

Figure 6.6 %TSS >200 µm of Sludge from the UofT Long-Term Study: Control Sludge Marked with Stars

Contained Significantly Greater %TS >200 µm ...... 119 Figure 6.7 Particle Size Distribution of Granules in the AE Sludge in UofT Long-Term Study: Degranulation after AE was Increased from 10% to 30% ...... 120 xii

Figure 6.8 Particle Size Distribution of Granules Collected during the 30% AE Test in the UofT Long-

Term Study: Lower %Particles 200-500 µm and Greater % Particles 1000-1500 µm in Control Sludge ...... 120 Figure 6.9 Granule Weakness of Sludge in the UofT Long-Term Study: AE Sludge Marked with Stars was Significantly Weaker than the Corresponding Control Sludge ...... 121 Figure 6.10 Clustering of Samples on the Jackknifed Tree Showing Good Reproducibility within each Triplicate Set ...... 123 Figure 6.11 PCoA Plots of the UofT Sludge Samples ...... 123 Figure 6.12 % Bacteria and Archaea in Sludge Collected in the UofT Long-Term Study: Sludge Treating 30% AE Contained Lower %Archaea than the Corresponding Control Sludge ...... 125 Figure 6.13 Distribution of Major Organisms (>2%) in Sludge Collected during Startup in the UofT Long-Term Study (Triplicate): Similar in Both Reactors ...... 125 Figure 6.14 Time Profiles of the Microbial Communities of the Control Sludge (Left) and AE Sludge (Right) in Different Phyla in the UofT Long-Term Study ...... 128 Figure 6.15 Cumulative Net Biogas Production in BMP Assays for Tests of Acclimation ...... 136

Figure 7.1 Dominant Microbial Groups Found in Both Continuous Studies and their Proposed Functions ...... 158

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LIST OF TABLES Table 2.1 Different Methanogens and their Substrates ...... 10 Table 2.2 Summary of Anaerobic Granulation Models ...... 12 Table 2.3 Examples of Characteristics of Pulp and Paper Processed Effluents in Literature ...... 14 Table 2.4 pK lw and Solubility of RFAs ...... 18 Table 2.5 Methanogenic IC50 of Various RFAs ...... 20 Table 2.6 Summary of Techniques Used to Study the Microbial Communities of Anaerobic Sludge ...... 26 Table 3.1 Compositions of Wood Chips Used in Various Types of Sulphite Pulp ...... 37 Table 3.2 Comparison: Tembec BCTMP Effluents vs. other CTMP Effluents in Literature ...... 40 Table 3.3 HPLC Analysis of Tembec BCTMP Effluents Collected in 2010 ...... 41 Table 3.4 Comparison between the Tembec AC and the AC (from Sulphite Pulping) in Literature ...... 42 Table 3.5 2009 Effluent Campaign: COD, BOD, Sulphite and Ammonium among Various Types of AEs ...... 44 Table 3.6 Heat Map (2009 Effluent Campaign): Comparing Concentrations of each Resin Acid in all AE Samples ...... 46 Table 3.7 Major Peaks in the HPLC Chromatograms of SW1 and SW2 AE ...... 48 Table 3.8 Summary of BCTMP Effluent, AC and SW1 AE ...... 51 Table 4.1 Vortex Duration, Frequencies and Sample Volumes in the Preliminary Granule Weakness Test ...... 60 Table 4.2 Statistic Summary of Pyrotag Sequencing of the FP Sludge ...... 70 Table 5.1 Chemical Characteristics of the Specific BCTMP Effluent, AC and AE Used in the FP Study76 Table 5.2 Feed Schedule, Compositions and Characteristics of Feeds in the FP Concentration Study ..... 78 Table 5.3 Sludge Samples and Analysis in the FPInnovations Study ...... 80 Table 5.4 Average of Specific Biogas Yield in the FP Reactors (L Biogas/g COD Degraded) ...... 87 Table 6.1 Compositions of the Synthetic Feed Used in the UofT Long-Term Study ...... 111 Table 6.2 Feeding Schedule in the UofT Study ...... 112 Table 6.3 Sludge Samples Collected in the UofT Study for Physical and Microbial Examinations ...... 113 Table 6.4 Setup of the BMP Assays ...... 114 Table 6.5 Pair-Wise Comparison of Sludge at Different Phylogenic Levels: AE sludge vs. Control Sludge ...... 131 Table 6.6 Organisms with Significant Changes in Percentages Following the Addition of AE (between Days 72 and 127) ...... 132 Table 6.7 Summary of RFA Concentrations in Feeds, Effluents and Sludge in the UofT AE Reactor ... 134

Table 7.1 Brief Summary of Wastewaters and Synthetic Feed Used in Both Studies ...... 141 Table 7.2 Concentrations and Loading Rates of AE and RFAs for the AE Reactors in Both Studies..... 142 Table 7.3 Summary of Reactor Performance and Physical Properties of Granules in Both Studies ...... 143

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LIST OF APPENDICES Appendix 2.1 List of Studies Using Statistical Tools in Correlation and Dynamics Studies based on Pyrotag Sequencing Data ...... 186 Appendix 3.1 Detailed List of Analyzed Streams, Data Sources and Analytical Methods ...... 187 Appendix 3.2 Detailed Methods of HPLC and IC Conducted at the University of Toronto ...... 188 Appendix 3.3 HPLC Results of Oct15 and Oct 25 ACs (SW1) ...... 190 Appendix 3.4 Tannin/Lignin and RFA Analysis of FP AE and UofT AE ...... 191 Appendix 4.1 Q-PCR Calibration Curves ...... 194 Appendix 4.2. Results of the Traditional Clone Library of Tembec Sludge ...... 195 Appendix 5.1 Supplementary Information of the Reactors and Feeds in FP Concentration Study ...... 196 Appendix 5.2 Time Profiles of Percentages TSS and VSS Contained in Granules Larger than 200µm in the FP Sludge ...... 197 Appendix 5.3 Results of ANOVA for Data in the Result Section of Physical Properties of the FP Sludge ...... 198 Appendix 5.4 Time Profiles of Particle Size Distribution in Granules (>200µm) in FP Sludge ...... 199 Appendix 5.5 Dominant Taxonomy in the FP Concentration Study ...... 200 Appendix 5.6 Combining Pyrotag Sequencing and q-PCR to Quantify the Abundance of Organisms ... 201 Appendix 5.7 Results of the Correlation Tests of Organisms Affected by Operational Parameters in the FP Concentration Study ...... 202 Appendix 6.1 Setup of the UofT Continuous Reactors ...... 204 Appendix 6.2 Summary of Elemental Analysis of IC Influent, Feed to FP Reactors and Literature Values ...... 205 Appendix 6.3 pH Measured during the UofT Concentration Study ...... 206 Appendix 6.4 VSS Concentrations in the Effluents from the UofT Reactors ...... 207 Appendix 6.5 Summary of Raw Sequences, Sequences before and after Chimera Removal ...... 208 Appendix 6.6 Alfa Diversity of Sludge Collected from the UofT Reactors...... 209 Appendix 7.1 Methanosaeta vs. Methanosarcina ...... 210

NOMENCLATURE

Abbreviations of sample names: BCTMP: bleached chemi-thermo-mechanical pulping AC: acid condensate AE: the alkaline effluent from sulphite pulping SW1, SW2, SW3 and HW: different types of sulphite pulp

Abbreviations of names of organizations: FP: FPInnovations UofT: the University of Toronto

Other technical terms: COD: chemical oxygen demand sCOD: soluble chemical oxygen demand TSS: total suspended solids VSS: volatile suspended solids HRT: hydraulic retention time OLR: organic loading rate SRT: sludge retention time

RFA: resin acid and long-chain fatty acid RA: resin acid LCFA: long-chain fatty acid DHA: dehydroabietic acid VFA: volatile fatty acid IC 50 : concentration at which 50% of the activity is inhibited OTU: operational taxonomy unit RDA: redundancy analysis db-RDA: distance-based redundancy analysis PCoA: principal coordinate analysis PC: principal coordinate bp: base pair

BMP: biochemical methane potential UASB: upflow anaerobic sludge blanket IC: internal circulation

PCR: polymerase chain reaction DGGE-PCR: denaturing gradient gel of products from PCR FISH: fluorescent in-situ hybridization q-PCR: quantitative PCR

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CHAPTER1. INTRODUCTION

1.1 Anaerobic Treatment of Pulp Mill Effluents and Anaerobic Granulation

Anaerobic treatment has several advantages over the conventional aerobic treatment: generation of methane as fuel, reduced production of biosolids and greenhouse gas, and lower requirements of energy and nutrients. In anaerobic wastewater treatment, bacteria hydrolyze and degrade complex organics into simpler 1- and 2-carbon compounds, and ultimately methanogens produce methane from these intermediates. Pulp mill effluents contain high concentrations of organics that can potentially be treated using anaerobic reactors. Since the first installation in the early 1980s, more than 360 anaerobic treatment plants have been constructed worldwide in the pulp and paper industry.

High rate reactors are preferable for anaerobic treatment of pulp mill effluents, as these reactors can deal with high organic loadings with relatively small footprints (Habets and Driessen,

2007). Upflow anaerobic sludge bed (UASB), expanded granular sludge bed (EGSB) and internal circulation (IC) are three typical high rate anaerobic reactors to treat pulp mill effluents. A characteristic common to all three is fast upward flows of liquid and gas inside the reactors, which may cause washout of sludge. Since anaerobic microorganisms have low growth rates, retention of biomass is extremely important, and greater retention can be achieved if sludge is present in the form of granules.

Anaerobic granules are spherical particles consisting of extracellular polymeric substances (EPS), bacterial and archaeal cells, and ash (approximately 30%). The diameter of the granules ranges from a few hundred microns to a few millimeters (Mussatsi et al. , 2005).

Compared to dispersed sludge, granulated sludge has several advantages: easier substrate transfer among closely located microorganisms, greater sludge retention due to the superior settleability,

1 and higher resistance to feed shocks (Subramanyam, 2013). The disintegration of granules lowers reactor performance, and costly reseeding with granular sludge from other sources is required.

1.2 Problem Definition

Despite of the importance of granulation in high rate anaerobic reactors, there were some knowledge gaps in the field of anaerobic treatment of pulp mill effluents and anaerobic granulation in general. In the studies of anaerobic treatment of pulp mill effluents, most of the published work focused on the treatability of wastewater and the inhibition of constituents, but there has been no direct investigation into the effect of treatment of pulp mill streams on granulation. Furthermore, little was known about the microbial response to the anaerobic treatment of pulp mill effluents. In terms of techniques applied in granulation studies, it is difficult to find a relatively simple approach to characterize the physical properties of granules in literature, as most of the current methods require relatively large sample volumes and have unknown or poor reproducibility.

The current study examines the anaerobic treatment of pulp mill effluents from Tembec’s

Temiscaming mill which includes a bleached-chemi-thermo-mechanical pulp (BCTMP) sector and a sulphite pulp sector. Since early 2006, two full scale IC reactors with granular sludge have been used to treat BCTMP effluent and acid condensate (AC) from sulphite pulping at the mill.

Batch studies and onsite IC reactor experience showed evidence of excellent anaerobic degradability of BCTMP effluent and AC. Since the treatment capacity of the IC reactors had not been fully utilized, the intention was to bring in other streams to increase biogas production and to decrease the organic loadings to the aerobic treatment plant. The alkaline effluent (AE) from sulphite pulping was one of the candidates. AE was characterized by a high COD concentration, and concentrations of resin acids and long-chain fatty acids (RFAs) and lignins which were significant, yet lower than those in the filtrate from the screw press to thicken pulp. Blending AE

2 with the IC reactor feed had been tried in the mill, but disturbance in the performance of IC reactors was noticed. In particular, the AE generated from sulphite pulping of softwood chips

(mainly spruce and jack pine) was a major concern, because the treatment of this AE using IC reactors led to process upsets.

1.3 Objectives

The overall objective is to study the effect of anaerobic treatment of pulp mill effluents on reactor performance and granular sludge.

Five specific objectives are included:

1. to develop methods to examine the physical properties and the microbial communities of

granular sludge

2. to investigate the impact of pulp mill streams and constituents on reactor performance and

granulation of sludge

3. to study the changes in the microbial communities and the organisms impacted by the

anaerobic treatment of pulp mill effluents

4. to identify the key microbial species involved in granulation and degranulation in the

anaerobic treatment of pulp mill effluents

5. to examine the ability of sludge to acclimate to pulp mill streams for enhanced treatability

and biogas production

1.4 Hypotheses

• The sludge-blanket-type reactor with a lower treatment efficiency also has poorer

granulated sludge

• Granulation is negatively influenced by toxic or inhibitory compounds in the pulp mill

effluents, e.g., resin acids and long chain fatty acids 3

• Treatment of the wastewater with poor degradability or toxicity alters the diversity and

compositions of the microbial communities, and the changes in the communities affect

the reactor performance

• Long term exposure to a specific pulp mill stream, i.e., AE, can help sludge acclimate to

the treated stream and improve the treatability

• Granulation depends on the abundance of certain microbial species

1.5 Research Approach

• Methods were developed to quantify granulation and to characterize the microbial

communities of granular sludge

• A continuous reactor study was conducted to evaluate the effect of the concentrations of

AE on reactor performance and granular sludge, with a one-month startup and a one-

month test of AE. This study is referred to as “the FP concentration study” or “FP study”

in short in this document. The FP concentration study was also used to examine if AE

could be blended with BCTMP effluent and AC in a rational way without compromising

granulation.

• A second continuous reactor study was carried out to investigate the relatively long-term

effect of AE on reactor performance and granular sludge, where AE was fed to the test

reactor in an increasing manner over a nine-month period after a two-month startup using

synthetic substrates. This study is referred to as “the UofT long-term study” in this

document. Compared to the FP concentration study, the UofT long-term study would

allow sludge to have sufficient time to acclimate to AE if acclimation was possible.

1.6 Thesis Outline

Following this introduction, a summary of literature review is provided in Chapter 2

to better understand the background of the project and the knowledge gaps in the field. The

results of the characteristics of BCMTP effluent, AC and AE are presented in Chapter 3,

which helps explain the observations in the continuous experiments in the later chapters.

Method developments to measure granule size and strength and to study the microbial

communities of the sludge are presented in Chapter 4. The investigation of the effect of

different concentrations of AE on reactor performance and granular sludge in the FP

concentration study is presented in Chapter 5. Chapter 6 is the UofT long-term study, in

which an eleven-month experiment was carried out to study the effect of AE on reactor

performance, granulation and possible acclimation. Chapter 7 is the overall discussion of this

research. The main conclusions, engineering and scientific significance and recommendations

are provided in Chapters 8.

1.7 Authorships and Contributors to this Research

• Minqing Ivy Yang was responsible for method development, microbial analysis and

physical examinations of sludge. Ivy also took part in reactor design, construction,

maintenance and some routine measurements (COD and TSS) in the UofT long-term

study. Ivy was the main investigator in the RFA analysis of the FP feed, effluent and

sludge samples.

• Dr. Torsten Meyer designed and built the UofT reactors. He was in charge of reactor

maintenance and was involved in routine measurements (pH and biogas) in the UofT

long-term study. He also developed the protocol for RFA analysis and examined the RFA

concentrations in the UofT effluent and sludge samples (Chapter 6).

• Allan Elliott and Talat Mahmood at the FPInnovations were responsible for setup and

maintenance of the reactors, and measurements of biogas, CODs and TSS in the FP

concentration study (Chapter 5).

• Renee Brunelle and Lyle Biglow at Tembec were involved in experiment planning, feed

formulation and wastewater preparation in the FP study (Chapter 5). They also supported

this research by sharing the data and analysis results of the pulp mill effluents and by

providing sludge and wastewater samples.

1.8 Publications and Academic Achievements Publications:

Yang, M.I. , Edwards, E.A, Allen, D.G. 2010. Anaerobic Treatability and Biogas Production Potential of Selected In-Mill Streams. Water Science and Technology. 62(10): 2427-34. (based on work done in Master’s degree)

Yang, M.I. , Elliott, A., Mahmood, T., Brunelle, R., Biglow, L., Edwards, E.A, Allen, D.G. Quantifying the Impact of Pulp Mill High Strength Wastewater on Anaerobic Digester Performance and Granulation. (submitted)

Yang, M.I. , Edwards, E.A, Allen, D.G. Microbial Community Studies of Anaerobic Sludge Treating Pulp Mill High Strength Wastewater (in preparation)

Conference Presentations:

1. Yang, M.I. , Edwards, A. E., Allen, D.G. The Effect of Anaerobic Treatment of Pulp Mill Wastewaters on the Microbial Community and Physical Properties of Granular Sludge.2nd International Conference on Water Research. Jan 21 to 23, 2013,Singapore 2. Yang, M.I. , Edwards, E.A, Allen, D.G. The Effect of Pulp Washer Effluent on Granulation of Sludge in the Anaerobic Conversion of Wastewater into Methane. The 2 nd International Forest Biorefinery Symposium, February, 2012, Montreal. 3. Yang, M.I. , Edwards, E.A, Allen, D.G. Microbial Communities in Granules and Treatment of Post Extraction Washer Water. The 1st BEEM Annual Research Meeting, November, 2010, Toronto. 4. Yang, M.I. , Edwards, E.A, Allen, D.G. Granulation in the Anaerobic Treatment of Pulp Mill Effluents. 12 th International Water Association (IWA) Specialist Conference on Anaerobic Digestion, November, 2010, Guadalajara, Mexico. 5. Yang, M.I. , Edwards, E.A, Allen, D.G. The Effect of Pulp Mill Effluents on the Microbial Properties of Anaerobic Granules. The 13 th International Symposium for Microbial Ecology, August, 2010, Seattle.

6. Yang, M.I. , Edwards, E.A, Allen, D.G. Granulation in the Anaerobic Treatment of Pulp Mill Effluents. The 11th Ontario-Quebec CSChE Biotechnology Meeting, June, 2009, Waterloo. 7. Yang, M.I Edwards, E.A, Allen, D.G. Anaerobic Treatment of Pulp Mill Effluents: Treatability of Selected Streams and Granulation. The 9th International Water Association – Symposium on Forest Industry Wastewaters, June, 2009, Fredericton, New Brunswick. 8. Yang, M.I. , Edwards, E.A, Allen, D.G. The Impacts of Effluent Type and Concentration on Methane Production from the Anaerobic Treatment of Pulp Mill Wastewaters. Energy Research Showcase, June, 2008, Toronto.

CHAPTER 2. LITERATURE REVIEW

In this chapter, the general steps and microorganisms involved in anaerobic degradation, as well as high rate anaerobic reactors, are introduced in section 2.1. Models of anaerobic granulation and the proposed theories of granule floatation and disintegration are explained in section 2.2. A literature survey of anaerobic treatment of pulp mill effluents is provided in section

2.3. Resin acids and long-chain fatty acids and their fate in anaerobic treatment are described in section 2.4. Different physical and microbial methods to examine granular sludge are presented in section 2.5. Concluding remarks to show the knowledge gaps and how this research helps fill those gaps are given in section 2.6.

2.1 Anaerobic Degradation and Treatment

In anaerobic degradation, complex organics are degraded into smaller molecules, which are eventually utilized by methanogens to produce methane. The degradation pathway and the microorganisms involved are presented in section 2.1.1. Anaerobic reactors, particularly internal circulation reactors, are introduced in section 2.1.2.

2.1.1 Pathway and Microorganisms

As illustrated in Figure 2.1, anaerobic degradation includes four steps: hydrolysis, fermentation, further breaking down of fermentation products into simple 1- or 2-carbon organics, and methanogenesis. Bacteria and archaea are involved in the degradation process.

Hydrolysis is carried out as extracellular enzymatic reactions by bacteria (Morgenroth et al. , 2002). Many anaerobic bacteria have hydrolytic functions, including Clostridia and Bacilli in

Firmicutes , and certain members in the phyla Bacteroidetes and Proteobacteria. Fermenting bacteria convert the hydrolysis products into simple molecules, e.g., volatile fatty acids (VFAs) and alcohols. Acidogens are the fermenting bacteria producing VFAs. Clostridium , Petrotoga

8 and Coprothermobacter are examples of acidogens found in the degradation of cellulose, starch and proteins respectively (Tatsuzawa et al., 2006).

Figure 2.1 Overall Anaerobic Degradation (Summarized based on McCarty & Smith, 1986; Aiyuk et al. , 2006)

Simple 1- and 2-C organics are produced from VFAs and alcohols by other groups of bacteria. The bacteria producing acetate are called acetogens. Homoacetogens produce acetate from 1-C compounds while consuming H 2. H2-producing acetogens produce both acetate and H 2.

In the final methanogenesis step, as listed in Table 2.1, CH 4 can be produced from CO 2 and H 2 by hydrogenotrophic methanogens, from acetate by acetoclastic methanogens, and from other 1- and

2-C organics. The Gibbs free energy of the conversion of a simple organic molecule into acetate and H 2 is positive. Removing H 2 from the system makes the reaction more thermodynamically favourable (McCarty and Smith, 1986). The dependence of the H 2 production by acetogens on the H 2 consumption by other species (i.e., homoacetogens and hydrogenotrophic methanogens) is a syntrophic relation.

In general, the archaeal classes of Methanomicrobia and Methanobacteria , and bacteria in the phyla Firmicutes , Proteobacteria , Chloroflexi , Spirochaetes and Bacteroidetes are commonly observed in the sludge from mesophilic anaerobic digestion (O’Flaherty et al ., 2010).

Table 2.1 Different Methanogens and their Substrates Methanogens Substrates Reference Methanosaeta Acetate only Angenent et al. , 2000 Methanomethylovorans Methanol, methylated amines, dimethyl sulphide and methane- De Bok et al. , 2006 thiol Methanobacterium Mostly H 2, formate for some strains (e.g. Worakit et al. , 1986; Ma et al. , Methanothermobacter wolfeii) 2005 Methanosarcina H2, methanol, mono-, di- and trimethylamine, acetate Grover, 2005; Marchaim, 1992; (especially at high concentration), pyruvate etc. Bock et al. , 1994; Simankova et al. , 2001

2.1.2 High Rate Anaerobic Reactors

High rate anaerobic reactors are preferred to treat high loadings of industrial wastewaters.

Upflow anaerobic blanket sludge (UASB) reactors, expanded granular sludge bed (EGSB) reactors, internal circulation (IC) reactors are typical high rate anaerobic reactors. In UASB, IC and EGSB reactors, biomass usually forms granular aggregates, so these reactors are classified as granular sludge-based reactors. Since the granule samples and the pulp mill effluent samples in this research were collected from a mill running IC reactors, the principles of an IC reactor are briefly described.

An IC reactor is characterized by its tall cylindrical shape, two compartments for phase separation, and a recirculation system as shown in Figure 2.2 (Driessen et al. , 2000). The lower compartment is the major region of anaerobic degradation. The upper compartment serves for post-treatment. The water/sludge mixture channeling to the gas-collector is redirected downward to the bottom of the lower compartment, forming a recirculation route and creating an expanded fluidized sludge bed (Driessen et al. , 2000; Kassam et al. , 2003). IC reactors occupy smaller footprints and have performance comparable or even superior to the conventional UASB reactors, making them suitable to treat wastewaters in many industries (Liu et al. , 2002). Nevertheless, the design of IC reactors is more complicated, so UASB reactors are used more frequently for in-lab studies, such as the research of anaerobic granulation.

Figure 2.2 Configuration of the Internal Circulation Reactor (IC Reactor) (Constructed based on Driessen et al. , 2000 )

There had been more than 360 anaerobic treatment plants constructed in the pulp and paper industry by 2012, an important fraction of which belonged to the granular sludge-based reactors (Totzke, 2012 ). As more granule-based reactors are adopted, maintaining granulation is important. Consequently, understanding anaerobic granulation and degranulation is crucial.

2.2 Anaerobic Granulation

Microorganisms in an anaerobic system can be dispersed or aggregated. Because there is a syntrophic relation in the microorganisms, a highly organized structure is favoured. Aggregates also have advantages in promoting a longer sludge retention time, and making a shorter hydraulic retention time with an effective COD removal feasible for the slow-growing anaerobic microorganisms (Mussati et al. , 2005). One typical example of microbial aggregation is granulation. In this section, several proposed models of granulation are introduced. Possible explanations for granule disintegration and floatation, and the proposed agents to facilitate granulation are also presented.

2.2.1 Different Models of Granulation

For over 30 years, research has been carried out to study the mechanisms of anaerobic granulation, but no consensus has been reached. Several models have been proposed to explain 11

granulation. As shown in Table 2.2, these models can be organized into three groups: physical,

thermodynamic and microbial.

Table 2.2 Summary of Anaerobic Granulation Models Model Reference Major Propositions Physical Models Selection Pressure Theory Hulshoff Pol Selection is due to the hydraulic loading rate and the gas loading rate et al. , 1983 Thermodynamic Models Surface Tension Model Thaveesri et In fluid with a high surface tension, methanogens adhere to each al. , 1995 other, and mixed conglomerates are formed In fluid with a low surface tension, fermenting bacteria surround methanogens to form a multi-layered structure Microbial Models EPS Theory –by Sam-Soon Sam-Soon et Methanobacterium strain AZ overproduce EPS (i.e. extracellular polymeric (Cape Town Hypothesis) al. , 1987 substances) proteins that can promote granulation EPS Theory – by Jia Jia et al. , Carbohydrate-utilizing sludge granulates better than acid-degrading sludge 1996 due to a higher production of EPS proteins by the fermenting organisms Spaghetti Theory Wiegant, Methanosaeta attach to each other or to inert surfaces at the core, and 1987 other cells adhere on the Methanosaeta surface or entrapped in the EPS network A high upflow velocity is required to form the round shape De Zeeuw’s Model De Zeeuw, • Methanosaeta colonize in the cavities of the Methanosarcina clumps, 1987 resulting in compact granules that are mainly consisted of Methanosaeta -like cells • A high selection pressure leads to filamentous granules that are in rough shape and loosely packed with intertwining Methanosaeta -like cells • Compact spherical granules are dominated by Methanosarcina , and the formation of these granules are independent on the selection pressure Multi-Layered Structure Model McLeod et • Methanosaeta are located at the core – by McLeod al. , 1990 • Homoacetogens and H 2-consuming organisms are in the middle layer • Fermenting bacteria and H 2-consuming organisms are located outside Multi-Layered Structure Model Vanderhaegen • Similar to McLeod’s version, but the sugar fermenting acidogens and – by Vanderhaegen et al. , 1992 the associated EPS can also act as the nucleation center

In summary, the importance of Methanosaeta , selection pressure and the extracellular

polymeric substances (EPS) has been repeatedly mentioned. Nevertheless, the influence of

physical, chemical and biological forces should be integrated when investigating the granulation

mechanisms.

2.2.2 Granule Disintegration and Floatation, and Agents to Enhance Granulation

The presence of certain compounds or particles might cause granule disintegration.

Washout of sludge may occur if the influent contains a high concentration of finely dispersed

12 suspended solids that can attach to microbial surfaces (Hulshoff Pol et al. , 2004). Long-chain fatty acids, such as oleic acid, may behave like surfactants to lower the surface tension within the granule, and thus may fragment the granules (Amaral et al. , 2004). Granules may also be disintegrated due to a sudden change in organic loading rate or upflow velocity (Araya-Kroff et al. , 2004).

Yoda and Nishimura (1997) proposed a mechanism of granule flotation. As illustrated in

Figure 2.3, cavities are created due to either cell lysis or EPS degradation. The produced biogas is entrapped in the cavities within the granules. Finally, a buoyant force causes granules to float

(Yoda and Nishimura, 1997).

Figure 2.3 Granule Floatation Mechanism (Yoda and Nishimura, 1997)

Various materials have been investigated as granulation agents. For example, the addition of chitosan or granulated activated carbon has been demonstrated to enhance granulation, shown as a shorter startup, larger granule and greater biomass retention (Yu et al. , 1999; Lertsittichai et al. , 2007).

2.3 Anaerobic Treatment of Pulp Mill Effluents

Anaerobic treatability of pulp mill effluents has been studied widely using lab-scale and full scale reactors in literature. The anaerobic treatability and toxicity of pulp mill effluents and constituents are presented in section 2.3.1. The previous studies of anaerobic granulation in the treatment of pulp mill effluents and constituents are summarized in section 2.3.2.

2.3.1 Anaerobic Treatability and Toxicity of Pulp Mill Effluents and Constituents

The constituents of pulp mill wastewaters can vary considerably, depending on the specific stream, the wood type and the processing technique. As shown in Table 2.5, sulphite spent liquor has the highest concentration of organics, mainly consisting of lignosulphonate and carbohydrates. Sulphite evaporator condensate contains numerous acetic acid and methanol, making the degradability as high as 90%. Tannin, resin acids, long-chain fatty acids and sulphur compounds were quoted as potential inhibitory compounds in pulp mill effluents. Resin acids and long-chain fatty acids will be explained in detail in section 2.4.

Table 2.3 Examples of Characteristics of Pulp and Paper Processed Effluents in Literature (Summarized based on Rintala and Puhakka, 1993) Wastewater COD Organic Composition Potential Inhibitory % COD (mg COD /l) (% of COD) Compounds Anaerobically Degradable Wet Debarking 1300-1400 Tannins 30-55; monomeric phenols 10-20; Tannins, resin acids 44-78 simple carbohydrates 30-40; resin compounds 5 Sulphite Spent Liquor 120000- 220000 Lignosulphonate 50-60; carbohydrates 15-25 Not Reported Not reported Sulphite Evaporator 7500-50000 Acetic acid 33-60, methanol 10-25, fatty acids Sulphur, organic sulphur 50-90 Condensate (SEC) <10 Chlorine Bleaching 900-2000 Chlorinate lignin polymers 65-75, methanol 1-27 Chlorinated phenols, 30-50 resin acids CTMP Effluent 2500-13000 Polysaccharides 10-15, lignin 30-40, organic Resin acids, long-chain 40-60 acids 35-40 fatty acids, sulphur

Lignin is mainly present in the form of lignosulphonate in sulphite pulping effluents. It was found that lignosulphonate caused a long lag period in CH 4 production, and increasing lignosulphonate concentration further inhibited methanogenesis. The amounts of sulphonate groups and phenolic hydroxyl groups in lignin might affect the molecular size range of the dominant lignin compounds that were inhibitory to anaerobic degradation (Yin et al. , 2000).

Sulphite pulping wastewaters usually contain a high concentration of leftover sulphite.

2- 2- The toxicity of sulphur compounds to VFA production by acidogens varied as SO 3 -S > SO 4 -S >

S2- (Lin and Hsiu, 1997). The inhibitory effect of sulphite was also found to depend on the

2- 2- COD:SO 3 ratio: a greater COD:SO 3 ratio was associated with a higher %COD removal

(Athanassopoulos et al. , 1989).

2.3.2 Studies of Granular Sludge Treating Pulp Mill Effluents and Constituents

The research of granular sludge treating pulp mill effluents is limited. Granulation studies in this field include the development of granules and granulation enhancement. In terms of the development of granules, Zhou et al . (2006) found that the seed granules were broken and replaced by newly formed granules in the treatment of kraft evaporator condensate in an air stripping-UASB reactor, suggesting that the compounds in the condensate altered the granule properties. In the treatment of wheat straw pulp black liquor, He et al. (1995) observed that a supplement of biodegradable carbohydrates shortened the granulation time. Similarly, Fukuzaki et al. demonstrated the positive effect of carbohydrates on granulation in comparison to VFAs, and suggested blending another stream with more carbohydrates to the feed to enhance granulation in the treatment of pulp mill effluents (Fukuzaki et al. , 1994).

Several studies were also conducted to investigate the effect of the inhibitory compounds in pulp mill effluents on granulation and degranulation. The studied impact of resin acids and long-chain fatty acids on granulation will be presented in section 2.4.2. The effect of lignosulphonate and sulphite on granulation is reviewed below.

The research conducted by Guiot et al. (1992) was the only published work to investigate the effect of lignin compounds on anaerobic granulation. In their study, one UASB reactor was fed with glucose, and a second one was fed with glucose and supplemented with the effluent from a neutral sulphite semi-chemical (NSSC) pulp mill that was rich in lignosulphonate. The granules in the reactor supplemented with the NSSC effluent showed an ordered and cluster-like structure, and were 4-5 times larger than those treating glucose solely, suggesting a positive impact of the NSSC effluent on granulation. However, other simple organics were also present in

15 the NSSC effluent, which might benefit the growth of microorganisms and granulation. Since possible inhibition of lignosulphonate to acetoclastic methanogenesis was proposed in other research (Yin et al. , 2000), lignosulphonate should be tested alone instead of NSSC effluent in order to evaluate the overall effect of lignosulphonate on anaerobic degradation and granulation.

Only one paper was related to the effect of sulphite on granulation. Blaszczyk et al.

(1994) used UASB reactors to treat a corn food wastewater. The reactor experienced a shock of a lower pH, a lower temperature, a higher total organic carbon, and a higher sulphite concentration.

The sludge concentration decreased after the shock, indicating degranulation and a loss of granule. Since the shock was a combined effect of a few sudden changes, the dominant factor could not be determined. Therefore, there seems to be no direct investigation into the effect of sulphite on anaerobic granulation.

In summary, granulation in the field of the anaerobic treatment of pulp mill effluents and constituents is poorly studied. In order to promote more anaerobic applications in the pulp and paper industry and to maintain the performance of the in-operation reactors, there is a need to better understand the effect of pulp mill effluents and the specific compounds on granulation.

2.4 Resin Acids and Long-Chain Fatty Acids (RFAs)

Resin acids and long-chain fatty acids (RFAs) belong to wood extractives, which are referred to wood constituents that are highly soluble in neutral organic solvents but hardly dissolved in water (Sjostrom, 1993; Back and Ekman 2000). RFAs have been found to exert inhibitory effect on anaerobic microorganisms. In this section, a general introduction to RFAs is given in subsection 2.4.1. Anaerobic degradability and toxicity of RFAs are described in subsection 2.4.2.

2.4.1 General Introduction to RFAs

Resin acids are diterpenoid carboxylic compounds, containing a hydrophobic skeleton and a hydrophilic carboxyl group (Liss et al ., 1997). As shown in Figure 2.4, resin acids are classified into two categories: the abietanes with isopropyl side chains (e.g., abietic, dehydroabietic, palustric, neoabietic and levopimaric acids) and the pimaranes with vinyl and methyl side chains (e.g., pimaric, isopimaric and sandaracopimaric acids) (Sjostrom, 1993;

Taylor et al. , 1988). Resin acids are only found in softwood species, accounting for 30% and 7% of the total wood extractives in wood and bark respectively (Fengel and Wegner, 1984a; Rudloff and Sato, 1963).

Figure 2.4 Chemical Formulas of RFAs (Drawn using the online tool www.emolecules.com)

The long-chain fatty acids (LCFAs) in wood are usually present as free forms of fatty acids or as esters, containing 16 to 24 carbons. Oleic, linoleic and linolenic acids are the common unsaturated LCFAs, and palmitic and stearic acids are examples of saturated LCFAs in wood.

LCFAs contribute to approximately 48% of wood extractives in hardwood species, and 33% and

13% in wood and bark in softwood species respectively (Fengel and Wegner, 1984a; Rudloff and

Sato, 1963). The LCFAs in softwood species are dominated by LCFAs with 18 carbons (i.e., oleic, linoleic and stearic acids) (Sundberg et al., 2009).

The RFAs in pulp mill effluents are mainly from the debarking and pulping processes, and from the bleaching process in some mills. The composition and concentration of RFAs in 17 pulp mill effluents depend on the wood species, the pulping technique, water usage and circulation, and the processing stage. The common resin acids in pulp and paper effluents include abietic acid, dehydroabietic acid (DHA), neoabietic acid, pimaric acid, isopimaric acid, sandaracopimaric acid, levopimaric aicd and palustric acid (Ali and Sreekrishnan, 2001). Oleic acid, palmitic acid, linoleic acid and stearic acid are the common LCFAs in pulp mill effluents

(Makris, 2003; Sierra-Alvarez et al ., 1994).

The solubility of RFAs varies, depending on temperature, metal ion concentration and pH (Strom, 2000). In pulp mill effluents, RFAs are frequently present as colloidal droplets, with the hydrophobic end pointing inward and the hydrophilic end (e.g., the carboxyl group) pointing

+ outward. Higher pH and lower salt content (i.e., Na ) help dissolve RFAs. The pK lw value is referred to the pH at which 50% of the RFA is present in the colloidal form and the rest is dissolved in water (Sundberg et al , 2009). The pK lw and solubility values of different RFAs are listed in Table 2.4. In general, LCFAs are less soluble than resin acids, and DHA is the most soluble resin acid.

