ABSTRACT CHENG, QIWEN. Understanding Microbial

ABSTRACT

CHENG, QIWEN. Understanding Microbial Transformations of Organic Matter Under Anaerobic Conditions: Experimental Evaluation of Mediating Capabilities of Pyrogenic Carbonaceous Materials and Metagenomic Characterization of Mixed Communities (Under the direction of Dr. Douglas Call).

Anaerobic microbial transformations play an essential role in organic matter degradation in the field of wastewater and solid waste treatment. Nevertheless, the application of anaerobic treatment is limited by the range of contaminants that can be targeted and the long timescales that are often required. These challenges encourage researchers to explore novel microbial pathways for contaminant degradation and energy production, and to identify microorganisms that allow engineers to shape microbial communities for environmental purposes.

This dissertation provides some insights into these research areas by conducting three projects: 1) investigating the impact of amending pyrogenic carbonaceous materials (PCMs) on methane generation from anaerobic bioreactors fed with high-strength wastewater; 2) evaluating an approach to cost-effectively enrich PCM-reducing microbial communities from a PCM- amended system; and 3) characterizing the impact of temperature on structures and functions of microbial communities in landfills. The first project (Chapter 2) shows for the first time that

PCM properties other than conductivity can largely explain how material amendments impact short-term batch reactor performance, which provides guidance for selecting optimal material types, sizes, and loadings for methane generation in anaerobic digesters. The second project

(Chapter 3) successfully enriched an activated carbon-reducing microbial community derived from a drinking water biological activated carbon system with activated carbon as the sole electron acceptor, which proposes a novel, low-cost method to grow exoelectrogens from mixed cultures. The third project (Chapter 4) constitutes the first study to comprehensively investigate the response of microbial communities to elevated temperatures in landfills, and provides guidance for operating landfills and recovering renewable biogas. Both experimental evaluation and computational analysis were performed in this dissertation to characterize microbial reactions and identify key microorganisms associated with anaerobic microbial transformations of organic matter.

All Rights Reserved Understanding Microbial Transformations of Organic Matter Under Anaerobic Conditions: Experimental Evaluation of Mediating Capabilities of Pyrogenic Carbonaceous Materials and Metagenomic Characterization of Mixed Communities

by Qiwen Cheng

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Civil Engineering

Raleigh, North Carolina 2020

APPROVED BY:

______Douglas Call Francis Lajara De Los Reyes Committee Chair

______Morton Barlaz Detlef Knappe

DEDICATION

This dissertation is dedicated to my parents, Shiqing Cheng and Shumin Cheng, who always love me unconditionally and whose good examples have taught me to work hard for the things that I aspire to achieve. This dissertation is also dedicated to those who devote their lives to protecting the environment. The world will become a better place because of you.

BIOGRAPHY

Qiwen Cheng was born in Jinan, Shandong Province, China in 1991. She received her bachelor’s degree in Environmental Engineering from Shandong University, China, in 2013. She started her graduate study in the same year, and received her master’s degree in Civil

Engineering from University of Washington in 2015. She then transferred to North Carolina

State University to pursue a doctoral degree in Civil Engineering, with a specialization in

Environmental, Water Resources and Coastal Engineering. Her research focuses on understanding the interactions between microorganisms and the environment, and exploring approaches to produce the desired microbial populations for environmental purposes. During her graduate study, she characterized microbial communities in engineered systems and investigated how their compositions and functions could be affected by environmental conditions. She also investigated electron transfer occurring in microbial communities and how it could be facilitated by amendments of pyrogenic carbonaceous materials (PCMs). In her research, she relies extensively on molecular biology techniques, metagenomics and advanced analytical approaches to characterize PCM properties as well as microbial community structures, physiology and metabolism.

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ACKNOWLEDGMENTS

I am deeply grateful for my committee members who were more than generous with their expertise and precious time. My deepest gratitude goes to Dr. Douglas Call, my committee chair, for his countless hours of reflecting, reading, encouraging, and most of all patience throughout the entire process. I would also like to show my gratitude to Dr. Francis de los Reyes, Dr. Detlef

Knappe, Dr. Morton Barlaz, and Dr. Owen Duckworth for serving on my committee and enlightening me with great research ideas.

I would like to thank the current and former Call Group members, Dr. Juan Fausto Ortiz

Medina, Dr. Fei Liu, Conner Murray, Victoria Tavares, Elvin Hossen, Sol Park, Mark Poole,

Yazeed Algurainy, Dr. Shan Zhu and Hezhou Ding, for all of your support on schoolwork and research. I would also like to thank my friends in Civil Engineering at North Carolina State

University, Binghui Li, Zisu Hao, Qianwen Liu, Amie McElroy, Zachary Hopkins, Asmita

Narode, Arpit Sardana, Mei Sun, Joe Weaver, Yue Zhi, Chuhui Zhang, for all the fun we have had during the past five years. Special thanks to Yi-Chun Lai, who has been a constant source of kindness, strength and inspiration for me. May peace, hope and love be with you all.

I would like to acknowledge the help from North Carolina State University Student

Health Services for taking care of my physical and mental health. I want to give my special thanks to Sooyoung Uhm, Shauna Campbell and Dr. Heather Rogers for guiding me through the challenges in graduate school. I would also like to acknowledge the staff members at North

Carolina State University, Dr. Lisa Castellano and Jake Rhoads in Civil Engineering, Chuck

Mooney, Roberto Garcia, Chuanzhen Zhou and Fred Stevie at Analytical Instrumentation

Facility, Hannah Jones, David Baltzegar and Kristen Fowler in Genomic Sciences Laboratory and Lisa Lentz in Environmental and Agricultural Testing Service, for their assistance in sample

analysis. I would like to further acknowledge the staff members, Charles Cocker at South

Durham Water Reclamation Facility, Jason Parker at North Cary Water Reclamation Facility,

Jess Brown and Jennifer Nyfennegger at Carollo Engineers, Pamela London-Exner at Veolia

North America, and Jeremy Ennis and Frederick Hughes at Dempsey E. Benton Water

Treatment Plant for their kind offer of water and carbon samples.

TABLE OF CONTENTS

LIST OF TABLES ...... vii LIST OF FIGURES ...... ix

Chapter 1. Introduction ...... 1

Chapter 2. Amending anaerobic bioreactors with pyrogenic carbonaceous materials: the influence of material properties on methane generation ...... 12 2.1. Abstract ...... 12 2.2. Significance ...... 13 2.3. Introduction ...... 13 2.4. Materials and methods ...... 15 2.5. Results and discussion ...... 20 2.6. Conclusions ...... 38 2.7. References ...... 40

Chapter 3. Developing microbial communities containing a high abundance of exoelectrogenic microorganisms using activated carbon granules ...... 46 3.1. Abstract ...... 46 3.2. Significance ...... 46 3.3. Introduction ...... 47 3.4. Materials and methods ...... 49 3.5. Results and discussion ...... 53 3.6. Conclusions ...... 66 3.7. References ...... 67

Chapter 4. Structures and functions of landfill microbial communities exposed to elevated temperatures ...... 74 4.1. Abstract ...... 74 4.2. Significance ...... 75 4.3. Introduction ...... 75 4.4. Materials and methods ...... 77 4.5. Results and discussion ...... 79 4.6. Conclusions ...... 104 4.7. References ...... 105

Chapter 5. Limitations and future research ...... 112

APPENDICES ...... 116 Appendix A. Supplementary information for Chapter 2 ...... 117 Appendix B. Supplementary information for Chapter 3 ...... 125 Appendix C. Supplementary information for Chapter 4 ...... 148

LIST OF TABLES

Table 2.1 Particle type, diameter, conductivity, and specific surface area ...... 16

Table 2.2 Properties of the swine wastewater mixture [1:30 (v:v), seed:feed] ...... 17

Table A.2.1 Soluble COD of particle-amended deionized water ...... 119

Table A.2.2 pH of particle-amended deionized water and swine wastewater ...... 120

Table B.2.1 PCMs used in this study ...... 131

Table B.2.2 Specifications of the csGAC (after air oxidation) used in this study ...... 132

Table B.2.3 Deconvolution results of C (1s) and O (1s) regions from XPS analysis of csGAC (after air oxidation) ...... 133

Table B.2.4 Alpha diversity metrics. Calculations are based on subsampling of 102,018 sequences (i.e., the size of the smallest library). The data presented represent the average of biological triplicates ± one standard deviation (n = 3). GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors; 1, 2 and 3 represent the incubation cycle...... 134

Table C.1.1 Pairwise t-test results showing the significance of alpha diversity metrics between each pair of LFA samples. A p-value of less than 0.05 was considered significant. “Excav” represents excavated samples ...... 148

Table C.1.2 Pairwise t-test results showing the significance of alpha diversity metrics between each pair of LFB samples. A p-value of less than 0.05 was considered significant. “Excav” represents excavated samples ...... 149

Table C.1.3 Pairwise permutational multivariate analysis of variance (PERMANOVA) results showing the significance of distances between each pair of samples. A p-value of less than 0.05 was considered significant ...... 150

Table C.1.4 Pairwise permutational multivariate analysis of variance (PERMANOVA) results showing the significance of distances between each pair of LFA samples. A p-value of less than 0.05 was considered significant. “Excav” represents excavated samples ...... 151

Table C.1.5 Pairwise permutational multivariate analysis of variance (PERMANOVA) results showing the significance of distances between each pair of LFB samples. A p-value of less than 0.05 was considered significant. Numbers in yellow represent p-values higher than or close to 0.05.“Excav” represents excavated samples ...... 152

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Table C.1.6 SIMPER analysis results showing OTUs in LFA incubated samples that contributed to the differences between each pair of incubation temperatures. Only the top five OTUs with highest contribution percentages between each pair of temperatures were listed. A p-value of less than 0.05 was considered significant ...... 153

Table C.1.7 SIMPER analysis results showing OTUs in LFB incubated samples that contributed to the differences between each pair of incubation temperatures. Only the top five OTUs with highest contribution percentages between each pair of temperatures were listed. A p-value of less than 0.05 was considered significant ...... 159

Table C.1.8 OTUs identified in LFA incubated samples explained by four environmental factors, including incubation temperature, excavation temperature, volatile solid concentration (VS) and ratio of cellulose to lignin content (CH/L). Numbers in the table represent the extent of explanation by a factor, and those with the largest absolute values are considered to be the predictor of an OTU. The plus sign indicates a positive correlation between the OTU abundance and factor, and the minus sign negative correlation. Samples with the excavation temperature of 70 °C were not included in this analysis due to lack of VS and CH/L data ...... 169

Table C.1.9 OTUs identified in LFB incubated samples explained by four environmental factors, including incubation temperature, excavation temperature, volatile solid concentration (VS) and ratio of cellulose to lignin content (CH/L). Numbers in the table represent the extent of explanation by a factor, and those with the largest absolute values are considered to be the predictor of an OTU. The plus sign indicates a positive correlation between the OTU abundance and factor, and the minus sign negative correlation. Samples with the excavation temperature of 70 °C were not included in this analysis due to lack of VS and CH/L data ...... 173

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LIST OF FIGURES

Figure 1.1 Conductor (A) and battery (B) mechanisms of PCM-associated electron transfer. Electrons can be directly conducted through PCMs, or stored in PCMs and then released to other electron acceptors ...... 4

Figure 2.1 Maximum methane production rates (Qmax) normalized to each respective batch no-particle control average as a function of particle type [graphite, biochar, activated carbon (AC), glass], size (granular or powdered) and (A) particle loading or (B) particle surface area. The x-axis labels in (A) refer to the three particle loadings tested (L – Low; M – Medium; H – High). Error bars represent the range of replicate experiments (n = 2). P-values indicate the statistical significance of the slopes ...... 25

Figure 2.2 Methane recoveries (rCH4) normalized to each respective batch no-particle control average as a function of particle type [graphite, biochar, activated carbon (AC), glass], size (granular or powdered) and (A) particle loading or (B) surface area. The x-axis labels in (A) refer to the three particle loadings tested (L – Low; M – Medium; H – High). Error bars represent the range of replicate experiments (n = 2). P-values indicate the statistical significance of the slopes ...... 26