Table 2.4 pK lw and Solubility of RFAs (Summarized based on Peng and Roberts, 1999; Strom, 2000; Robb, 1966) RFA pK lw (30°C) Solubility in Water (mg/L) Palmitic 7.7 0.04 (25 °C) Linoleic 8.1 0.14 (25 °C) Oleic 8.4 Not found (frequently quoted as insoluble) Abietic 7.5 2.75 (20 °C) Neoabietic 7.3 2.31 (20 °C) Levopimaric Not found 2.54 (20 °C) Palustric 7.6 2.41 (20 °C) DHA 6.0 5.11 (20 °C)

2.4.2 RFAs in Anaerobic Treatment

The fate of RFAs in anaerobic treatment has been studied extensively by previous researchers. A literature survey of the anaerobic degradability and toxicity of RFAs is presented in this section.

• Long-Chain Fatty Acids (LCFAs)

Degradation of LCFAs under anaerobic conditions has been reported previously (Hanaki,

1981; Pereira et al ., 2002). The main degradation mechanism is β-oxidation, i.e., the two carbons at the end of the skeleton are removed in each step (Weng and Jeris, 1976). For example, oleic acid is first converted to the saturated stearic acid, followed by the production of palmitic acid via

β-oxidation. While β-oxidation of stearic acid to palmitic acid is relatively fast, the conversion from oleic acid to stearic acid has been proposed to be the rate-limiting step (Lalman and Bagley,

2001).

Accumulation of palmitic acid on sludge treating LCFAs was found in multiple studies

(Pereira et al ., 2002; Salminen et al ., 2001). Pereira et al . (2005) observed that the treatment of palmitic acid caused local deposition of palmitic acid precipitates on granular sludge, while the treatment of oleate led to encapsulation of primarily palmitic acid around the granules. After being washed and centrifuged, the sludge previously encapsulated with LCFAs was able to degrade the adsorbed matter and produce CH 4 (i.e., mineralization) when no more oleate was fed to the system. However, addition of extra oleate to the washed sludge led to slower and lower

CH 4 production, suggesting that the added oleate inhibited further β-oxidation of the palmitic acid associating with sludge (Pereira et al ., 2002). Pereira et al . (2005) proposed that the encapsulation of LCFAs on sludge created a physical barrier, resulting in transport limitation and poor methanogenic activity.

LCFAs have also been demonstrated to exert inhibitory effect on methanogens, particularly to the acetoclastic methanogens (Hanaki, 1981). The methanogenic IC 50 values of linoleic, linolenic and oleic acids are shown in Table 2.5. In Hanaki’s study, LCFAs inhibited

CH 4 production from acetate with the appearance of a lag period. Adding soluble calcium salts

19 helped restore the methanogenic activity that was previously affected by the presence of LCFAs, possibly due to the precipitation of the calcium-LCFA salts (Hanaki, 1981; Koster, 1987).

Table 2.5 Methanogenic IC50 of Various RFAs RFA Methanogenic IC 50 (mg/L) Substrate Reference 278 Pyruvate Demeyer and Henderickx, 1967 Linolenic Acid 501 H2/CO 2 Prins et al ., 1972 Linoleic Acid 897 H2/CO 2 Prins et al ., 1972 Oleic Acid 1235 Acetate Koster and Kramer, 1987 Acetate/ propionate/ Sierra-Alvarez and Lettinga, Abietic Acid 89-235 butyrate 1990 Acetate/ propionate/ Sierra-Alvarez and Lettinga, DHA 43-123 butyrate 1990

In addition to inhibition, LCFAs were also found to have negative effect on granulation.

Pereira et al. (2003) and Amaral et al. (2004) studied the effect of oleate concentrations on granules that were obtained from a brewery treatment plant: as more LCFAs were adsorbed, granules migrated to the top of the reactor, leading to granule floatation; granule disintegration was also noticed, indicated by the increasing amount of fine aggregates and size reduction of the granules. The authors addressed that deterioration of granules fed with oleate took place even at oleate concentrations far below the toxicity limit, and suggested that the granule disruption could occur prior to inhibition.

• Resin Acids

Biotransformation of resin acids can occur under anaerobic conditions, but anaerobic degradation of the skeleton with the three fused ring in resin acids has not be reported (Martin et al. , 1999). Tavendale et al. (1997a) fed resin acids to sediment from a lake receiving the effluent from a bleached kraft mill. Fifty percent of the total resin acids were slowly removed over 264 days. DHA was converted to retene with CH 4 as byproduct, and no further degradation was identified (Tavendale et al ., 1997b). Liver and Hall (1996) used unacclimated anaerobic sludge to treat a mixture of five resin acids. During the 25-day test period, an increase in DHA and a

20 decrease in palustric acid were observed without any clear detectable change in the concentration of total resin acids, suggesting that interconversion between species of resin acids, rather than degradation, possibly took place in the system. Furthermore, no pure culture has been found to utilize resin acids as their energy or carbon source (Martin et al ., 1999). In general, resin acids and the products that they transform into usually resist complete degradation under anoxic conditions (Mohn et al ., 1999).

Besides biotransformation, adsorption onto biosolids (e.g., sludge) also contributes to the removal of resin acids. In the study performed by Patoine et al . (1997), adsorption onto sludge accounted for 10% of the DHA removal in an UASB reactor seeded with acclimated granular sludge and fed with 20mg/L DHA and abietic acid. Accumulation of resin acids in anaerobic reactors treating mechanical pulping wastewaters was also reported by Ho (1988) and McFarlane and Clark (1988). With both active and inactivated non-acclimated anaerobic sludge, Hall and

Liver (1996) found that the partitioning of resin acids onto sludge was a two-step process: the first rapid attachment to sludge occurred within one hour, followed by a slower secondary removal phase that required up to nine days; majority of the partitioning of resin acids onto sludge took place during the first phase.

In addition to recalcitrance, resin acids are also inhibitory compounds in anaerobic treatment. Kennedy et al. (1992) conducted anaerobic toxicity assays to examine the impact of resin acids on CH 4 production from an acetate/propionate mixture, and observed 41 to 59% reduction in methanogenic activity when increasing resin acid concentrations from 20 to 320 mg/L. Sierra-Alvarez and Lettinga (1990) conducted standard toxicity assays and demonstrated that resin acids had stronger inhibitory effect on methanogenesis than LCFAs, and DHA was more toxic than abietic acid as suggested by their IC 50 values in Table 2.5. The toxic impact of

21 abietic acid on methanogens was also stated in the studies performed by Andersson and Welander

(1985) as well as by Field et al (1988).

Previous investigation into the impact of resin acids on granulation is limited. One example was the treatment of CTMP effluent using UASB reactors conducted by Richardson et al. (1991). The CTMP effluent contained fines that included largely resin acids and small amounts of long-chain fatty acids. After 120-day treatment, the sludge in the UASB reactor treating the CTMP effluent containing fines showed poorer settleability (i.e., lower setting velocity) than the seed sludge and the sludge treating the fine-free CTMP effluent.

In general, a literature review of resin acids and long-chain fatty acids is provided in section 2.4. RFAs are mainly hydrophobic, most of which are poorly soluble in water. Both resin acids and long-chain fatty acids inhibit anaerobic microorganisms, and the formers are more toxic.

While long-chain fatty acids are degradable under anaerobic conditions, the three-ring skeleton of resin acids is resistant to anaerobic degradation. Adsorption of resin acids and long-chain fatty acids onto biomass was noticed, partially explaining the total removals of RFAs in anaerobic treatment.

2.5 Methods to Characterize Anaerobic Granules

In order to study the effect of pulp mill effluents and constituents on anaerobic granular sludge, methods to characterize the physical properties and the microbial communities are required. Various assays to examine the physical properties of granules are presented in section

2.5.1. Different molecular techniques for community studies are introduced in section 2.5.2.

2.5.1 Physical Examinations of Anaerobic Granules

Physical properties of sludge are important indices to quantify granulation. In this section, methods to measure particle size distribution and granule strength are described. Brief summaries of tests to measure granule settleabiltiy, permeability and porosity are also provided.

There are five major types of methods to measure particle size distribution of granular sludge: wet-sieving, image analysis, a test with a laser particle size analyzer, gelation, and estimation based on mathematical models. Wet-sieving, the most straight forward method, requires sieving dishes and a drying oven (Laguna et al. , 1999). If microscopic image analysis is used for particle size distribution, Abreu et al. (2007) and Araya-Kroff et al. (2004) suggested that at least 100 images per sample should be taken, which can be tedious and time consuming. A laser particle size analyzer is usually expensive, and its detection limit requires the collaboration of another particle size analysis method, e.g., image analysis. In the gelation method, granules are fixed in a transparent gel before scanning. Gelation is the least labor-intensive method, but the accuracy depends on the scanning resolution and the homogeneousness of the gel itself, such as few air bubbles and a homogeneous transparency. It has been pointed out that reproducibility is important when working with granular sludge (Laguna et al. , 1999). However, most published work presenting the granule size distribution data did not include any statistical analysis or error bars. Therefore, it is difficult to compare the reproducibility of each method.

Granule strength is related to their mechanical stability (Liu et al ., 2009). In literature, granule strength is referred to either compressive strength or shear strength. Van Hullebusch et al.

(2007) examined the resistance of granule samples against the compression exerted by a moving piston in a vertical cylinder: disintegration of granules caused a sudden increase in the resistance to compression, and the pressure at which the sudden increase happened was taken as the compressive strength of the granules. Pereboom (1997) used the abrasion rate coefficient to

23 represent the shear strength of granules: shear in stirred vessels and bubble columns caused breaking of relatively large granules (1-3mm) into fine particles (<0.2mm); the abrasion rate coefficient was calculated by measuring the reduced fractions of large particles at six shear rates during a 10-minute period. In the study conducted by Teo et al. (2000), the shear strength of granules was measured as the change in the turbidity of the sample supernatant after shaking at

150 rmp for 2 minutes. Among these three methods, the former two methods showed <10% variations within duplicates or triplicates, while the reproducibility of the Teo’s test was unknown as the experiment was conducted without any replicate. However, Teo’s method was relatively simple and only required basic equipment commonly found in many wastewater test laboratories. Besides compressive strength and shear strength, mathematical models were also used to estimate granule strength (Wu et al., 2006).

Other physical examinations of anaerobic granules include tests of settleability, permeability and porosity. Settleabilty can be assessed by measuring the upflow velocity of granules, the rate of granule accumulation due to gravity, the sludge volume index and the zone settling velocity (Show et al ., 2004; Vlyssides et al ., 2008; Liu et al ., 2006). Permeability and porosity of anaerobic granules in published work were often estimated based on settling velocity using mathematical models, or assessed by conducing size exclusion chromatography where the column was packed with anaerobic granules (Mu et al ., 2006).

In summary, various methods to examine the physical properties of anaerobic granules are reviewed in section 2.5.1, with a focus on particle size distribution and granule strength.

Despite of the improving knowledge of the physicochemical characteristics of anaerobic granules, as addressed by Liu et al (2009), further research is needed to understand the links between the physical properties and the microbial characteristics of sludge to better explain the granulation phenomena.

2.5.2 Microbial Examinations of Anaerobic Sludge

The molecular methods used in microbial community studies are presented in this section, including the traditional clone library based on Sanger sequencing, denaturing gradient gel electrophoresis of products from polymerase chain reaction (PCR-DGGE), fluorescent in-situ hybridization (FISH), quantitative PCR (q-PCR) and pyrotag sequencing. A summary is also included to present the state of the art of each method applied to investigate the communities related to anaerobic degradation.

• Approaches in Community Studies based on the 16S rRNA Genes

One common approach in community studies is based on the 16S ribosomal RNA (16S rRNA) genes. There are a few advantages of using the 16S rRNA genes as biomarkers: these genes are present in all bacteria and archaea; they include both the variable and highly conserved regions; more and more sequences of these genes become available in public database for comparison and alignment (Rastogi and Sani, 2011).

Traditional clone library and PCR-DGGE used to be the common tools to study the microbial community compositions. They have been gradually replaced by the newly developed high throughput techniques such as pyrotag sequencing. Q-PCR is frequently conducted to quantify the abundance of organisms of interest. FISH is performed to roughly estimate and locate specific organisms in a sample. The working theory, applications and examples of each method are summarized in Table 2.6.

The traditional clone library method is based on Sanger sequencing. The PCR products of the 16S rRNA genes are recombined with plasmid vectors and transformed into E. coli .

Colonies are derived from the growth of a single E. coli cell (Sanz and Kochling, 2007). The plasmids are extracted and sequenced. The species are identified using the public database as references. The amplicons are usually 1000 to 1500 base pair (bp) long, covering most region of 25 the 16S rRNA genes. Typically, using sequence similarity cut-off values of 80, 85, 90, 92, 94 or

98%, sequences are assigned to phylum, class, order, family, sub-family (genus), or species respectively (DeSantis et al . 2007). The results of clone library are normally used for identification purposes rather than quantification.

Table 2.6 Summary of Techniques Used to Study the Microbial Communities of Anaerobic Sludge Studies related to anaerobic treatment of pulp and paper effluents are highlighted in blue. Method Basic Working Theory Application s Examples Survey of the archaeal PCR products of the 16S rRNA communities in 44 anaerobic Traditional Identification of organisms genes are recombined into digesters treating various types Clone Library in the communities with vectors, cloned into E coli , of wastewater, including pulp (with Sanger resolution up to the species multiplied, then extracted and mill effluents in Canadian and Sequencing) level sequenced French mills (Leclerc et al ., 2004) Examination of the community Fingerprinting of the of a full scale UASB reactor PCR products are separated in community; visualization treating paper mill wastewater DGGE gels based on different of major differences among (Roest et al ., 2005); assessment PCR-DGGE melting temperatures as a samples; identification of of the changes in a constructed consequence of the GC content species requires further consortium treating alkaline of the PCR products cloning and sequencing; black liquor (Yang et al ., mostly qualitative 2008) Similar to the traditional clone Identification and library, but multiplication takes quantification (i.e., Microbial diversity analysis of Pyrotag place in emulation oil droplets in percentage) of organisms in sludge in an EGSB reactor Sequencing the 454 platform instead of E the communities with treating nitrate rich wastewater coli ; high throughput sequencing resolution up to the genus (Liao et al ., 2013) is conducted level Examination of ammonia - oxidizing archaea and bacteria in sediments receiving the Fluorescent dye is incorporated Quantification of the effluent from a pulp and paper Quantitative into the product and the signal is absolute abundance of plant (Abell et al ., 2014); PCR (q-PCR) monitored in each PCR cycle specific organisms investigation of the microbial population in close water circuits in two recycle paper mills (Öqvist et al ., 2008) Determination of the methanogenic and sulphate Probes with fluorescent dye are reducing bacterial population in hybridized to the denatured sludge from full scale anaerobic FISH single stranded 16s rRNA genes; Identification and location contact reactor treating signal is detected using an combined white and black epifluorescent microscope liquor in a pulp mill (Ince et al ., 2007)

PCR-DGGE provides a general fingerprint of the community. In the PCR for DGGE, either the forward or the reverse primer contains a 5’GC clamp (30-50 nucleotides). In the DGGE gel, the PCR products migrate downward in an applied electric field. The denaturing agent causes

PCR products to dissociate except the 5’GC clamp. The intact GC clamp prohibits the largely

26 dissociated PCR products from migrating downward, so different species are separated on the gel.

The dissociation of a PCR product depends on its GC content. One band on the gel often represents one organism, and the band intensity reflects the relative abundance. Therefore, DGGE is often used to visualize the major differences in species among various samples. Further phylogenetic identification requires the bands of the DGGE gel to be excised, amplified using cloning or PCR, and sequenced. The length of the amplicons in PCR-DGGE is usually shorter than 500 bp (Rastogi and Sani, 2011).

The Roche 454 pyrosequencing platform has been developed as a high throughput and automatic system to examine microbial communities. As indicated in Figure 2.5, after DNA extraction, PCR is conducted using special primers, whose ends contain adapter sequences that can bind to the beads in the oil droplets in the 454 platform. One oil droplet contains one bead that only binds to one PCR fragment. Emulsion PCR is conducted with each oil droplet as an independent PCR chamber to obtain multiple copies of the same amplicon. The oil droplets are separated into different holes, thereafter undergo high throughput sequencing. The 454 platform allows massive sequencing in parallel. By incorporating 5- or 10-bp sequences (i.e., barcode) in the PCR primer in conjunction with the adapter sequences, multiple environmental samples can be pooled into one sequencing region of the machine. The third generation of the 454 sequencing platform (i.e., 454 GS FLX Titanium) reads sequence lengths typically ranging between 100 and

450 bp depending on the PCR primers, and yields approximately 400 million bases in a 10-hour instrumental run with accuracy >99.96% (Metzker, 2010).

Figure 2.5 Process of Pyrotag Sequencing

Quantitative PCR (q-PCR) is a popular technique to quantify specific microbial groups in a sample. The setup of q-PCR is similar to that of a regular PCR, except the addition of a reporter probe (e.g., a fluorescent molecule) that binds to the PCR products. In each PCR cycle, the number of amplicons is doubled, and so is the fluorescent signal. A threshold line is chosen when the growth of the fluorescent signal is still in the exponential range. The cycle at which the threshold line intercepts with the fluorescent signal curve is recorded. A calibration curve is obtained by plotting the cycle number at which the signal curve crosses the threshold line for each standard against its known DNA concentration. The DNA concentration of the test sample is estimated based on its threshold cycle using the calibration curve (Alberts et al. , 2002).

Another method to obtain the microbial fingerprints of the communities is fluorescent in situ hybridization (FISH). The FISH probes are generally 18-35 bp long with fluorescent dye at the 5’ end. The probes bind to specific regions of the 16S rRNA genes in the cells. The cells of interest, with the fluorescent signal, are detected using an epifluorescent microscope (Rastogi and

Sani, 2011).

The approaches presented above may have bias at every step: incomplete or preferential lyses of cells in the DNA extraction step, preferential hybridization and interference of inhibitory compounds during PCR, possible loss of PCR products during purification, formation of PCR 28 artifacts and sequencing error. It was recommended to use replicates, pooled DNA extracts and

PCR products when taking the culture-independent approaches (Rastogi and Sani, 2011).

• Applications of the Culture-Independent Approaches in the Community Studies

Related to Anaerobic Degradation

In the published studies, traditional clone library with Sanger sequencing, FISH and

PCR-DGGE were the major tools to characterize the microbial communities, while q-PCR was used to quantify specific microbial members of interest. For example, Satoh et al. (2007) used clone library with traditional Sanger sequencing to identify the microorganisms present in a lab- scale reactor treating powered skim milk. Based on the clone library results, the authors designed

FISH probes to study the spatial distribution of certain organisms in granules with layered structure. O’Reilly (2010) conducted q-PCR and PCR-DGGE, and observed the dominance of

Methanomicrobiales during granulation of sludge in bioreactors fed with glucose-based and

VFA-based wastewaters at 15°C.

Since 2007, there have been over 1200 published community studies involving pyrotag sequencing of the 16S rRNA genes. Among these publications, approximately 120 papers investigated the communities in samples from anaerobic treatment, anaerobic sediment or anaerobic culture degrading specific compounds. As shown in Figure 2.6, the number of the published studies using pyrotag sequencing to study anaerobic communities has increased dramatically since year 2008.

Figure 2.6 Distribution of Publications Using Pyrotag Sequencing to Study Anaerobic Communities Data generated based on searching results in the database of Compendex, Inspec, PaperChem, Geobase and GeoRef (up to October 2014)

One ultimate goal of the microbial characterization is to identify the functional roles of the organisms in their communities in order to better operate the reactors or to perform more efficient anaerobic treatment. Investigating the dynamics of the communities and correlating the abundance of organisms to the environmental factors and operation data can provide insight into the functional roles of organisms. The massive sequences generated by pyrotag sequencing offer an opportunity for systematic and statistical correlation and dynamic studies. Among the ~120 published studies using pyrotag sequencing to examine anaerobic communities, only seven papers statistically investigated the correlation between the phylogenetic abundance and the environmental parameters, and only two papers statistically examined the dynamics of organisms

(Appendix 2.1). Most of the remaining papers only focused on community compositions and microbial diversity (i.e. species richness). There is room for improving the understanding of the functional roles of anaerobic microorganisms.

Despite of the growing interest in applying anaerobic biotechnology to treat pulp and paper wastewaters and the increasing number of implementations in the industry, the studies of anaerobic sludge treating pulp and paper mill effluents are limited. As highlighted in blue in

Table 2.6, most of the work in this field remains at the stage of preliminary identification using the traditional clone library or FISH, and visualization of the major shifts using PCR-DGGE. 30

Little research effort has been dedicated to identify the possible organisms degrading constituents of pulp mill effluents, except the studies of a few methanogens, sulphate reducing bacteria and ammonia oxidizing organisms as listed in Table 2.6. Although pyrotag sequencing is a powerful tool to study a complex microbial system, there has been no publication of applying pyrotag sequencing to analyze the anaerobic sludge treating pulp mill effluents.

2.6 Concluding Remarks

The referenced physical and microbial examination methods provide us with tools to evaluate the degree of granulation and to characterize the microbial communities. Many of these methods have unknown reproducibility. As addressed by Laguna et al. (1999), reproducibility of the test is a critical factor when examining granular sludge. We need to select or develop reproducible methods of physical and microbial examinations that are suitable to our sludge samples.

Despite that anaerobic treatability and toxicity of pulp mill effluents and constituents have been studied extensively, the knowledge of their effect on granulation is limited. Further investigation into how granulation (e.g., granule size and strength) is impacted by anaerobic treatment of pulp mill effluents and constituents helps better understand the process failure and propose possible feeding strategies towards greater treatment efficiency and methane production.

Although the general steps in anaerobic degradation are well understood, compositions and dynamics of the sludge treating pulp mill effluents anaerobically are not known. Therefore, the microorganisms responsible for hydrolysis, fermentation, acetogenesis and methanogenesis in such treatments are still considered as a black box (Roest et al ., 2004). Further in-depth community studies benefit the development and optimization of anaerobic digestion systems on the macro scale. Using statistical tools to systematically assess the correlations between the

31 relative abundance of organisms and the environmental parameters and to investigate the dynamics of the communities, we can propose the possible functional roles of the major organisms in the anaerobic sludge digesting pulp mill effluents. Furthermore, by coupling the community studies and the physical properties of granules, we may be able to identify organisms possibly linked to granulation, and we can propose strategies to enhance granulation or reduce excessive degranulation in the treatment of pulp mill effluents.

CHAPTER 3. CHARACTERISTICS OF PULP MILL EFFLUENTS

3.1 Introduction

Three major waste streams from the pulp and paper mill Tembec Temiscaming were examined in this research. One stream was the integrated effluent from the bleached-chemi- thermal-mechanical pulping (BCTMP) plant. The other two streams came from the sulphite pulping plant: acid condensate (AC) from the evaporators and the alkaline effluent (AE). In the mill, primarily four types of sulphite pulp are produced: SW1, SW2, SW3 and HW. The SW1 pulp is the major sulphite pulping product. Currently, the mill uses two full scale internal circulation (IC) reactors to treat both BCTMP effluent and AC, as well as some grades of AE.

The main goal of this chapter is to understand the characteristics of the pulp mill effluents.

The constituents of BCTMP effluent, AC and AE were characterized by external labs, and at the

University of Toronto. The results are presented in this chapter. The knowledge of the constituents of pulp mill effluents helps better explain the differences in anaerobic treatability of various streams and their impact on granulation.

Following the introduction, the rest of the chapter is divided into seven parts. Brief descriptions of the pulping processes, the sources of BCTMP effluent, AC and AE, the wood chip compositions for various grades of sulphite pulp and general operational data of the full scale IC reactors are provided in section 3.2. Section 3.3 includes the information on the data source, the examination methods, and the details of the analyzed streams. The results of the organic constituents of BCTMP effluents, AC and AE are summarized in sections 3.4, 3.5 and

3.6 respectively. At the end of the chapter, the characteristics of BCTMP effluent, AC and AE are compared and summarized in section 3.7.

3.2 Process Overview, Effluent Sources and Various Grades of Sulphite Pulp

The knowledge of the pulping processes and materials is important to understand the stream compositions. The production processes in the BCTMP plant and in the sulphite pulping plant are introduced, and the sources of BCTMP effluent, AC and AE are stated (section 3.2.1).

The wood species used to produce various grades of sulphite pulp are also explained (section

3.2.2). The operational data of the full scale IC reactors in the mill are briefly summarized

(section 3.2.3).

3.2.1 BCTMP and Sulphite Pulping Processes

The BCTMP process is illustrated in Figure 3.1, where the pink arrows indicate the additions of chemicals, the brown lines represent the flows of wood fibres, and the blue lines show the flows of effluents. There were three in-mill streams generated from BCTMP: the effluent from the chip washer, the white water from the pulp cooker, and a minor stream from the presser and dryer after bleaching. All waste streams flew to a settler to remove fibres and other large particles. The effluent from the settler, which was the integrated effluent of the entire

BCTMP plant, was treated anaerobically.

The sulphite pulping process is explained in Figure 3.2, where the brown lines represent the flows of wood fibres, the pink arrows indicate the additions of chemicals, and the blue lines show the flows of effluents. The wood chips were first cooked with ammonium and sulphurous acid. The cooking chemicals, lignin compounds, as well as most of the soluble carbohydrates

(e.g., mannose and glucose), were washed off to the spend liquor. The crude spent liquor was sent to the evaporators for chemical recovery, while the remaining pulp was thickened. The thickened pulp was cooked again at a high pH to extract hemicellulose and the remaining resin acids and long-chain fatty acids (RFAs), followed by a washing step. The effluent generated from

34 this washing step was the alkaline effluent, which is also called AE in this study. The waste streams from sulphite pulping were subject to either aerobic treatment or anaerobic treatment, depending on the anaerobic toxicity and the volumes of the effluents. Batch assay results showed that the effluent from the screw press was inhibitory to anaerobic degradation (Yang et al ., 2010).

Therefore, the screw press effluent was treated aerobically. AE was characterized by a high concentration of organics (i.e. >20000mg COD/L, where COD stands for chemical oxygen demand), and concentrations of RFAs and lignins which were significant, but lower than those in the screw press effluent. The mill was in the process of optimizing the anaerobic treatment of AE using the IC reactors, by carefully selecting the appropriate AE from the production of various grades of sulphite pulp.

Figure 3.1 Simplified Diagram of the BCTMP Plant in the Mill

Figure 3.2 Simplified Diagram of the Sulphite Pulp Plant in the Mill

Acid condensate was generated from the chemical recovery process in sulphite pulping, where the spent liquor was boiled to recover sulphite. Figure 3.3 is a brief diagram of the chemical recovery process in the sulphite pulp plant in the mill. Three groups of products were formed: the vapours with high concentrations of acetic acid and SO 2, the remaining spent liquor with high concentrations of sugars, and the lignosulphonate and other particles depositing on the walls of the evaporators. The vapors were directed to a condenser, from which most of the SO 2 was recycled for sulphite pulping. The leftover SO 2 and acetic acid were diluted with water, and transferred to the spray tank. The remaining spent liquor after evaporation with high concentrations of sugars was directed to the alcohol plant. The evaporators were washed to remove particles on the walls using the liquor from the spray tank. The early wash effluent, containing majority of the lignosulphonate, was treated aerobically. The later wash effluent was relatively ‘clean’, so it was sent to the AC tank and was later treated anaerobically. The AC used in this study is referred to this clean rinsing wastewater in the AC tank. 36

Figure 3.3 Simplified Diagram of the Chemical Recovery Process in the Sulphite Pulp Plant in Tembec

3.2.2 Wood Species Used to Produce Various Types of Sulphite Pulp

As mentioned in the introduction, the sulphite pulp plant in the mill primarily produced four grades of pulp: SW1, SW2, SW3 and HW. The major compositions of wood chips used for each type of sulphite pulp are listed in Table 3.1. SW1, SW2and SW3 pulp were mainly produced from the softwood species spruce and pine. HW pulp was mainly produced from poplar that belongs to the hardwood class. Since resin acids only naturally exist in softwood (Markris and Banerjee, 2001) and AE contained large amounts of resin acids that were washed off from pulp, it was expected to see higher concentrations of resin acids in SW1, SW2 and SW3 AEs as compared to HW AE.

Table 3.1 Compositions of Wood Chips Used in Various Types of Sulphite Pulp Pulp Type Wood Chip Composition SW1 Mainly spruce and jack pine SW2 Mostly pine, spruce and balsa SW3 Mainly pine and spruce-balsa HW Mainly poplar

3.2.3 General Operation of the Full Scale Internal Circulation (IC) Reactors in the Mill

In early 2006, two full scale internal circulation reactors were implemented in the mill to treat in-mill streams and to generate methane. Each of the IC reactors has a 2600m 3 working volume, with 27m height and 11m diameter. As shown in Figures 3.1 and 3.2, BCTMP effluent and AC were mainly treated using the IC reactors (i.e., the anaerobic treatment plant shown in the flow charts), whose treated effluents were then directed to the aerobic treatment plant for further removals of organics. As mentioned in section 3.2.1, some AEs were treated using the IC reactors, while other AEs were treated aerobically, depending on the observed impact on the performance of the IC reactors (e.g., biogas production, sCOD removal and suspended solid content of the treated effluent).

Based on the operation data between year 2006 and 2010, BCTMP effluent, AC and AE were blended in a 2:1:1 volumetric ratio on average. The hydraulic retention time (HRT) was targeted at 4 to 7 hours. However, HRT was occasionally extended to longer hours (e.g., >10 hours) when the IC reactors experienced process upsets. The average organic loading rate was approximately 39kg COD*day -1*m 3 reactor -1. The total sludge inventory in both reactors was approximately 128 ton volatile suspended solids (VSS) on average, with approximately 50% fluctuations. The average specific loading rate was 0.81kg COD*day -1*kg VSS -1, with a maximum at 1.90kg COD*day -1*kg VSS -1.

3.3 Data Sources, Analytical Methods and Stream Samples

Analysis data of effluent characteristics mainly came from three sources: the analysis conducted in an external lab Exova (http://www.exova.com/) as retained by either the mill or the

University of Toronto, the measurements carried out at FPInnovations, and the examinations performed at the University of Toronto. A detailed list of all effluent samples, as well as data sources, is provided in Appendix 3.1.

Resin acid, long-chain fatty acid (LCFA) and tannin/lignin contents in the effluent samples were measured by FPInnovations and Exova. At FPInnovations, resin acid and long- chain fatty acid (RFA) concentrations were measured using gas chromatography (GC) according to Voss and Rapsomatiotis’ method (1985). Concentrations of tannin/lignin and various types of

RFAs in the effluents were also evaluated by Exova. In 2009, Exova tested several AC and AE samples associated with different types of sulphite pulp, which was referred to as ‘the 2009

Effluent Campaign’. In addition to the 2009 Effluent Campaign, Exova also measured the individual RFA in the AE used in the two continuous experiments in this research (i.e., FP AE and UofT AE). However, the detailed examination methods are not specified here, as they are proprietary to Exova.

At the University of Toronto, high performance liquid chromatography (HPLC) tests were conducted to quantify volatile fatty acids (VFAs), alcohols and simple sugars. The HPLC method was similar to the one used by Vascondelos de Sa et al. (2011). Detailed methods and calibration curves are presented in Appendix 3.2. Alcohols and simple sugars were only detectable in the refractive index (RI) channel, so the calibration curves for these compounds were constructed using the RI signals. In contrast, the UV channel (at 210nm) was more sensitive to VFAs than the RI channel, so the calibration curves for VFAs were constructed using the UV signals. Using 31 standard compounds as references, peaks in the chromatogram of each effluent sample were identified based on two factors: the retention time of the peak, and the relative signal strength (i.e., peak area) between the RI channel and the UV channel.

In addition to HPLC analysis, standard Bradford tests (Bradford, 1976) and phenol- sulphuric acid tests (Dubois et al. , 1956) were also carried out at the University of Toronto to measure the total protein content and the total carbohydrate content in the effluent samples.

3.4 Results of the Characteristics of BCTMP Effluent

The characteristics of the Tembec BCTMP effluents and similar streams found in literature are compared in Table 3.2 which includes five BCTMP effluent samples from Tembec and three CTMP effluents from literature. The COD concentrations of the Tembec BCTMP effluents were similar to those in the literature. VFAs contributed to approximately a third of the soluble COD (sCOD) in the Tembec BCTMP effluents, consistent with the literature values.

Lignin/tannin compounds contributed to 15-30% of total COD in the Tembec BCTMP effluents, which were slightly lower than the published values. The percentages of carbohydrates and RFAs in the Tembec BCTMP effluents also seemed to be lower than the literature values. The different concentrations might be due to the differences in the examination methods, the wood materials used for pulping, and the streams in the pulp plant that were included in the final effluent.

Table 3.2 Comparison: Tembec BCTMP Effluents vs. other CTMP Effluents in Literature Dubeski et Pichon et Oct 15, 18 May 2010, Habets et al. June 2006, al. al . and 25, 2010 Tembec (FP (1991) Tembec (2001) (1988) Tembec Study) CTMP with CTMP with CTMP with CTMP with CTMP CTMP Wastewater peroxide peroxide peroxide peroxide (softwood) (softwood) bleaching bleaching bleaching bleaching Total COD NA 5900-8300 10000 13500 (s) 5700-7700 (s) ~9600 (s) (mg/L) Carbohydrates NA NA 9-14 NA ~6 (s) NA (%COD) VFA (%COD) NA 15-43 37 NA 35-42 (s) NA Alcohols NA NA NA NA 3-4 (s) NA (%COD) Lignin/Tannin NA NA 40-50 15 NA 30 (%COD) Total Resin 11-16 2-22 NA NA Acids (%COD) Total Long- 0.2 0.3 chain Fatty 5-10 NA NA NA Acids (%COD) NA: data not available (s): soluble COD

Table 3.3 summarizes the HPLC results of three BCTMP effluent samples collected from

Tembec in 2010. All the major peaks, each with area > 2% of the total area in the chromatogram, were identifiable in each BCTMP effluent sample. The identified compounds together

40 contributed to 43 to 50% of the total sCOD in BCTMP effluents. Acetate was the most dominant simple organic detected in BCTMP effluent, accounting for approximately one third of the sCOD in each sample, consistent with the findings by Habet et al. (1991). Glucose was the most dominant carbohydrate detected in BCTMP effluents, followed by xylose. Methanol contributed to 3-4% of the effluent sCOD. Small amounts of succinate, formate and citrate were also present in BCTMP effluents.

Table 3.3 HPLC Analysis of Tembec BCTMP Effluents Collected in 2010 BCTMP BCTMP BCTMP Unit Oct 15, 2010 Oct 18, 2010 Oct 25, 2010 mg COD/L 2600 2290 2410 Acetate %sCOD 34 40 33. mg COD/L 250 220 230 Glucose %sCOD 2 4 3 mg COD/L 140 120 150 Xylose %sCOD 3 2 2 mg COD/L 240 210 230 Methanol %sCOD 3 4 3 Succinate , Formate mg COD/L 130 120 120 and citrate %sCOD 2 2 2 Please note that sCOD was used here, as the original BCTMP effluent had high fibre content. The total COD of the BCTMP effluent after removing particles > 500µm by sieving was about 25% higher than its sCOD value

When examining the variations among different BCTMP samples, for each compound identified using HPLC, the concentrations across all samples were similar (Table 3.3), implying that the constituents in BCTMP effluent were relatively stable. Nevertheless, since these three

BCTMP effluent samples were collected within a relatively short period of time, the long-term variations in the BCTMP effluents were unknown.

3.5 Results of the Characteristics of Acid Condensate (AC)

The characteristics of the Tembec AC are presented in this section. First, the major organic constituents of the Tembec AC are compared to those found in literature. Secondly, the

AC samples associated with different grades of sulphite pulp are examined. In particular, the lignin/tannin and RFA contents in various ACs are compared, as these compounds are cited

41 inhibitors in the anaerobic treatment of pulp mill effluents. Moreover, the HPLC results of two

SW1 AC samples are also compared to study the consistency in AC constituents.

The major constituents of the Tembec AC are compared to those of two similar streams in literature in Table 3.4. The COD concentrations of the Tembec ACs were similar to the literature values. Acetate was the largest organic constituent in the Tembec AC, contributing to approximately 30% of the total sCOD, consistent with Aivasidis’ results (1983). Similar to both streams in literature, the Tembec AC also contained methanol, ethanol and furfural. Furthermore, although lignin/tannin compounds and carbohydrates were not measured in the studies conducted by Aivasidis (1983) and Benjamin et al . (1984), these compounds were found in Tembec AC. As mentioned earlier, the Tembec AC included the rinsing waste stream from cleaning the evaporator walls, which could be the source of lignin/tannins and carbohydrates.

Table 3.4 Comparison between the Tembec AC and the AC (from Sulphite Pulping) in Literature Aivasidis Benjamin Nov 11 and 12, Dec 2009 to Jan Oct 15, and 25,

(1983) et al.(1984) 2008, Tembec 2010 2010 Tembec Total COD 7500- 11380-12950 4700 8250-17600 8150-13400 (s) (mg/L) 50000 (s) SW1, SW2, SW3, Type of AC Unknown Unknown SW1 SW1 HW Carbohydrates NA NA NA NA 4-9(s) (%COD) Acetate 27-68 >57 NA 15-31 27-31 (s) (%COD) Propionate NA 3-6 NA NA Not detected (%COD) Methanol 1-9 16-27 NA NA 4-6 (s) (%COD) Ethanol NA 3-5 NA NA 7-11 (s) (%COD) Furfural 2-9 5-9 NA NA 6-12 (s) (%COD) Lignin/Tannin NA NA 6-26 (s) 6-13 NA (%COD) Total RFA NA NA ~0.2 (s) 0.1-0.4 NA (%COD) NA: data not available (s): soluble COD

The concentrations of tannin/lignin and RFAs in different Tembec AC samples are compared in Figures 3.4 and 3.5 respectively. The concentrations of tannin/lignin contained in

42 various types of ACs were not significantly different (p values in two-tailed t-tests >0.16). On average, these compounds made up approximately 5-9% of the total COD in AC. SW1 and SW2

ACs might contain less RFAs as compared to SW3 and HW ACs. However, due to the relatively large variations within the triplicates, the differences among various ACs were not statistically significant. Nevertheless, RFAs were only present in minor amounts in all AC samples, i.e., <50 mg/L and <0.5% of total COD.

% Total COD due to Lignin-Tannin 1500 Lignin-Tannin Concentrations 15

1000 10

mg COD/L mg 500 5 %Total COD %Total 0 0 SW1 SW2 SW3 HW Figure 3.4 Concentrations of Tannin-Lignin (Blue Bars) and %Total COD due to Tannin-Lignin (Red Bars) in Various Types of AC in the 2009 Effluent Campaign (Exova): not Significantly Different Error bars: 95% confidence intervals of samples collected on three different days

% Total COD due to RFAs 70 RFA Concentrations 0.5 60 0.4 50 40 0.3

30 0.2 mg COD/L mg 20 COD %Total 0.1 10 0 0.0 SW1 SW2 SW3 HW Figure 3.5 Concentrations of RFAs (Blue Bars) and %Total COD due to RFAs (Red Bars) in Various Types of AC in the 2009 Effluent Campaign (Exova): not Significantly Different Error bars: 95% confidence intervals of samples collected on three different days

HPLC tests were conducted to study the simple organics present in two SW1 AC samples and to evaluate the variations between them (figures presented in Appendix 3.4). The concentrations of the major constituents (i.e., acetic acid, methanol, ethanol and furfural) were 43 comparable in both samples (<30% difference), implying that the constituents of the SW1 AC were relatively stable.

3.6 Results of the Characteristics of Pulp Washer Effluent (AE)

The characteristics of AE are explained in this section. Variations among different types of AE were compared in the 2009 Effluent Campaign (measured by Exova). Because SW1 was the major sulphite pulp product, HPLC tests were performed to investigate the simple organic constituents and the variations among several SW1 AE samples. Additional analysis was also carried out to estimate the concentrations of total carbohydrates and total proteins in SW1 and

SW2 AE samples.

3.6.1 Comparison of Various Types of AE

In the 2009 Effluent Campaign, each type of AE included three samples collected on different days. The results of COD, BOD, sulphite and ammonium are shown in Table 3.5 as a heat map: reddest colour indicates the highest concentration in the row; greenest colour is for the lowest concentration; a white cell implies no data available. As shown in the table, compared to other samples, SW1 AE contained relatively more COD, BOD, sulphite and ammonium. In other words, SW1 AE was a concentrated stream.

Table 3.5 2009 Effluent Campaign: COD, BOD, Sulphite and Ammonium among Various Types of AEs Notes: red: highest concentration in the row; green: lowest concentration in the row; white: no data available

As shown in Figure 3.6, approximately 2000-4000mg COD/L tannin and lignin were contained in AE, contributing to 10-30% of the total COD in the effluents. In terms of variations,

44 the concentrations of tannin/lignin contained in SW1 AE were not significantly different than other types of AE (p values in two-tailed t-tests >0.18).

SW1 AE contained significantly more long-chain fatty acids as compared to other streams

(P values in one-tailed t-tests <0.008). As displayed in Figure 3.7, approximately 60mg COD/L

LCFAs were included in SW1 AEs, which was at least six times higher than the concentrations in other types of AE.

% Total COD due to Lignin-Tannin Lignin-Tannin Concentrations 6000 50

5000 40 4000 30 3000 20 2000 mg COD/L mg %Total COD %Total 1000 10 0 0 SW1 SW2 SW3 HW Figure 3.6 Concentrations of Lignin-Tannin (Blue Bars) and %Total COD due to Lignin-Tannin (Red Bars) in Various Types of AE in the 2009 Effluent Campaign (Exova): Not Significantly Different Error bars: 95% confidence intervals of samples collected on three different days

% Total COD due to LCFAs 0.3 LFA Concentrations 80

60 0.2

40 0.1

20 COD %Total mg COD/L mg

0 0.0 SW1 SW2 SW3 HW Figure 3.7 Concentrations of Long-Chain Fatty Acids (Blue Bars) and their %Total COD due to LCFAs (Red Bars) in Various Types of AE in the 2009 Effluent Campaign (Exova): Significantly Higher in SW1 AE Error bars: 95% confidence intervals of samples collected on three different days

SW1, SW2 and SW3 AEs contained more resin acids than HW AE, as shown in Figure

3.8. The lower resin acid content in HW AE was due to the lower percentage of softwood chips used to produce the HW pulp as compared to other grades. Resin acids contributed to up to 3.6% of the total COD in AEs. The resin acids detected in AEs included pimaric, sandaracopimaric,

45 isopimaric, palustric, dehydroabietic, abietic and neoabietic acids. Table 3.6 shows the concentration of each resin acid in all samples in a heat map form: in each row, a redder cells means a higher concentration; a greener cells indicates a lower concentration; a white cell means that the concentration was under the detection limits. For most of the detected resin acids, the highest concentrations were found in SW1 AE, and the lowest concentration was often found in

HW AE.

% Total COD due to Resin Acids Resin Acid Concentrations 500 4

400 3 300 2 200 mg COD/L mg 100 1 %Total COD %Total 0 0 SW1 SW2 SW3 HW Figure 3.8 Concentrations of Resin Acids (Blue Bars) and % Total COD due to Resin Acids (Red Bars) in Various Types of AE in the 2009 Effluent Campaign (Exova): Significantly Lower in HW AE Error bars: 95% confidence intervals of samples collected on three different days

Table 3.6 Heat Map (2009 Effluent Campaign): Comparing Concentrations of each Resin Acid in all AE Samples Notes: redder: higher concentration in the row; greener: lower concentration (above detection limit) in the row; white: under detection limit

Although the data were not presented here, analyses of tannin/lignin and RFAs were also conducted for two other SW1 AE samples: FP AE and UofT AE (Appendix 3.4). The concentrations of tannin/lignin and RFAs in the UofT AE were comparable to those in the SW1

AEs in the 2009 Effluent Campaign. The FP AE was found to have higher concentrations of tannin/lignin and RFAs than other SW1 AEs.

3.6.2 Results of the HPLC Analysis of SW1 AE

As mentioned earlier, SW1 accounted for nearly 50% of the sulphite pulp production in

Tembec. HPLC tests were conducted to study the major organics present in a few SW1 AE samples collected at various time points: the FP AE (May 2010), Oct 15 and 25 AE (October

2010) and the UofT AE (January 2012).

In general, similar peaks were observed for all samples in the RI channel and the UV channel respectively, implying that the major constituents in all tested AE samples were similar

(Appendix 3.5). An example of the chromatogram is presented in Figure 3.9, and the concentrations of the identified compounds and the values of the area under the unknown peaks are summarized in Table 3.7. As illustrated in Figure 3.9, eight major peaks in the UV channel could not be identified, each of which showed stronger responses to the UV detector at 210nm than to the RI detector. Therefore, these compounds might contain ester, aldehyde, carboxyl, sulphoxide, nitro or nitrile groups (Karger and Hancock, 1996). The concentrations of each major

VFA in most AE were similar, and the area values of each unknown peaks were also comparable.

FP AE was one exception, which showed a higher concentration of acetic acid and lower concentrations of formic acid and xylose as compared to other SW1 AEs. Interestingly, no substantial difference was observed between SW1 AE and SW2 AE. AEs

It should be noted that the separation of compounds in HPLC depends on the interaction among the mobile phase, the packing material and the analytes. If two compounds have similar interactions with the packing material and the mobile phase, they might be eluted at the same time. If these two compounds also have similar responses to the RI and UV channels, they could be mistakenly identified as one compound. For example, xylose and lactate were added to AE samples as internal standards, and their presence in AE was confirmed by using the current

HPLC method (Appendix 3.6). However, the so-called xylose and lactate peaks of AE might also

47 be compounds that interacted with the column and the mobile phase in ways similar to xylose and lactate. Therefore, it is recommended to run a different type of test to confirm the identified peaks listed in Table 3.7. The presence of acetic acid and formic acid in the AE samples was confirmed using ionic chromatography (IC) (Appendix 3.1).

Figure 3.9 HPLC Chromatograms of SW1 AE (Oct25): RI Channel on the Left and UV Channel on the Right UnID: unidentified peaks

Table 3.7 Major Peaks in the HPLC Chromatograms of SW1 and SW2 AE Relative Retention FP AE UofT AE Oct15 Oct25 Oct18

Time * (SW1) (SW1) (SW1) (SW1) SW2 Total COD (mg/L) 31800 20800 23800 22800 22100

Formic Acid (mg/L) 0.93 1700 5200 3300 5100 6000 Acetic Acid (mg/L) 1.00 3600 2100 2600 2400 2200 Lactic Acid (mg/L) 0.87 5300 3700 4400 4200 4200 Maleic Acid (mg/L) 0.61 250 200 250 200 250 Xylose (mg/L) 0.66 90 250 480 270 370 Total Contribution to AE COD 30 45 35 35 36 (%) Unidentified 1 (UV, mAU) 0.42 2350 3040 2670 1020 1220 Unidentified 2 (UV, mAU) 0.52 370 510 454 445 460 Unidentified 3 (UV, mAU) 0.56 150 180 1160 170 170 Unidentified 4 (UV, mAU) 0.69 110 80 100 90 90 Unidentified 5 (UV, mAU) 0.76 240 170 200 200 240 Unidentified 6 (UV, mAU) 0.83 140 100 80 110 50 Unidentified 7 (UV, mAU) 0.97 110 60 100 110 90 Unidentified 8 (UV, mAU) 1.07 60 50 60 60 70 * relative retention time = retention time of the compound / retention time of acetate

3.6.3 Total Carbohydrates and Total Proteins in SW1 and SW2 AEs

Xylose was the only identified carbohydrate detected in SW1 and SW2 AEs using HPLC.

The phenol-sulphuric acid test (PSA) was conducted to detect mono-, oligo- and polysaccharides in AE (DuBois et al ., 1956). One SW2 and two SW1 AE samples were examined using the PSA test. In this study, glucose was used to construct the calibration curve, so the results of the PSA test were expressed in terms of glucose equivalent (mg/L).

As shown in Figure 3.10, all samples contained carbohydrate contents greater than

1000mg glucose equivalent /L. The HPLC results showed that the concentrations of xylose in

SW1 AE collected on Oct 15 and Oct 25was 480 and 270 mg/L respectively, which seemed to be lower than the concentrations estimated using the PSA test. Therefore, besides xylose, AE also contained carbohydrates that were not detected using the current HPLC method. SW2 AE had a lower concentration of carbohydrates as compared to the SW1 AEs, while the two SW1 samples shared similar carbohydrate concentrations.

In Bradford tests, SW1 AEs were found to have higher protein concentrations than SW2

AE. As shown in Figure 3.10, approximately 1100mg COD/L proteins were contained in SW1

AE, contributing to 4-5% of effluent sCOD. On the other hand, the protein concentration in SW2

AE was only 560mg COD/L, accounting for 2% sCOD in the sample.

PSA Protein 1500 1500

1000 1000

500 500 Carbohydrates Carbohydrates (mg glucose eq/L) glucose (mg 0 0 COD/L) (mg Proteins Oct 15 (SW1) Oct 25 (SW1) Oct 18 (SW2) Figure 3.10 Results of Phenol-Sulphuric Acid Tests (Red) and Total Protein Tests (Blue): Lower in SW2 AE Error bars: 95% confidence intervals of triplicate tests

3.6.4 Summary of the AE Characteristics

Various types of AE were compared in the first half of this section. While there was no significant difference in the concentration of tannin/lignin among different types of AE, higher concentrations of COD, BOD, sulphite, ammonium and long-chain fatty acids were observed in

SW1 AE. HW AE was also found to contain lower concentrations of resin acids than the softwood-based AE. Furthermore, the HPLC results demonstrated that both SW1 AE and SW2

AE consisted of similar simple organics. However, the results of the total carbohydrate test and total protein test suggested that SW1 AE contained higher concentrations of sugars and proteins than SW2 AE. In general, SW1 AE was a concentrated stream. Within SW1 AE itself, the constituents of several SW1 AE samples seemed to be relatively comparable.

In terms of the major compositions of SW1 AE, the VFAs and xylose identified in HPLC contributed to up to 45% of sCOD in SW1 AE. SW1 AE consisted of 4-5% proteins on a COD basis. Moreover, the carbohydrate contents in SW1 were equivalent to 1000mg glucose/L.

Tannin/lignin, resin acids and long-chain fatty acids were also present in SW1 AE.

3.7 Summary of the Characteristics of BCTMP Effluent, AC and AE

A summary of BCTMP effluent, AC and SW1 AE is given in Table 3.8. Volatile fatty acids were the most dominant constituent in all effluents, accounting for one third to half of sCOD in each wastewater. SW1 AE contained the most VFAs, followed by AC then BCTMP effluent. While VFAs in BCTMP effluent and AC were largely dominated by acetic acid, acetic, formic and lactic acids were present at comparably high concentrations in SW1 AE. Alcohols were contained in both BCTMP effluent and AC, but were not identified in SW1 AE. AC contained six times more alcohols than BCTMP effluent. Methanol was the only detected alcohol in BCTMP effluent. Ethanol and furfural were only noticed in AC. AC contained the most detected simple carbohydrates. AC and BCTMP effluent included both xylose and glucose, but

SW1 AE only contained xylose. VFAs, alcohols and carbohydrates are all useful to anaerobic

treatment, as these components can be degraded by anaerobic microorganisms to produce

methane. A higher concentration of these compounds implies a greater methane production

potential.

Table 3.8 Summary of BCTMP Effluent, AC and SW1 AE Unit BCTMP AC SW1 AE mg sCOD/L ~2600 2600-3600 10200-12100 VFAs (from HPLC) 35-42 (acetic 27-31 (almost 30-43 (acetic, formic and lactic % sCOD acid dominant) solely acetic acid) acids) mg COD/L ~230 ~1400 ND Alcohols (from HPLC) % sCOD 3-4 11-17 ND Carbohydrates (Monomers, mg COD/L ~380 500-700 250-480 from HPLC) % sCOD ~6 ~12 ~2 Furfural (from HPLC) % sCOD ND 10-14 ND mg COD/L 1800-3000 520-1100 2380-5470, ~15500 (FP AE) Tannin/Lignin (Exova, FP) %COD* 14-30 6-13 9.5-14, 44% (FP AE)** mg COD/L 15 8-11 140-420, ~1500 (FP AE)** Total Resin Acid (Exova, FP) %COD 0.1 ~0.1 0.6-1.4, 4.3(FP AE)** Total Long-Chain Fatty mg COD/L 0.3 ~1 36-138, 385 (FP AE)** Acids (Exova, FP) %COD* 0.01 ~0.01 0.2-0.7, 1.1 (FP AE)** Proteins (Bradford) %sCOD NA NA 4.4 %COD Indentified %COD* 58-82 56-80 47-68 NA: data not available; ND: not detected; *: sCOD; **: measured by FPInnovations

On the other hand, tannin/lignin compounds, resin acids and long-chain fatty acids are not

preferable for anaerobic treatment, as they are either undegradable or inhibitory to anaerobic

microorganisms. Tannin/lignin compounds were observed in all effluent samples, contributing to

6-30% of the effluent COD. Resin acids and long-chain fatty acids were also detected in all

effluent samples. The considerably high concentrations of resin acids and long-chain fatty acids

were the most distinct feature of SW1 AE as compared to BCTMP effluent and AC.

In summary, the organics measured in various assays and tests conducted by Exova and

FPInnovations and at the University of Toronto contributed to approximately 50-80% of the

COD in BCTMP effluent, AC and SW1 AE. The remaining unknown COD contents in AC and

BCTMP effluent might belong to proteins or carbohydrates that could not be detected using

HPLC. The remaining unknown COD portions of SW1 AE could be the compounds corresponding to the unidentified peaks.

Variations within effluents were also examined. The greatest variation was found among

AE samples associated with different types of sulphite pulp. SW1 AE was a concentrated stream, containing higher concentrations of COD, sulphite, ammonium, long-chain fatty acids and many resin acids than other types of AE. Compared to the variations among different types of AE, the constituents of each of SW1 AE, BCTMP effluent and AC were relatively stable, i.e., the difference was not statistically significant or only minor variations were observed.

CHAPTER 4. DEVELOPMENT OF METHODS TO STUDY THE PHYSICAL PROPERTIES AND THE MICROBIAL COMMUNITIES OF GRANULAR SLUDGE

4.1 Introduction

In this research, the effect of pulp mill effluents on granular sludge was investigated by comparing the physical properties and the microbial communities of different sludge samples.

Therefore, it was important to choose the appropriate methods to quantify granulation and to examine the microbial communities of sludge. The chosen methods should provide reproducible results with minimum amounts of sludge samples.

As presented in Chapter 1, the first objective of this research was to develop methods for physical examinations and microbial studies. In this chapter, the assessment and evaluation of various methods are presented. The development of the physical examinations mainly focused on particle size distribution and granule strength. Several molecular methods were investigated to evaluate and identify the best method for microbial studies. The rest of this chapter is divided into four sections: the method development for particle size distribution analysis (section 4.2), the method development for granule weakness test (section 4.3), the evaluation of different molecular methods (section 4.4), and an overall summary of the chapter (section 4.5).

4.2 Development of Methods to Test Particle Size Distribution

4.2.1 Sludge Samples and Methods

Two types of anaerobic sludge were used to develop the methods of physical examinations of sludge: the sludge collected from the full scale internal circulation (IC) reactors in Tembec treating pulp mill effluents (Tembec sludge), and the sludge collected from an anaerobic wastewater treatment plant dealing with potato food waste streams (food sludge). As

53 shown in Figure 4.1, compared to Tembec sludge, particles in food sludge were larger in size and had more defined round shape.

Figure 4.1 Food Sludge was Larger than Tembec Sludge

The particle size distribution of sludge was examined using a combined method of wet- sieving and image analysis. Each sample was tested in four replicates for its particle size distribution. As shown in Figure 4.2, a sludge sample was first sieved using a sieving dish with a

200µm 1 pore size. Two hundred micrometers was chosen as the cut-off value because of both the detection limit of the camera and the definition of ‘fine particles’ in literature (Pereboom, 1997;

Batstone and Keller, 2000). Granules (> 200µm) were transferred to a clear Petri dish and submerged in water. Granules were manually separated from each other to ensure clear visualization. A digital image was captured using G:BOX (by SYNGENE) and GeneSnap software, with the following settings: close iris = 7.1, zoom out = 19.9, focus = 95, exposure time

= 80ms, lighting source = upper white, no filter. Images were saved in JPEG format. The images were processed and analyzed using the software ImageJ to calculate the numbers of particles present in the size ranges of 200-500µm, 500-1000µm, 1000-1500µm, 1500-2000µm, and >

2000µm. Furthermore, total suspended solid (TSS) and volatile suspended solid (VSS) tests were carried out for both portions (> 200µm and <200µm) according to standard methods (Eaton et al. ,

1998). The percentages of TSS and VSS present in particles larger than 200µm in a sludge sample were calculated using Equations 4.1 and 4.2.

1 500µm was used in the preliminary tests, but later sludge from continuous reactors was sieved using 200µm pore size 54

Figure 4.2 Procedures in the Combined Method of Wet-Sieving and Image Analysis

%TSS>200µm = ∗ % Equation 4.1 %VSS>200µm = = ∗ % Equation 4.2

In addition to wet sieving and image analysis, a laser scattering analyzer (Horiba LA-920) was used as a different method to measure particle size distribution of sludge.

4.2.2 Results of the Method Development for Particle Size Distribution Analysis

As shown in Figure 4.1, food sludge was larger in size, and had a more defined round- shape structure than Tembec sludge. Therefore, the particle size distribution test was expected to show that food sludge had significantly greater percentages of larger particles than Tembec sludge.

Three methods were attempted to analyze particle size distribution of food sludge and

Tembec sludge, including wet-sieving, image analysis, and a test using a laser scattering analyzer.

Neither image analysis nor wet-sieving produced satisfactory reproducibility. The sizes of the standard deviations of four replicates exceeded one third of the mean values. Consequently, neither wet-sieving nor image analysis was able to show that food sludge was significantly larger than Tembec sludge. More replicates would be needed in order to reduce the sizes of the standard

55 deviations. However, this could be challenging, as more replicates also implied more labour input and a larger amount of sample. A laser scattering analyzer was also used to measure particle size distribution of sludge. When the same sludge sample was examined twice using the analyzer, higher percentages of smaller particles were observed in the second round compared to the first run, probably due to the grinding effect of the impeller in the sample mixing chamber. Therefore, none of these tested methods alone was suitable to study the particle size distribution of sludge.

In the next step, a method combining wet-sieving and image analysis was examined. Four sludge samples were prepared: the intact Tembec sludge, the vortexed Tembec sludge, the intact food sludge, and the vortexed food sludge. The vortexed sample was the intact sample vortexed at 30Hz for 5 minutes. The vortexed samples experienced a mechanical disruption, so the particles in the vortexed sample were smaller than those in the corresponding intact sample. The particles in each sample were separated into two size ranges using wet sieving: ‘< 500um’ and

‘>500um’. The following observations were expected:

• in the tests of the percentages of total suspended solids and volatile suspended

solids contained in particles larger than 500um in a sample, the intact food sludge

should have greater %TSS>500um and %VSS>500um than the intact Tembec sludge,

and the intact sample should also have greater %TSS>500um and %VSS>500um than

the corresponding vortexed sample;

• in the image analysis for particles larger than 500um, the intact food sludge should

contain higher percentages of larger particles than the intact Tembec sludge; the

intact sample should contain greater percentages of larger particles than the

corresponding vortexed sample.

As shown in Figure 4.3, the results of %TSS>500um and %VSS>500um agreed with our expectation. The intact food sludge contained significantly higher %TSS>500um and %VSS>500um 56 than the intact Tembec sludge. Intact food sludge and intact Tembec sludge also showed significantly greater %TSS>500um and %VSS>500um than the corresponding vortexed samples (all P values <0.03 in one-tail t-tests).

90 TSS% (>500µm) VSS% (>500µm) 60

30 Weight % Weight

0 Intact Food Sludge Vortexed Food Intact Tembec Vortexed Tembec Sludge Sludge Sludge

Figure 4.3 %TSS>500um and %VSS>500um : Intact Food Sludge > Intact Tembec Sludge; Intact Sludge > the Corresponding Vortexed Sludge Error bars: 95% confidence intervals of 4 replicates

The results of particle size distribution from image analysis are presented in Figure 4.4.

Most of the intact food sludge had diameters between 1 and 2mm. The majority of the vortexed food sludge had diameter between 0.75 and 1.5mm. The distribution of the intact Tembec sludge mainly fell in the range of 0.75 to 1.5mm. The intact Tembec sludge had significantly greater percentage of particles larger than 1.5mm than the vortexed Tembec sludge. The comparison between the intact food sludge and the intact Tembec sludge, as well as intact samples vs. vortexed samples, agreed with our expectation.

In terms of sample volume, as low as 20mg TSS sludge was sufficient for one replicate in the combined method of wet-sieving and image analysis. This small sample volume allowed the usage of ≥4 replicates, even in the case of batch assays where the amount of sludge was limited

(e.g., < 100 mg TSS sludge per bottle).

In general, a combined method of wet-sieving and image analysis was able to show the expected differences in particle size distribution among samples, with reasonable reproducibility and relatively small sample size. This method can be used as a basic tool to examine the degree of granulation. In later experiments, after method refinement and improvement, 200 µm was used 57 as the new cut-off size in wet-sieving, as 200 µm was the lowest detection limit of the camera used in the image analysis in this research and aggregated solids smaller than 200 µm were frequently quoted as fine particles instead of granules (Pereboom, 1997; Batstone and Keller,

2000).

Figure 4.4 Distribution for Particle > 500um from Image Analysis: Intact Food Sludge Larger than Intact Tembec Sludge; Intact Sludge Larger than the Corresponding Vortexed Sludge Error bars: 95% confidence intervals of 4 replicates

4.3 Development of Granule Weakness Test

4.3.1 Sludge Samples and Methods

Food sludge and Tembec sludge, as shown in Figure 4.1, were used to develop the granule weakness test.

Physical disruption causes an increase in turbidity due to the release of biomass from granules. The degree of increase is affected by the strength of granules. If a sample contains weaker granules, there is more detached biomass after disruption. Usually the detached biomass does not settle as well as the biomass in the granules, so a higher turbidity results. In general, a sample is said to have weaker granules if a greater change in turbidity is observed after disruption

(Teo et al ., 2000).

The granule weakness test used in this study is a modified version of Teo’s method

(2000). In Teo’s study, granules were shaken at 150 rpm for 3 minutes, and the change in 58 turbidity was measured using a turbidimeter. It was found that shaking at 150 rpm for 3 minutes was not aggressive enough to cause any noticeable change in the turbidity of Tembec sludge.

Therefore, vortexing was chosen as the physical disruption instead of shaking. Furthermore, in this research, the turbidity of supernatant was evaluated by measuring the absorbance at 650nm using a spectrophotometer instead of a turbidimeter.

In the modified granule weakness test, a sludge sample was transferred to a clear glass tube (16mm Hach glass vial), capped, diluted to 8ml with distilled water, and allowed to settle overnight at 4 oC. On the next day, the sample was brought back to room temperature. The sample was thoroughly mixed by gently inverting the tube six times, and allowing it to settle for three minutes. The initial turbidity after settling was measured as the absorbance at 650nm using a spectrophotometer (Rose Scientific Ltd., Model 6320D). The sludge ample was then subject to physical disruption by vortexing.

In the preliminary test, various sample volumes, vortex frequencies and duration were examined, as shown in Table 4.1. After vortexing, the sample was gently inverted, and the final turbidity was measured again as described above. In a granule weakness test, each sludge sample was tested in four replicates, with a fifth tube serving as the blank for the turbidity measurement.

The blank sample was exempt from physical disruption. In addition to the weakness of the granules, the amount of TSS present in a sample also influenced the change in turbidity.

Therefore, the change in turbidity (i.e., absorbance) after vortexing was further normalized to the

TSS of the sludge sample to minimize the bias caused by the sample size, as shown in Equation

4.3. The normalized change in turbidity was used as an indicator of granule weakness. A greater change in the turbidity (normalized) implied a weaker sludge sample.

– Normalized Change in Absorbance = Equation 4.3

Table 4.1 Vortex Duration, Frequencies and Sample Volumes in the Preliminary Granule Weakness Test Sample Volume 1ml, 2ml, 4ml and 6ml Vortex Duration 1 minute, 2 minutes, and 3 minutes Vortex Frequency 10Hz, 20Hz, 30Hz and 40Hz

4.3.2 Results of Method Development of the Granule Weakness Test

In the preliminary experiment, various vortex frequencies, vortex duration, and sample volumes were tested, and the results are presented in this section. The chosen test parameters were then applied to food sludge and Tembec sludge to investigate the sensitivity and reproducibility of the method.

Different vortex duration and vortex frequencies were examined to identify the optimal test conditions, as shown in Figure 4.5. When samples were vortexed at 10Hz, the tubes were barely shaken. When a vortex frequency of 40Hz was applied, the tube holder could not secure the tubes properly. Therefore, 10Hz and 40Hz were excluded. Vortexing at 30Hz led to larger variations within replicates compared to the samples vortexed at 20Hz for the same duration, so

20Hz was preferred over 30Hz. In terms of vortex duration, longer vortexing led to greater changes in absorbance, due to more detachment of biomass. For samples vortexed at 20Hz, one- minute vortexing caused little change in absorbance. Based on the results of the tested parameters, vortexing at 20Hz for 2-3 minutes was recommended.

The effect of sample volume was also studied. The changes in the absorbance shown in

Figure 4.5 were values after normalization to the TSS of sludge using Equation 4.3.

Normalization to sample TSS minimized the bias in the changes in absorbance caused by variations in sample volumes. In other words, it was expected that the normalized changes in absorbance should stay relatively the same regardless of the sample volume. This was observed for the chosen test conditions described above, i.e., vortexing at 20Hz for 2-3 minutes. The independence of the normalized change in absorbance on sample volume had the advantage of using a minimum amount of sludge to examine granule weakness. As little as 20mg TS sludge, 60 which was less than 20% of the inoculum normally contained in a regular batch assay bottle, was sufficient for one replicate in the granule weakness test.

Figure 4.5 Effect of Vortex Duration, Vortex Frequency and Sample Volume on the Granule Weakness (Calculated as the Change in Absorbance Normalized to TSS) Error bars: 95% confidence intervals of 4 replicates The raw readings for a 6ml sample vortexed at 30Hz and 20 Hz for 3 minutes were beyond the upper reading limit, so no data were recorded.

In order to verify that the granule weakness test described above could be used to detect the differences in weakness of sludge, food sludge and Tembec sludge were examined using this method. Food sludge was observed to be stronger than Tembec sludge. Consequently, it was expected to see lower granule strength (i.e., higher granule weakness) in Tembec sludge using this granule weakness test. As shown in Figure 4.6, Tembec sludge was significantly weaker than food sludge (p = 0.0023 in one-tail t-test), which agreed with the expectation.

In summary, various vortex frequencies and duration in the granule weakness test were examined. Vortexing at 20Hz for 2-3 minutes was found to have the least variations within replicates among all the tested combinations. Vortexing at 20 Hz for 2 minutes was used to examine Tembec sludge and food sludge. The expected difference was observed, and was shown to be statistically significant. Therefore, in later experiments, granule weakness was determined by measuring the normalized change in absorbance after vortexing at 20Hz for 2-3 minutes.

9 4

5 2 Normalized Change in Ab.

0 Tembec Food Figure 4.6 Food Sludge Stronger than Tembec Sludge Using the Established Granule Strength Test Error bars: 95% confidence intervals of 4 replicates

4.4 Evaluations of Various Molecular Methods of Microbial Community

Analysis

Four major molecular methods to study the microbial communities of sludge were evaluated, including the traditional clone library based on Sanger sequencing, quantitative- polymerase chain reaction (q-PCR), denaturing gradient gel electrophoresis of PCR products

(PCR-DGGE) and pyrotag sequencing. The description of sludge samples and methods is provided in section 4.4.1, and the results are presented in section 4.4.2.

4.4.1 Sludge Samples and Methods

• Sludge Samples

The sludge samples collected from the full scale IC reactors in Tembec and the sludge collected from the FP concentration study were used to evaluate various molecular techniques.

• DNA Extraction

DNA was extracted from the sludge samples using the UltraClean ® Soil DNA Kit and the

PowerSoil ® DNA Kit (MoBio Labouratories, Inc.) for the Tembec IC sludge and the sludge used in the FP concentration study (i.e., FP sludge) respectively according to the manufacturer’s

 instructions. DNA concentrations in the extracted samples were measured using a NanoDrop

Spectrophotometer at the wavelength of 260nm.

• Traditional Clone Library based on Sanger Sequencing

Two different primer sets were used in PCR to target the 16S rRNA genes in archaea and bacteria: 8F and 1512R for most bacteria (Felske et al. , 1997) and 1Af and 1100Ar for most archaea (Embley et al. , 1992). The amplification was verified by running PCR products with agarose gel electrophoresis. The PCR products were cleaned up using the Sigma GenElute TM

PCR Clean-Up Kit, before ligating them into the Invitrogen’s PCR2.1 Vector and transforming into One-Shot TOP10 Chemically Competent E. coli cells. Clones were selectively grown overnight on agar plates containing Luri-Bertani (LB) broth and Ampicillin in an incubator at

37 ℃. The positive colonies were randomly selected and transferred to a LB broth containing

Ampicillin, and grown overnight in shaker (200rpm) at 37 ℃. The plasmid DNA in the grown E. coli was isolated using the GenElute TM Plasmid Miniprep Kit. PCR with primer set T7f/M13r and agarose gel electrophoresis were conducted to verify the vector insertion (Duhamel and Edwards,

2006). To construct the clone libraries, 57 archaeal clones and 65 bacterial clones were randomly selected, and sent for sequencing. The sequencing results were then compared to the known 16S rRNA gene sequences in the database of the National Center for Biotechnology Information

(http://blast.ncbi.nlm.nih.gov/Blast.cgi ) and the 16S Ribosomal DNA Project

(http://rdp.cme.msu.edu ) for taxonomy identification. The sequencing results were screened for chimeras using the Mallard program, and clustered using the BioEdit program.

In order to assess how much of the microbial diversity, including both richness and evenness, was covered by the sequencing results (e.g., clone library data), the coverage value C was calculated using Equation 4.4

C = 1 – (n/N) (Ravenschlag et al., 1999) Equation 4.4 Where C is the coverage of the clone library; n is the number of unique clones (OTUs); N is the total number of clones sequenced

• Quantitative Polymerase Chain Reaction (q-PCR)

The primers used in q-PCR were from published papers, including: 787f/1059r for general archaea (Yu et al. , 2005), 170f/390r for Methanosaeta , 166f/413r for

Methanomethylovorans , 418r/269f for Methanobacterium , 180f/511r for Methanosarcina

(Duhamel and Edwards, 2006), and 1055f/1392r for general bacteria (Dionisi et al. , 2003). All primers were synthesized by Sigma-Aldrich ®.

The DNA extracted from each sludge sample was diluted ten times and run in triplicates.

Calibration curves were constructed using known concentrations of plasmid DNA containing the corresponding 16S rRNA gene insert. Each calibration curve consisted of serial ten-fold dilutions of the plasmid DNA for the organism of interest, from 10 9 to 10 3 copies /mL.

 The DNA templates were mixed with the Sigma SYBR Green JumpStart TM Taq

Readymix TM . Reactions were conducted in the DNA Engine Opticon TM 2 with the program

Opticon Monitor 3. Details of the reaction setup and the thermocycling program used for q-PCR were described elsewhere (Duhamel and Edwards, 2007). The calibration curve and the amplification efficiency for each run are presented in Appendix 4.1.

The percentage of total archaea in the community was calculated using Equation 4.5. The percentage of specific methanogens in the archaeal community was calculated using Equation 4.6.

%Arc in Total Population Estimated Using q-PCR / = *100% Equation 4.5 / /

% Specific Methanogens in the Archaeal Community Estimated Using q-PCR = *100% Equation 4.6

• PCR and Denaturing Gradient Gel Electrophoresis (PCR-DGGE)

The bacterial 16S rRNA genes were amplified using PCR with the primer set 357f-GC and 518-r (Muyzer et al. , 1993). The PCR products were purified using the GeneJET TM PCR

Purification Kit (Fermentas) according to the manufacturer’s instructions. Clean PCR products were loaded on 8% polyacrylamide gels with a 30-70% urea-formamide gradient. Electrophoresis was conducted in a DCode TM Mutation Detection System (Bio-Rad) for 16 hours at 80V and 60 oC. The DGGE gels were stained using SYRB ® Gold Nucleic Acid Gel Stain (Invitrogen), and visualized using the UV setting in the G:BOX (by SYNGENE) with the GeneSnap software.

• Pyrotag Sequencing

PCR was conducted with primer sets 926f/1392r to target both general bacteria and general archaea (Kunin et al. , 2010). The forward primer contained a 30 bp adapter sequence

(CCT ATC CCC TGT GTG CCT TGG CAG TCT CAG). The reverse primer also contained a

30bp adapter sequence (CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG), as well as a 10 bp multiplex identifier barcode (MID) for distinguishing multiple samples pooled within one sequencing region. PCR reactions were prepared and conducted as described by Ramos-Padrón et al. (2011). The PCR products were purified using the GeneJET TM PCR Purification Kit

 (Fermentas). The concentrations of the purified DNA were measured using a NanoDrop

Spectrophotometer. Besides NanoDropping, the PCR products were loaded on 2% agarose gels with a serial dilution of ladder DNA with known concentrations. The gel images were processed using ImageJ to quantify the DNA concentrations. The purified and quantified PCR products were sent to the Genome Quebec and McGill University Innovation Centre. In Genome Quebec, samples were checked for quality control, pooled, and subject to unidirectional sequencing (i.e.