Figure 2.3 Maximum methane production rates (Qmax; normalized to each respective batch no-particle control average) as a function of particle electrical conductivity. G – granular; P – powdered; Low, Med, and High refer to particle loadings. Error bars represent the range of replicate experiments (n = 2) ...... 28

Figure 2.4 (A) TCOD removals (normalized to the no-particle control average) in reactors amended with graphite, biochar, activated carbon (AC), or glass particles. The x-axis labels refer to the three particle loadings tested (L – Low; M – Medium; H – High). (B) TCOD removals in sterilized swine -1 wastewater [particle loading of 2.2 g particles (g VSseed) ]. All bottles started with a TCOD equal to the no-particle control (empty columns). The final supernatant TCOD was recorded after a 19-day incubation (diagonal lines). G – granular; P – powdered. Error bars represent the range of replicate experiments (n = 2) ...... 30

Figure 2.5 Electron balances in reactors amended with (A) graphite, (B) biochar, (C) activated carbon (AC) and (D) glass. Balances are based on the COD distributions at the end of the incubation relative to the initial TCOD (TCODi). All values are normalized to the no-particle controls. Values to the right of zero show an increase relative to the control, while those to the left indicate a decrease. G – granular; P – powdered. Low, Med, and High refer to particle loadings. Error bars represent the range of replicate experiments (n = 2) ...... 33

Figure 2.6 Cumulative methane production using a non-pH adjusted wastewater (pH = 7.2) and a sample adjusted to pH 8.1 using NaOH. No particles were added. Error bars represent the range of replicate experiments (n = 2) ...... 35

Figure 2.7 Scanning electron micrographs of (A) G-graphite, (B) P-graphite, (C) G-biochar, (D) P-biochar, (E) G-AC, (F) P-AC, (G) G-glass and (H) P-glass. Images show new particles that were not added to the reactors. White scale bar represents 50 µm. G – granular; P – powdered ...... 37

Figure 3.1 Dissolved organic carbon (DOC) in the rinsate from the coconut shell-based GAC (csGAC) rinse cycles. The data represent the average of triplicates ± one standard deviation (n = 3). EEC: electron exchange capacity ...... 54

Figure 3.2 (A) Acetate consumption by G. sulfurreducens when csGAC was provided as the terminal electron acceptor. Acetate concentrations (mg L-1) at different time points were normalized to the initial concentrations at Day 0. Error bars represent one standard deviation of triplicate experiments (n = 3). (B) Scanning electron micrograph of G. sulfurreducens on the csGAC surface at the end of incubation. White scale bar represents 10 µm...... 56

Figure 3.3 (A) Acetate consumption in the first incubation, and (B) in the Acetate + GAC + cells reactors in all three incubations when acetate and csGAC or oxygen (O2) were provided to the BAC culture. Acetate concentrations (mg L-1) at different time points were normalized to the initial concentrations at Day 0. Error bars represent one standard deviation of triplicate experiments (n = 3) ...... 58

Figure 3.4 Scanning electron micrographs of (A) raw csGAC with no cells, (B) csGAC after the first incubation, (C) csGAC after the second incubation, and (D) csGAC after the third incubation. White scale bar represents 10 µm ...... 60

Figure 3.5 Taxonomic distribution of microorganisms at the genus level in the BAC inoculum, on the csGAC surface and in suspension when csGAC was the electron acceptor. Abundances are based on averages of biological triplicates. Genera with relative abundances of less than 2% and unclassified genera were grouped into the “Others” category. GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors; 1, 2 and 3 represent the incubation cycle ...... 61

Figure 4.1 Methane yields from LFA and LFB and VFA production from LFB at multiple incubation temperatures. The relative yield values were normalized to the maximum yield detected for each excavated sample. Error bars represent one standard deviation of 16 reactors (n = 16) ...... 80

Figure 4.2 Microbial community composition in excavated samples from LFA. No replicate measurements were performed for excavated sample at each

temperature (n = 1). Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category ...... 82

Figure 4.3 Archaeal community composition in LFA samples incubated at multiple temperatures. Abundances are based on average of biological duplicates or single sample when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category ...... 83

Figure 4.4 Bacterial community composition in LFA samples incubated at multiple temperatures. Abundances are based on average of biological duplicates or single sample when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category ...... 84

Figure 4.5 Microbial community composition in excavated samples from LFB. No replicate measurements were performed for excavated sample at each temperature (n = 1). Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category ...... 86

Figure 4.6 Archaeal community composition in LFB samples incubated at multiple temperatures. Abundances are based on average of biological duplicates or single sample when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category ...... 88

Figure 4.7 Bacterial community composition in LFB samples incubated at multiple temperatures. Abundances are based on average of biological duplicates or single sample when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category ...... 88

Figure 4.8 Alpha diversity in excavated and incubated samples from (A) LFA and (B) LFB. Excav on the x axis represents excavated samples (not incubated), and the numbers represent the temperatures used during the incubations. The Observed index counts the number of distinct OTUs present in a sample. The Chao1 index estimates the number of distinct OTUs by giving more weight to rare OTUs. The Shannon index takes into account both abundance and evenness of OTUs in a sample and assumes that all OTUs are represented and randomly sampled. The Simpson index measures the probability that two reads randomly selected from a sample belong to different OTUs. A p-value of less than 0.05 was considered significant ...... 92

Figure 4.9 Non-metric multidimensional scaling (NMDS) illustrating beta diversity in excavated and incubated samples from LFA (A) and LFB (B) based on Bray-Curtis dissimilarity (a quantitative measure of community differences).