Lib-l chemistry) of the 16S rRNA gene libraries using the Roche 454 GS FLX Titanium technology.

The raw sequences generated from pyrotag sequencing were processed using the software

QIIME (Caporaso et al ., 2010). In general, the following steps were included in QIIME: filtering chimera sequences, clustering of sequences into operational taxonomy units (OTUs), classifying 65 the representatives for the OTUs, constructing the summary of the microbial communities, calculating the microbial diversity, and explicitly comparing the microbial communities among samples. In addition to QIIME, the statistics software R was used to identify the organisms that were affected by the environmental factors (e.g. wastewater loadings and culture time). The steps in QIIME and R will be described in detail in Section 5.2.5.

The percentages of Methanomethylovorans in the archaeal community were also calculated using Equation 4.7 with the pyrotag sequencing data.

%. % Vorans from pyrotag = *100% Equation 4.7 %.

4.4.2 Results: Evaluation of Different Molecular Methods of Microbial Community

Analysis

The results of the clone-library based on Sanger sequencing, PCR-DGGE, q-PCR and pyrotag sequencing are presented in this section. These methods are compared in order to choose the most suitable technique to study the microbial communities of granular sludge with reasonably high reproducibility, good resolution and great coverage. It should be noted that this section mainly focuses on the discussion of various molecular methods, instead of the investigation of specific data relating to an experiment.

• Clone Library based on Sanger Sequencing to Identify the Major Species

The first molecular technique attempted in this research was the traditional clone library based on Sanger sequencing. An archaeal clone library and a bacterial clone library of the

Tembec sludge were constructed by randomly selected 57 archaeal clones and 65 bacterial clones for Sanger sequencing respectively. The archaeal community was found to be relatively simple, mainly consisting of three species ( Methanomethylovorans , Methanosaeta and

Methanobacterium , Appendix 4.2). The bacterial clone library was found to consist of 36 species, most of which were present in minor fractions, i.e., =1/65 occurrence (Appendix 4.2).

The coverage values of the clone libraries above were calculated using Equation 4.4 to assess how well the diversity of the selected clones represented the diversity of the actual population. The coverage value C for the archaeal clone library was 0.97, but was only 0.45 for the bacterial clone library. Therefore, the number of clones sequenced to construct the bacterial clone library was insufficient to represent the diversity of the population. In general, clone library based on Sanger sequencing only provided a way to identify the major species present in the communities. Other molecular techniques would be required for quantification of the specific groups.

• PCR-Denaturing Gradient Gel Electrophoresis (PCR-DGGE)

Denaturing gradient gel electrophoresis was conducted using the PCR products

(generated using general archaea and bacteria primers) to obtain fingerprints of the communities.

The major species in a community can be distinguished from the minor ones based on the band intensity on the DGGE gel. To illustrate the application of this method, the DGGE image of the bacterial communities in triplicate samples (FP sludge) is displayed in Figure 4.7. Similar band patterns were observed within each triplicate set, meaning good reproducibility of the test. The dark bands indicated the dominant bacteria in the communities. The gel image also reveals a few bacterial species shared by all samples (orange arrows) and a few shifts of the major species

(pink arrows).

Figure 4.7 DGGE Image of the Bacterial Community of the FP Sludge: Good Reproducibility among Triplicates

Sequencing of the DNA contained in the bands was required to identify the corresponding species. However, because the bands on the gel were closely located to each other, extracting the

DNA from the desired bands was challenging. Furthermore, because the universal bacteria primers were highly sensitive, when amplifying the extracted DNA using PCR, undesired contamination was often amplified as well. As a result, cloning was frequently required before sequencing, which was time-consuming and labour intensive. Therefore, it was decided to use alternatives with greater automation and better quantification.

• Quantitative PCR (Q-PCR)

Quantitative PCR is often performed to quantify the absolute abundance of specific groups of organisms in the communities. For example, q-PCR was conducted to quantify the abundance of total bacteria, total archaea and Methanomethylovorans in three sets of sludge samples collected from the FP concentration study. The percentages of archaea in the total population and the percentage of Methanomethylovorans in the archaeal communities were calculated using Equations 4.5 and 4.6. The results are shown in Figure 4.8. The good 68 reproducibility of q-PCR was reflected by the relatively small variations within each triplicate set.

Nevertheless, for quantification of microbial compositions, the choice of q-PCR primer sets depended on the basic knowledge of the members present in the communities, e.g.,

Methanomethylovorans in the case above. Other techniques were therefore needed in order to provide this primary knowledge.

30 %Methanomethylovorans in the Archaeal Community 25

Percentage 10 % Archaea in Total Population

0 FP Sludge 1 FP Sludge 2 FP Sludge 3 Figure 4.8 q-PCR Results: FP Sludge 1 Contained Higher %Archaea in the Population and Higher % Methanomethylovorans in the Archaeal Communities Error bars: 95% confidence intervals of triplicate sludge samples in each set

• Pyrotag Sequencing

Pyrotag sequencing is a more automated and powerful technique than the traditional clone library. Pyrotag sequencing was conducted to identify and quantify the microbial compositions in the FP sludge samples same as the ones presented in Figure 4.8. The coverage of the population diversity, the taxonomic resolution and the reproducibility of pyrotag sequencing are evaluated.

A brief statistical summary of the pyrotag sequencing results is provided in Table 4.2.

Benefiting from the high throughput power of pyrotag sequencing, more than a hundred thousand reads were generated per sequencing region, resulting in a few thousand sequence reads per sample. Using 97% sequence similarity as the cut-off, sequences were clustered into operational taxonomy units (OTUs). Approximately half of the OTUs were identified to the levels of genus.

The resolution of pyrotag sequencing could not get to the species level, as opposed to the clone 69 library, because shorter amplicons were generated in pyrotag sequencing (i.e., 480bp in pyrotag sequencing vs. 1200bp in clone library). However, the taxonomic resolution of the data from pyrotag sequencing was still sufficient to identify the differences and the similarities in the microbial compositions among samples. Using Equation 4.4, the pyrotag sequencing data were found to have an average coverage value C of 80%. This coverage was an important improvement as compared to that of the clone library for sludge samples (i.e., 45%). In addition to the relatively high coverage and reasonable resolution, all results from pyrotag sequencing showed great reproducibility, indicated by the close similarity among triplicates (sections 5.5.1 and 6.5.1).

Table 4.2 Statistic Summary of Pyrotag Sequencing of the FP Sludge Number of Sequences without Chimeras 104466 Average Reads in One Sample 4353 Minimum OTU in One Sample 608 Maximum OTU in One Sample 1200 Average OTU in One Sample 822 Highest Coverage* of Population Diversity in Samples 70% Lowest Coverage* of Population Diversity in Samples 89% Average Coverage* of Population Diversity in Samples 80% * Coverage was calculated using Equation 4.4 The accuracy of quantification using pyrotag sequencing data was investigated by comparing the results from pyrotag sequencing to those from q-PCR. The percentages of archaea in the total population are presented in Figure 4.9, along with the percentages of

Methanomethylovorans in the archaeal communities as calculated using Equation 4.7.

30 %Methanomethylovorans in Archaea Community 25

10 Percentage % Archaea in Total Population 5

0 FP Sludge 1 FP Sludge 2 FP Sludge 3

Figure 4.9 %Archaea in the Total Population and %Methanomethylovorans in the Archaeal Community: both Lower in FP Sludge 2 and 3 Error bars: 95% confidence intervals of triplicate sludge samples in each set

Comparing Figure 4.9 to Figure 4.8, the percentages of Methanomethylovorans from pyrotag sequencing were similar to those from q-PCR in general, but differences in the percentages of archaea in the total population were observed between the two tests. In most cases, the percentages of archaea from q-PCR were approximately double of those reported in pyrotag sequencing. The differences could be due to different binding specificities of primers to the desired 16S rRNA genes (Baker et al., 2003). All primers used in q-PCR had 100% matches with the desired 16S rRNA genes. In pyrotag sequencing, both the forward and reverse primers matched the corresponding regions of general bacteria genes perfectly, but the forward primer had one mismatch with the general archaea gene, possibly leading to a lower binding efficiency of the forward primer to archaea and a consequent underestimation of archaea. Another cause of the differences could be errors in q-PCR. In published papers, it was proposed that the errors in the baseline estimation could affect the observed PCR efficiency values and thus influence the estimated concentrations of the template DNA (Ruijter et al., 2003). Nevertheless, both q-PCR results and pyrotag results showed similar trends, i.e., lower %archaea and % Methanomethylovorans were found in FP sludge 2 and FP sludge 3 from both pyrotag sequencing and q-PCR. 71

To summarize section 4.4.2, different molecular techniques were attempted to study the microbial communities of granular sludge. Sanger sequencing of PCR-clones provided the best resolution, i.e., the sequences could be identified to the species level. However, Sanger sequencing was labour intensive and functioned poorly in quantification. Q-PCR provided quantification data of specific groups of organisms, but a general picture of the community was required in order to choose the appropriate primer sets. PCR-DGGE was used as a prescreening tool to visualize the important shifts in the dominant species. However, identification of organisms associating with the interesting bands was time consuming and labour intensive.

Pyrotag sequencing was a relatively new technique compared to other tested methods. Pyrotag sequencing was highly automated, and its great sequencing power allowed relatively reliable quantification of the microbial compositions. Good reproducibility was also achieved in pyrotag sequencing. Furthermore, almost half of the sequences could be clustered to the genus level, providing sufficient resolution to compare the microbial communities of different samples.

Moreover, the results of pyrotag sequencing were generally consistent with those revealed in q-

PCR. Therefore, pyrotag sequencing was chosen as the main tool to study the microbial communities of sludge in this research.

4.5. Summary of Method Development

Physical examinations of granulation focused on particle size distribution and granule weakness. A combined method of wet-sieving and image analysis was chosen to examine particle size distribution because of its relatively high reproducibility and the relatively small sample volume required. Granule weakness was evaluated by measuring the change in turbidity after vortexing. These methods were proven to be applicable to the Tembec granules. Different degrees of granulation between food sludge and Tembec sludge were detected using these developed methods. The relatively basic setup, simple procedures, and small sample volumes of

72 the chosen physical tests enable their usage in future granulation studies in batch systems, contiguous reactors or even full scale treatment plants.

Among the tested molecular techniques for the microbial examinations of granular sludge, pyrotag sequencing provided good reproducibility, relatively high coverage of the population diversity and reasonable resolution. The community composition, diversity and variations among samples could be assessed using the massive pyrotag sequencing data. The biological and environmental factors that altered the microbial community ecology could also be determined. Furthermore, the results from pyrotag sequencing were generally consistent with those from q-PCR, implying that pyrotag sequencing was reliable. Therefore, it was decided to conduct pyrotag sequencing for the sludge collected from both the FP concentration study and the UofT long-term study in order to investigate the effect of the treatment of pulp mill effluents on the microbial communities.

CHAPTER 5. THE FP CONCENTRATION STUDY: THE EFFECT OF DIFFERENT CONCENTRATIONS OF AE

5.1 Introduction

Two full scale internal circulation (IC) reactors seeded with granular sludge have been used to treat bleached-chemi-thermo-mechanical pulping (BCTMP) effluent and acid condensate

(AC) from sulphite pulping at the mill since early 2006. The mill would like treat more streams to achieve higher methane production and to reduce the loadings to the aerobic treatment. One candidate stream was the alkaline effluent from sulphite pulping (AE) that was characterized by a high concentration of organics (i.e., >20000 mg COD/L, where COD stands for chemical oxygen demand). However, compared to most other in-mill streams, higher concentrations of inhibitory compounds, such as resin acids and long-chain fatty acids (RFAs), were also contained in AE, particularly when pulping softwood chips. In this chapter, the AE refers to the one generated during the production of SW1 sulphite pulp from softwood chips.

The overall objective of the FP concentration study was to assess the implications of the addition of this softwood AE on reactor performance and granular sludge. Four lab-scale upflow anaerobic reactors were set up, with a one-month AE-free startup and a one-month treatment of the softwood AE in three test reactors while the fourth served as an AE-free control. There were three detailed objectives: 1) to study the impact of AE on reactor performance and granulation; 2) to investigate the effect of AE on the microbial communities of sludge; 3) and to examine the fate of RFAs in the anaerobic treatment of AE.

Following the introduction, section 5.2 contains the description of reactor setup, characteristics and compositions of feeds, and the analytical methods. The results of reactor performance, the physical properties of sludge, and the microbial community studies are presented and discussed in sections 5.3, 5.4 and 5.5 respectively. The results of the RFA analysis are explained in section 5.6. A summary of the chapter is provided in section 5.7. 74

5.2 Materials and Methods

In this section, reactor setup, materials and methods are described, including information regarding to reactor setup and seed sludge (section 5.2.1), feeds to the reactors and the feed schedule (section 5.2.2), routine examinations of feeds and effluents (section 5.2.3), sampling, storage, and physical and chemical examinations of sludge (section 5.2.4), and the main methods to study the microbial communities of sludge (section 5.2.5).

5.2.1 Reactor Setup and Seed Sludge

Four identical upflow anaerobic digesters operating at 35oC were set up at FPInnovations, each with a liquid volume of 5L and a headspace volume of 1.5L (Figure 5.1). Each reactor was equipped with an external clarifier to settle sludge. A reversed flow was applied from each clarifier to the corresponding reactor approximately once a week to return the settled sludge back to the reactors. Wastewater was continuously fed to the bottom of the reactors at a rate of 15L/d.

A fluidized bed of sludge was generated by circulating biogas at a flow rate of approximately

2.5L/min for 30 seconds every 15 minutes and by circulating treated effluent at a flow rate of

5L/min for one minute twice a day. The sludge bed was also mixed and re-suspended three times a week by withdrawing and re-injecting the sludge using a 60mL syringe. NaOH or HCl was added to the reactor feeds to maintain pH ≈ 7 inside the reactors. The hydraulic retention time was approximately 8.5 hours.

The seed sludge was collected from the full-scale IC reactors treating pulp mill effluents at Tembec Temiscaming. At FPInnovations, the amount of sludge seeded to each anaerobic digester was approximately 250g total suspended solids (TSS), or 185g volatile suspended solids

(VSS).

Figure 5.1 Schematic of the Upflow Anaerobic Digester Used in the FP Concentration Study

5.2.2 Feeds: Pulp Mill Effluents and Additional Nutrients

Reactors were fed with effluents collected from Tembec Temiscaming, including BCTMP effluent, and AC and AE from the sulphite pulping of fresh softwood chips as noted. All effluents were directly shipped to FPInnovations from the mill, and were stored at 4 oC until required for feed preparation. BCTMP effluent was sieved (pore size = 599µm) to remove fibres before feed preparation. The information on the effluent characteristics is presented in Table 5.1. In general,

AE was a concentrated stream with high concentrations of COD, RFAs and lignins.

Table 5.1 Chemical Characteristics of the Specific BCTMP Effluent, AC and AE Used in the FP Study Parameter AC AE BCTMP pH 3.3 12.2 6.6 Chemical Oxygen Demand, COD, mg/L 11860 36160 9580 Soluble Chemical Oxygen Demand, sCOD, mg/L 11800 34640 7060 Total Suspended Solids, TSS, mg/L 80 430 560* Total Resin Acids and Long-Chain Fatty Acids (RFAs), mg/L 20 790 10 Lignin, mg/L 3310 6470 1640 *Total suspended solids of BCTMP was measured after fibres were removed by sieving

BCTMP effluent, AC and AE were blended as reactor feeds. BCTMP effluent and AC were mainly blended in a 2:1 volumetric ratio to mimic the feeding condition in Tembec, except in the feeds to R3 and R4 during the AE test. All reactors were fed with the same amounts of AC and BCTMP effluent during startup (days 1 to 27). R1 remained as the AE-free control reactor for the entire course of the experiment. During the AE test (days 28 to 62), R2 and R3 were fed with 35% and 64% AE on a sCOD basis respectively. R4 was fed with 100% AE between days

28 and 41. It should be noted that the naming of the reactors in the rest of this chapter is based on the % PEW COD in each reactor feed. Characteristics of the blended feeds are described in Table

5.2. It should be noted that the total sCOD concentration in each feed was different. The total sCOD concentration and total organic loading rate were higher when more AE was contained in the feed. Consequently, the sludge in the reactor with a greater addition of AE experienced a higher specific loading rate, e.g., 0.8kg sCOD *day-1 *kg VSS sludge -1 in the control reactor and

2.5kg sCOD *day -1 *kg VSS sludge -1 in R4 with 100% PEW. The specific loading rate in the control reactor was comparable to the average value of the full scale IC reactors. The specific loading rates in R2 and R3 were at the high end of the operation range of the IC reactors (section

3.2.3). The specific loading rate in R4 was hardly applied to the IC reactors in the mill, so it was possible that R4 was overloaded with organics. Additional nutrients were also added to provide

Fe, Ca, Ni, Co and P, as presented in Appendix 5.1. The upflow velocity in each reactor was also estimated in Appendix 5.1.

Table 5.2 Feed Schedule, Compositions and Characteristics of Feeds in the FP Concentration Study All Reactors during R2 R3 R4 Startup (Days 1-27) (35% AE) (64% AE) (100% AE) and R1 (Days 1-62) (Days 28-62) (Days 28-62) (Days 28-41) AC 3900 3400 800 0 mg COD/L BCTMP 6400 5500 5700 0 AE 0 5000 12000 34600 AC 38 25 4 0 %COD BCTMP 62 40 32 0 AE 0 35 64 100 AC 33 29 7 0 % Volume BCTMP 67 57 60 0 AE 0 14 33 100 TCOD, mg/L 10390 13680 17600 35690 sCOD, mg/L 9810 13120 16700 30700 Organic Loading Rate 29 39 50 92 (kg sCOD *Day -1 *L Reactor -1) Specific Loading Rate 0.8 1.1 1.4 2.5 (kg sCOD * kg VSS -1 *Day -1) TSS, mg/L 610 680 1000 1310 Resin Acids and Long-Chain 13 97 240 630 Fatty Acids, RFAs, mg/L Volatile Fatty Acids, VFA, mg/L 2330 2400 2440 Not Measured Lignin, mg/L 2190 2800 3360 6470 Sludge Retention Time (Day)2 37 33 26 Not Calculated

5.2.3 Analysis of Feeds and Effluents

VFA concentrations in feeds were analyzed using the Dionex DX-500IC, equipped with the Ion Pac ICE-AS1 column, the AMMS-ICE II anion suppressor and a conductivity detector.

Heptafluorobutyric acid (0.8mM) was used as the eluent at a flow rate of 0.8mL/min.

Tetrabutylammonium hydroxide was used as the regenerant at a flow rate of 3-5mL/min. RFA concentrations were measured according to Voss and Rapsomatiotis’ method (1985).

Biogas production, the concentrations of sCOD and TSS of feeds and effluents were examined on a daily basis at FPInnovations. Biogas was monitored using wet tip gas meters

(wettipgasmeter.com), which measured gas production based on liquid displacement. TSS and sCOD tests were conducted according to the Standard Methods for Examination of Water and

Wastewater (Eaton et al. , 1998). It should be noted that during the first two weeks, the operation

2 Sludge retention time was calculated based on the TSS of seed sludge, influent TSS, effluent TSS and sludge withdrawn for sampling during the whole experiment 78 was mainly focused on establishing the reactor run and troubleshooting, so daily routine measurements were started on day 15.

Concentrations of resin acids and long chain fatty acids (RFAs) were also examined once per week at the University of Toronto using a liquid chromatography-mass spectrometry (LC-

MS). RFA analysis of the effluents included the concentrations in both the dissolved phase and the particulate phase. In the RFA analysis, samples were first spun down at 8000G at 20 oC.

Supernatant was separated from the settled pellet, and its pH was adjusted between 9.3 and 9.8.

After pH adjustment, the liquid sample was filtered using an Acrodisc syringe filter with 0.2µm pore size. The filtered sample was mixed with methanol in a 1:1 volumetric ratio before being analyzed using the LC-MS equipped with a C8 column. The pellet with the particulate phase was re-suspended with methanol, sonicated for 30 minutes, filtered using an Acrodisc syringe filter

(0.2µm), and mixed with water (1:1 on a volume basis) before the LC-MS run (Meyer et al ., submitted).

5.2.4 Sampling, Storage, and Physical and Chemical Analyses of Sludge

Well-mixed sludge samples were collected weekly from the sludge bed in each reactor using a 60mL syringe with a catheter tip. Sludge samples were shipped to the University of

Toronto in coolers with ice on the sampling days. Physical examinations of the sludge samples were conducted immediately after the samples arrived at the University of Toronto. The sludge samples remained after the physical tests were distributed to 1.5ml Eppendorf tubes, and centrifuged at 13000G for 20 minutes. The supernatant in each tube was discarded, and the tubes with the sludge pellet were preserved at -80 oC for later RFA analysis and microbial studies.

Particle size distribution analysis and the granule weakness tests were conducted. The percentages of TSS contained in particles larger than 200 µm (%TSS>200 µm) were measured. The distribution of particles in different size ranges (i.e., 200-500 µm, 500-1000 µm, 1000-1500 µm,

1500-2000 µm, and >2000 µm) was evaluated using image analysis. Granule weakness was assessed by examining the change in turbidity after vortexing. The details of both methods were previously described in sections 4.2.1 and 4.3.1 respectively.

The RFA concentration in the sludge bed was estimated using the weekly sludge samples preserved. The spun-down sludge pellet was re-suspended with methanol, and prepared and analyzed using the LC-MS method as described previously for the particulate phase of effluent samples.

Sludge sampling time and the analyses performed are listed in Table 5.3.

Table 5.3 Sludge Samples and Analysis in the FPInnovations Study Day since Particle Size Granule RFA Microbial Study Operation Notes Startup Distribution Test Strength Test Analysis 0 Yes Yes Yes Yes Seed sludge 11 Yes Yes Yes No All reactors received 20 Yes No Yes No same amounts of BCTMP 26 Yes No Yes Yes effluent and AC 32 Yes Yes Yes No R1 served as AE-free 39 Yes Yes Yes No control, while R2, R3 and 46 Yes Yes Yes No R4 treated different 53 Yes Yes Yes No concentrations of AE 62 Yes Yes Yes Yes Last day of reactor run

5.2.5 Microbial Examinations of the FP Sludge

The microbial communities of the seed sludge (Day 0), the sludge collected at the end of startup (Day 26) and at the end of the study (Day 62) were examined. DNA was extracted from each sample using the PowerSoil ® DNA Kit (MoBio Labouratories, Inc.) according to the manufacturer’s instructions. Quantitative PCR (q-PCR) were conducted to estimate the concentrations of (i.e., copies of the 16s rRNA genes/mL) of general archaea and general bacteria in the samples, as previously described in section 4.4.1. Pyrotag sequencing was performed to investigate the distributions of different microbial groups in the samples. Methods of PCR and sample preparation for pyrotag sequencing were presented in detail in section 4.4.1.

Analysis of pyrotag sequencing data was conducted using the open source software package QIIME (http://qiime.org/), which allowed the analysis of high throughput community 80 sequencing data (Caporaso et al. , 2010). In the first step, undesired sequences were eliminated using the following criteria: 1) sequence had quality score lower than 25; 2) the length of the sequence was shorter than 220 base pairs; 3) sequence contained ambiguous base(s); 4) sequence contained mismatch in primer sequence; and 5) sequence contained more than 8 homopolymers.

After de-multiplexing, the remaining sequences were passed through two chimera identifiers to identify the false products (sequences) created by amplification mistakes in PCR: Chimera Slayer

(in QIIME) and Decipher (Write et al., 2012). The common sequences identified as chimeras in both programs were removed from the dataset. The remaining sequences were grouped into operational taxonomy units (OTUs) based on 97% similarity using UCLUST. A representative sequence was selected for each OTU, and its taxonomy was assigned using the RDP Classifier.

The summary of the taxonomic composition of each community was generated at different levels, from kingdom to OTU. The alpha diversity was calculated using the observed species metric.

Beta diversity was calculated for the explicit comparison of microbial communities based on their compositions. Due to the large variations in sequencing effort of the FP sludge (e.g., min

= 2637 sequence/sample, max = 8137 sequence/sample), rarefaction was conducted to remove sample heterogeneity and to standardize the sequencing data. A common size of 500 sequences per sample and a total run of 100 rarefactions were taken (Bowers et al. , 2010; Agler, et al. ,

2012). Samples were clustered based on the dissimilarity matrixes calculated using the weighted

Unifrac method. Principal coordinate analysis was conducted to visualize the clustering of samples on three 2D graphs, where the axes were the first three principal coordinates with the greatest contribution to the variations among samples. A beta Jackknifed tree was also constructed to visualize the dissimilarity distance among samples based on the 100 rarefactions.

Correlation tests were conducted using QIIME to identify the organisms that were correlated to incubation time (i.e., days since startup) and AE loadings. The significance scores (p-values) and

81 the correlation coefficients were calculated. An organism was said to significantly correlate to

AE loadings or incubation time if its significant score was smaller than 0.01.

In order to estimate the abundance of organisms in the sludge samples, the quantities measured using q-PCR was combined with the percentages reported from pyrotag sequencing, using Equations 5.1 and 5.2.

BAC-specific (gene copy /ml) =

% * total BAC (gene copy/ml, from q-PCR ) Equation 5.1 %

ARC-specific (gene copy /ml) =

% * total BAC (gene copy/ml, from q-PCR) Equation 5.2 %

In addition to the correlation studies available in QIIME, distance-based redundancy analysis (dbRDA) was also conducted using the statistical software R (R Core Team, 2013) to investigate the impact of operational parameters (e.g., total organic loadings, AE loadings, day, and concentrations of lignin and RFAs in feeds) on the abundance of organisms in sludge samples. The dbRDA was conducted according to the standard species analysis techniques as described by Legendre and Anderson (1999), McArdle and Anderson (2001), Anderson and

Willis (2003) and Miller et al. (2005). In short, the Bray-Curtis index was used to calculate a dissimilarity matrix between the abundance and richness of species per sample. Next, a principal coordinate analysis (PCoA) was done on the matrix. Finally, the eigenvalues from the PCoA were correlated (multiple linear regression) with the operational parameters. The final result was projected into 3-space reflecting both the dissimilarity index, and its relationship to the operational parameters. This procedure produced a pseudo-F value which could be used as a measure of overall significance of the analysis, and was found to be significant for all cases here

(i.e., F < 0.001). As a result, it was valid to use variations in the listed operational parameters across samples to explain the variations in distributions of organisms in the samples. A biplot

82 was created with the organisms as points and the operational parameters as vectors on the figure.

If an organism is lying in the same direction as the vector, there is a strong dependence of the organism on that vector. Statistical significance of the relationship between vectors and organisms was also produced by dbRDA calculation, as well as how much variation each axes accounted for.

5.3 Effect of the Addition of AE on Reactor Performance

The effect of the addition of AE at different concentrations on reactor performance was evaluated from the following perspectives: solid removal, percentage removal of soluble COD

(%sCOD removal), daily biogas production and specific biogas yield. The results are presented and discussed in this section.

5.3.1 Removal of Total Suspended Solids (TSS)

The changes in TSS concentration, expressed as ∆TSS and calculated as TSS in feed less

TSS in effluent, were used to investigate the fate of suspended solids in the treatment. A positive

∆TSS value may imply removal of suspended solids after treatment, which can be a result of solids in the feed being immobilized in the sludge bed or transformed into the dissolved phase. In contrast, a negative ∆TSS value may be a sign of washout of sludge from the reactor.

The results of ∆TSS are shown in Figure 5.2, where the dots indicate the actual values and the lines represent the trends of 5-day moving averages. During the startup period, negative

∆TSS was observed in all reactors, implying possible washout of sludge from the reactors.

After the AE test was started, increases in ∆TSS were observed in reactors 1, 2 and 3, while ∆TSS decreased in reactor 4 with 100% AE. In R1 and R2, ∆TSS gradually increased to zero and above, implying that stable sludge retention was regained. In R3, the addition of 64%

AE led to a surprisingly large increase in ∆TSS (to a maximum of 200mg/), which might imply that either the suspended solids in the feed became soluble in the effluent, or they attached to the 83 sludge bed and were immobilized. In R4, a sudden decrease in ∆TSS was observed when its feed was changed to 100% AE, which might indicate severe washout of sludge. The feed to R4 was

100% AE. Its organic loading rate (kg COD*L reactor-1 * day -1) was 1.5 times of what was usually applied to the full scale IC reactors in the mill, and its specific loading rate (kg COD*kg

VSS sludge -1 * day -1) was also almost triple of the average value of the IC reactors. Therefore, besides the harmful effect of AE, overloading of organics to R4 could also be one reason of the failure operation of that reactor.

Addition of AE

600

, R3 (64% AE) 400 Effluent

200 R1 (Control) –TSS Feed

mg/L 0

-200 TSS= TSS TSS=

∆ -400

-600 R2 (35% AE) R4 (100% AE) -800

-1000 14 19 24 29 34 39 44 49 54 59 64 Day since Startup

Figure 5.2 Delta TSS in the FP Concentration Study: Severe Washout when Treating 100% AE Points: actual measurements; lines: trends of 5-day moving average

5.3.2 Removal of Soluble COD (sCOD)

The results of %sCOD removal are shown in Figure 5.3, where the dots indicate the actual measurements and the lines represent the trends of 5-day moving averages. During the AE- free startup (until day 27), similar decreasing trends in %sCOD removal were noticed in all reactors. The decreased %sCOD removal was probably due to the initial washout of sludge.

Addition of AE R1 Feed (Control) 9 R2 Feed (35% AE)

R3 Feed (64% AE) 8

R4 Effluent (100% AE) R1 Effluent (Control) R2 Effluent 7 (35% AE)

R3 Effluent (64% AE) Reactor pH Reactor 6

R4 Feed (100% AE)

5 0 5 10 15 20 25 30 35 40 45 50 55 60 65 Day since Startup

Figure 5.3 %sCOD Removals in the FP Concentration Study: Lower %sCOD Removal When Receiving more AE Points: actual measurements; lines: trends of 5-day moving averages

During the AE test, the AE-free control reactor showed the highest %sCOD removal

among all reactors. The greatest %sCOD removal in the control reactor R1 in this study was

about 53%. The reported treatability of BCTMP effluent and AC in a one-month batch assay was

about 80-100% (Yang et al. , 2010). The full scale IC reactors at the mill also had higher %sCOD

removal than the control reactor in this study, due to its higher ratio of biomass concentration to

organic loading rate and greater biomass retention as a result of the two-compartment structure of

the IC reactors. Another possible explanation for the poorer sCOD removal in R1 might be the

relatively short startup and experimental duration in contrast to the mill’s operation.

Among the reactors treating AE, %sCOD removal was highest in R2 with 35% AE, and

lowest in R4 with 100% AE. In R3, %sCOD dropped dramatically to <10% after the addition of

64% AE. The %sCOD in R3 seemed to slightly increase towards the end of the experiment,

which might be a result of improved degradation of soluble organics due to acclimation of

microorganisms to toxicants, or physical removal of certain soluble organics, e.g., partitioning

into biomass or precipitation. Between days 28 and 41, sCOD removal in R4 treating solely AE

85 was lower than 5%. In a two-month batch assay to treat a pulp mill stream containing 350mg/L

RFAs, 20% degradability was observed (Yang et al. , 2010). The low %sCOD removal in the treatment of AE (in R4), which also contained significant concentrations of RFAs, was consistent with the published batch assay results.

5.3.3 Biogas Production

In anaerobic digestion, COD is degraded and biogas is produced, which mainly consists of methane and carbon dioxide, and a minor fraction of hydrogen sulphide (<5%). Daily biogas production is shown in Figure 5.4, where the dots indicate the actual values and the lines represent the trends of 5-day moving averages. During the AE-free startup period, all reactors produced similar amounts of biogas, suggesting a good reproducibility among reactors. Similar to the decreasing trends observed in %sCOD removals, biogas production decreased in all reactors over the startup period, possibly due to sludge loss caused by washout.

Addition of AE R1 (AE-Free Control) 30

20 R2 (35% AE)

Biogas Production, L/day Production, Biogas R3 (64% AE) R4 (100% AE) 0 14 19 24 29 34 39 44 49 54 59 Day since Startup Figure 5.4 Daily Biogas Production in the FP Concentration Study: Less Biogas Produced from Reactor Fed with a Higher Concentration of AE Points: actual measurements; lines: trends of 5-day moving averages

After the AE test was started, with more AE contained in the feed, the less biogas was produced from the reactor. The biogas production from R1 gradually increased, implying recovery of the system. The biogas production from R3 decreased after AE was added to the

86 feeds, and remained approximately stable after day 37. In R4, immediately following the addition of 100% AE, biogas decreased to < 3L/day, which was the lowest among all reactors.

5.3.4 Specific Biogas Yields

In anaerobic degradation, products from fermentation, such as VFAs, alcohols and hydrogen, can be utilized by acetogens and methanogens, as well as sulphate and nitrate reducers if electron acceptors like sulphate or nitrate are present (Rittmann and McCarty, 2001). Nitrate- and sulphate-reducers outcompete acetogens and methanogens for fermentation products.

Specific biogas yield, calculated as the ratio between the biogas production to the amount of sCOD removed, is often used to estimate the portion of sCOD that is converted to methane.

Assuming that 60-80% of biogas is methane and electrons are only used by methanogens, complete degradation of 1g COD yields 0.49-0.65L biogas at 35 oC at 1atm (Speece, 1996).

The average specific biogas yield for each reactor is listed in Table 5.4. Similar specific biogas yields were observed in all reactors during the startup period. After the AE test was started, generally a higher specific biogas yield was found in the reactor receiving less AE. The specific biogas yields in R1 (control) and R2 (35% AE) were close to the maximum theoretical values, implying that most of the removed soluble organics were consumed for methane production. The specific biogas yield in R3 was slightly lower, because AE contained a considerable amount of sulphate (840 mg/l). Sulphate reduction could contribute to as much as 17% of the sCOD removal in R3. Furthermore, R3 effluent contained more TSS, so more sCOD might be removed by sorption onto solids and by washout, resulting in lower specific biogas yields in R3.

Table 5.4 Average of Specific Biogas Yield in the FP Reactors (L Biogas/g sCOD Degraded) R1 (Control) R2 (35% AE) R3 (64% AE) R4 (100% AE) During Startup 0.41 0.43 0.44 0.41 During AE Test 0.47 0.43 0.38 0.15

5.3.5 Summary of Reactor Performance

In summary, the results of the FP concentration study demonstrated the negative impact of the addition of AE on reactor performance, shown as lower biogas production and poorer sCOD removals. A low specific biogas yield was found when more AE was loaded to the reactor.

The reactor treating 100% AE experienced severe washout of sludge. As shown in Table 5.2, the most distinct feature of the feeds containing AE, as compared to the feed to the control reactor, was the presence of numerous resin acids and long-chain fatty acids. RFAs have been found to exert toxic effect on anaerobic microorganisms (Sierra-Alvarez and Lettinga, 1990), so RFAs could be one reason for the negative effect of the addition of AE. Some monomeric lignin compounds could also be inhibitory to anaerobic microorganisms (Sierra-Alvarez and Lettinga,

1991). Therefore, lignins might also contribute to the poor performance of the reactors treating

AE. Nevertheless, since the feed with more AE also had a higher total sCOD concentration, overloading of sCOD could also contribute to the poor performance of reactors treating AE.

5.4 The Effect of the Addition of AE on Granulation of Sludge

The effect of the addition of AE on the physical properties of sludge was evaluated from three perspectives: the percentages of sludge contained in particles > 200 µm, particle size distribution for sludge > 200 µm, and granule weakness. The results are presented and discussed in this section. It should be noted that the continuous experiment in R4 was terminated two weeks earlier than other reactors, so the results of the sludge collected at the end of the study only included samples from R1, R2 and R3.

5.4.1 TSS and VSS Contained in Particles Larger than 200 µm (%TSS>200 µm and %VSS>200 µm)

The first test to examine granulation was to estimate the portions of total suspended solids and volatile suspended solids contained in particles larger than 200 µm (%TSS>200 µm and %VSS>200 µm) in each sludge sample. In literature, sludge smaller than 200 µm was often 88 referred to as fine particles, while sludge larger than 200 µm was classified as granules (Pereboom,

1997; Batstone and Keller, 2000). Therefore, %TSS>200 µm and %VSS>200 µm show the fractions of solids and biomass contained in the granule portion in each sludge sample. A sludge sample is said to granulate better if it contains larger %TSS>200 µm and %VSS>200 µm. The detailed %TSS>200 µm and %VSS>200 µm measured at various time points are presented in Appendix

5.3. The results presented here only focus on the sludge samples collected at the end of the startup period and at the end of the study.

At the end of startup, as presented in Figure 5.5, %TSS>200 µm and %VSS>200 µm were similar in all reactors, implying good reproducibility. In each reactor, more than 90% of total suspended solids and biomass were contained in granular sludge (i.e., particles > 200 µm).

%TSS >200 µm %VSS >200 µm

100

m 90 µ

80 98 98 98 96 94 95 70 93 93 % Solids >200 >200 Solids % 60

50 R1 (Control) R2 (35% AE) R3 (64% AE) R4 (100% AE) Figure 5.5 %TSS>200 µm and %VSS>200 µm at the End of Startup in the FP Concentration Study: Similar in all Reactors Error bars: 95% confidence intervals of 4 replicates

The results of %TSS>200 µm and %VSS>200 µm at the end of the study are illustrated in Figure

5.6. Compared to the sludge collected at the end of startup (Figure 5.5), the end point sludge from

R2 and R3 (Figure 5.6) contained greater fractions of fine particles and lower fractions of granules, suggesting degranulation in these two reactors after AE was added to the reactors. At the end of the study, the sludge from the control reactor showed significantly higher %TSS>200 µm and %VSS>200 µm than the sludge treating AE (p values >0.05 in one-tail t-tests, in Appendix 5.3), indicating better granulation of sludge in the control reactor.

%TS >200 µm %VS >200 µm 100

90 m µ 80

94 95 70 87 90 82 81

% Solids >200 >200 Solids % 60

50 R1 (Control) R2 (35% AE) R3 (64% AE) Figure 5.6 TSS>200 µm and %VSS>200 µm at the End of the FP Concentration Study: Significantly Higher in the Control Sludge Error bars: 95% confidence intervals of 4 replicates

5.4.2 Particle Size Distribution for Granules (>200µm)

A second index to quantify granulation is the particle size distribution of granules (i.e., particles >200 µm) using image analysis. The detailed results of particle size distribution of sludge at various time points are illustrated in Appendix 5.4. The results presented here focuses on the sludge collected at the end of the startup and at the end of the study.

At the end of the startup, as illustrated in Figure 5.7, there was no significant difference in the particle size distribution among sludge samples collected from all reactors (all p values >0.05 in two-tail t-tests, Appendix 5.3). In all sludge samples, approximately 45% of the granules had diameters between 500 and 1000 µm.

60 R1 (AE-Free Control) R2 (35% AE) R3 (64% AE) R4 (100% AE)

50 m µ 40

% among Particles>200 Particles>200 among % 0 200-500 500-1000 1000-1500 1500-2000 >2000 Particle Size ( µm) Figure 5.7 Particle Size Distribution of Granules at the End of the Startup in FP Concentration Study: Similar in all Reactors Error bars: 95% confidence intervals of 4 replicates

The results of particle size distribution of granules in the sludge collected at the end of the experiment are illustrated in Figure 5.8. Compared to the results in Figure 5.7, the sludge collected at the end (Figure 5.8) from R2 and R3 contained greater percentages of granules between 200 and 500 µm and lower percentages of granules > 1500 µm, suggesting disintegration of large granules and formation of small particles. When comparing the particle size distribution of various sludge at the end of study, the percentage of particles between 200 and 500 µm was significantly lower in the sludge from the control reactor (p values <0.05, Appendix 5.3).

R1 (AE-Free Control) R2 (35% AE) R3 (64% AE) 55

25 % of Particles of % 15

-5 200-500 500-1000 1000-1500 1500-2000 >2000 Particle Size ( µm) Figure 5.8 Particle Size Distribution of Granules at the End of the FP Concentration Study: Control Sludge Contained the Lowest %Particles in the Smallest Tested Size Range (200-500µm) Error bars: 95% confidence intervals of 4 replicates

5.4.3 Weakness of Granular Sludge

The resistance to physical disruption (i.e. vortexing) is an indirect measure of the ability of granular sludge to withstand the shear inside the reactor where high upward flows of liquid and biogas are present. In the granule weakness test, a greater change in absorbance after vortexing implies a weaker sludge. The results of the granule weakness test are shown in Figure

5.9. The weakness of the sludge sample from each reactor collected on day 11 (i.e. during startup) was not significantly different from that of the seed sludge (p values >0.05, Appendix 5.3).

During the AE test, after day 38, the sludge samples from the control reactor and R2 treating 35%

AE shared similar granule weakness, while the sludge from R3 treating 64% AE was always the weakest at each sampling event.

2.0 Addition of AE

R3 (64% AE)

1.5

1.0 R2 (35% AE)

0.5 Weakness of Sludge Sludge of Weakness R4 (100% AE) R1 (AE Free Control)

(Normalized Change in Absorbance) in Change (Normalized 0.0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 Day since Startup Figure 5.9 Granule Weakness of Sludge from the FP Concentration Study: Sludge Treating 64% AE was Weaker Error bars: 95% confidence intervals of 4 replicates

5.4.4 Summary of the Physical Properties of Sludge

The results from the physical examinations of sludge indicated the negative impact of the addition of softwood AE on granulation. The sludge treating a higher percentage of AE contained greater fractions of total suspended solids and biomass in undesired fine particles (< 200µm).

Compared to the control sludge, the sludge treating AE also contained greater percentages of particles in the lowest size range in image analysis (i.e., 200-500µm). Furthermore, the sludge treating 64% softwood AE was significantly weaker than the granules treating the AE-free feed.

5.5 Effect of the Addition of AE on the Microbial Communities of Anaerobic

Sludge

Pyrotag sequencing of the 16S rRNA genes of various sludge samples was conducted to study the effect of the addition of AE on the microbial communities of anaerobic sludge in the FP reactors. Three sets of sludge were examined: the seed sludge, the sludge collected at the end of

92 the startup and the sludge collected at the end of the AE test. Since the AE test in R4 only lasted ten days instead of one month, the results of the sludge from R4 are excluded. In addition to pyrotag sequencing, q-PCR was conducted to quantify total bacteria and total archaea in the sludge samples. The results of pyrotag sequencing and q-PCR are presented and discussed in this section. The microbial studies included the following perspectives: the reproducibility of sampling and sequencing, the microbial diversity, the similarity among various sludge samples, the microbial compositions, and the effect of the operational parameters on organisms.

5.5.1Reproducibility of Sampling and Sequencing

The jackknifed tree was constructed to show the clustering of samples based on the abundance and phylogenetic similarity of operational taxonomic units (OTUs, constructed based on 97% similarity of sequences). In such a tree, samples that are more alike are clustered under one branch. As shown in Figure 5.10, samples within each triplicate set were clustered under one branch, implying good reproducibility of sampling and sequencing.

Figure 5.10 Jackknifed Tree based on the Relative Abundance and Phylogenetic Similarity of the OTUs: Samples within each Triplicate Set were Highly Reproducible Jackknife score at each node: out of 1, based on 100 simulations Scale bar shows distance between clusters: distance = 0 implying identical samples; distance = 0.5 implying samples with no common OTUs

5.5.2 Microbial Diversity

In order to compare the microbial diversity of the sludge samples, the number of observed species (i.e., OTU) was plotted again the sequences per sample simulated using rarefactions. As shown in Figure 5.11, for the same number of sequences, fewer species were revealed in the sludge treating AE (dark blue for R2 and orange for R3). In other words, the microbial communities of the AE sludge were less diverse than those of the AE-free sludge, implying the possible enrichment effect of AE at the tested concentrations.

Figure 5.11 Microbial Diversity of Sludge Collected from the FP Concentration Study: Sludge Treating AE was Less Diverse than the AE-Free Sludge

5.5.3 Microbial Composition

The distributions of major organisms (>2% population) in sludge sample investigated using pyrotag sequencing data are presented in Figure 5.12. The term ‘unclassified’ refers to the case when a highly matching (>97%) sequence was found in the GreenGene database using

QIIME, but there was no classification assigned to that reference sequence. The term

‘undetermined’ refers to the case when our sequence matched to a few sequences in the database 94 with similar identity scores, so the classification of our sequence could not be determined. The dominant phyla, classes, orders, families, genera and OTUs are presented in Appendix 5.5 in detail. Background knowledge of the microbial groups identified in this section and their possible functions in the communities will be described in Chapter 7.

The largest group of methanogens was an unclassified Methanomicrobiale (OTU585), contributing to 2-4% of the total population and accounting for 30 to 40% of total archaea.

Blasting on the NCBI website, OTU585 was found to be highly similar to Methanosaeta concilli and Methanosaeta soehngenii (both with 99% coverages and 100% identity scores).

Seed End of R1 End R2 End R3 End 0% AE Startup 0% AE 35% AE 64% AE (Triplicate) 0% AE (Triplicate) (Triplicate) (Triplicate) (R1, 2, 3)

Figure 5.12 Distribution of Major Organisms in Sludge Samples from the FP Concentration Study (based on Pyrotag Sequencing) Taxonomy names: genera of the organisms except for the unclassified and undetermined organisms For each unclassified or undetermined organism, the percentage was only the value of the indicated OTU

Firmicutes was the largest bacterial phylum in the sludge treating 64% AE, and comprised the second in the control sludge and the sludge treating 35% AE. Oscillospira was the largest genus in the sludge treating AE, accounting for 7-17% of the total population.

Bacteroidetes was the largest phylum in the control sludge and in the sludge treating 35%

AE, and was the second largest phylum in the sludge treating 64% AE. An unclassified

Bacteroidales (OTU2804) was the largest member of Bacteroidetes in the seed sludge (5-8%).

The genus Prevotella was the largest Bacteroidetes group in all sludge collected at the end of the study, contributing to 11-21% of the total population.

The phyla Proteobacteria , Chloroflexi , Spirochaetes were also present at relatively high percentages. In all samples, Proteobacteria were mainly found in the genus Desulfovibrio . The sludge treating 35% AE also contained relatively large percentages of the genus Propionivibrio .

The genus T78 in the family Anaerolinaceae was the largest group of Chloroflexi in the sludge treating. In the AE-free sludge, both genera T78 and Anaerolinea were present at comparably high percentages. Spirochaetes in the seed sludge were mainly detected in the genus Treponema in the class Spirochaetales and in the genus W5 in the candidate order Cloacamonales in the class

WWE1 . At the end of the study, Spirochaetes in the AE sludge were mainly present in the genus

Sphaerochaeta .

The absolute abundance of organisms was estimated by combining the results of pyrotag sequencing and q-PCR using Equations 5.1 and 5.2. The overall trends of the absolute abundance of organisms are very similar to those in the percentages of organisms: the AE-free sludge contained more archaea (mainly methanogens) and Treponema , and less Oscillospira than the sludge from R2 and R3 treating AE (Appendix 5.4).

5.5.4 Similarity and Variations among Different Sludge Samples

Principal coordinate analysis (PCoA) was conducted based on the differences in the relative abundance and phylogenetic relation of OTUs in different samples. As shown in Figure

5.13, the variations represented by principal coordinates (PC) 1 and 2 were 51% and 16% respectively. Along PC1, sludge treating AE were located on the negative region, while the AE-

96 free sludge samples were located on the positive region. Samples collected at the end of the experiment had negative PC2 values, while earlier samples had positive PC2 values. Separation of samples along PC1 and PC2 suggested that both AE loadings and culture time (i.e., days since startup) contributed to the variations in the microbial communities.

Figure 5.13 PCoA Plot of the FP Sludge: Samples Clustering Affected by AE Loadings and Culture Time

5.5.5 Identification of Microorganisms Affected by Operational Parameters

Distance-based redundancy analysis (dbRDA) was performed as a statistical tool to indentify the organisms affected by operational parameters, using sludge samples collected at the end of the startup and at the end of the AE test. The abundance of organisms, calculated based on the results of q-PCR and pyrotag sequencing, were entered to the dbRDA. The names of organisms were plotted as dots in the diagrams. Operational parameters were plotted as vectors.

Any organism aligning in the same direction as the vector was positively correlated to that vector.

Three sets of dbRDA were conducted: in the first dbRDA, organisms were investigated at the phylum level, and the operational parameters included AE loadings, total organic loadings and

97 culture time; in the second dbRDA, organisms were analyzed at the genus level 3, and the operational parameters were the same as the first set; in the third dbRDA, the organisms were also examined at the genus level, and the operational parameters contained concentrations of lignin and total resin acids and long-chain fatty acids (RFAs) in the feeds.

The effect of AE loadings, total organic loadings and culture time on different phyla is investigated using Figure 5.14. The vectors of total organic loadings and AE loadings pointed to similar direction in the ordination diagram, implying that their effect on the distribution of organisms could not be distinguished based on the input experimental data using dbRDA. In general, these two factors showed strong positive correlations to the phylum Firmicutes . They also had positive correlations to the phylum Proteobacteria and negative correlations with the phyla Euryarchaeota (e.g., methanogens), Chloroflexi and Spirochaetes .

Figure 5.14 dbRDA for the FP Concentration Study: Effect of AE Loadings, Organic Loadings and Culture Time on Various Phyla

3 Only genera > 1.5% of the total population were analyzed; if an OTU contributed to >1.5% of total population but with unclassified or unidentified taxonomy naming, the quantity of OTU was used. 98

The impact of AE loadings, total organic loadings and culture time on different major organisms (i.e., >1.5% total population) at the genus level is examined using Figure 5.15. The diagram on the left is the original view, and the figure on the right is the zoom-in view of the original figure. Similar to Figure 5.14, the effect of total organic loadings and AE loadings could not be distinguished. As shown in the original figure on the left, the genus Oscillospira in the phylum Firmicutes was strongly positively correlated to both AE loadings and total organic loadings, and the genus Prevotella in the phylum Bacteroidetes was strongly correlated to culture time. As presented in the zoom-in figure on the right, a few organisms were negatively correlated to total organic loadings and AE loadings, such as Treponema , Methanomethylovorans , and possibly an unclassified Bacteroidetes (OTU2804) and an unclassified methanogen (OTU595).

In contrast, organisms belonging to the genus HA73 in the phylum Synergistetes were positively correlated to total organic loadings and AE loadings. The zoom-in figure also reveals that

Sphaerochaeta was positively correlated to culture time.

Figure 5.15 dbRDA for the FP Concentration Study: Effect of AE Loadings, Organic Loadings and Culture Time on Various Major Organisms (>1.5% Total Population) at the Genus Level

As explained in the literature review (sections 2.3.1 and 2.4.2), lignins, and resin acids and long-chain fatty acids (RFAs) are often cited as inhibitory compounds present in pulp mill effluents. As mentioned in the chapter of the characteristics of pulp mill effluents (section 3.7), 99

SW1 AE contained considerably higher concentrations of lignins and RFAs than BCTMP effluent and AC. Therefore, dbRDA was conducted to investigate whether any major organism

(>1.5% total population) was correlated to the lignin and RFA contents in reactor feeds. As shown in Figure 5.16, Oscillospira was found to be strongly positively correlated to the RFA concentrations in reactor feeds (left, original view), while the genera Parabacteroides and HA73 showed positive correlations to the lignin content in feeds (right, zoom-in view).

Figure 5.16 dbRDA for the FP Concentration Study: Effect of Lignins and RFAs on Various Major Organisms (>1.5% Total Population) at the Genus Level

In addition to dbRDA, correlation tests were also conducted to examine the effect of AE loadings, total organic loadings and culture time on the abundance and percentages of organisms at the phylum and genus levels using the built-in function in QIIME. The correlation coefficients and the significant scores (p-values) were calculated. The results are summarized in Appendix

5.7. In general, the results of the correlation tests were consistent with those shown in the RDA diagrams: Oscillospira and total Firmicutes were strongly positively correlated to AE loadings;

Treponema , Methanomethylovorans , total Chloroflexi and total Spirochaetes were negatively correlated to AE loadings; Prevotella were strongly positively correlated to culture time.

5.6 RFAs in Anaerobic Treatment of Pulp Mill Effluents

Resin acids and long-chain fatty acids (RFAs) are frequently cited as the important inhibitors in anaerobic treatment (Rintala and Puhakka, 1994; Sierra-Alvarez et al ., 1994). Resin 100 acids are diterpenoid carboxylic acids present primarily in softwood species (Liss et al ., 1997).

Abietic acid and dehydroabietic acid (DHA) are often the major resin acids contained in pulp mill effluent. Oleic and linoleic acids are typical long-chain fatty acids (LCFAs) found in pulp mill effluents (Leach and Thakore, 1973). In this section, the concentrations of RFAs contained in reactor feeds, treated effluents and in the sludge bed are presented. The following RFAs were examined using LC-MS: DHA, palustric acid, the sum of abietic, pimaric and sandaracopimaric acids, the sum of levopimaric and neoabietic acids, palmitic acid, linoleic acid, oleic acid and stearic acid.

5.6.1 RFAs in Reactor Feeds and Effluents

The concentrations of total RFAs (i.e., sum of the particulate phase and the dissolved phase) are displayed in Figure 5.17, where the red lines represent the concentrations in feeds and the green lines show the concentrations in effluents. Since the concentrations of stearic acid, palustric acid and the sum of levopimaric and neoabietic acids were minor as compared to other

RFAs, the results of these acids are not presented here. Furthermore, the analysis conducted by

Exova demonstrated that the concentration of abietic acid in AE was twenty times higher than those of pimaric and sandaracopimaric acids (Appendix 3.4), so the peak of abietic, pimaric and sandaracopimaric acids was believed to mainly represent abietic acid.

As shown in Figure 5.17, the most dominant RFAs in the non-AE feed (i.e., BCTMP effluent and AC) were linoleic acid and abietic acid, while oleic acid, linoleic acid, abietic acid and DHA were all present at comparably high concentrations in the feeds containing AE. DHA was mainly present in the dissolved phase in the feed, while majority of palmitic acid was contained in the particulate phase. The difference in phase partitioning between DHA and palmitic acid was due to their different solubility and hydrophobicity (Peng and Roberts, 1999;

Strom, 2000; Robb, 1966).

101

The effluent from R3 contained the highest concentrations of RFAs, while the lowest

concentrations of RFAs were observed in the effluent from R1 whose feed did not have the RFA-

rich AE.

Figure 5.17 RFA Concentrations (Dissolved +Particulate Phase) in Feed (Red) and Effluents (Green) in the FP Concentration Study

102

Removals of certain RFAs were observed in R2 and R3 during the AE test. The feeds to

R2 and R3 contained at least twice as much linoleic and abietic acids than the corresponding treated effluents, implying removals of these two acids in the AE reactors. Approximately 13% less DHA was contained in the effluents from R2 and R3 than the corresponding feeds, consistent with the low removal rates of DHA ( ≤10%) in literature (McFarlane and Clark, 1988).The concentrations of oleic acid in the feeds to R2 and R3 were also 15-30% higher than those in the treated effluents, suggesting possible removals of oleic acid as well. The concentrations of palmitic acid in the feeds to R2 and R3 were a magnitude lower than those in the effluents, imply generation of palmitic acid in the AE reactors.

5.6.2 RFA Concentrations in Sludge Samples

As shown in Figure 5.18, compared to the sludge collected at the end of startup, more

RFAs were contained in the final sludge samples from R2 and R3 treating AE. The highest concentration of each detected RFA was found in the sludge from R3 treating 64% AE. The RFA concentrations in the sludge bed were positively correlated to the AE content in its feed.

In the sludge treating AE (i.e., R2 and R3), the most abundant RFA was palmitic acid, followed by oleic acid and DHA. In the study conducted by Pereira et al . (2005), oleate was fed to an expended granular sludge bed reactor, and palmitic was found to be the most dominant

RFA in the sludge. Lalman and Bagley (2000, 2001) suggested that palmitic acid was the primary oxidation product from beta-oxidation of oleic and linoleic acids. Therefore, the great amounts of palmitic acid in the sludge from R2 and R3 was likely due to the oxidation of oleic and linoleic acids during the anaerobic treatment of AE.

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Figure 5.18 RFA Concentrations in FP Sludge: Lowest in the Control Sludge, Highest in the Sludge Treating 64% AE

5.6.3 Summary of the RFA Analysis

The effluents from the AE reactors contained higher concentrations of resin acids and long-chain fatty acids than the effluent from the control reactor. By examining the reactor feeds and effluents, clear reductions of abietic and linoleic acids, as well as minor removals of DHA and oleic acid, were observed, while generation of palmitic acid was noticed in all reactors.

Higher concentrations of RFAs were associated with the sludge treating 35% and 64% AE as compared to the sludge collected at the end of the startup. In particular, a remarkable increase in palmitic acid concentration was detected in the AE sludge. RFA partitioning onto biomass was proposed to play an important role in the removals of abietic, linoleic, oleic acids and DHA. In addition, since high concentrations of palmitic acid were observed in the treated effluents and in the sludge from the AE reactors, conversion from linoleic and oleic acids to palmitic acid might also explain the observed removals of the former two LCFAs.

5.7 Summary of the FP Concentration Study

In this chapter, the continuous experiment of AE conducted at FPInnovations was described. The AE used in this study was the problematic softwood AE (SW1) mentioned in

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Chapter 3, a concentrated stream of COD, tannin and lignin compounds, and resin acids and long-chain fatty acids (RFAs). Four upflow anaerobic reactors were set up, with a one-month startup period when all reactors treated BCTMP effluent and AC, and a one-month test of AE in three reactors, while the forth one served as the AE-free control.

Poorer treatment efficiency and granulation were found in the reactor receiving a higher percentage of AE. The more AE was contained in the feed, the lower the %sCOD removal and biogas production were, i.e., the daily biogas production in R3 treating 64% AE was 40-50% of that in the AE-free control reactor, and the %sCOD removal in R3 was 70% lower than that the control reactor. In R4 treating 100% AE, the effluent contained more TSS than its feed, implying washout of sludge. Compared to the AE-free sludge, the sludge treating AE was smaller and weaker. Therefore, the addition of AE had negative impact on treatment efficiency and granulation of anaerobic sludge, suggesting that AE or a similar stream should be further diluted

(<35%) or pretreated prior to anaerobic treatment.

The AE sludge was found to have very different microbial communities than the AE-free sludge. The AE sludge was less diverse, implying the possible enrichment effect of AE at the tested concentrations. The clustering of sludge samples on the principal coordinate plot suggested that both AE loadings and culture time had important impact on the microbial communities of sludge. The AE sludge had less and lower percentages of archaea (methanogens) than the AE- free sludge. Addition of AE also influenced the distribution and dominance of certain organisms.

Correlation tests and distance-based redundancy analysis were carried out to indentify the organisms affected by AE loadings. Total Firmicutes and Oscillospira in the phylum Firmicutes were found to have strong positive correlations with AE loadings and the RFA concentrations in feeds. Future investigation of the roles of these organisms will be highly valuable.

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Since the most distinct feature of the feeds containing AE, as compared to the AE-free feed, was the presence of numerous resin acids and long-chain fatty acids, analysis was conducted to evaluate the fate of RFAs in the FP reactors. The effluent and sludge from the reactor receiving a higher AE loading contained more RFAs in general. When comparing the

RFA concentrations in the feed and in the effluent for each AE reactor, removals of abietic, linoleic, dehydroabietic and oleic acids were observed. Since the sludge treating AE collected at the end of the study contained 5-10 times more RFAs than the sludge collected at the end of startup, it was believed that partitioning of RFAs onto sludge played an important role in the removals of RFAs mentioned above. In contrast, generation of palmitic acid was observed.

Therefore, the conversion of long-chain fatty acids into palmitic acid could also partially account for the removals of oleic and linoleic acids.

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CHAPTER 6. THE UOFT LONG-TERM STUDY: THE EFFECT OF CONTINUOUS TREATMENT OF AE

6.1 Introduction

In the FP concentration study, the effect of adding different proportionsof SW1 AE, the alkaline effluent from sulphite puling of softwood, on reactor performance and granulation of sludge was investigated. Although the negative impact of SW1 AE was confirmed, there were two major drawbacks of the FP concentration study: the total COD loading in each reactor was not the same (i.e., 30 kg COD/m3/day in the control reactor and 95 kg COD/m 3/day in the reactor treating 100% AE), so it was difficult to distinguish the effect of AE from the effect of overloading COD; the AE test only lasted one month, making it impossible to assess the long- term effect of AE on sludge.

In order to better understand the effect of AE on granular sludge and to attempt to adapt a microbial community to AE, a continuous experiment was conducted at the University of

Toronto with two anaerobic upflow digesters. During the two-month startup period, both reactors were supplied with a synthetic feed containing organics that were similar to the major constituents in the bleached chemi-thermo-mechanical pulping (BCTMP) effluent and acid condensate (AC) from sulphite pulping, which were feeds to the full scale internal circulation (IC) reactors in the mill. After the startup period, one reactor was fed with the same synthetic feed, while the feed to the test reactor (i.e., the ‘AE reactor’) was gradually replaced by 10, 15, 30 and

40% AE on a COD basis.

Some objectives of this UofT long-term study were similar to those in the FP concentration study, such as to examine the effect of the addition of AE on reactor performance, the physical properties and the microbial communities of anaerobic sludge. In addition to the shared objectives, there were two specific goals of the UofT long-term study: to assess the

107 microbial dynamics of the sludge treating AE in a longer time span, and to evaluate the ability of sludge to acclimate to AE after several months of treatment of AE.

There are three sections following this introduction. Materials and methods are presented in section 6.2. The results of reactor performance, the physical properties of sludge, and the microbial communities are presented and discussed in sections 6.3, 6.4 and 6.5 respectively. The fate of resin acids and long-chain fatty acids (RFAs) during the treatment of AE in the UofT experiment is investigated in section 6.6. The results of batch assays to verify acclimation are presented in section 6.7. An overall summary of the chapter is provided in section 6.8.

6.2 Materials and Methods

This section is subdivided into five parts: reactor setup (section 6.2.1), feed characteristics (section 6.2.2), feeding schedule (section 6.2.3), a brief description of the methods

(section 6.2.4), and the setup of the batch assays to test acclimation (section 6.2.5).

6.2.1 Reactor Setup

Two identical upflow anaerobic reactors were set up at the University of Toronto to test the long-term effect of AE exposure on sludge, as shown in Figure 6.1. Each reactor was made of acrylic, with 10cm outer diameter and 8.9cm internal diameter, a working volume of 4.4L and a headspace of 1.2L. Reactors were maintained at 37 oC with water jackets. Feeds and nutrients were continuously pumped to the bottom of the reactors with a hydraulic retention time (HRT) between 11 and 15 hours. Sludge was suspended by continuous recirculating the biogas from the headspace to the bottom of the reactors at a flow rate of 100mL/min. A porous ceramic disk (2- inch diameter, by Refractron Technologies Corp) was used inside each reactor to support the sludge bed and to help distribute the biogas. Biogas production was monitored using bubble counters. The reactor effluent was sent to a capped sludge settler, before flowing to the waste

108 effluent collector. Approximately 800mL of sieved Tembec granules (>200 µm)4 was added to each reactor, with sludge retention time ranging between 30 and 40 days.

Figure 6.1 Schematic of the Continuous Reactors Used in the UofT Long-Term Study

6.2.2 Synthetic Feed, AE and Additional Nutrients

There were some advantages of using a synthetic feed to replace the actual BCTMP effluent and AC. In the FP concentration study, similar flow rates were applied in order to achieve comparable HRTs across all reactors. Different total organic loading rates (OLR) to the reactors were resulted, as a higher COD concentration was included in AE as compared to

BCTMP effluent and AC. One advantage of using a synthetic feed to replace the actual BCTMP effluent and AC was the ability to control similar OLRs and HRTs in different reactors by manipulating the concentration of the synthetic feed. Compared to the actual effluents, another advantage of a synthetic feed was its known and consistent constituents. In addition, a synthetic feed was only needed to be prepared until required to be used. Therefore, a synthetic feed was

4 The TSS or VSS concentrations of the seed sludge were not measured at the time. Based on other Tembec sludge samples, the amount of the added sludge was estimated to be around 100g volatile suspended solids (VSS). 109 more practical and preferred over the actual BCTMP effluent and AC from the perspectives of feed preservation and storage as well as waste disposal.

Three factors were considered when formulating the synthetic feed: the major organics should be similar to those in the actual BCTMP effluent and AC; the hydrolytic, acidogenic, acetogenic and methanogenic functions of the sludge should be preserved; and the organic compounds on the recipe should not have known toxic effect on anaerobic organisms or human for handling. Based on the effluent characteristics described in Chapter 3, the simple organics identified in BCTMP effluent and AC mainly consisted of acetate, glucose, xylose, methanol and ethanol. These compounds were substrates for fermenting bacteria and methanogens. It was decided to include the soluble sodium carboxymethyl cellulose (CMC, Sigma-Aldrich, Cat#

419311) in the synthetic feed, because it could help preserve the hydrolytic function of the sludge and polysaccharides were also important compounds in the BCTMP effluent. Nevertheless, acetate, glucose, xylose, methanol, ethanol, as well as CMC, did not add up to 100% of the total

COD content in BCTMP effluent and AC. Therefore, the corresponding fractions were scaled up to sum up to 100%. It should be noted that the actual AC also contained furfural, which was classified as a hazardous compound on the material safety data sheet (MSDS) and required special disposal procedures. Given the large volume of effluent involved in the entire experiment

(e.g., ~7m3 for an 11-month run), it was decided to exclude furfural from the synthetic feed.

Based on all the considerations listed above, a synthetic feed was formulated, as shown in Table

6.1. It should be noted that this synthetic feed was blended with de-ionized water for the control reactor or PEW for the test reactor in a 1:1 volumetric ratio as feeds to the reactors using peristaltic pumps, as shown in Appendix 6.1.

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Table 6.1 Compositions of the Synthetic Feed Used in the UofT Long-Term Study Constituent Concentration (g/L) Acetic acid 14.6 Methanol 0.6 Ethanol 0.4 Xylose 1.0 Glucose 0.9 CMC 1.6

Additional minerals and micronutrients were also provided to the reactors, based on their concentrations in the influents to the Tembec IC reactors, the feed to the control reactor in the FP concentration study, as well as what other researchers used in the published papers (Appendix

6.2). N, Ca, Mg, Mn, Al, Cu, Zn, B and Mo were supplemented for cell growth. KH 2PO 4 and

K2HPO 4 were added as sources of P and buffering agents. Yeast extract was also added to provide amino acids and vitamins that were essential to cells. Although bicarbonate was added in some published studies (Tay and Yang, 1994) as a second buffering agent, it was decided to not to supplement bicarbonate to the UofT reactors for two reasons: no additional bicarbonate was added to the Tembec IC reactors, and bicarbonate could be produced during anaerobic degradation by microorganisms (Espinosa et al ., 1995). The effluent pH was closely monitored on a daily basis. In the event when a pH upset (i.e., <6.8) was noticed (Appendix 6.3), the feed flow rate was reduced to increase HRT for recovery.

The AE used for the entire experiment was from the same batch shipment from Tembec, with the following characteristics: 19g COD/L, 120mg ·sulphite/L, 1570mg tannin and lignin compounds /L, 100mg resin acids/L and 20mg long-chain fatty acids/L (Section 3.3.4 and

Appendix 3.4). After arriving at the University of Toronto, the AE was well mixed, divided into smaller portions, and frozen at -20°C until required for feed preparation.

6.2.3 Organic Loading Rates and Feeding Schedule

During the course of the experiment, the organic loading rate was maintained at approximately 30g COD * L reactor -1 *day -1. HRT was approximately 11 hours. The specific 111 loading rate was approximately 1.2kg sCOD* kg VSS sludge -1*day -1. The volumetric organic loading rate was comparable to the average value in the mill, and the specific loading rate based on the sludge concentration was at the high end of the actual value of the full scale IC reactors

(Section 3.2.3).

The feeding schedule is presented in Table 6.2. The control reactor always received the

AE-free synthetic feed. On the other hand, AE was blended to the feed to the test reactor at a step-wise increasing manor: 10% between days 80 and 93, 15% between days 94 and 111, 30% between days 112 and 239, and finally 40% between days 240 and 343.

Table 6.2 Feeding Schedule in the UofT Study Day Feed to Control Reactor Feed to AE Reactor (%: Based on COD) 0-79 100% synthetic feed 100% synthetic feed 80-93 100% synthetic feed 90% synthetic feed + 10% AE 94-111 100% synthetic feed 85% synthetic feed + 15% AE 112-239 100% synthetic feed 70% synthetic feed + 30% AE 240-343 100% synthetic feed 60% synthetic feed + 40% AE

6.2.4 Sample Collection and Routine Measurements

COD, total suspended solids (TSS) and volatile suspended solid (VSS) in feeds and effluents were measured 2-3 times per week according to standard methods (Eaton et al. , 1998).

The biogas production was monitored using bubbler counters (Digital Flow Meter by Challenge

Technology).

Sludge was sampled approximately once per month. The sludge in the reactors was well mixed by recirculating headspace gas at a flow rate of 2000mL/min for 1 minute, before the sludge samples were withdrawn from the sludge sampling port. The sludge samples and the assays conducted are listed in Table 6.3. Particle size distribution analysis, granule weakness test and pyrotag sequencing were performed as previously described in sections 4.2.1, 4.3.1 and 5.2.5 respectively. The RFA concentrations in feeds, effluents and sludge samples were estimated using LC-MC as described in sections 5.2.3 and 5.2.4.

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Table 6.3 Sludge Samples Collected in the UofT Study for Physical and Microbial Examinations Sampling Day Notes Regarding to the AE Fed to Measurements (since Startup) the Test Reactor Size Distribution, granule weakness, 72 0% AE pyrotag sequencing 107 Size Distribution and granule weakness 13 days into the 15% AE Test Size Distribution, granule weakness, 127 15 days into the 30% AE Test pyrotag sequencing Size Distribution, granule weakness, 149 37 days into the 30% AE Test pyrotag sequencing Size Distribution, granule weakness, 169 57 days into the 30% AE Test pyrotag sequencing Size Distribution, granule weakness, 197 87 days into the 30% AE Test pyrotag sequencing 217 Size Distribution, granule weakness, 105 days into the 30% AE Test 342 Size Distribution, granule weakness 100 days into the 40% AE Test

6.2.5 Batch Assay Setup

Biochemical methane potential (BMP) assays (Owen et al. , 1978 .) have been widely used as a standard method to evaluate the anaerobic degradability and the biogas productivity of various substrates (Chynoweth et al. , 1993; Luna-del Risco et al. , 2011). In a BMP assay, as shown in Figure 6.2, culture medium, substrates and inocula are placed in 160ml anaerobic serum bottles capped with rubber stoppers and crimped. Substrates are degraded to generate biogas

(mainly CO 2 and CH 4), which is then released to the headspace.

Figure 6.2 Basic Setup of a Biochemical Methane Potential (BMP) Assay

In the UofT long-term study, BMP assays were conducted to investigate if the final sludge from the reactor treating AE could degrade AE better than the sludge from the control reactor. The detailed setup is shown in Table 6.4. The inocula were sludge samples collected from the control reactor and the AE reactor at the end of the UofT continuous experiment: after 113 the continuous experiment was terminated, the sludge was transferred and stored in anaerobic serum bottles at 4 oC for two weeks; prior to the setup of the BMP assays, the sludge was brought back to room temperature for three days for reduction of organic residues in the supernatant.

Culture medium was added to the serum bottles to provide phosphate buffer, trace minerals, redox indicator, bicarbonate, vitamins, FeS etc., as described by Edwards and Grbic-Galic (1994).

Four sets of triplicate bottles were included for each type of sludge: negative control, positive control, acetate and AE. No substrate was added to the negative control bottles, where biogas was mainly produced from the endogenous decay of cells. The positive control bottles were fed the synthetic feed as explained in section 6.2.2. The feed in the ‘AE-Feed’ bottles was 40% AE blended with 60% synthetic feed on a COD basis, which was identical to the feed for the AE reactor during the 40% AE test. Initially, 120 mg COD substrates were fed to all bottles except the negative control. The pH in all bottles was adjusted to approximately 6.8 at time zero. All bottles were incubated at 37 oC.

Table 6.4 Setup of the BMP Assays Total Volume Medium Inocula AE Acetate Synthetic Water Substrate Headspace Gas (ml) (ml) (ml) (ml) (ml) Feed (ml) (ml) Negative Control 90%N 2 +10%CO 2 98 54 40 0 0 0 4

Positive Control 90%N 2 +10%CO 2 98 54 40 0 0 3 1

Acetate 90%N 2 +10%CO 2 98 54 40 0 3 0 1

AE 90%N 2 +10%CO 2 98 54 40 2 0 2 0 VSS sludge per bottle: 70mg; COD per bottle (except the negative control): 120 mg

Biogas production was monitored using the gas displacement method (Yang et al ., 2010), as shown in Figure 6.2. Sampling was performed at 1 atm at 35°C. The biogas production reported in the section for BMP assays (section 6.7) was the net value after subtracting the biogas production in the negative control. In theory, if the substrates degraded are solely used for methanogenesis, complete degradation of 1g COD generates a maximum of 350ml CH 4 at standard conditions (Speece, 1996).