Excav in legends represents excavated samples (not incubated), and numbers represent the temperatures during the incubations. The ellipses represent 95% confidence intervals around their centroids. The stress values of 0.16 and 0.23 provide a fair representation in reduced dimensions ...... 94

Figure 4.10 Distance-based redundancy analysis (db-RDA) showing the impact of incubation temperature, excavation temperature, volatile solid concentration (VS) and ratio of cellulose to lignin content (CH/L) on microbial compositions in incubated (A) LFA and (B-C) LFB samples. The plots (A) and (B) were colored with relative methane yields, and (C) with relative volatile fatty acid (VFA) concentrations. The gray dots in each plot represent OTUs. Samples with the excavation temperature of 70 °C were not included in (A) analysis due to lack of VS and CH/L data. Samples with no VFA measurements were not shown in (C) ...... 97

Figure 4.11 Relative abundances of methanogenesis-related genes in (A) LFA and (B) LFB samples incubated at various temperatures, predicted by Piphillin. Each gene was normalized to the number of total genes detected in a sample. A p-value of less than 0.05 was considered significant. K00399: mcrA gene; K00401: mcrB gene; K00402: mcrG gene; K03421: mcrC gene; K03422: mcrD gene. All of these genes encode coenzyme-B sulfoethylthiotransferase ..... 100

Figure 4.12 Relative abundances of cellulose degradation-related genes in (A) LFA and (B) LFB samples incubated at various temperatures, predicted by Piphillin. Each gene was normalized to the number of total genes detected in a sample. A p-value of less than 0.05 was considered significant. K01179: gene encoding endoglucanase; K01181: gene encoding endo-1,4-beta-xylanase; K19355: gene encoding mannan endo-1,4-beta-mannosidase ...... 101

Figure A.3.1 Maximum methane production rates as a function of particle loadings from (A) granule-amended reactors and (B) powder-amended reactors. Methane recoveries as a function of particle loading from (C) granule-amended reactors and (D) powder-amended reactors. Error bars represent the range of replicate experiments (n = 2). Correlation coefficients greater than 0.7 are shown ...... 121

Figure A.3.2 Volatile fatty acid (VFA) adsorption in sterile swine wastewater amended with particles. The minimum detection limit was 20 mg L-1. Concentrations below this limit are not shown. The lowest particle loading (2.2 g particles per g VSseed before sterilization) was used. G – granular; P – powdered. Error bars represent the range of replicate experiments (n = 2) ...... 122

Figure A.3.3 Abiotic methane adsorption in deionized water amended with particles. Blue diagonal lines represent the methane concentrations after adsorption with 100% methane initially injected. Yellow horizontal lines represent the methane concentrations after adsorption with 10% methane initially

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injected. The highest particle-to-working volume ratio (3.4 g particles per 100 mL; n = 1) was used. A 24-hour incubation was used. G – granular; P – powdered ...... 123

Figure A.3.4 Total ammonia nitrogen (TAN) adsorption in NH4Cl solutions amended with particles under abiotic conditions. The highest particle-to-working volume ratio (3.4 g particles per 100 mL) was used. A 19-day incubation was used. G – granular; P – powdered. Error bars represent the range of replicate experiments (n = 2) ...... 124

Figure B.3.1 Schematic diagram of the Tampa Bay Regional Surface Water Treatment Plant. The biologically active carbon (BAC) filter samples were collected from the biofiltration unit shown in the diagram ...... 135

Figure B.3.2 Dissolved organic carbon (DOC) in the rinsate of lignite coal-based GAC (lcGAC) during the preparation steps. The data represent the average of triplicates ± one standard deviation (n = 3). EEC: electron exchange capacity ..... 136

Figure B.3.3 (A) XPS survey spectrum, (B) C (1s) spectrum, and (C) O (1s) spectrum of csGAC ...... 137

Figure B.3.4 The mEAC values of multiple PCMs calculated from acetate degradation by Geobacter sulfurreducens. Error bars represent one standard deviation of triplicate experiments (n = 3) on mEAC estimations from acetate for csGAC and lcGAC, and error bars represent range of duplicate experiments (n = 2) on other mEAC estimations. rhBC: rice husk-based biochar; hwBC: hardwood-based biochar; pwBC: pine wood-based biochar; csGAC: coconut shell-based GAC; lcGAC: lignite coal-based GAC; bcGAC: bituminous coal-based GAC ...... 138

Figure B.3.5 Correlation between COD and acetate measurement in bottles with Geobacter sulfurreducens medium and acetate ...... 139

Figure B.3.6 Acetate consumption in the abiotic and biotic controls in the (A) second and (B) third incubations. Acetate concentrations (mg L-1) measured at different time points were normalized to the initial concentrations at Day 0. Error bars represent one standard deviation of triplicate experiments (n = 3) ...... 140

Figure B.3.7 Scanning electron micrograph of the original BAC culture (from the pilot-scale system) attached to csGAC. White scale bar represents 10 µm ...... 141

Figure B.3.8 Alpha rarefaction curves based on observed OTUs. The sampling depth was 102,018, which was the size of the smallest library of all samples. GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors; 1, 2 and 3 represent the incubation cycle. Rarefaction curves for all samples approached

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saturation, indicating that the sampling depth fully covered the diversity of microbial communities ...... 142

Figure B.3.9 (A) Weighted unifrac distance-based PCoA plot displaying the distances between community compositions and (B) pair-wise PERMANOVA results showing the significance of distances between each pair of samples. A p-value of less than 0.05 was considered significant. The numbers 1, 2 and 3 and arrows in (A) represent the incubation cycle. GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors ...... 143

Figure B.3.10 Taxonomic distribution of microorganisms in the BAC inoculum and when oxygen (O2) was the electron acceptor. Genera with the relative abundance of less than 2% and unclassified genera are grouped into the “Others” category. The abundances represent averages of biological triplicates .. 144

Figure C.2.1 Experimental design of (A) LFA experiments and (B) LFB experiments. The tables show the excavation depth and temperature of each excavated sample, and the structure charts show the incubation temperatures for each excavated sample under laboratory conditions. Each incubation was performed in duplicate. DNA was extracted from each excavated sample (n = 1) and incubated sample in duplicate (n = 2) ...... 177

Figure C.2.2 Core microbiota analysis showing genus names of the most prevalent OTUs in (A) excavated samples and (B) incubated samples in LFA ...... 178

Figure C.2.3 Core microbiota analysis showing genus names of the most prevalent OTUs in (A) excavated samples and (B) incubated samples in LFB ...... 179