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6.3 Effect of the Addition of AE on Reactor Performance.

The impact of the addition of AE on reactor performance was evaluated based %sCOD removal, biogas production and TSS content in the effluent. It should be noted that two major upsets occurred during the course of the experiment. A few days upon around day 139, there was a leakage in the phosphate feed line and a consequent low pH (<6.8) in the control reactor

(Appendix 6.3). After the leakage was fixed on day 141, OLR to both reactors was reduced for five days to facilitate the recovery of the control reactor. At the beginning of the 40% AE test, severe clogging of the gas distribution disc was noticed in both reactors. The reactors were open for a short duration to clean up the clogging on day 254.

6.3.1 %sCOD Removal

The results of %sCOD removals are presented in Figure 6.3, where the dots represent the actual calculated values and the lines show moving averages of 9 samples. The frequency of nine samples was chosen, because approximately nine samples were collected every month for COD measurements. During the startup period, the %sCOD removals in the AE reactor seemed to be slightly higher than those in the control reactor, but the difference was not statistically significant

(p = 0.57 in two-tail t-test). During the 30% AE test, the control reactor showed significantly higher %sCOD removals than the AE reactor (p = 5*10-7 in one-tail t-test). The differences in the %sCOD removals between the two reactors were even larger right after 40% AE was added to the AE reactor.

The %sCOD removals in both reactors decreased after day 255 (until day 280), which was about the time when the reactors were opened for cleaning the gas distribution discs. The decrease in the %sCOD removals in the AE reactor was smaller. Both percentages slowly increased towards the end of the experiment, which could be a sign of recovery of both systems.

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AE Addition

Figure 6.3 %sCOD of Control Reactor (Red) and AE Reactor (Blue) in the UofT Long-Term Study: Poorer %sCOD Removal in AE Reactor during the 30% and 40% AE Test Dots: actual calculated values; lines: moving averages of 9 samples

6.3.2 Daily Biogas Production

Before day 218, daily biogas production was estimated by manually counting bubbles produced in a one-minute time interval then scaling up to 24 hours. We believed that gas production was highly heterogeneous on a time basis, so the one-minute sampling time interval might not be sufficient or representative enough to calculate the daily production, which could lead to inaccurate estimation.

Starting on day 219, biogas production was estimated using a bubble counter coupled with an automated reader. As shown in Figure 6.4, there was a reduction in biogas production in both reactors around day 250, when the clogged gas distributors were taken out for cleanup.

Nevertheless, the control reactor was found to produce significantly more biogas than the AE reactor after day 219 (p = 6*10 -7 in one-tail t-test).

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AE Addition

Figure 6.4 Biogas Production in Control Reactor (Red) and AE Reactor (Blue) in UofT Long-Term Study: Control Reactor Produced more Biogas than the Reactor Treating 30% and 40% AE (after Day 219) Dots: actual calculated values; lines: moving averages of 10 samples (equivalent to two-week)

6.3.3 TSS in Reactor Effluents

Since the TSS concentrations in both AE and synthetic feed were low (i.e., <10 mg/L), the TSS present in reactor effluents mainly reflected the amount of biomass washed out from the reactors. As shown in Figure 6.6, towards the end of the 30% AE addition and during the treatment of 40% AE, the effluent from the AE reactor contained significantly more TSS than the effluent from the control reactor (p = 0.006 in one-tail t-test). Similarly, significantly higher VSS concentrations were also observed in the effluent from the AE reactor treating 30% and 40% AE

(p = 0.01 in one-tail t-test, VSS Figure shown in Appendix 6.4). The results of the effluent TSS and VSS implied that more sludge was washed out due to the addition of AE.

In summary, the results in section 6.3 demonstrated that the reactor with the addition of a relatively high %AE (i.e., 30 and 40%) generally showed poorer performance than the control reactor. During the treatment of 10% and 15% AE, both reactors performed similarly. During the treatment of 30% and 40% AE, the control reactor showed higher %sCOD removals and biogas production and less sludge washout than the AE reactor, which confirmed the negative impact of

117 the addition of AE on reactor performance. Nevertheless, the biogas production from the AE reactor was greater than what could be produced from the degradation of the synthetic feed portion in its feed, suggesting that some organics in the AE wastewater were degraded towards

CH 4 production.

AE Additio n

Figure 6.5 Effluent TSS from Control Reactor (Red) and AE Reactor (Blue) in UofT Long-Term Study: Reactor Treating 30% (after day 210) and 40% AE Showed Greater Washout than Control Reactor Dots: actual calculated values; lines: moving averages of 9 samples

6.4 Effect of the Addition of AE on the Physical Properties of Granular

Sludge

Tests of particle size distribution and granule weakness were conducted to investigate the effect of the addition of AE on granulation. Two parameters were used to measure particle size distributions: the percentage of total suspended solids contained in sludge> 200 µm (%TSS>200 µm), and the size distribution of granules (> 200 µm) using image analysis. Granule weakness was evaluated by examining the change in the sample turbidity after vortexing. As mentioned in the previous section, an upset took place at the beginning of the 40% AE test. Therefore, only samples collected before this upset took place and at the end of the entire continuous experiment were considered for the physical examinations of sludge in this section.

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6.4.1 Percentage of Total Suspended Solids Contained in Sludge > 200 µm (%T SS>200 µm)

In literature, sludge particles smaller than 200 µm were classified as ‘fine particles’, and were not considered as good granules (Pereboom, 1997). A better granulated sludge sample should have a greater portion of total suspended solids contained in particles larger than 200 µm.

The results of TSS>200 µm ar e presented in Figure 6.6. During the 30% AE test , the control sludge collected on days 169 and 197 contained significantly lower percentages of fine particles than the

AE sludge (p = 0.002 and 0.03 in one -tail t-tests). At the end of the study when the 40 % AE test had been conducted for 103 days, the sludge from the control reactor also contained a significantly lower percentage of fine particles than the AE sludge (p = 0.01 in one -tail t-test).

%TSS PEWAE %TSS Control 100

80 %Solides >200um %Solides 75 100 150 200 250 300 350 400 Day since Startup Figure 6.6 %TSS>200 µm of Sludge from the UofT Long-Term Study: Control Sludge Marked with Stars Contained Significantly Greater %TS >200 µm Stars: significant difference obtained in t -test using α=0.05

6.4.2 Particle Size Distribution of Sludge Based on Image Analysis

The particle size distribution of granules (> 200 µm) was assessed using image analysis.

As displayed in Figure 6.7, increasing the percentage of AE from 15 to 30 in the feed led to a significant increase in the percentage of particles between 200 and 500 µm (p = 0.00 2 in one-tail t-test) and a significant decreases in the percentage of particles between 1000 and 1500 µm (p =

5*10 -5 in one-tail t-test) between days 107 and 169, suggesting disintegration of granules in the

AE reactor.

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70 10% 15% 30% 40% AE Addition 60 500 -1000um 50 40 30 200 -500um 20

Percentage (%) Percentage 1000 -1500um 10 1500 -2000um 0 >2000um 0 100 200 300 400 Day since Startup Figure 6.7 Particle Size Distribution of Granules in the AE Sludge in UofT Long-Term Study : Degranulation after AE was Increased from 10% to 30% Error bars: 95% confidence intervals of triplicates; Stars: significant difference obtained in t-test using α=0.05

Figure 6.8 Particle Size Dis tribution of Granules Collected during the 30% AE Test in the UofT Long-Term Study: Lower %Particles 200-500 µm and Greater % Particles 1000-1500 µm in Control Sludge Error bars: 95% confidence intervals of triplicates

For the sludge collected during the 30% AE test on days 149, 169 and 197 , as shown in

Figure 6.8, the control sludge contained significantly lower percentages of particles between 200 and 500 µm and significant ly higher percentage s of particles between 1000 and 1500 µm than the

AE sludge (all p values in one -tail t-tests < 0.05). The results of image analysis once again demonstrated that the sludge collected from the control reactor was larger than the sludge tre ating

AE.

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6.4.3 Granule Weakness

The results of granule weakness tests are illustrated in Figure 6.9, where a greater normalized change in absorbance implies a weaker sludge sample. As shown in the figure, the

AE sludge collected on day 127 (15 days into the 30% AE test), day 197 (87 days into the 30%

AE test ) and at the end of the study (3 -month treatment of 40% AE ) were found to be significantly weaker than the corresponding control sludge .

AE Sludge Control Sludge 2.5 10% 15% 30% 40% AE Addition 2.0

1.5

1.0

0.5 Nor. ChangeNor. inAbsorbance 0.0 0 50 100 150 200 250 300 350 400 Day since Startup Figure 6.9 Granule Weakness of Sl udge in the UofT Long-Term Study: AE Sludge Marked with Stars was Significantly Weaker than the Corresponding Control Sludge Error bars: 95% confidence intervals of four replicates Stars: p values < 0.05 in one-tail t-test

To summarize section 6.4, the sludge treating 30% AE was generally smaller and weaker than the control sludge collected at the same time . The sludge collected at the end of the study treating 40% AE was also weaker and contained a higher percentage of fine particles than the corresponding control sludge. Therefore, it was concluded that the addition of AE negatively impacted granulation.

6.5 Microbial Communities of Sludge in the Anaerobic Treatment of AE

Pyrotag sequencing of the 16S rRNA genes was performed to study the microbial communities of sludge collected from the UofT continuous reactors . For each reactor, five sets of

121 sludge samples were examined: the sludge collected on day 72 during startup, and the sludge collected on days 127, 149, 169 and 197 during the 30% AE test. A statistical summary of reads is provided in Appendix 6.5. The microbial community studies included the similarity among different sludge samples, the microbial diversity, the major microbial compositions of the sludge at various time points, and the organisms affected by the addition of AE. Detailed discussion of the possible functions of the named organisms will be included in Chapter 7.

6.5.1 Clustering of Samples, and Microbial Diversity of Sludge

The similarity among different sludge samples was evaluated by constructing the jackknifed tree and the principal coordinate analysis (PCoA). The beta-jackknifed tree was constructed based on the abundance and phylogenetic distance of operational taxonomy units

(OTUs, i.e., clusters of sequences with similarity ≥97%). In the jackknifed tree as shown in

Figure 6.10, most of the triplicates were clustered under the same parent branch, implying reproducible sampling and sequencing.

Clustering of samples was also visualized on the PCoA plots as shown in Figure 6.11, where principle coordinates (PC) 1, 2 and 3 explained 43%, 15% and 9% of the total variations respectively. In the left PCoA plot with PC 1 as the x-axis and PC2 as the y-axis, almost all sludge samples treating AE were located on the top-right region, and were separated from the control sludge. In the right PCoA plot with PC3 as the x-axis and PC2 as the y-axis, samples collected later generally had PC3 values ≥ 0, except the control sludge collected on day 149.

Therefore, both the treatment of AE and culture time seemed to impact the microbial communities of the sludge.

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Figure 6.10 Clustering of Samples on the Jackknifed Tree Showing Good Reproducibility within each Triplicate Set Jackknife score at each node: based on 100 simulations Scale bar: distance = 0 implying identical samples; distance = 0.5 implying samples with no common OTUs

Figure 6.11 PCoA Plots of the UofT Sludge Samples

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The microbial diversity was studied by plotting the number of observed species (i.e., distinct OTUs) against the simulated sequencing effort. Similar results were found in the AE sludge and the control sludge collected at the same time, implying that the treatment of AE, at the tested concentrations, did not alter the number of the observed species of the sludge (Appendix

6.6).

6.5.2 Microbial Compositions and Dynamics of the Sludge Samples

As shown in Figure 6.12, in all sludge samples, the percentage of bacteria was at least six times greater than the percentage of archaea. In the control sludge, % archaea fluctuated between days 72 and 197. In the AE sludge, after the 30% AE test started on day 112, %archaea decreased, reached a minimum at ~7% on day 149, then gradually increased to ~12% towards day197. A lower percentage of total archaea was present in the sludge treating 30% AE as compared to the control sludge collected at the same time.

The distributions of major organisms in the sludge samples collected during the startup period are illustrated in Figure 6.13. The term ‘unclassified’ referred to the case when a highly matching (>97%) sequence was found in the GreenGene database using QIIME, but there was no classification assigned to that sequence. The term ‘undetermined’ referred to the case when our sequence matched to a few sequences in the database with the same similarity scores, so the classification of our sequence could not be determined. As shown in the figure, the communities of the control sludge and the sludge from the AE reactor were similar, implying good reproducibility between the two reactors during the AE-free startup. The largest genus was

Ruminococcus in the phylum Firmicutes , accounting for approximately 20% of the total population. An unclassified Porphyromonadaceae (OTU103) belonging to Bacteroidetes and an unclassified Lachnospiraceae (OTU6583) belonging to Firmicutes were also present at relatively

124 high percentages. Methanosarcina was the largest group of archaea, followed by an unclassified

Methanomicrobiales (OTU25).

AE AE AE AE AE

Figure 6.12 % Bacteria and Archaea in Sludge Collected in the UofT Long-Term Study: Sludge Treating 30% AE Contained Lower %Archaea than the Corresponding Control Sludge

Figure 6.13 Distribution of Major Organisms (>2%) in Sludge Collected during Startup in the UofT Long-Term Study (Triplicate): Similar in Both Reactors

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The time profiles of the percentages of the phyla Euryachaeota, Firmicutes , Bacteroidetes ,

Proteobacteria , Chloroflexi and Spirochaetes are presented in Figure 6.14, where the left column is for the control sludge and the right column is for the AE sludge. For each phylum, the percentages of that phylum and the major members are displayed.

In both types of sludge, archaea were mainly present as Euryachaeota . Methanosarcina was the largest group of methanogens (>2%) in all sludge samples, except in the AE sludge collected on day 149 when % Methanospaera was greater than % Methanosarcina .

Firmicutes , mainly consisted of Clostridiales , was the most abundant bacterial phylum in all sludge samples. In the control sludge, no particular predominant group was observed on day

127; Butyrivibrio , Dorea and the Lachnospiracea with undetermined genus (OTU6583) were present at comparable percentages (2-3%) on day 149; the latter became the largest member afterwards. In the AE sludge, Dorea contributed to the highest percentage of Firmicutes between days 127 and 169, and Butyrivibrio replaced Dorea and became the largest group of Firmicutes on day 197.

Bacteroidetes , mainly consisted of Bacteroidales , was the second largest bacterial phylum in all sludge samples. In the control sludge, the leading Bacteroidetes belonged to an unclassified

Porphyromonadaceae (OTU103) by day 149; thereafter, this unclassified Porphyromonadaceae and an unclassified Bacteroidales (OTU7993) were present at comparably high abundance. In the

AE sludge, the unclassified Porphyromonadaceae (OTUs 103) was always present at the highest percentage among all Bacteroidetes .

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AE Sludge

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Figure 6.14 Time Profiles of the Microbial Communities of the Control Sludge (Left) and AE Sludge (Right) in Different Phyla in the UofT Long-Term Study Values presented in the figures were averages of the triplicates in each sample set

Other relatively large phyla included Proteobacteria , Chloroflexi and Spirochaetes . The largest group in Proteobacteria in the control sludge was an unclassified Geobacteraceae

(OTU5903). Propionivibrio was often found to be the predominant Proteobacteria in the AE sludge, except on day 149 when similar percentages of Pseudomona and Propionivibrio were observed. In all samples, at least half of Chloroflexi was assigned to the genus T78 in the family

Anaerolinaceae . Within the phylum Spirochaetes , the genus W22 in the candidate family

Cloacamonaceae and the genus Treponema in the family Spirochaetaceae were primary

128 members in the control sludge. In the sludge treating 30% AE, W22 and Treponema were present at comparably high percentages. Decreasing trends were observed in the percentages of W22 ,

Treponema and total Spirochaetes as the 30% AE was carried out.

Organisms in other phyla with percentages >2% are also illustrated in Figure 6.14. In the control sludge, an unclassified Endomicrobia (OTU2959) in the phylum Elusimicrobia increased after day 72, and reached a maximum on day 149. More than 2% of the population in sludge from both reactors was assigned to the genus HA73 in the family Dethiosulfovibrionaceae in the phylum Synergistetes , expect on day 149 when sudden decreases were observed in both reactors.

In the AE sludge, the undetermined bacteria (OTU719) accounted for >2% of the population, except on day 149 when it decreased by 50%.

6.5.3 Organisms Affected by the Addition of AE

In the UofT long-term study, two approaches were taken to identify the organisms affected by the addition of AE. In the first approach, the percentages of the organisms present in the control sludge and in the sludge treating 30% AE at each sampling event were compared. In the second approach, the organisms showing increasing or decreasing percentages between day

72 (startup) and day 127 (15 days into the 30% AE test) in each reactor were assessed, and the results were compared between the two reactors in order to identify the affected organisms.

The results of the pair-wise comparison in the first approach are presented in Table 6.5.

The sludge treating 30% AE contained significantly lower percentages of total archaea than the control sludge. At the phylum level, the AE sludge contained significantly lower percentages of

Spirochaetes and significantly higher percentages of Firmicutes and Proteobacteria (except day

197) than the control sludge, and generally lower percentages of Bacteroidetes were also found in the sludge treating AE. At the genus level, significantly greater percentages of Methanosphaera ,

Dorea and Oscillospira in the phylum Firmicutes , and Desulfovibrio and Propionivibrio in the

129 phylum Proteobacteria were detected in the AE sludge, while significantly greater fractions of

Methanomethylovorans , Methanosarcina , and Treponema and W22 in the phylum Spirochaetes were observed in the control sludge.

The results of the organisms with noticeable changes in percentages between days 72 and

129 are presented in Table 6.6. Only organisms with different trends between the two types of sludge are included in the table. If the organism showed significantly increased percentages only in the AE sludge and not in the control sludge, this organism was positively impacted by the addition of AE. On the other hand, the organism only showing significantly decreased percentages in the AE sludge was negatively affected by the treatment of AE. The phyla

Firmicutes and Proteobacteria , and the genera Dorea , Oscillospira , Desulfovibrio and

Propionivibrio all demonstrated positive responses to the addition of AE. The percentages of total archaea, the phyla Euryarchaeota , Chloroflexi and Spirochaetae , and the genera

Methanomethylovorans , Methanosarcina and Treponema were all suppressed by the addition of

AE.

The results of the first approach were consistent to those of the second approach. The organisms showing greater percentages in the AE sludge than in the corresponding control sludge from the first approach were generally the ones found to be positively affected by the addition of

30% AE from the second approach. Examples of the positively impacted organisms included

Firmicutes , Proteobacteria , Dorea , Oscillospira , Desulfovibrio and Propionivibrio . The negatively impacted organisms included archaea (as total), Chloroflexi , Spirochaetes ,

Methanomethylovorans , Methanosarcina and Treponema .

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Table 6.5 Pair-Wise Comparison of Sludge at Different Phylogenic Levels: AE sludge vs. Control Sludge Phylogeny %in AE Sludge (as Compared to Control Sludge) Representative OTU Kingdom__Archaea Significantly Lower (except Ctrl.0%.D169.rep3) p__Euryarchaeota c__Methanomicrobia o__Methanosarcinales; f__Methanosarcinaceae aaaa g__Methanomethylovorans Always Significantly Lower 5639 g __Methanosarcina Always Significantly Lower 7959 o__Methanobacteriales f__Methanobacteriaceae g__Methanosphaera Always Significantly Higher 3394 Kingdom__Bacteria Significantly Higher (except Ctrl.0%.D169.rep3 p__Firmicutes Always Significantly Higher c__Clostridia o__Clostridiales f__Lachnospiraceae g__Butyrivibrio Significantly Higher (except day 149) 1352 g__Undetermined Significantly Lower since day 149 6583 g__Dorea Always Significantly Higher 758 f__Ruminococcaceae g__Oscillospira Always Significantly Higher 396/8637 p__Bacteroidetes Significantly Lower on Days 169 and 197 c__Bacteroidia o__Bacteroidales f__Bacteroidaceae g__Bacteroides Always Significantly Higher 6596 p__Proteobacteria Significantly Higher except day 197 c__Deltaproteobacteria o__Desulfovibrionale f__Desulfovibrionaceae g__Desulfovibrio Always Significantly Higher 3912 o__Desulfuromonadales f__Geobacteraceae g__Unclassified Always Significantly Lower 5903 c__Betaproteobacteria o__Rhodocyclales f__Rhodocyclaceae g__Propionivibrio Always Significantly Higher 5904 p__Chloroflexi Slightly lower p__Spirochaetes Always Significantly Lower c__Spirochaetes o__Spirochaetales f__Spirochaetaceae g__Treponema Always Significantly Lower 1595,225 c__WWE1 o__[Cloacamonales] f__[Cloacamonaceae] g__W22 Always Significantly Lower 7844

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Table 6.6 Organisms with Significant Changes in Percentages Following the Addition of AE (between Days 72 and 127) Change in % in Control Reactor Change in % in Control Reactor Phylogeny between Days 72 and 127 between Days 72 and 127 Kingdom__Archaea Significantly Increased Significantly Decreased p__Euryarchaeota Significantly Increased Significantly Decreased c__Methanomicrobia o__Methanosarcinales; f__Methanosarcinaceae aaaa g__Methanomethylovorans Significantly Increased Significantly Decreased g __Methanosarcina Significantly Increased Significantly Decreased Kingdom__Bacteria Significantly Decreased Significantly Increased p__Firmicutes Significantly Decreased Significantly Increased c__Clostridia o__Clostridiales f__Lachnospiraceae g__ Dorea No Significant Change Significantly Increased c__Clostridia o__Clostridiales f__Ruminococcaceae g__Oscillospira No Significant Change Significantly Increased p__Proteobacteria No Significant Change Significantly Increased c__Deltaproteobacteria o__Desulfovibrionale f__Desulfovibrionaceae g__Desulfovibrio No Significant Change Significantly Increased c__Betaproteobacteria o__Rhodocyclales f__Rhodocyclaceae g__Propionivibrio No Significant Change Significantly Increased p__Chloroflexi No Significant Change Significantly Decreased p__Spirochaetes Significantly Increased Significantly Decreased c__Spirochaetes o__Spirochaetales f__Spirochaetaceae g__Treponema Significantly Increased Significantly Decreased c__WWE1 o__[Cloacamonales] f__[Cloacamonaceae] g__W22 Significantly Increased No Significant Change

6.5.4 Summary of the Microbial Studies

The microbial communities of the sludge collected from the UofT long-term study were investigated by conducting pyrotag sequencing of the 16S rRNA genes. The microbial diversity was similar in the control sludge and in the sludge treating 30% AE, but the major organisms were very distinct. The AE sludge contained higher percentages of Firmicutes and

Proteobacteria and lower percentages of archaea, Becteroidetes , Chloroflexi and Spirochaetes than the control sludge. For example, significantly lower percentages of Methanomethylovorans ,

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Treponema and Methanosarcina and higher percentages of Propionivibrio , Desulfovibrio and

Oscillospira were contained in the sludge treating 30% AE.

Dynamic microbial communities were revealed in both types of sludge. The sludge collected during the startup period had very different microbial compositions than the sludge collected later. As shown in the time profiles in Figure 6.14, major changes took place around day 149, when the control reactor experienced the first major process upset. In order to help the control reactor recover, the HRT was increased and the OLR was decreased for five days for both reactors. The changes in HRT and OLR very likely affected the microbial compositions. In addition the changed HRT and OLR, the temporary lack of phosphate and the consequent low pH could also impact the microbial communities of the control sludge.

6.6 The Fate of Resin Acids and Long-Chain Fatty Acids (RFAs)

The concentrations of resin acids and long-chain fatty acids (RFAs) in feeds, effluents and sludge samples were measured. RFAs were of interest because they were found to be inhibitory to anaerobic microorganisms. The detailed results of the RFA analysis in the UofT long-term study were presented in the paper by Meyer et al. (in preparation). This section is a brief summary of the results.

The RFA concentrations in feeds, effluents and sludge in the AE reactor are summarized in Table 6.6. The concentrations in feeds and effluents are the sums of the RFAs present at both the dissolved phase and the particulate phase. During the AE test, each tested RFA was gradually accumulating on the sludge from the AE reactor. The most abundant RFA associated with the AE sludge was palmitic acid, while noticeable amounts of dehydroabietic acid (DHA), linoleic, oleic and abietic acids were also present in the AE sludge.

DHA, palmitic, oleic, abietic and linoleic acids were major RFAs in the effluents from the

AE reactor, with palmitic acid being the most dominant RFA. Higher concentrations of DHA,

133 linoleic acid and abietic acid were observed in the feed than in the corresponding effluent, indicating removals of these acids from the treatment. In contrast, the effluent contained a higher concentration of palmitic acid than its feed, suggesting generation of palmitic acid during the treatment of AE. It has been reported that palmitic acid adsorbed on sludge was produced from the degradation of other long-chain fatty acids with longer chains, e.g., oleic and linoleic acids

(Lalman and Bagley, 2001).

Table 6.7 Summary of RFA Concentrations in Feeds, Effluents and Sludge in the UofT AE Reactor Concentration in Feed Concentration in Sludge Average Concentration in Effluent RFA (mg/L) (mg RFA/g TSS Sludge) (mg/L) 30% AE 40% AE 30% AE 40% AE 30% AE 40% AE Increased from Increased from DHA 15 20 12 15 0.8 to 1.7 3.8 to 6.6 Increased from Increased from Linoleic Acid 26 35 8 15 0.9 to 1.5 4.2 to 9.7 Increased from Increased from Oleic Acid 12 16 12 17 1.5 to 3.5 7.1 to 11.5 Increased from Increased from Palmitic Acid 2.2 2.9 20 25 2.4 to 5.3 14 to 19 Sum of Non- Aromatic RA Increased from Increased from 35 47 9 14 (Primarily 0.6 to 1.4 4.7 to 7.5 Abietic Acid)

6.7 Batch Assays to Evaluate Acclimation towards Better Treatment of AE

Batch assays were conducted to investigate if the sludge from the AE reactor was able to degrade AE better than the control sludge after a nine-month incubation of AE in the AE reactor.

Biogas production was used as the indicator of treatment of AE, i.e., greater biogas production implied better degradation of AE. Synthetic substrates and AE were added in such a way that all bottles had the same maximum theoretical methane production of 47ml at 1atm and 37°C. The substrates used in the positive control were the same as the feed for the control reactor in the

UofT long-term study (i.e., the synthetic feed). In the bottles labeled with ‘AE-Feed’, 40% AE was mixed with 60% synthetic feed on a COD basis, which was identical to the blended feed to the AE reactor during the 40% AE test.

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The net cumulative biogas production is plotted against time in Figure 6.15. Assuming that methane contributed to 60-80% of the biogas in headspace, the maximum biogas production from complete degradation of the feeds was expected to range between 57 and 78mL at 1atm and

37°C. The biogas production from the positive control and the acetate-feed bottles was close to the expected range. Therefore, the substrates in these feeds were likely completely degraded. The

AE-Feed bottles produced slightly less biogas than the positive control bottles, as the wastewater

AE contained lignin/tannin and RFAs that were recalcitrant or inhibitory to anaerobic degradation (Yin et al. , 2000; Hanaki, 1981; Kennedy et al ., 1992).

Comparing the biogas production from the AE-Feed bottles to that from the positive control bottles, degradation of the organics in the AE and synthetic feed mixture was proposed to be a two-step process. Most of the degradation in the AE-Feed bottles took place within the first

68 hours, same as the time when the biogas production in the positive control bottles started to plateau. As mentioned above, the synthetic substrates added to the AE-Feed bottles were 60% of the amounts fed to the positive control-Feed bottles. However, the biogas produced from the AE-

Feed bottles was greater than 60% of what was produced from the positive control bottles, implying that some organics contained in the wastewater AE were degraded. Hence, the compounds degraded in the AE-Feed bottles within the first 68 hours were simple organics present in both the wastewater AE and the added synthetic substrates. Between 68 and 547 hours, further biogas was slowly generated in the AE-Feed bottles. The substrates utilized within this period could be the complex compounds in AE that required longer time to be hydrolyzed.

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Figure 6.15 Cumulative Net Biogas Production in BMP Assays for Tests of Acclimation Error bars: 95% confidence intervals of triplicates

In the AE-Feed bottles, the amount of biogas produced by the two types of inocula was not significantly different (p >0.13 in two-tail t-test). The rates of biogas production by the control sludge and by the AE sludge were also comparable. Therefore, there was no significant advantage of the AE sludge in degrading the wastewater AE as compared to the control sludge.

In other words, at the tested concentration, there was no evidence to demonstrate that the AE sludge was acclimated to AE after a nine-month continuous treatment of AE in the UofT long- term study for enhanced biogas production.

Interestingly, when examining the positive control feed bottles, despite the fact that the bottles inoculated with the control sludge did not produce biogas after 68 hours, increasing biogas production was still observed in the bottles inoculated with the AE sludge. At the end of the batch assay, the AE sludge produced approximately 40% more biogas than the control sludge in the positive control set. The plateau of the biogas produced by the control sludge suggested that the degradation of the feed was completed, so the extra biogas produced by the AE sludge was from other substrates, e.g., biomass in the sludge. The data presented in Figure 6.15 were the 136 net biogas production, which was the remaining value after subtracting the noise from the endogenous decay. Therefore, it was proposed that some compounds in the synthetic feed, such as the added sugars and alcohols, helped the AE sludge release and digest some organics that were previously trapped in the sludge bed towards methane production. This is a very preliminary proposition. Further experiments will be required in the future to verify this proposition.

6.8 Summary of the Chapter

In the UofT long-term study, two upflow anaerobic sludge digesters were set up to test the effect of AE in a relatively long term, i.e., two-month startup and nine-month test of AE. A synthetic feed mainly consisting of xylose, glucose, acetate and alcohols were fed to both reactors during startup. After startup, one reactor was kept as the AE-free control and received the same synthetic feed, while 10, 15, 30 and 40% AE was blended with the synthetic feed on a

COD basis and fed to the test reactor.

The addition of AE negatively impacted the reactor performance and granulation. The addition of 10 and 15% AE did not cause any noticeable changes in the AE reactor as compared to the control reactor within the test duration. However, %sCOD removals were significantly lower in the AE reactor treating 30% AE than in the control reactor. The differences in %sCOD removals between the two reactors became more noticeable after 40% AE was added to the AE reactor. Using the automated bubble counters, the biogas production from the AE reactor treating

30% and 40% AE was found to be significantly lower than that from the control reactor. In addition, effluents from the AE reactor contained significantly higher concentrations of TSS and

VSS than the control reactor at the end of the 30% AE test and during the 40% AE test, implying poorer settling of the AE sludge. Furthermore, the sludge treating 30% and 40% AE was generally smaller and weaker than the AE-free control sludge.

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Pyrotag sequencing was conducted to study the effect of the addition of AE on the microbial communities of sludge using samples collected during the startup period and during the

30% AE test (days 127-197). The microbial diversity of the AE sludge was similar to that of the control sludge collected at the same time during the 30% AE test. The sludge treating 30% AE had very different microbial communities than the corresponding control sludge. The sludge treating AE contained higher percentages of total Firmicutes , total Proteobacteria ,

Propionivibrio , Desulfovibrio and Oscillospira , and lower percentages of total archaea, total

Bacteroidetes , total Chloroflexi , total Spirochaetes, Methanomethylovorans, Treponema and

Methanosarcina than the control sludge. In both types of sludge, varying percentages of organisms and changes in the predominant groups were observed. However, the results of triplicates were reproducible, suggesting that the changes and fluctuations were not random and were likely linked to the operation disturbances.

Analysis was performed to study the fate of RFAs during the treatment of AE. Removals of DHA, abietic and linoleic acids were observed, while palmitic acid was generated during the anaerobic treatment of AE. Accumulation of RFAs on sludge was observed in the AE reactor, with palmitic acid present at the highest concentration.

In the BMP assays, the sludge collected at the end of the UofT long-term study (day 343) was used to degrade synthetic substrates blended with 40% AE. The control sludge and the AE sludge produced similar amounts of biogas at comparable rates from this blended feed, suggesting that the microorganism in the AE sludge was not acclimated to AE for better degradation of AE and greater biogas production from it. Comparing the biogas production from the positive control bottles to the AE-Feed bottles, it was suggested that some organics in the wastewater AE was degraded at the tested concentration.

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CHAPTER 7. OVERALL DISCUSSION OF THE ANAEROBIC TREATMENT OF PULP MILL EFFLUENTS

The overall objective of this research is to investigate the impact of anaerobic treatment of pulp mill effluents on reactor performance, and the physical properties and the microbial communities of granular sludge. The alkaline effluent from sulphite pulping of softwood (SW1

AE) was of particular concern, as it was a COD-rich stream and contained resin acids and long- chain fatty acids (RFAs) that were often cited as the inhibitory compounds present in pulp mill effluents.

The experimental results were presented in detail in chapters 3 to 6. The characteristics of various types of in-mill streams were described (Chapter 3). Appropriate methods to examine the microbial communities and to quantify granulation were developed (Chapter 4). Continuous experiments were conducted to examine the effect of the addition of different concentrations of

AE on reactor performance and granular sludge, with a one-month startup and a one-month test of AE (Chapter 5). Furthermore, two reactors were set up to investigate the effect of continuous treatment of AE, with a two-month startup using synthetic substrates as feeds and a nine-month test of AE where increasing amounts of AE was blended with synthetic substrates and fed to the test reactor (Chapter 6).

This chapter mainly focuses on the similarity and differences between the two continuous studies in order to draw some overall conclusions. Wastewater characteristics and the background knowledge from literature are also reviewed to better understand these similarity and differences and to explain the observed effect of AE. Four sections are contained in this chapter. A brief summary of the characteristics of pulp mill effluents and the synthetic feed is provided to facilitate the discussion in the reactor performance and granulation (section 7.1). The effect of the addition of AE on reactor performance and granulation is discussed (section 7.2). The microbial

139 communities of sludge treating pulp mill effluents are assessed (section 7.3). Possible acclimation of the microbial communities to AE and feeding strategies of AE for higher treatment efficiency are investigated (section 7.4). In addition to the comparisons between the two continuous studies, the developed methods used in the physical and microbial examinations of sludge are also commented (section 7.5).

7.1 Summary of Characteristics of Pulp Mill Effluents and Synthetic Feed

The organic constituents of the pulp mill effluents and the synthetic feed used in this research are briefly summarized in Table 7.1. A colour bar is included in each numerical cell for better visualization of the gradients across each row: the longest bar is given to the cell with the maximum value; in other cells, the length of the colour bar is reduced based on the ratio between its value and the maximum value. It should be noted that different sets of BCMTP effluent and

AC samples were used by Exova and FPInnovations in the lignin tests, which partially explained the differences in the reported lignin concentrations. The method of lignin test used in Exova was unknown, because it was proprietary to Exova. When examined by Exova only, various types of

AC were found to have comparable lignin concentrations (Figure 3.4), suggesting that the differences in the lignin concentrations in AC measured by Exova and by FPInnovations might be caused by variations in the assay methods. Therefore, only the concentrations measured by the same institute (i.e., values in the same row) should be compared.

Compared to AE, the most distinct feature of the AE-free streams (i.e., BCTMP effluent,

AC and the synthetic feed) was the substantially lower concentrations of resin acids and long- chain fatty acids (LCFAs). More specifically, the concentrations of abietic acid and dehydroabietic acid (DHA) in the blended BCTMP/AC feed were an order of magnitude lower than those in AE. AE-free feeds contained lower concentrations of lignins/tannins than AE, but the differences in the lignin/tannin concentrations were not as dramatic as those in the

140 comparisons of RFAs. Furthermore, glucose, methanol and ethanol were only contained in the

AE-free feeds, while lactate was only present in AE. Nevertheless, acetate was a large component in all wastewaters and in the synthetic feed, with concentrations greater than 2100 mg/L and contributing to 10-30% of total soluble CODs in the streams. The major organic constituents in the FP AE and the UofT AE were similar, although the exact concentrations were different.

Table 7.1 Brief Summary of Wastewaters and Synthetic Feed Used in Both Studies

Colour bars show the gradients in value size in each row; longest bar is dedicated to the maximum value

The concentrations and loadings of AE and RFAs fed to the AE reactors in both studies were calculated. The total COD and RFAs in the UofT AE were only 30-50% of those in the FP

AE, and the HRT in the UofT reactors was 1.5 times of what was used in the FP reactors.

Therefore, as presented in Table 7.2, even at the highest tested level (i.e., 40%), the AE and RFA loadings for the UofT AE reactor were still lower than those for R2 (35% AE) in the FP concentration study. In other words, the AE sludge in the FP concentration study, even at the lowest AE concentration tested (i.e., 35%), still suffered more RFA stress than the sludge from the UofT AE reactor.