Figure C.2.4 Non-metric multidimensional scaling (NMDS) illustrating beta diversity in excavated and incubated samples in LFA and LFB based on the Bray-Curtis dissimilarity (a quantitative measure of community dissimilarity). The rarefaction depth is 10,106. The ellipses represent 95% confidence intervals around their centroids. The stress value of 0.22 provides a fair representation in reduced dimensions ...... 180

Figure C.2.5 Relative abundances of four major metabolic pathways in LFA samples incubated at various temperatures, predicted by Piphillin. Each pathway was normalized to the number of total pathways detected in a sample ...... 181

Figure C.2.6 Relative abundances of four major metabolic pathways in LFB samples incubated at various temperatures, predicted by Piphillin. Each pathway was normalized to the number of total pathways detected in a sample ...... 182

Figure C.2.7 Relative abundances of the starch and sucrose metabolic pathway (part of carbohydrate metabolism) and methane metabolic pathway (part of energy

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metabolism) in (A) LFA and (B) LFB samples incubated at various temperatures, predicted by Piphillin. Each pathway was normalized to the number of total pathways detected in a sample. A p-value of less than 0.05 was considered significant ...... 183

Chapter 1. Introduction

Anaerobic microbial processes are crucial in the field of wastewater and solid waste treatment. Under anoxic or microaerobic conditions, microorganisms are capable of degrading or rendering harmless many environmental pollutants using their natural biological activity. In some systems, they can produce renewable energy (e.g., biogas) as a “byproduct” during the stabilization of organic matter. Anaerobic biotechnologies provide cost-effective and eco- friendly solutions to the increasing environmental pollution and energy crisis. Examples of widely used technologies include anaerobic digestion (AD) for sludge stabilization and biogas production, anaerobic membrane reactors (AnMBR) for wastewater treatment, and anaerobic ammonium oxidation (anammox) technology for nitrogen removal. However, the application of anaerobic treatment is often limited by the range of contaminants that can be targeted and the long timescales that are often required. These challenges motivate researchers to explore novel microbial pathways for contaminant degradation and energy production, and to identify the operational “knobs” that allow engineers to shape microbial communities for targeted environmental outcomes.

Electron transport is a key process in anaerobic biotechnologies. Chemotrophic microorganisms carry electrons from the electron donating substances (e.g., organic matter) to electron acceptors other than oxygen, while conserving energy for cell growth.1,2 Many environmental contaminants can support microbial oxidation-reduction (redox) reactions as either electron donors or acceptors, and thus become degraded. In addition, microbial production of renewable energy often involves redox reactions, in which electrons are released from their original sources in the form of electron-carrying intermediates (e.g., hydrogen and formate), and

finally stored as chemical energy (e.g., methane). Much research effort has been invested to select the appropriate electron acceptors/donors to stimulate the redox degradation of targeted compounds, and electron exchange between anaerobic microorganisms and insoluble electron acceptors/donors has been extensively studied because of its environmental significance and practical applications. Microorganisms such as Geobacter and Shewanella have been found to reduce iron and manganese oxides in aquatic sediments by oxidizing a variety of organics.3,4 In engineered systems, some microorganisms can respire on electrodes to degrade waste compounds while generating hydrogen gas, electricity or valuable chemicals.5 These microorganisms can donate or receive electrons via membrane-associated proteins and filaments.4,6,7 These cell structures have broad redox potential ranges that permit both electron donation and uptake.8 Applying insoluble electron acceptors/donors can effectively improve contaminant bioremediation in anoxic environments (e.g., contaminated groundwater), and enhance energy production efficiency in engineered systems (e.g., microbial fuel cells, anaerobic digesters).8,9 Moreover, dosing insoluble materials can lower the risk of secondary pollution, compared with soluble compounds that can diffuse freely in the environment.10

Other than minerals and electrodes, pyrogenic carbonaceous materials (PCMs) can also exchange electrons with microorganisms. PCMs [e.g., biochar, activated carbon (AC) and graphite] are made through the pyrolysis of biomass or fossil fuels.11–13 PCMs have been widely utilized to improve soil fertility, sequester carbon and remove contaminants from gas and water.11,14–16 These benefits have been mainly attributed to PCM’s large surface area, buffering capability and adsorption capacity. Their ability to transfer electrons, however, have only been recently studied as a potentially significant property that mediates many biogeochemical and envirotechnical redox reactions, including iron reduction, dechlorination and reductive

degradation of nitrated explosives.17–20 Applying PCMs to contaminated sites and engineered bioreactors has been shown to accelerate microbial redox reactions for organic pollutant

17,19–24 degradation, metal reduction and CH4 production. Since many PCMs (e.g., biochar) are barely heat-treated natural carbons, amending them on a large scale may be economically feasible and environmentally safe.25

Electron transfer between microorganisms and PCMs involves, although debatably, two major mechanisms (Figure 1.1). The first mechanism suggests that PCMs serve as electrical conduits (conductor mechanism, Figure 1.1A).21,22,26–29 In other words, PCMs are not direct electron acceptors/donors, but rather “conductors” that allow electrons to pass through from electron sources to sinks. This mechanism relies on conjugated p-electron systems that are associated with aromatic ring structures in PCMs.30 PCM’s electrical conductivity can range from 2.1 μS cm-1 to 4000 S cm-1.21,22,31 Previous studies have shown that PCMs added to

Geobacter metallireducens and Methanosarcina barkeri co-cultures could conduct electrons

21,22 from Geobacter to Methanosarcina to facilitate CH4 production. The other mechanism indicates that PCMs can store electrons from electron sources, and then release them to other electron sinks (battery mechanism, Figure 1.1B).17,19,20,24,26,32,33 In this way, PCMs can be the terminal electron acceptors or direct electron donors. This mechanism has been associated with the redox-active moieties (e.g., quinones, phenols) on the PCM surface, and has only been recently suggested to impact microbial respiration.17,34,35 For example, G. metallireducens could utilize biochar as the electron acceptor for acetate oxidation, and then retrieve these electrons from biochar for nitrate reduction.33 An anaerobic methanotrophic archaea (ANME) species,

36 ANME-2d, could also couple biochar reduction to CH4 oxidation. These reversible redox reactions were likely catalyzed by the quinone groups in biochar.

Although researchers have uncovered many unknowns about PCM-mediated electron transfer, significant knowledge gaps remain. First, it is unclear if electron conduction and storage can be affected by other PCM properties. For example, biochar and AC are both effective sorbents for abiotic removal of organic contaminants, which potentially change the bioavailability and biodegradation of these substances.13 Besides, most studies on PCM-mediated electron transfer have focused on known exoelectrogens (e.g., Geobaceter and Shewanella), which are electrochemically active bacteria that have been suggested to power microbial fuel cells via electron transfer.6 However, the prevalence of PCM-mediated electron transfer in natural and built environments amended with PCMs is unknown. PCMs such as biochar and AC have been widely applied to soils, waste streams and drinking water systems to facilitate biodegradation of pollutants.16,37 Whether PCMs can support specific microbial communities due to their redox-active nature remains to be discovered. Third, although exoelectrogens have wide applications in environmental engineering fields, approaches to obtain their cultures are limited to applications of costly electron acceptors such as electrodes, metals and fumarate. While PCMs 4

are economic alternatives to these materials, whether they can serve as the electron acceptor to reproduce exoelectrogens requires careful evaluation.

To address these knowledge gaps, this dissertation focused first on the impact of PCM amendments on methane generation (Chapter 2). The objective was to systematically investigate the impact of PCM amendments on methane generation from real-wastewater digestion, and to examine the relationships between multiple PCM’s properties (e.g., electrical conductivity, adsorptive capacity, surface properties and buffering capacity). Roles of three types of PCMs

(graphite, biochar and activated carbon) on swine wastewater digestion were investigated.

Moreover, this dissertation discussed the enrichment of PCM-reducing microbial communities from a PCM-amended system (Chapter 3). The objective was to identify and enrich exoelectrogenic microbial communities that could quickly and effectively use PCMs as the electron acceptor. Microbial communities naturally present in biological activated carbon (BAC) filter systems were examined, and activated carbon was utilized as a representative PCM.

Another area of research regarding electron transport is to characterize the donor- acceptor interactions between microorganisms in nature as well as in built environments. A mixed culture of microorganisms can partner in close metabolic association and exchange electrons in the form of electron carriers, such as hydrogen and formate.8 This process, often termed syntropy, enables metabolic reactions that do not occur when each microbe acts separately, and allows microorganisms to function under a range of environmental conditions.38

A classic example is the cooperation between microbial fermenters and methanogens in methanogenic environments, such as wetlands, paddy soils, anaerobic digesters and landfills. In these environmental systems, the complex organic substrates (e.g., polysaccharides, proteins and lipids) can be hydrolyzed and fermented to smaller intermediate compounds (e.g., sugars, amino

acids and fatty acids), as well as hydrogen, formate and acetate that can be directly used for methanogenesis. The intermediates can be further degraded by fermenters to provide substrates for methanogens.38

Due to the heterogeneity of many environmental systems, the syntrophic methanogenic process can be dynamically affected by many environmental factors, such as temperature, substrate types and concentrations. Understanding the interactions between microorganisms and environmental conditions will allow microbial processes to be better engineered for targeted environmental proposes. Nevertheless, our knowledge on how these factors can shift microbial compositions and metabolic activities is very limited, because methods for efficient characterization of complex microbial processes in the real environment were lacking. With the development of modern technologies such as next-generation sequencing (NGS) and computational genomics, it is now possible for researchers to describe the microbial reactions more completely and accurately. Therefore, a revisit to the environmental systems of interest is necessary to better take advantage of microbial processes for pollution control and bioenergy recovery. This dissertation examined the impact of temperature on compositions and functions of methanogenic cultures obtained from elevated temperature landfills (ETLFs) (Chapter 4). These landfills can experience temperatures of 80 – 100 °C, leading to elevated odor emission, increased leachate quantity and strength, and reduced methane production.39,40 Till now, only the methanogenic cultures in related engineered systems (e.g., anaerobic digesters) have been extensively characterized and correlated with temperatures,41,42 while little research has focused on the response of landfill microbial communities to elevated temperatures. Therefore, the objective of this work was to characterize these microbial communities using modern metagenomic approaches. The microbial communities in two landfill systems exposed to

elevated temperatures were sampled and cultured in lab-scale reactors that mimicked real landfill environments. The community structures and functions were analyzed with 16S rRNA amplicon sequencing.

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Chapter 2. Amending anaerobic bioreactors with pyrogenic carbonaceous materials: the influence of material properties on methane generation

2.1. Abstract

Amending anaerobic digesters with pyrogenic carbonaceous materials (PCMs) has been suggested to improve methane production by enabling electron conduction between fermenters and methanogens. This enhancement has been attributed to the electrical conductivity of some

PCMs. Properties other than conductivity have received little attention, leaving uncertain the primary drivers of the observed improvements. Accordingly, the objective of this study was to relate the electrical conductivity, adsorptive behavior, pH effects, and surface properties of

PCMs to methane production rates and methane recoveries in swine wastewater-fed, short-term anaerobic batch reactors. Three types of PCM particles [graphite, biochar, activated carbon

(AC)] at two size ranges and three particle loadings were tested and compared to non-conductive glass particles and no-particle controls. Graphite amendments resulted in higher methane production rates and methane recoveries than the no-particle controls and showed improvements with increased particle loadings. The impact of biochar and AC amendments depended on particle sizes and loadings, with granule amendments generally exceeding or matching the controls. Powdered biochar and AC amendments substantially decreased performance. Material conductivity was a poor predictor of methane production rates, and bulk solution pH changes due to particle amendments could not explain reactor performance discrepancies. Adsorption was a critical material property controlling the fate of wastewater chemical oxygen demand (COD).

Powdered biochar and AC, which adsorbed more COD than graphite and glass, resulted in lower

COD to methane conversion. These results indicate that PCM properties other than conductivity,

such as adsorption, can strongly impact short-term bioreactor performance. These properties should be taken into consideration when selecting and optimizing PCMs for biological-based technologies.