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Table 7.2 Concentrations and Loading Rates of AE and RFAs for the AE Reactors in Both Studies AE Contained in AE Loading RFA Concentration AE Level in the UofT RFA Loading Feed (mg AE in Blended Feed Long-Term Study (mg/L/d) (mg AE COD/L) COD/L/d) (mg/L) 10 3000 2000 90 60 15 4500 3000 140 100 30 9000 6000 280 190 40 12000 8000 370 260 AE Contained in AE Loading RFA Concentration AE Level in the FP RFA Loading Feed (mg AE in Blended Feed Concentration Study (mg/L/d) (mg AE COD/L) COD/L/d) (mg/L) 35% 4800 14000 263 790 64% 11260 33000 620 1862 100% 35700 96000 1880 5640

7.2 Effect of the Addition of AE on Reactor Performance and Granulation

Reactor performance and the physical properties of sludge are summarized in Table 7.3.

The results of reactor performance include average %sCOD removal, grams of sCOD removal per day, daily biogas production, specific biogas yield, and TSS concentration in effluents. It should be noted that the synthetic feed and the AE used in the UofT long-term study contained relatively low concentrations of suspended solids, so the effluent TSS approximately reflected the amount of sludge loss. On the other hand, the actual BCTMP effluent and AC contained suspended solids that were not negligible. Therefore, for the FP study, TSS removals, calculated as influent TSS less effluent TSS, and are provided in the brackets. The evaluation of granulation is based on granule weakness, the percentage of total suspended solids contained in the desired granular particles (> 200µm), and the fractions of small granules (i.e., 200- 500µm, the smallest size range reported in image analysis).

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Table 7.3 Summary of Reactor Performance and Physical Properties of Granules in Both Studies * indicated that the differences between the AE sludge and the control sludge were significant (p > 0.05 in t-tests) NA: not available FP Concentration Study UofT Long-Term Study

AE AE AE R1 R2 35% R3 64% R4 100% Control Control Control Control S tage AE Stage 4 Stage 1 Stage 2 Stage 3 Control AE AE AE Stage 1 Stage 2 Stage 3 4 (40%) (10%) (15%) (30%) Dropped from Dropped to 50% to 35%, 30% then %sCOD 41 24 11 2.5 40-64 40-54 45-61 46-55 52* 42 then increased increased to removal to 40% 40% Significantly higher in control Dropped to 35, Dropped to 32, sCOD then increased then increased Removal Rate 59 54 30 0 54-60 52-65 61-94 60-91 62* 52 to 45 to 43 (g/d) Significantly higher in control Biogas Production 27 21 12 3 Ctrl* > AE (with automated bubble counter) Not available due to inaccurate (L/day) manual readings of bubbles Biogas Yield

(m 3 biogas/ kg 0.45 0.42-0.45 ~0.4 0 Ctrl *> AE (with automated bubble counter) COD removed) Significantly 1531 Effluent TSS 675 755 888 Between 400 to 800 mg/L, slightly higher in AE at (-400 to Significantly higher in AE (mg/L) (0 to 100) (0 to 150) (200) higher in AE effluent the end of the -600) 30% AE test Significantly lower in AE on 88* %TSS 94 88* 82* NA NA Comparable 92 >200µm days 169 and (day 343) 197 Significantly %Particles: lower in AE on 60 51* 54* NA NA Comparable Comparable (day 343) 200-500µm days 149, 169 and 197 Ctrl sludge significantly End: Granule 0.3 0.4 0.6* NA NA Comparable stronger than AE 0.5 2.0* (day 343) weakness sludge on days 127and 197

Poorer sCOD removals were observed in the AE reactors as compared to the control

reactors. In the FP concentration study, the more AE the feed contained, the lower the %sCOD

removals and sCOD removal rates were. In the UofT reactors, despite the fluctuations due to

process upsets, increasing %AE in the feed to the test reactor generally led to poorer sCOD

removals. The %sCOD removals in the UofT AE reactor were comparable to those in the FP

control reactor, and were higher than those in the FP AE reactors, likely due to the lower strength

of the UofT AE and the lower loadings of RFAs in the UofT long-term study (Table 7.2).

Nevertheless, the degradation of the synthetic feed in the UofT control reactor was not as good as

expected. In the work conducted by Kalyuzhnyi et al ., approximately 96-97% sCODs were 143 removed in the UASB reactor treating glucose and acetate mixture at an organic loading rate similar to our UofT control reactor (Kalyuzhnyi et al. , 1995). The %sCOD removal in the UofT control reactor was lower than the value reported by Kalyuzhnyi et al ., possibly due to the lower amounts of seeding sludge, i.e., twice sludge (TS) was seeded to Kalyuzhunyi’s reactor.

In both studies, the control reactor also produced greater amounts of biogas as compared to the AE reactors. In particular, biogas production was found to have significant negative correlations with loadings of AE and RFAs for the FP reactors (for RFA: p = 0.004 and r = -0.96; for AE COD: p = 0.005 and r = -0.95). Although the total organic loading rate (OLR) for each FP reactor was different, OLR did not seem to significantly correlate to biogas production (p =

0.132). Therefore, instead of OLR, AE and likely RFAs contributed to the poorer performance in the AE reactors. In the UofT long-term study where both reactors had comparable OLR, significantly higher biogas production was observed in the control reactor, confirming the negative effect of adding AE on biogas production. In addition to biogas production, the control reactors had higher biogas yields than the AE reactors in both studies, suggesting that methanogenesis governed the organic removal in the control reactors while other mechanisms also existed in the AE reactors to remove organics.

The negative impact of the addition of AE on granulation was observed at most of the tested concentrations. In the FP concentration study, significantly greater amounts of sludge were present in fine particles (<200µm) and in relatively small granules (200-500 µm) in the reactors treating 35% and 64% AE as compared to the control sludge. In the extreme case, severe washout of sludge took place immediately after the feed was switched to 100% AE (i.e., in R4). In the

UofT long-term study, the particle size distribution and the granule weakness of both types of sludge were comparable during the treatment of 10% and 15% AE, so the addition of AE at these two concentrations did not seem to substantially impact granulation. After the 30% AE test had

144 been conducted for 15 days (day127), the AE sludge was weaker than the control sludge, but the two shared similar particle size distributions. After the 30% AE test had been conducted for 57 days (day169), the AE sludge was significantly smaller and weaker than the control sludge.

The most distinct feature of AE was the high concentrations of RFAs, which were found to be significantly negatively correlated to biogas production. Therefore, it is proposed that RFAs played an important role in the observed negative impact of the addition of AE. RFAs are known inhibitory compounds present in pulp mill wastewaters (Rintala and Puhakka, 1994). In the UofT

AE and in the FP AE, the most dominant RFA was abietic acid, followed by dehydroabietic acid

(DHA), oleic acid and linoleic acid. In literature, the concentrations of DHA, abietic acid, linoleic acid and oleic acid causing 50% inhibition of methanogenic activity (IC 50 ) were found to be 43-

123, 83-235, 897 and 1235 mg/L respectively (Sierra-Alvarez and Lettinga, 1990). In the RFA analysis for R3 treating 64% AE in the FP concentration study, it was found that the effluent contained 43-53mg/L DHA, 60 to 80mg/L abietic acid, 50mg/L oleic acid and 15-50mg/L linoleic acid. The concentrations of DHA and abietic acid in the R3 effluent were close to their

IC 50 values. In contrast, the concentrations of oleic and linoleic acids in the R3 effluent were at least a magnitude lower than the reported IC 50 values. In batch assays studying the anaerobic treatment of pulp mill effluents, the degradability of pulp mill streams were found to be negatively correlated to the DHA concentration (Yang et al ., 2010). Therefore, we propose that

DHA, possibly abietic acid as well, played an important role in the inhibitory effect of AE.

In both studies, the sludge from the AE reactors also contained higher concentrations of

RFAs than the AE-free sludge. The RFAs in the AE sludge primarily belonged to palmitic acid, while noticeable amounts of oleic acid, DHA, linoleic acid and abietic acid were also detected.

Palmitic acid accounted for 40-50% of the total RFAs embedded in the AE sludge (Figure 5.23 and Table 6.6). The dominance of palmitic acid in the RFAs associating with the AE sludge was

145 consistent with the study conducted by Pereira et al. (2005) in which palmitic acid was found to be the principal long-chain fatty acids (LCFAs) in anaerobic sludge. Pereira et al. proposed that the negative impact of LCFAs on methanogenesis was partially due to the accumulation of these acids on sludge fed with oleate, by creating a physical barrier and interrupting the transfer of substrates and products. Meyer et al. (paper in preparation) conducted a RFA survey of sludge collected from a full scale reactor treating pulp mill effluents, and found that the RFA concentrations in sludge were significantly negatively correlated to %sCOD removal. Therefore, the accumulation of RFAs on the AE sludge could be another reason for the poorer performance of the AE reactors in our studies. Furthermore, fragmentation and floatation of anaerobic granules were also observed in reactors treating oleate in other studies (Amaral et al. , 2004;

Pereira et al. , 2003). Hence, the LCFAs in AE were possibly linked to poorer granulation as well.

It should be noted that besides RFAs, AE also contained relatively high concentrations of sulphite (50 to 140 mg/L) and lignin compounds (2500-5500 mg/L measured by Exova and

15000 measured by FPInnovations). Sulphite has been reported as compounds toxic to anaerobic microorganisms (Lin and Hsiu, 1997). Lignin compounds, such as lignosulphonate, were shown to inhibit methanogenesis (Yin et al. , 2000). However, comparing the concentrations of RFAs, sulphite and lignin compounds present in BCTMP effluent, AC and AE, the difference in RFA concentrations between AE and the other two streams was the most dramatic. Therefore, as a first step to track the negative effect of AE, this research mainly focused on RFAs. In future, more in- depth studies should be conducted to investigate the roles of sulphite and lignin in the negative impact of the addition of AE on reactor performance and granulation.

It should also be pointed out that various organic loadings and specific loadings were applied in different reactors in the FP study (Table 5.2). Therefore, it was difficult to distinguish the effect of overloading organics from the impact of PEW. Nevertheless, in the UofT long-term

146 study, both reactors were subject to similar organic loading rates and specific loading rates. The negative impact of PEW and RFAs were confirmed in the UofT long-term study.

7.3 Microbial Communities of Sludge in the Anaerobic Treatment of Pulp

Mill Effluents

The results of both continuous studies suggested that the treatment of pulp mill effluents affected the microbial compositions and influenced the predominant species in the sludge.

Furthermore, as the continuous experiments proceeded, dynamic microbial communities were revealed. The discussion in this section is subdivided into five parts: the predominant microbial members in each type of sludge from both studies (section 7.3.1), the microbial groups responding to the AE loadings in each experiment (section 7.3.2), the dynamics of the microbial communities (section 7.3.3), the organisms possibly linked to granulation (section 7.3.4), and a summary of the key findings in the microbial studies (section 7.3.5).

7.3.1 Predominance of Microbial Groups in Sludge

In the sludge collected from both studies, Euryarchaeota , Firmicutes , Bacteroidetes ,

Proteobacteria , Chloroflexi and Spirochaetes were the major phyla in the communities, each accounting for at least 5% of the total population. The predominant groups in each phylum are summarized. Background knowledge of the microbial species from literature is reviewed to better understand the functions of these microorganisms in the communities.

• Predominant Methanogens in the Archaeal Communities

Methanosaeta -like organisms (OTU585) was found to be the largest group of methanogens in the sludge from the FP concentration study, contributing to up to 50% of the archaeal population in each sample. Methanosarcina-like organisms was found to be present at the highest

147 percentages among all methanogens in almost all sludge collected from the UofT long-term study, with the AE sludge collected on day 149 as the only exception, in which Methanosphaera was found to be the predominant methanogen. Methanosarcina , with optimal working pH close to 7, use acetate as their substrate to produce methane (Maestrojuan and Boone, 1991). At high acetate concentrations (>70mg/l), Methanosarcina outcompete another group of acetoclastic methanogens, Methanosaeta . Methanosarcina can also grow on other substrates, such as H2, methanol, mono-, di- and trimethylamine and pyruvate (Grover, 2005; Marchaim, 1992; Bock et al. , 1994; Simankova et al ., 2001). Methanosphaera have an optimal pH between 6.5 and 6.9

(Miller and Wolin, 1985). Certain Methanosphaera , such as Methanosphaera stadtmanae , have very restricted energy metabolism, as they can only produce methane by reducing methanol while using hydrogen as the electron donor and acetate as the carbon source (Fricke et al , 2006) .

• Predominant Organisms in Major Bacterial Phyla: Firmicutes and Bacteroidetes

Firmicutes and Bacteroidetes were the two largest phyla in the communities of all sludge samples, together contributing to 40 to 70% of the whole population. Based on literature,

Firmicutes and Bacteroidetes in anaerobic sludge mainly function as hydrolytic organisms and volatile fatty acid (VFA) producers (from fermentation). Compared to methanogens, Firmicutes and Bacteroidetes have faster growth rates, and they are less sensitive to environmental shocks

(Noike et al ., 1995; Mata-Alvarez et al ., 2000). In particular, many Firmicutes , with the production of endospores, can survive in a harsh environment that kills many other organisms

(Silhavy et al , 2010).

Firmicutes , mainly consisting of Clostridiales , was the largest phylum in all UofT sludge and in the FP sludge treating 64% AE. In the UofT long-term study, Clostridiales mainly consisted of Lachnospiraceae and Ruminococcaceae , many of which were identified as important organisms to degrade dietary fibre to produce VFAs in literature (Hooda et al ., 2012). 148

In the FP concentration study, most Clostridiales were present in the Ruminococcaceae family.

In particular, high relative abundance of Oscillospira (8-17%) was found in the sludge treating 35% and 64% AE. Oscillospira also contributed to 1-3% of the total population in the UofT AE sludge.

Oscillospira were frequently observed in cattle and sheep rumen (Mackie et al ., 2003). There has been little published work on pure cultures of Oscillospira , so its ecological role and physiological properties still remain unclear (Yanagita et al ., 2003). Nevertheless, Clarke (1979) observed that Oscillospira and other large bacteria colonized on the cuticular surface of leaf residues in sheep rumen, and suggested that these microorganisms were able to grow on the nutrients and substances excluded and leached from leaves.

Besides Oscillospira , large portions of Firmicutes in the UofT AE sludge also belonged to the genera Butyrivibrio and Dorea. Butyrivibrio are commonly found in the gastrointestinal systems of animals. Butyrivibrio play an important role in the degradation of fibre, biohydrogenation of fatty acids and proteolysis, and they can produce butyrate, acetate and lactate (Abubakr et al., 2014). Organisms belonging to Dorea are frequently contained in fecal or intestinal samples. In general, Dorea can ferment glucose, fructose galactose, lactose, maltose and other relatively simple sugars, but they are not capable of hydrolyzing large molecules such as starch, cellulose and gelatin (Blaut et al ., 2002). Interestingly, despite the observation that

Dorea was an important component in the UofT AE sludge, it was not detected in the FP sludge, due to its extremely low abundance in the seed sludge (i.e., not detectable by pyrotag sequencing).

Bacteroidetes , primarily consisted of Bacteroidale , was the largest phylum in the UofT sludge and in the FP sludge treating 35% AE. In the FP concentration study, Prevotella was the largest group of Bacteroidetes in the sludge collected from all reactors at the end of the experiment. Prevotella are often found in rumen and the gut of animals. Prevotella were also

149 detected in the sludge from an anaerobic lagoon treating swine wastewater (Ducey and Hunt,

2013). In general, Prevotella species are essential players in the metabolisms of starch, proteins, peptides, hemicellulose and pectin (Flint et al ., 2000). In particular, many Prevotella are saccharide degraders while producing acetate and succinate. In the UofT study, Bacteroidales in the control sludge were predominated by an unclassified Porphyromonadaceae (UofT-OTU103) and an unclassified Bacteroidales (UofT-OTU7993), and the same unclassified

Porphyromonadaceae was also the largest Bacteroidetes group in the UofT AE sludge. The unclassified Porphyromonadaceae (UofT-OTU103) was similar to an uncultured bacterium found in the sludge from mesophilic anaerobic digestion of beet silage in the NCBI database

(coverage 100%, identify 99%) (Krakat et al ., 2011). In general, Porphyromonadaceae require saccharides to grow, and volatile fatty acids are the degradation products (Krieg, 2011).

• Other Major Bacterial Phyla: Proteobacteria , Chloroflexi and Spirochaetes

Proteobacteria is the most diversed bacterial phylum according to literature, consisting of approximately 460 genera. Deltaproteobacteria was the main component of Proteobacteria in all control sludge samples, while both Deltaproteobacteria and Betaproteobacteria were the predominant Proteobacteria classes in all AE sludge samples. Desulfovibrio and unclassified

Geobacteraceae were the principal Deltaproteobacteria in the FP sludge and in the UofT sludge respectively. This divergence in the predominant group of Deltaproteobacteria was likely due to the difference in the seed sludge, i.e., %Desulfovibrio was roughly a magnitude greater than % Geobacteraceae in the seed sludge in the FP study, while it was the opposite case in the

UofT study. Desulfovibrio are commonly involved in sulphate and sulphite reduction while oxidizing VFAs and alcohols (Sarti et al ., 2010). Desulfovibrio are aerotolerant, although they grow much faster in the absence of oxygen, i.e., fast doubling time of 3-5 hours (Madigan and

Martinko, 2005). Desulfovibrio is proposed to contribute to the reduction of sulphate and sulphite 150 and the production of H 2S in our reactors. Geobacteraceae , on the other hand, are frequently known as metal reducers (Cummings et al ., 2003). Some Geobacter strains, such as Geobacter metallireducens , can degrade VFAs, alcohols and mono-aromatic compounds while reducing iron (Fe 3+ ) (Tremblay et al ., 2011). Propionivibrio was the principal Betaproteobacteria in the

AE sludge from both studies. Propionivibrio are strictly anaerobic propionate producers (Brune et al ., 2002). Certain Propionivibrio are able to utilize fumarate, L-malate, succinate, maleate and even hydroaromatic compounds such as quinic and shikimic acids (Tanaka et. al , 1990; Brune et al ., 2002).

Anaerobic bacteria in the Chloroflexi phylum can use halogenated organics as energy source, and they are also acidogens in anaerobic degradation. The genus T78 in the family

Anaerolinaceae was the largest group of Chloroflexi in both studies. The only exception was the control sludge from the FP concentration study, in which the primary Chloroflexi was an unclassified Dehalococcoidaceae (FP-OTU845). The knowledge of the T 78 genus was limited, and so was it role in anaerobic degradation. In one paper, T78 species was detected in the sludge producing methane while degrading phenol (Ju and Zhang, 2014). Dehalococcoidaceae , most of which are assigned to the genus of Dehalococcoides , use H 2 as energy source to carry out reductive dehalogenation (Loffler et al ., 2013).

Spirochaetes are fermenting bacteria in anaerobic degradation. For example, Spirochaetes

Zuelzerae convert glucose to lactate, acetate, succinate, CO2 and H 2 (Schlegel, 1993).

Spirochaetes in the control sludge from both studies mainly consisted of the genera W22 and

W55 in the WWE1 class and the genus Treponema in the Spirochaetes class. In both AE sludge, the genus Sphaerochaeta were also present at relatively high percentages similar to Treponema .

WWE1 is a relatively novel class proposed in 2005. Organisms belonging to WW1 were found in anaerobic sludge treating municipal wastewater (Chouari et al ., 2005). More insight into W22

151 was provided in the study conducted by Ju and Zhang (2014), in which W22 was one of the dominant organisms in the sludge anaerobically degrading phenolic solution at mesophilic conductions. Based on their phenol degradation experiment and the knowledge of W22 –related organisms, Ju and Zhang proposed that the role of W22 was to metabolize amino acids and oxidize VFAs (e.g., butyrate) to CO 2, hydrogen and acetate in a syntrophic environment.

Carbohydrates are the only energy source for most Treponema . For example, Treponema bryantii , which are highly mobile Spirochaetes that degrade cellobiose to produce succinate, often interact with cellulolytic bacteria for improved breakdown of cellulose (Lechine, 1995). Li et al . (2014) proposed that Treponema might specialize in substrate hydrolysis in the fermentation of straw.

7.3.2 Microbial Groups Responding to AE

In Chapter 5, distance-based redundancy analysis and correlation tests were conducted to identify the organisms positively or negative correlated to AE loadings in the FP concentration study (section 5.5.5). In Chapter 6, the percentages of organisms in the AE sludge and in the control sludge at each sampling event were compared and the changes in the percentages after the addition of AE were examined to investigate the organisms responding to the UofT AE (section

6.5.6). This section mainly focuses on the organisms consistently negatively impacted by the addition of AE or consistently positively influenced by the addition of AE in the both continuous studies.

A few phyla were negatively impacted by the addition of AE. In the FP concentration study, the abundance and percentage of total Euryarchaeota (methanogens) were negatively correlated to AE loadings. In the UofT long-term study, lower percentages of methanogens were found in the AE sludge as compared to the control sludge, and the addition of AE also reduced the percentage of total methanogens in the AE reactor. The percentages of total Chloroflexi and total Spirochaetes were significantly lower in the AE sludge as compared to the control sludge in

152 both studies. Total Chloroflexi and total Spirochaetes were presence at lower abundance in the reactor receiving a higher AE loading in the FP concentration study. Decreases in the percentages of these two phyla were noticed in the UofT AE reactor after the addition of 30% AE. Therefore,

Chloroflexi and Spirochaetes were generally negatively affected by the addition of AE.

Some phyla had positive responses to the addition of AE. The percentages of total

Firmicutes were higher in the sludge treating AE in both studies, and the abundance of total

Firmicutes was also strongly positively correlated to AE loadings in the FP concentration study.

A higher percentage of Proteobacteria in the AE sludge, as compared to the control sludge, was noticed in both studies, mostly due to greater fractions of Desulfovibrionale . The higher abundance of Desulfovibrionale (mainly Desulfovibrio ) in the AE sludge was probably due to the higher sulphate and sulphite contents in the feeds containing more AE. The changes in the percentages of organisms after the addition of 30% AE in the UofT long-term study also confirmed the positive effect of adding AE on total Firmicutes and total Proteobacteria .

Among all genera with percentages > 2% of total population, Oscillospira , Treponema and Methanomethylovorans were of particular interest. In the FP concentration study, the percentages and the abundance of Treponema and Methanomethylovorans were significantly negatively correlated to the AE loadings, while the percentage and abundance of Oscillospira were significantly positively correlated to the AE loadings. In the UofT long-term study, significantly lower percentages of Treponema and Methanomethylovorans and significantly higher percentages of Oscillospira were contained in the sludge treating 30% AE as compared to the control sludge, and the percentage of Oscillospira increased and the percentages of

Treponema and Methanomethylovorans decreased significantly in the AE sludge after the addition of AE (day 72 sample vs. day 127 samples). As mentioned earlier, Treponema are carbohydrate consumers. Since AE contained relatively lower concentrations of carbohydrates

153 than the synthetic feed and the actual BCTMP effluent and AC (Table 3.8 and Table 7.1), lower absolute abundance and percentage of Treponema in the AE sludge were expected.

Methanomethylovorans are methanogens generally sensitive to toxic compounds. Since AE contained higher concentrations of RFAs and lignin with known inhibitory effect to anaerobic organisms, lower percentages of Methanomethylovorans in the AE sludge were expected. The ecological role and physiological properties of Oscillospira were poorly understood. Since the addition of AE positively influenced the absolute and relative abundance of Oscillospira, it was possible that Oscillospira grew on compounds that were particularly contained in AE. Further experiments will be required to confirm the role of Oscillospira in the anaerobic treatment of AE.

7.3.3 Dynamics of the Microbial Communities

In the FP concentration study, despite that different concentrations of AE were fed to the reactors, the results of the correlation tests and the distance-based redundancy analysis indicated that a few organisms were affected by culture time in all reactors (Figures 5.14 and 5.15). For example, larger populations of Prevotella and Sphaerochaeta were observed as the experiment proceeded. The predominant groups in some phyla also changed. For instance, the unclassified

Bacteroidales (FP-OTU 2804) was the dominant Bacteroidetes in the seed sludge, but Prevotella became the most dominant Bacteroidetes in the sludge collected at the end of the study.

Dynamic microbial communities were revealed in the sludge from the UofT long-term study. Major shifts in the microbial compositions of the UofT sludge took place between days

127 and 169, mainly around day 149. In the UofT control sludge, the changes were reflected by concave-down decreases in the relative abundances of total archaea, total Bacteroidetes , total

Proteobacteria , total Chloroflexi and total Spirochaetae , as well as a concave-up increase in the percentage of total Firmicutes . Similarly, a sudden increase in phyla Firmicutes and decreases in phyla Chloroflexi and Spirochaetae around day 149 were also observed in the UofT AE sludge.

154

Reactor operation notes were reviewed in order to explain the shifts in the microbial communities in the UofT long-term study. On day 139, there was a leakage in the phosphate pump to the control reactor, leading to lower pH in the control reactor for a few days. In order to help the control reactor recover, the hydraulic retention time was reduced in both reactors, resulting in lower organic loading rates. The control reactor also suffered a few accidental exposures to oxygen. Therefore, the dramatic changes in the communities in the control sludge and in the AE sludge in the UofT long-term study around day 149 were likely caused by the process upsets and the changes in operation.

7.3.4 Granulation of Anaerobic Sludge in the Treatment of Pulp Mill Effluents: the

Microbial Perspectives

Generally, our granular sludge was relatively small, with sizes mainly ranging between

500 to 1000µm. In literature, particles with sizes between 1 and 2mm dominated in the sludge treating a kraft pulp mill effluent (Mahadevaswamy et al ., 2006). The granules degrading slaughterhouse wastewater had diameters between 1 and 4 mm (Torkian et al , 2002). Well granulated sludge was also found in the treatment of olive mill effluent, whose size range primarily fell between 3 and 8mm (Ubay and Ozturk, 1997).

All sludge samples in this research contained relatively low percentages of Methanosaeta .

In the FP sludge, the concentrations of Methanosaeta -like organisms were 2E7 to 5E7 copies 16s rRNA gene/ml sample, contributing to 2-4% of total population and 30- 40% in the archaeal community (based on pyrotag sequencing). Methanomethylovorans were also present at relatively high abundance in the FP sludge (i.e., up to 1.8% of total population based on pyrotag sequencing). In the UofT long-term study, Methanosarcina -like organisms were the most dominant methanogen (i.e., accounting for up to 10% of total population based on pyrotag sequencing), and were 2-3 times more abundant than Methanosaeta . Methanosaeta are solely

155 acetoclastic. Methanosarcina can utilize H2, methanol, mono-, di- and trimethylamine and pyruvate. When growing on acetate, Methanosarcina have a greater specific growth rate and a larger half saturation coefficient (Appendix 7.1, Conklin et al. , 2006). Methanosarcina dominate at high acetate concentrations (>70mg/L), and Methanosaeta dominate at low acetate concentration (<70mg/L) (Marchaim, 1992). Methanosarcina , Methanomethylovorans and

Methanosaeta also have different morphology: Methanosarcina are irregular spheroid bodies commonly found as clumps of cells; Methanomethylovorans are mostly irregular cocci.

Methanosaeta are rod-shaped cells with flat ends, and usually form long filaments for cell aggregation.

The filamentous Methanosaeta have been proposed to play a key role in anaerobic granulation in literature. Wiegant et al . (1987) proposed that Methanosaeta intertwined with each other as precursors to form the core of granules for other microorganisms to attach. In the study conducted by Zheng et al . (2005), two UASB reactors were seeded with sieved fine sludge

(particle size < 100un) which then slowly aggregated to form granules. By conducting fluorescent in situ hybridization (FISH) to examine the microbial communities, Zheng et al. concluded that the increase in granule size was closely related to the increasing abundance of

Methanosaeta (i.e. from 10% to 20% of total populations). In an UASB reactor seeded with partially granulated sludge to treat leachate from acidogenic fermenters, Shin et al . (2001) observed large granules (1 to 2.5mm) after 280 days, which were dominated by Methanosaeta . In the study conducted by El-Mamouni et al . (1995), the sludge mainly consisting of

Methanosarcina was found to have smaller size and poorer settleability than the Methanosaeta - based sludge. The relatively low abundance and percentage of Methanosaeta might contribute to the general poor granulation of our sludge.

156

Furthermore, Jia et al . (1996) found that the sludge degrading glucose presented a higher degree of granulation than the sludge fed with acetate, possibly due to the higher production of extracellular proteins and carbohydrates in acidogenesis as compared to acetogenesis and methanogenesis. As a result, the poor granulation of our sludge might also be caused by the relatively low production of extracellular proteins and carbohydrates due to the presence of large amounts of VFAs in the feeds.

7.3.5 Summary of the Microbial Studies

In Figure 7.1, the major microbial groups in the sludge collected from both continuous studies are listed, along with their proposed functions in the communities. The organisms highlighted in red were the ones showing higher percentages in the AE sludge, and the ones highlighted in blue were the ones present at lower percentages in the AE sludge. In general,

Firmicutes , Bacteroidetes and Spirochaetes , such as Butylrivibrio , Dorea , Oscillospira ,

Prevotella , Treponema and W22 , might be responsible for hydrolysis and fermentation of organics in the pulp mill effluents and in the synthetic feed. Desulfovibrio , Geobacteraceae , and

Dehalococcoidaceae and T8 might function as sulphate and sulphite reducer, metal reducers and degraders of halogenated compounds respectively. In the archaeal community, Methanosarcina ,

Methanomethylovorans , Methanosphaera and Methanosaeta were the main methane producers.

The percentages of total Spirochaetes , total Chloroflexi and total methanogens were lower in the sludge treating AE, while the AE sludge contained higher percentages of total Firmicutes and total Proteobacteria . Firmicutes were present at higher percentages and abundance in the sludge treating AE, because these organisms can survive in a harsher environment than other bacteria. In contrast, methanogens are more sensitive to toxic shocks, leading to their lower percentages in the sludge fed with AE. The comparisons of AE sludge vs. control sludge in each study suggested that Oscillospira , Treponema and Methanomethylovorans were the key genera

157 affected by the addition of AE. Future investigation is required to understand and confirm the detailed mechanisms of how AE affected these organisms.

Figure 7.1 Dominant Microbial Groups Found in Both Continuous Studies and their Proposed Functions Notes: organisms highlighted in blue: lower percentages with the addition of AE; organisms highlighted in red: higher percentages with the addition of AE

The microbial communities of the sludge in both studies were found to be dynamic.

Certain organisms were identified to significantly correlate to the culture time in the FP concentration study. The fluctuating percentages and changes in the predominant organisms in 158 the UofT sludge were likely due to the operational disturbances and a longer period of experiment run.

Two explanations were proposed for the generally small-size granules in both studies from the microbial perspectives. One was the relatively low percentage and abundance of

Methanosaeta , which were found to be the crucial organism in other granulation studies. Another possible explanation could be the presence of large amounts of VFAs in reactor feeds, which might cause relatively low production of extracellular proteins and carbohydrates from the acidogenesis step.

7.4 Investigation of Possible Sludge Acclimation on AE and Feasible

Strategies of Blending AE to the Reactor Feed

One common goal between the FP and UofT studies was to investigate whether acclimation of sludge took place for better digestion of AE. If acclimation did happen, both an improved %sCOD removal and greater biogas production could have been detected in R2 and R3 in the FP study and in the UofT AE reactor towards the end of the experiments. In addition to the continuous experiments, the results of the batch assays, whose inocula were the control sludge and the AE sludge collected at the end of the UofT continuous experiment, also provided evidence whether the AE sludge could treat AE better than the control sludge.

In R3 treating 64% AE in the FP concentration study and in the UofT AE reactor treating

40% AE, enhanced %sCOD removals were observed towards the end of the experiments, while biogas production stayed essentially unchanged. This divergence of %sCOD removal and biogas production suggested that the enhanced %sCOD removals towards the end were due to mechanisms not related to methanogenesis. As discussed previously, soluble organics could be removed by sulphate reduction or sorption onto solids. For example, previous research had found

159 that partitioning on biosolids played an important role in the removals of many RFAs, regardless of their solubility in water (Hall and Liver, 1996). Attachment of RFAs onto sludge and suspended solids might be one reason for the higher sCOD and solid removals towards the end of the experiments. Furthermore, in the UofT long-term study, similar trends of increasing %sCOD removals were observed in both reactors, implying that the enhanced sCOD removal in the UofT

AE reactor might also be due to its recovery from the process upsets.

In the batch assays to test acclimation of AE sludge, the biogas production from the degradation of AE using AE sludge and the control sludge were not significantly different. In other words, the AE sludge, after a nine-month exposure to AE, did not seem to have any great advantage in treating AE as compared to the control sludge. Combining the results of the continuous experiments and the batch assays, it was concluded that there was no clear sign of acclimation of the sludge to treat AE with an improved treatability.

Another goal of the research was to investigate if there would be any strategy to blend AE to the existing feed (e.g., BCTMP effluent and AC) without compromising sCOD removal, biogas production and granulation. The results of the %sCOD removal, biogas production and the

BMP assays in the UofT long-term study indicated that some CODs in AE, possible the sugar and VFA fractions, were degradable under anaerobic conditions. If these readily degradable compounds in AE are treated in the IC reactors anaerobically, the mill will benefit from a greater methane production and lower organic loadings to the aerobic secondary treatment.

The FP concentration study clearly demonstrated that if AE was blended as additional

CODs, at the tested concentrations, AE had a detrimental effect on reactor performance and caused degranulation within one month, suggesting that AE should be further diluted to reduce its harmful effect. The AE used in the UofT study was a more diluted stream than the FP AE, and it was fed to the reactor at a lower loading rate. When 10% AE was blended with the synthetic feed,

160 no clear negative impact on granulation or reactor performance was observed within the 15-day test duration. When the blending ratio was increased to 30%, weaker sludge was observed 15 days after the 30% AE test started. Therefore, the AE content should be diluted to a concentration similar to that in the 10% AE test in the full scale treatment system. However, blending AE at such a high dilution rate implies that most of the useful and degradable fractions of AE are lost and not utilized for methane production. Therefore, simple dilution is not a good strategy for adding AE to the existing feeds to the IC reactors.

Pretreatment of AE is proposed to be a better strategy than dilution. If the toxicants in AE can be removed prior to anaerobic treatment, AE will be a good stream for methane production due to its high COD content. For example, possible methods to reduce the RFA content in AE include fungal treatment, ozone treatment, and precipitation of RFAs by acidification (Hodgson et al ., 1998; Roy-Arcand and Archibald, 1996).

7.5 Discussion on the Developed Methods for Physical and Microbial

Examinations

In this research, the methods to examine the physical properties and the microbial communities of sludge were developed. A combined method of wet-sieving and image analysis was chosen to evaluate the particle size distribution of sludge. The granule weakness or strength was assessed by measuring the change in sample turbidity after vortexing. Both the combined method of wet-sieving and image analysis and the granule weakness test are relative simple, not labour intensive or time consuming. Most of the apparatus, such as the vortex machine and the spectrophotometer, are common equipment in many laboratories analyzing wastewater, so no extra equipment at a high cost is required. Even with limited sample volumes, the results of these

161 developed methods still showed reasonable reproducibility, detecting the significant differences among samples.

Using the granule weakness test and the combined method of wet-sieving and image analysis in the FP and UofT studies, it was observed that noticeable increases in granule weakness often took place before larger fractions of undesired particles were detected in the sludge. For example, in the FP concentration study, the sludge treating 64% AE was significantly weaker than the sludge from the control reactor on day 39 when both sludge shared similar particle size distributions; in the next sampling event on day 46, the sludge treating 64% AE was found to be significantly smaller than the control sludge. In the UofT long-term study, the AE sludge collected on day 127 was significantly weaker than the control sludge when the particle sizes in both types of sludge were comparable; in the next sampling event on day 149, the AE sludge contained significantly larger fractions of small granules (i.e., 200-500µm) than the control sludge. Based on our observations, it was proposed that sludge particles, under the stress of AE, first became weaker, then disintegrated into smaller particles. If this proposition is true, the granule weakness test can be conducted as a routine analysis to provide an early warning sign of degranulation.

Pyrotag sequencing was carried out to study the microbial communities of granular sludge in this research. Many sequences were identified to the level of genus, implying reasonable phylogenetic resolution. Comparing the results of pyrotag sequencing to those of q-PCR, similar trends were observed, suggesting that the quantification using pyrotag sequencing data was generally reliable. Furthermore, the sample clustering on the Jackknifed trees and on the PCoA plots suggested good reproducibility of sampling and pyrotag sequencing of sludge. By conducting correlation study, the organisms affected by AE loadings and culture time were identified. By constructing the Bi-plot based on db-RDA, the degree of the influence of the

162 operational parameters on phyla and major organisms was visualized. In general, pyrotag sequencing and the downstream data analysis help us better understand the microbial communities in the sludge treating pulp mill effluents.