2.2. Significance

Anaerobic digestion is a widespread technology for treating high-strength wastewaters and sludge. Improvement in methane production upon addition of PCMs is often attributed to the material’s electrical conductivity. This study constitutes the first systematic investigation of the role of PCMs on the anaerobic digestion of high-strength wastewater. It shows that material properties other than conductivity largely explain how material amendments impact short-term batch reactor performance, which provides guidance for selecting optimal material types, sizes, and loadings for methane generation. This work has been published in Environmental Science:

Water Research & Technology 4 (11), 1794-1806, and a literature review related to this work has been published in Environmental Science: Processes & Impacts 18 (8), 968-980.

2.3. Introduction

Anaerobic digestion (AD) is a biological-based technology that simultaneously treats and recovers energy from high-strength wastewaters and sludge. This technology has received great interest worldwide to stabilize wastes, produce renewable biogas, reduce greenhouse gas emissions and recover nutrients.1,2 Despite these benefits, AD is largely underutilized due to digester startup challenges, low methane yields, generally unfavorable economics, and sensitivity to operating conditions.2,3

Amending anaerobic methane-generating cultures with pyrogenic carbonaceous materials

(PCMs) can improve anaerobic reactor performance and enhance methane yields. PCM is made through the pyrolysis of biomass or fossil fuels, and includes natural and engineered carbons, such as charcoal, soot, biochar, activated carbon (AC), and graphite.4,5 Among these materials,

AC,6 biochar7, and carbon cloth8 stimulated methane production in defined syntroph-methanogen co-cultures. Similarly, graphite,9 carbon cloth,9,10 biochar,11,12 AC,10,13,14 and carbon nanotube15,16 amendments expedited methane production and reduced lag phases in undefined mixed cultures.

However, undefined cultures, where real wastes are used, typically do not yield the methane production rate increases seen in defined co-cultures.17 For example, several studies on PCM- amended digesters reported no changes or even decreases in methane production in the presence of PCMs.18–20 The large variability across studies suggests that we do not yet have a robust understanding of PCM functionality in anaerobic bioreactors.

Multiple PCM properties can potentially affect microbial activity. For example, PCM surfaces and pores can support microbial colonization and biofilm formation.21,22 Some PCMs provide inherent buffering capabilities that alleviate microbial stress from acids and high

+ 19,20,23 ammonium (NH4 ) concentrations. PCMs can also contain bioavailable macro- and micro- nutrients (e.g., labile carbon and minerals).24–26 Electrical conductivity is frequently cited as a

PCM property that may promote direct interspecies electron transfer (DIET) between fermenting

10,11 bacteria and methanogens, leading to more rapid methane production. Toxic constituents

(e.g., heavy metals and polycyclic aromatic hydrocarbons) may also be present in PCMs.27,28

Materials such as biochar and AC are known sorbents and can potentially change the bioavailability of nutrients and inhibitors.24,25,29–32 In most studies, the impact of one particular

PCM property (e.g., electrical conductivity) on methane production is considered, while others

(e.g., adsorption) are largely overlooked. A comprehensive understanding of how PCM properties impact methane production is therefore needed to aid in the design and selection of

PCMs for biological technologies.

Accordingly, the overall objective of this study was to relate a variety of PCM properties to methane production during the anaerobic degradation of high-strength wastewater. Three types of PCM particles (graphite, biochar and AC) were investigated, all of which have been suggested to increase methane production, and were compared to glass particles (a non- conductive control) and reactors without particles. Each particle type was tested under three different particle loadings for each of two different sizes (granular and powdered). Swine wastewater was used as a representative wastewater in this study because it is an abundant waste across the US and is an underutilized resource for AD.33,34 Performance was measured as methane production rates and methane recoveries, and electron balances were conducted to determine the fate of wastewater chemical oxygen demand (COD) for each PCM. Overall, PCM properties, namely adsorption, were found to have a profound impact on methane production under short-term batch conditions.

2.4. Materials and methods

2.4.1. Particle types and sizes

Three different types of PCMs were used in this study (Table 2.1). Granular graphite

(Graphite Sales Inc., Chagrin Falls, OH, USA), biochar (pine wood-based, pyrolyzed under

980 °C; Waste To Energy Inc., Slocomb, AL, USA) and AC (peat-based, steam-activated;

Sigma-Aldrich, St. Louis, MO, USA) were sieved to 2.0 – 2.4 mm [8 – 10 mesh; granular (G)].

Powdered graphite was purchased directly and was sieved to 0.21 – 0.25 mm [60 – 70 mesh;

powdered (P)]. P-biochar and P-AC were ground with a mortar and pestle from granules and sieved to the size range of P-graphite. Non-conductive glass beads (Corpuscular Inc., Cold

Spring, NY, USA) with diameters of 2.0 mm and 0.21 – 0.25 mm were included as G and P controls, respectively.

Table 2.1. Particle type, diameter, conductivity, and specific surface area.

Activated Unit Graphite Biochar Glass carbon 2.0 (G); 0.21 – Diameter mm 2.0 – 2.4 (G); 0.21 – 0.25 (P) 0.25 (P) Conductivitya S cm-1 17 ± 2.6 0.22 ± 0.046 1.2 ± 0.25 1E-16 – 1E-12 0.38 ± 0.050 (G) 451 ± 11 (G) 530 ± 116 (G) 1.2E-03 (G) Surface areab m2 g-1 0.52 ± 0.23 (P) 531 ± 31 (P) 652 ± 13 (P) 1.0E-02 (P) a Average ± standard deviation (n = 10). The glass conductivity was obtained from literature.69 b Average ± range (n = 2). The glass surface area was calculated from the diameter provided by the manufacturer. G – granular; P – powdered.

2.4.2. Swine wastewater properties

Swine sludge (seed) and wastewater (feed) were collected from the NC State Swine

Educational Unit on Apr 15, May 17 and Jun 13, 2016. Samples were stored at 4 °C prior to use.

The seed and feed were mixed to a final volume ratio of 1:30 (seed:feed) and added to each reactor. The total chemical oxygen demand (TCOD), total solids (TS), volatile solids (VS), pH,

+ and total ammonia nitrogen (TAN, including NH4 -N and NH3-N) were recorded for the mixture

(Table 2.2). The swine wastewater used in this study was similar to the diluted swine slurries reported in other studies,35,36 but with lower TCOD, solids and TAN than undiluted swine manure.37–40

Table 2.2. Properties of the swine wastewater mixture [1:30 (v:v), seed:feed].