163

CHAPTER 8. CONCLUSIONS, SIGNIFICANCE AND RECOMMENDATIONS

The effect of anaerobic treatment of pulp mill effluents on reactor performance and granulation was studied in this project. The research mainly focused on the anaerobic treatment of a alkaline effluent (AE) from sulphite pulping of softwood chips, which was a concentrated stream of COD, and resin acids and long-chain fatty acids (RFAs). The effect of the addition of

AE on reactor performance was evaluated from the perspectives of organic removal, biogas production and effluent solid content. Granulation was quantified by measuring particle size distribution and granule weakness. The impact on the microbial communities of sludge was examined by conducting pyrotag sequencing and q-PCR. In addition, the fate of RFAs in anaerobic treatment of AE was also studied. The overall conclusions, significance and recommendations are presented in this chapter.

8.1. Overall Conclusions of this Research

The following conclusions are obtained based on experimental work, data analysis and literature review:

1. AE was a complex and variable wastewater stream, containing many unidentified

compounds and compounds known to be inhibitory to microbial activity, including RFAs.

Other compounds in the AE included volatile fatty acids (VFAs, mainly acetate, formate

and lactate), xylose and lignin, making up ~50 to 70 % of the measured COD. Moreover,

AE varied with the grade of pulp being produced at the mill, e.g., substantially higher

concentrations of RFAs were contained in AE when the softwood-based SW1 pulp was

produced.

164

2. Addition of the RFA-rich AE had a negative impact on both reactor performance and

granulation of sludge, as shown in the FP concentration study and in the 30% and 40%

tests of AE in the UofT long-term study (Objective 2)

a. Significantly poorer %sCOD removals and biogas production were observed in

the reactor with a greater %AE addition

b. The sludge treating AE was weaker and smaller compared to the AE-free sludge

c. In the UofT long-term study, the effluent from the AE reactor contained

significantly higher TSS concentrations than the effluent from the control reactor.

In the FP concentration study, in the extreme case, treatment of 100% AE led to

an immediate washout of sludge

3. RFAs are proposed to play an important role in the poorer performance of the AE reactors

(Objective 2)

a. The RFA loadings were significantly negatively correlated to the biogas

production in the FP concentration study

b. In both studies, the sludge receiving a higher AE loading and showing lower

treatment efficiency contained larger amounts of RFAs, particularly palmitic acid,

so the accumulation of RFAs on the AE sludge could be one reason for the

negative impact of AE

c. The poorer performance of the AE reactor could also be caused by the inhibitory

effect of dehydroabietic acid contained in AE

4. The sludge treating AE showed very distinct microbial communities compared to the AE-

free sludge (Objective 3)

165

a. In the FP concentration study where loadings of AE and RFAs were relatively

high, the microbial communities in the sludge treating AE were less diverse than

the AE-free sludge

b. The AE sludge was found to contain higher percentages and abundance of

Firmicutes and Proteobacteria , and lower percentages and abundance of

methanogens, Spirochaetes and Chloroflexi as compared to the AE-free sludge.

c. In particular, % Oscillospira was significantly positively correlated to AE

loadings, while % Treponema and % Methanomethylovorans were significantly

negatively correlated to AE loadings. Higher absolute abundance of Oscillospira

and lower absolute abundance of Treponema and Methanomethylovorans were

also found in the sludge treating a higher %AE.

5. There was no clear evidence of the sludge acclimated to AE for enhanced biogas

production from the degradation of AE (Objective 5)

a. No enhanced biogas production was observed towards the end of the experiments

in both continuous studies.

b. In the BMP assays to test acclimation, similar amounts and rates of biogas

production were observed in both sets of bottles inoculated with the control

sludge and the AE sludge collected from the UofT reactors at the end of the 9-

month continuous experiment.

6. The granular sludge in the this research was generally small as compared to the mature

granules reported in many published papers, possibly due to the relatively low

percentages of Methanosaeta and possibly the relatively low production of extracellular

proteins and carbohydrates by acidogens likely caused by the high VFA content in the

reactor feeds (Objective 4).

166

7. Methods were developed to examine granulation based on size and strength with

reasonable reproducibility using small sample sizes. The modified granule weakness test

could provide an early warning sign of degranulation. Protocols were also established for

pyrotag sequencing and the downstream data analysis to enable comparisons of the

communities of various samples and to identify the key species (Objective 1).

8.2 Engineering and Scientific Significance

Almost all the published work to study anaerobic treatment of pulp and paper wastewaters focused on the treatability, despite the fact that granulation of sludge plays an important role in high rate reactors and directly affects treatment efficiency. In the work presented in this thesis, we performed systematic analysis to quantify granulation in addition to reactor performance. This fills the knowledge gap between the treatability of pulp mill effluents and their effect on granulation.

The dual approach of quantification of granulation and examination of treatment efficiency also helps to define operational parameters for a full scale digester, because biogas production tests alone do not provide data on the propensity to degranulation. The approach is applicable to the evaluation of other wastewaters being considered as feeds to high rate anaerobic digesters.

To our best knowledge, this research is the first community study using high throughput pyrotag sequencing to examine the microbial communities of sludge anaerobically treating pulp and paper mill effluents. In literature, community studies of the anaerobic sludge treating pulp mill effluents are rather limited, so the microorganisms involved in hydrolysis, fermentation and methanogenesis are poorly understood in such treatment. Using the data generated from pyrotag sequencing and combining the knowledge from literature, we proposed the key organisms

167 involved in different stages of anaerobic degradation of various constituents of pulp mill effluents.

In particular, we identified Oscillospira as a key organism, as it was significantly positively impacted by the addition of the high strength wastewater AE.

We developed relatively simple and reproducible methods to quantify granule size and strength. The granule weakness test based on the change in turbidity after vortexing can be used to provide an early warning sign of degranulation. These methods to quantify granulation can be employed by the operators of anaerobic treatment plants to monitor the health of the granules in their systems, and can also be adopted by other researchers in the future to study anaerobic granulation.

We also established protocols of preparing samples for pyrotag sequencing and downstream analysis. Using these protocols, the community compositions, the microbial diversity (species richness) and the clustering of samples based on similarity of their communities can be assessed. In addition, we also demonstrated the utilization of correlation study and distance-based redundancy analysis to identify the organisms affected by operational parameters. These established protocols have been adopted by various researchers among three lab groups in the Department for community studies.

8.3 Recommendations for Future Studies

Recommendations are offered to both the operation of anaerobic treatment plants in the pulp and paper industry and to future research in the field:

• the granule weakness test can be conducted as part of the routine analysis to provide

an early warning sign of degranulation;

168

• in order to utilize its easily degradable organic content to produce methane, an RFA-

rich stream, such as AE presented in this document, should be pretreated before

entering the anaerobic reactor to achieve a relatively low RFA loading rate (i.e., <64

mg RFA/L/day in this research) to minimize its RFA content prior to anaerobic

treatment;

• in order to achieve better granulation, other carbohydrate-rich stream(s) can be co-

digested to promote the growth of the EPS producing acidogens and Methanosaeta ;

• further investigation can be conducted to examine the functional role of Oscillospira

in the anaerobic treatment of pulp mill effluents, and to assess if Oscillospira is linked

to the degradation of RFAs or other specific compounds present in AE for possible

bioaumentation in the future;

• tests using scanning electron microscope (SEM) and transmission electron microscope

(TEM) can be performed in the future to verify the proposed encapsulation effect of

RFAs on anaerobic granules;

• based on the community studies using pyrotag sequencing data, specific probes can be

designed for fluorescent in situ hybridization (FISH) assays to detect and locate

organisms of interest and to better understand their roles in granulation and

degranulation during the treatment of pulp mill effluents;

• further experiments can be carried out to study the impacts of other AE constituents,

such as sulphite and lignin compounds, on anaerobic granules.

169

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APPENDICES

Appendix 2.1 List of Studies Using Statistical Tools in Correlation and Dynamics Studies based on Pyrotag Sequencing Data Statistic Statistic Method Method Used in Used in Author Paper Title Source Dynamics Correlation and Year Study Study A pilot scale two-stage anaerobic digester Correlation treating food waste leachate (FWL): Kim et al., Process Biochemistry. (using the R Performance and microbial structure analysis 2014 49(2): 301-6 package) using pyrosequencing Temporal variation in methanogen Non-metric communities of four different full-scale Lee et al., Bioresource 168: 59- multidimensiona anaerobic digesters treating food waste- 2014 63 l scaling recycling wastewater Predominance of cluster I Clostridium in Bioresource and Linear regression Park et al., hydrogen fermentation of galactose seeded Technology. 157: 98- in Excel 2014 with various heat-treated anaerobic sludges 106 Molecular characterization of anaerobic PCoA and Bioresource digester microbial communities identifies Town et Spearman's Rank Technology. 151: 249- microorganisms that correlate to reactor al., 2014 analysis 57 performance Journal of Non-metric Microbial community structure and dynamics Meesap et Biomedicine and multidimensional during anaerobic digestion of various al., 2012 Biotechnology. scaling agricultural waste materials 97(11): 5161-74 Canonical Canonical Bacterial and methanogenic archaeal Bioresource and Jang et al., correspondence correspondence communities during the single-stage anaerobic Technology. 165: 174- 2014 analysis analysis digestion of high-strength food wastewater 82 Applied Redundancy Impact of long-term diesel contamination on Sutton et Environmental

Analysis soil microbial community structure al., 2012 Microbiology. 79(2): 619-30 Linear or PH is a good predictor of the distribution of Soil Biology and Feng et quadratic anoxygenic purple phototrophic bacteria in Biochemistry. 74: 193- al., 2014 regression Arctic soils 200

186

Appendix 3.1 Detailed List of Analyzed Streams, Data Sources and Analytical Methods The rows in the table below are organized according to the type of effluent: BCTMP, followed by AC, then various types of AE. Within each type, the samples are listed according to the sample time (second column).

Sample Name Date of Collection Data Source Method Type of Analysis BCTMP June 2006 Exova (data Unknown Tannins and lignins, obtained from sulphite, RFA Tembec) BCTMP May 5, 2010 (feed to FP FPInnovations GC RFA (total) reactor) BCTMP Oct 15 and 25, 2010 UofT HPLC; total reducing VFA, alcohols, sugars sugar AC Nov 12 and 13, 2008 Exova (data Unknown Tannins and lignins, obtained from sulphite, RFA Tembec) AC Dec 2009 to Jan 2010 Exova (data Unknown Tannins and lignins, obtained from sulphite, RFA, phenol, Tembec) BTEX AC May 5, 2010 (feed to FP FPInnovations GC RFA (total) reactor) AC Oct 15 and 25, 2010 UofT HPLC; total reducing VFA, alcohols, sugars sugar AE (SW1) Nov 11 and 12, 2008 Exova (data Unknown Tannins and lignins, obtained from sulphite, RFA Tembec) AE (SW1) Dec 10, 11 and 14, 2009 Exova (data Unknown Tannins and lignins, obtained from sulphite, RFA, phenol, Tembec) BTEX AE (SW1) May 5, 2010 (feed to FP FPInnovations GC RFA (total) reactor) AE (SW1) May 5, 2010 (feed to FP Exova (exam paid Unknown RFA (individual) reactor) and Jan, 2012 by UofT) (feed to UofT reactor) AE (SW1) June 7, 2010 (R4 UofT HPLC VFA, alcohols, sugars effluent, used to represent the Alfa95a AE) AE (SW1) Oct 15 and 25, 2010 UofT HPLC; total reducing VFA, alcohols, sugars; sugar test; Bradford protein test AE (SW2) Dec 20, 21 and 22, 2009 Exova (data obtain Unknown Tannins and lignins, from Tembec) sulphite, RFA, phenol, BTEX AE (SW2) Oct 18, 2010 UofT HPLC; total reducing VFA, alcohols, sugars; sugar test; Bradford protein test AE Dec 29 and 30, 2009 Exova (data obtain Unknown Tannins and lignins, (SW3) from Tembec) sulphite, RFA, phenol, BTEX AE Jan 7, 8 and 11, 2010 Exova (data obtain Unknown Tannins and lignins, (HW) from Tembec) sulphite, RFA, phenol, BTEX

187

Appendix 3.2 Detailed Methods of HPLC and IC Conducted at the University of Toronto HPLC was conducted with a guard column (Biorad) and an Aminex HPX-87H column (Biorad,

#125-0410) to separate the compounds, using the following settings: column temperature at

50°C, 5mM sulphuric acid as the eluant at a flow rate of 0.6 ml/min, a reflective index (RI)

detector and an UV detector at 210nm (Vascondelos de Sa et. al. , 2011).

Standards for alcohols, sugars and VFAs were purchased from Sigma and Biorad (purity >99%).

Xylose: y = 0.1512x + 0.0401 R² = 0.9994 10 Ethanol: y = 0.4116x - 0.4399 Sorbitol: y = 0.1872x - 0.0999 9 R² = 0.9931 R² = 0.9994 8 Methanol: y = 1.3899x - 0.3133 7 R² = 0.9999 Arabinose: y = 0.1261x - 0.0121 6 R² = 1 Boric Acid: y = 0.3177x + 0.0025 5 R² = 1 4 3 2

Concentration (mg/ml) Concentration 1 0 0 10 20 30 40 50 60 70 Area

188

10Succinate: y = 0.0493x - 0.2668 R² = 0.9986 Butyrate: y = 0.0483x - 0.1359 9 R² = 0.995 Lactate: y = 0.0357x + 0.0185 R² = 0.9997 8 Acetate: y = 0.0338x + 0.0105 7 R² = 0.9997 6 Propionate: y = 0.0344x + 0.0144 R² = 0.9996 Formate: y = 0.0264x - 0.1608 5 R² = 0.9989 4 3 Formamide: y = 0.0198x - 0.0083 R² = 1 2 Concentration (mg/ml) Concentration 1 0 -1 0 50 100 150 200 250 300 350 400 Peak Area

An IonPac AS18 column (4mm) was used to separate the constituents in effluent samples with the following settings: 33 mM KOH as eluent at a flow rate of 1mL/min, column temperature at 30°C, a 25µL injection volume, and 82mA auto-suppression

189

Appendix 3.3 HPLC Results of Oct15 and Oct 25 ACs (SW1) Concentrations of AC Constituents on a COD Basis

AC Oct 15 AC Oct 25 4000

3000

2000 3586

1000 2534 1613 Concentration(mgCOD/l) 893 870 573 102 48 667 430 455 504 0 Xylose Acetate Ethanol Furfural Glucose Methanol

AC Organic Constituents based on HPLC

AC Oct 15 AC Oct 25 40

20 31 27 % Total COD % 10 12 11 1 08 3 6 6 4 7 0 Xylose Acetate Ethanol Furfural Glucose Methanol

190

Appendix 3.4 Tannin/Lignin and RFA Analysis of FP AE and UofT AE Unit UofT AE FP AE COD mg/L 20000 35000 Linoleic Acid mg/L 30 76 Linolenic Acid mg/L UD UD Oleic Acid mg/L 99 275 Stearic Acid mg/L 9 34 9,10-Dichlorostearic Acid mg/L UD UD Pimaric Acid mg/L 16 45 Sandaracopimaric Acid mg/L 6 18 Isopimaric Acid mg/L 23 86 Palustric Acid mg/L 17 50 Levopimaric Acid mg/L UD UD Dehydroabietic Acid (DHA) mg/L 136 360 Abietic Acid mg/L 270 913 Neoabietic Acid mg/L 7 25 12 & 14-Chlorodehydroabietic mg/L UD UD Acid 12,14-Dichlorodehydroabietic Acid mg/L UD UD Total Fatty Acids mg/L 138 385 Total Resin Acids mg/L 474 1496 Total Resin and Fatty Acids mg/L 612 1881 Lignin and Tannin mg/L 2618 3 15497 3 %LCFA Accounted for Total COD % 0.7 1.1 %RA Accounted for Total COD % 2.4 4.3 % RFA accounted for Total COD % 3.1 5.4 %Tannin/Lignin Accounted for % 13 44 Total COD Note: 1. UD: under detection limit 2. All RFA examinations were performed by Exova 3. Tannin/Lignin tests: the UofT AE was tested by Exova; the FP AE was tested by FPInnovations

191

Appendix 3.5 Chromatograms of SW2 and SW1 AEs in RI Channel

All SW1 and SW2 AE Samples Showed Similar Chromatograms

192

Appendix 3.6: Addition of Lactate and Xylose as Internal Standards

193

Appendix 4.1 Q-PCR Calibration Curves Calibration for Gen. Bac 30 y = -3.64x + 39.97 R² = 0.99 25 Efficiency = 89% 20

Cq 15

0 0 2 4 6 8 10 Log Quantity (copy gene/ml)

Calibration for Gen Arc 30 y = -3.76x + 38.70 25 R² = 0.995 Efficiency =84% 20

15 Cq

0 0 2 4 6 8 10 Log Quantity (copy gene/ml)

Calibration for Methanomethylovorans 30 y = -3.54x + 37.99 25 R² = 0.996 Efficiency =92% 20

Cq 15 10 5 0 0 2 4 6 8 10 Log Quantity (copy gene/ml)

194

Appendix 4.2. Results of the Traditional Clone Library of Tembec Sludge

Methanosarcina , 2% ExilispiraUnclassified, 25% (AF402980.1), 25% Methanosaeta , Methano- 41% bacterium , 18%39%

Propionivibrio , 8%

Acidobacteriaceae, Methano- 6% methylovorans , Unclassified 39% Clostridia , Sporobacter , 5% 5%

195

Appendix 5.1 Supplementary Information of the Reactors and Feeds in FP Concentration Study Table 5A.1. Nutrients Added to Anaerobic Digesters

Nutrient FennoNutri*, mg Micronutrient*, Phosphoric Ca (as Calcium Fe/L µL/L Acid, mg/L Hydroxide), mg/L Dosage 3.0 1.5 7.0 150 *FennoNutri and Micronutrient were pre-mixed nutrients supplied by the manufacturer of the IC in Tembec Temiscaming. Major compositions could be found in Table 5A.2.

Table 5A.2. Metals Contained in FennoNutri and Micronutrient

Concentration in Concentration in Micronutrient (mg/L) FennoNutri (mg/L) Total Aluminum (Al) Not Detected 220 Total Calcium (Ca) Not Detected Not Detected Total Cobalt (Co) 17600 27 Total Copper (Cu) Not Detected Not Detected Total Iron (Fe) Not Detected 190000 Total Magnesium (Mg) Not Detected 270 Total Manganese (Mn) Not Detected 78 Total Nickel (Ni) 4480 35 Total Potassium (K) Not Detected Not Detected Total Sodium (Na) 2720 Not Detected Total Zinc (Zn) Not Detected Not Detected

Table 5A.3 Other Operational Parameters

All Reactors during R2 R3 R4

Startup and R1 (35% PEW) (64% PEW) (100% PEW) Upflow Velocity from 0.09 0.07 0.04 0.01 Produced Biogas (m/h) Upflow Velocity based on 0.05 0.05 0.05 0.05 Feed (m/h) Upflow Velocity from 12.2 12.2 12.2 12.2 Circulated Biogas (m/h) Upflow Velocity from 24.5 24.5 24.5 24.5 Circulated Effluent (m/h)

196

Appendix 5.2 Time Profiles of Percentages TSS and VSS Contained in Granules Larger than 200µm in the FP Sludge

Percentages TSS and VSS Contained in Granules Larger than 200 µm: Time Profile for Control Reactor (A), Reactor with 35% AE (B), Reactor with 64% AE (C), Reactor with 100% AE (D). Error bars: 95% confidence interval of 4 replicates; Vertical red line indicated the end of startup period

197

Appendix 5.3 Results of ANOVA for Data in the Result Section of Physical Properties of the FP Sludge ANOVA For %TSS>200 µm and %VSS>200 µm: End of Study %TSS>200 µm %VSS>200 µm P=0.001 P=0.0005 Mean Variance Mean Variance R1 93.65 1.02 95.01 1.31 R2 86.63 5.94 89.76 1.8 R3 81.65 10.1 81.12 27.27 ANOVA For Particle Size Distribution: End of Startup 200-500 µm 500-1000 µm 1000-1500 µm 1500-2000 µm >2000 µm 0.47 0.91 0.966 0.07 0.07 ANOVA For Particle Size Distribution: End of Study 200-500 µm 500-1000 µm 1000-1500 µm 1500-2000 µm >2000 µm P=0.009 P=0.28 P=0.27 P=0.09 P=0.48 For 200-500 µm Mean Variance R1 40.904 12.79 R2 49.613 4.27 R3 46.5 6.32 ANOVA For Particle Size Distribution: End of Startup P= 0.68 ANOVA For Granule Weakness Test: End of Startup P=0.02 mean Variance R1 0.34 0.01 R2 0.40 0.14 R3 0.99 0.00

198

Appendix 5.4 Time Profiles of Particle Size Distribution in Granules (>200µm) in FP Sludge

Particle Size Distribution of Granules Larger than 200 µm: Time Profile for Control Reactor (A), Reactor with 35% AE (B), Reactor with 64% AE (C), Reactor with 100% AE (D) Error bars: 95% confidence interval of 4 replicates; Vertical red line indicated the end of startup period

199

Appendix 5.5 Dominant Taxonomy in the FP Concentration Study FP Control AE (End:35% and 63%) p_Euryachaeota(1st in Archaea: all time) p_Euryachaeota(1st in Archaea: all time) c_Methanomicrobia(1st: all time) c_Methanomicrobia(1st: both AE%) o_Methanomicrobiales(1st: all time) o_Methanomicrobiales(1st: both AE%) f__(1st: all time) f__(1st: both AE%) FP-OTU585(1st: all time) FP-OTU585(1st: both AE%) p_Firmicutes(2nd in Bacteria: all time) p_Firmicutes(1st (64%AE), 2nd (35%AE) in Bacteria) c_Clostridia(1st: all time) c_Clostridia(1st: both AE%) o_Clostridiales(1st: all time) o_Clostridiales(1st: both AE%) f__Ruminococcaceae(1st: all time) f__Ruminococcaceae(1st: both AE%) g__Other(1st: all time) g__Oscillospira(1st: both AE%) FP-OTU3639(1st seed) FP-OTU4284(1st: both AE%) FP-OTU2234(1st: startu ) g__Other(1st: 35% AE) FP-OTU200(1st: 64% AE) FP-OTU3034(1st: 35% AE) p_Bacteroidetes(1st: all time in Bacteria) p_Bacteroidetes(1st (35%AE),2nd (64%AE) in Bacteria) c_Bacteroidia(1st: all time) c_Bacteroidia(1st: both AE%) o_Bacteroidales(1st: all time) o_Bacteroidales(1st: both AE%) f__f__(1st: all time) f__Prevotellaceae(1s5: 64% AE) FP-OTU2804(1st: all time) g__Prevotella(1st: both AE%) FP-OTU2598(1st: both AE%) p_Proteobacteria(4th: seed; 5th: startup&end in Bacteria ) p_Proteobacteria(3rd in Bacteria) c_Deltaproteobacteria(1st: all time) c_Deltaproteobacteria(1st: both AE%) o_Desulfovibrionales(1st: Startup, end) o_Desulfovibrionales(1st: both AE%) f__Desulfovibrionaceae(1st: all time) f__Desulfovibrionaceae(1st: both AE%) g__Desulfovibrio(1st: all time) g__Desulfovibrio(1st: both AE%) FP-OTU2558(1st: all time) FP-OTU2558(1st: all time) c_Betaproteobacteria(2nd: 35% AE) o_Rhodocyclales(1st: all time) f__Rhodocyclaceae(1st: all time) g__Propionivibrio(1st: all time) UT-OTU212(1st: all time) p_Chloroflexi(5th: seed; 3rd: startup;4th: end in Bacteria ) p_Chloroflexi(6th in Bacteria) c__Anaerolineae(2nd:seed;1st: Startup, end) c__Anaerolineae(1st: both AE%) o__Anaerolineales(1st: Startup, end) o__Anaerolineales(1st: both AE%) f__Anaerolinaceae(1st: all time) f__Anaerolinaceae(1st: both AE%) g__T78(1st seed) g__T78(1st: both AE%) FP-OTU1490(1st: all time) FP-OTU1490(1st: both AE%) g__Anaerolinea(1st: Startup, end) FP-OTU1462(1st: all time) c__Dehalococcoidetes(1st seed; 2nd: startup, end) o__Dehalococcoidales(1st: all time) f__Dehalococcoidaceae(1st: all time) g__(1st: all time) FP-OTU845(1st: all time) p_Spirochaetes(3rd: seed; 4th: startup;3rd: end in Bacteria ) p_Spirochaetes(4th in Bacteria) c__Spirochaetes(1st seed) c__Spirochaetes(1st: both AE%) o__Spirochaetales(1st seed, startup) o__Spirochaetales(2nd both AE%) f__Spirochaetaceae(1st: all time) f__Spirochaetaceae(1st: both AE%) g__Treponema(1st: all time) g__Treponema(1st: both AE%) FP-OTU3221(1st: all time) FP-OTU3221((1st: 35% AE) FP-OTU676(1st: both AE%) o__Sphaerochaetales(1st: startup, end) o__Sphaerochaetales(1st: both AE%) f__Sphaerochaetaceae(1st: all time) f__Sphaerochaetaceae(1st: all time) g__Sphaerochaeta(1st: all time) g__Sphaerochaeta(1st: both AE%) FP-OTU1307(1st: all time) FP-OTU2438(1st: both AE%) c__WWE1(1st: Startup, end) o__[Cloacamonales](1st: all time) f__[Cloacamonaceae](1st: all time) g__W5(1st: all time) FP-OTU4084(1st: all time) 200

Appendix 5.6 Combining Pyrotag Sequencing and q-PCR to Quantify the Abundance of Organisms

2.E+08 1.E+08 1.E+08 1.E+08 8.E+07 6.E+07 4.E+07 2.E+07 0.E+00 … … … … … … … … … Gene copy Genecopy /ml Sample 0 0 0 0 - - - 35 35 35 35 64 64 64 ------

Seed End of R1 End R2 End R3 End

(Triplicate) Startup R1 End R1 End R1 End SeedRep1 SeedRep2 SeedRep3

0% AE 35%R2 End AER2 End R2 End R3 End 64%R3 End AE R3 End 0%EndStartR1 AEEndStartR2 EndStartR3 (Triplicate) (Triplicate) (Triplicate) (R1,2,3) Table A5.6.1 Abundance of Archaea in the FP Sludge Estimated based on Q-PCR

Figure A5.6.2 Abundance of Major Organisms in the FP Sludge Estimated based on both Pyrotag Sequencing Data and Q-PCR Results

201

Appendix 5.7 Results of the Correlation Tests of Organisms Affected by Operational Parameters in the FP Concentration Study Table A5.7.1 Correlation between the Abundance of Different Types of Microorganism and AE Loadings Significant Score Correlation Organisms (p-Values) Coefficient Spirochaetes 6.5E-06 -0.94 Phylum Firmicutes 5.7E-05 0.90 Level Euryarchaeota 3.3E-03 -0.77 Chloroflexi 6.5E-03 -0.73 Oscillospira 1.1E-11 1.00 Parabacteroides 2.4E-07 0.97 Genus Unclassified Bacteroidales (OTU2804) 3.7E-04 -0.86 Level HA73 1.1E-03 0.82 Treponema 1.9E-03 -0.80 Methanomethylovorans 2.4E-03 -0.79

Table A5.7.2: List of Major Organisms (>1.5% Population) that were Significantly Correlated to Concentrations of Lignin and RFAs in Reactor Feeds Lignin Significant Score Correlation Organisms (p-Values) Coefficient Oscillospira 5.1E-09 0.99 Parabacteroides 2.1E-08 0.98 HA73 9.7E-04 0.82 RFA Significant Score Correlation Organisms (p-Values) Coefficient Oscillospira 6.50E-12 1 Parabacteroides 6.50E-07 0.96 HA73 1.30E-03 0.81

Table A5.7.3: List of Major Organisms (>1.5% Population) that were Significantly Correlated to Time OTU p-Value Correlation Coefficient Highest % in Population (Pyrotag) Prevotella 3.1E-04 0.87 22 Sphaerochaeta 1.3E-04 0.89 4

202

Table A5.7.4: Correlation between % Microorganism and AE Loadings

Phylogeny % in AE Sludge Correlation with AE Loadings Kingdom: Archaea Lower Not S.S p__Euryarchaeota; aa c__Methanomicrobia; aaa o__Methanosarcinales ; aaaaa f__Methanosarcinaceae aaaa aaaaaa g__Methanomethylovorans Lower Not S.S Kingdom: Bacteria p__Firmicutes Higher P= 1.2E-6, r=0.99 aa c__Clostridia; aaa o__Clostridiales Higher P= 1.1E-7, r=0.99 aaaaa f__Lachnospiraceae; aaaaaaa g__Butyrivibrio Lower P= 7.1E-4, r= -0.91 aaaaa f__Ruminococcaceae; aaaaaaa g__Oscillospira Higher P= 3E-10, r=1.00 p__Proteobacteria Higher P= 9.2E-3, r=0.81 ac__Deltaproteobacteria; ao__Desulfovibrionale Higher Not S.S aaa o__Desulfuromonadales Higher Not S.S p__Chloroflexi Lower P= 6.1E-3, r= -0.83 aa c__Dehalococcoidetes; aaa o__Dehalococcoidales; aaaaa f__Dehalococcoidaceae; aaaaaaa g_Unclassified Lower P= 2.6E-3, r= -0.87 aaa o__Anaerolineales; aaaaa f__Anaerolinaceae; aaaaaaa g__Anaerolinea Lower P= 9.9E-3, r= -0.80 p__Spirochaetes Lower P= 3.1E-3, r= -0.86 aa c__Spirochaetes; aaa o__Spirochaetales; aaaaa f__Spirochaetaceae; aaaaaaa g__Treponema Lower P= 1.3E-3, r= -0.89 p__Synergistetes Lower P= 1.6E-3, r= 0.88 aa c__Synergistia; aaa o__Synergistales; aaaaa f__Dethiosulfovibrionaceae; aaaaaaa g__HA73 Lower P= 3.0E-3, r= 0.86 Not S.S: not statistically significant as p>0.01 Phylogeny abbreviations: p: phylum; c: class; o: order; f: family; g: genus

Table A5.7.5: List of Major Organisms (>1.5% Population) With % Significantly Correlated to Time OTU p-Value Correlation Coefficient Highest % in Population (Pyrotag) Prevotella 3.0E-06 0.91 22 Sphaerochaeta 5.0E-05 0.85 4

203

Appendix 6.1 Setup of the UofT Continuous Reactors

204

Appendix 6.2 Summary of Elemental Analysis of IC Influent, Feed to FP Reactors and Literature Values Compound Literatur Needed for the IC FP Compound e Range Desired Reason for the Proposed Amount (based on mg/g Elements mg per mg per proposed for (mg per Element COD calculation) g COD g COD the element g COD) Concentration (mg/l feed) K HPO 163.2 P is essential for cell metabolism; the ranges in IC and 0.13- 0.18- 2 4 Total P 1.4-10.7 FP seem much lower than lit range, so we propose to 0.19 0.39 KH PO 208.8 2 4 add the amount close to the low end of the lit value.

Total N 5.2-100 5.0-7.3 8.4-19.6 NH 4Cl 2000 Within the ranges in IC, FP and lit values In the ranges for IC and FP; since BCTMP contains MgSO *7H O 210 4 2 high SO , so total S is higher as compared to the lit Total S 4 value. However, since sulfate reduction competes with (from 0-3.6 5.0-7.6 2.0-4.5 methanogenesis, so we propose to add slightly lower SO ) MnSO *H O 40 4 4 2 than the low end value of FP; roughly the mid-point of lit range 0.008- 0.01- Al (SO )3• Total Al 2 4 7.5 Similar to high end of FP and mid-point of lit range 0.011 0.02 16H 2O Close to the high end of IC and FP, in which Ca 2+ is mainly from the addition of Ca(OH)2; here we propose Total Ca 0-9.1 18-26 11.0-23 CaCl 2*2H 2O 2500 to add CaCl 2 due to its higher solubility than Ca(OH) 2; note that this number is much higher than the lit value Close to the high end of IC and FP concentrations, in 0.03- 0.02- Total Co 0-0.1 Micro 0.07 ml which Co is mainly from the addition Micro nutrient 0.04 0.04 from Paque; within the lit range 0.007- 0.002- Similar to high end of FP and IC, and mid-point of lit Total Cu 0-0.03 CuCl 0.6 0.012 0.01 2 range Similar to the high end of FP and IC in which Fe 2+ is 0.29- 0.19- Total Fe 2+ 0.5-2 Fenno 0.07 ml mainly from the addition of Fenno from Paque; close 0.43 0.42 to the low end of lit range MgSO * Similar to the high end of FP, and within the lit range, Total Mg 0.2-2.7 1.0-1.5 0.3-0.7 4 210 7H 2O lower than the low value of IC MnSO * Similar to the high end of FP and IC, and within the lit Total Mn 0.01-3.1 0.6-0.9 0.2-0.4 4 40 H2O range 0.008- 0.005- Similar to the high end of FP and IC, and within the lit Total Ni 0-0.09 Micro 0.07 ml 0.012 0.011 range 0.08- 0.02- similar to the high end of FP and within lit range; Total Zn 0-0.1 ZnCl 4 0.12 0.05 2 slightly lower than the low end of IC Yeast 0-40 0 0 Yeast extract 30 similar to values found in 2 references (1 mg /g COD) extract H3BO 3 0.01-0.2 0 0 H3BO 4 4.5 within the lit range 0.004- (NH ) Mo O Mo 0 0 4 6 7 24 0.45 most papers have values close to 0.01 mg/ gCOD 0.11 •4H 2O

205

Appendix 6.3 pH Measured during the UofT Concentration Study

The pH in both reactors mainly lay between 6.8 and 7.6, slightly higher than the optimal range of pH 6.8-7.2 for anaerobic degradation (Angelidaki and Sander, 2004). During the

30% AE test and the first 10 days of the 40% AE test (i.e., days 112-250), the pH in the control reactor was significantly higher than that in the AE reactor (p = 0.002 in one-tail t-test). After day

250, the effluents from both reactors had similar pH. The upset in pH was mainly observed between days 139 and 141, as well as days 250 and 280.

AE Addition

pH of Effluents from Control Reactor (Red Square) and AE Reactor (Blue Diamond) in UofT Study

206

Appendix 6.4 VSS Concentrations in the Effluents from the UofT Reactors

900 Control 800 Reactor AE Reactor 700

600

500

400

VSS (mg/L) VSS 300

200

100

0 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345 360 Day since Startup

207

Appendix 6.5 Summary of Raw Sequences, Sequences before and after Chimera Removal

Number of Raw Sequences 273986 Number of Sequences Including Chimeras 214083 Number of OTU Including Chimeras 9063 Number of Sequences without Chimeras 209528 Number of OTU without Chimeras 7842

Name of Sample Sequences / Sample OTUs / Sample Ctrl.Feb.Rep1 7025 1050 Ctrl.Feb.Rep2 6084 804 Ctrl.Feb.Rep3 6335 857 AE.Feb.0.Rep1 6533 996 AE.Feb.0.Rep2 7365 822 AE.Feb.0.Rep3 6196 882 Ctrl.Apr.Rep1 6209 1008 Ctrl.Apr.Rep2 6720 1016 Ctrl.Apr.Rep3 6650 1037 AE.Apr.30begin.Rep1 6317 1048 AE.Apr.30begin.Rep2 5529 908 AE.Apr.30begin.Rep3 6011 993 Ctrl.May3.Rep1 6364 845 Ctrl.May3.Rep2 6934 906 Ctrl.May3.Rep3 6667 873 AE.May3.30.Rep1 6287 859 AE.May3.30.Rep2 6051 816 AE.May3.30.Rep3 5855 737 Ctrl.May22.Rep1 6963 861 Ctrl.May22.Rep2 6267 871 Ctrl.May22.Rep3 7423 862 AE.May22.30.Rep1 6580 902 AE.May22.30.Rep2 5798 895 AE.May22.30.Rep3 6296 896 Ctrl.Jun.Rep1 6706 892 Ctrl.Jun.Rep2 6190 883 Ctrl.Jun.Rep3 6903 833 AE.Jun.30.Rep1 8128 1154 AE.Jun.30.Rep2 6463 853

208

Appendix 6.6 Alfa Diversity of Sludge Collected from the UofT Reactors.

Error bars: standard deviations of triplicates

209

Appendix 7.1 Methanosaeta vs. Methanosarcina

Methanosaeta Methanosarcina Specific growth rate, k, (mg COD/mg VSS/day) 10.1 12.2 Half saturation coefficient, Ks, (mg COD/L) 49 280 Cell yield (mg VSS/ mg COD) 0.019 0.048

Adopted based on Conklin et al., 2006

210