Mixturea Unit TCOD 4.7 ± 1.5 g L-1 TS 7.1 ± 0.63 g L-1 VS 4.7 ± 0.83 g L-1 pH 7.4 ± 0.22 TAN 220 ± 36 mg L-1 a Average ± standard deviation (n = 3). Averages were calculated from the three sampling dates.

2.4.3. Anaerobic reactor setup and operation

Glass serum bottles (125 mL total volume), filled with 100 mL of the wastewater mixture, were used to mimic short-term batch reactors. Three different particle loadings were added for each particle size tested, and all the reactors were run in duplicate. Granular loadings

(normalized to the initial seed VS) and their abbreviations used in this study were 2.2 (low; G-

-1 Low), 5.5 (medium; G-Med) and 11.1 (high; G-High) g particles (g VSseed) . The powdered particles were 2.2 (low; P-Low), 4.8 (medium; P-Med) and 9.6 (high; P-High) g particles (g

-1 VSseed) . The particle loadings were chosen based on previous studies that showed enhanced methane production with these loadings.9,11,12,41 Each reactor was sealed with a butyl rubber stopper and aluminum cap, and the headspace flushed with pure nitrogen for five minutes to remove oxygen. The bottles were placed on shakers (100 rpm) at 30 °C for 19 days. The gas production rate and total volume (30 °C, 1 atm) from each bottle were recorded in real-time using a respirometer (BPA-800; Challenge Technology, Springdale, AR, USA), with the gas collected in a gas bag (500 mL; Calibrated Instruments, Inc., McHenry, MD, USA). Gas samples were periodically taken from the gas bag to determine the composition. At the end of the incubation (day 19), the shaker was stopped and the solids were allowed to settle for 30 minutes.

Supernatant was then collected (60 mL) and analyzed for volatile fatty acids (VFAs), pH and

TCOD.

2.4.4. Analytical methods

Particle Brunauer-Emmett-Teller (BET) surface areas were determined using N2 adsorption (Autosorb-1; Quantachrome Instruments, Boynton Beach, FL, USA). Glass particle surface areas were calculated based on their reported diameters. TS and VS were determined according to Standard Methods for the Examination of Water and Wastewater.42 TCOD was determined using Hach Method 8000, and TAN was measured using Hach Method 10031

(DR/890 Portable Colorimeter; Hach, Loveland, CO, USA). The pH was measured using an

Orion 3-Star benchtop pH meter equipped with an Orion ROSS Ultra Refillable pH/ATC Triode

(Thermo Scientific, Waltham, MA, USA). Supernatant VFAs were analyzed using a Dionex

ICS-5000+ Ion Chromatography system with a conductivity detector and Dionex IonPac AS11-

HC column (Thermo Scientific, Waltham, MA, USA). The electrical resistivity of particles was determined with a digital multimeter (RadioShack, Fort Worth, TX, USA) using the two-probe method,43 and the conductivity was calculated as the inverse of resistivity. The G size of the

PCM particles were cut into cylinders with a knife before the conductivity measurement, and it was assumed that the G and P sizes had the same conductivity. Scanning electron micrograph

(SEM) images were taken using a Hitachi S3200N variable pressure scanning electron microscope (VPSEM) with an Everhart-Thornley secondary electron detector (Hitachi High

Technologies America, Schaumburg, IL, USA).

Biogas composition was analyzed by a gas chromatograph (Model 8610C; SRI

Instruments, Torrance, CA, USA) equipped with a thermal conductivity detector (TCD) and

CTR I Column. The TCD detector current was 167 mA. Helium was the carrier gas with a flow rate of 88 mL min-1. The modified Gompertz model was used to determine the methane production rate:

R × e M (t) = P × exp #−exp % max × (λ − t) + 1() (2.1) P

-1 where M (t) [mL (g VSseed) ] is the cumulative methane production at time t (d), P [mL (g

-1 -1 -1 VSseed) ] is the maximum methane potential, Rmax [mL (g VSseed) d ] is the maximum methane production rate, λ (d) is the lag phase period, and e is exp (1). The methane production rate at

-1 -1 time t [Q (t), mL (g VSseed) d ] was obtained from the slope of the fitted model, and the

-1 -1 maximum production rate, Qmax [mL (g VSseed) d ], was defined as the maximum Q (t) value from each reactor over the 19-day incubation period. For some treatments, Rmax values were predicted from a limited set of experimental points (e.g., reactors with long lag phases after particle amendments); thus, we used Qmax instead of Rmax for all treatments.

Methane recovery (rCH4, %) was calculated as:

V r = CH4 × 100% (2.2) CH4 V CH4, max

where VCH4 (mL) is the total methane obtained from each reactor, and VCH4, max (mL) is the

-1 theoretical maximum methane based on TCOD removal [0.39 L CH4 (g TCOD) at 30 °C and 1 atm]. TCOD removal was calculated as the difference between initial TCOD (swine wastewater slurry) and the final TCOD (reactor supernatant after shaking was stopped).

The COD balance was conducted based on the TCOD, VFA, and methane measurements, with the following equation:

TCODi = CODCH4 + CODVFA + TCODother + TCODsolids (2.3)

-1 -1 where TCODi (mg L ) is the initial TCOD added at the start of each batch, CODCH4(mg L ) is

-1 the COD of collected methane gas, CODVFA (mg L ) is the COD of VFAs in the final

-1 supernatant quantified by ion chromatography, TCODother (mg L ) is the TCOD of other constituents in the supernatant (e.g., proteins, polysaccharides) calculated as the difference

-1 between supernatant TCOD and CODVFA, TCODsolids (mg L ) is the TCODi utilized for biomass generation, adsorbed to particles, or directly stored as electrons within particles,44,45 which is calculated from Equation (2.3). Hydrogen gas was not measured and thus not included in the electron balance calculations.

2.4.5. Statistical methods

The fitting and corresponding R-squared and p values were determined using the statistical software package R. Student’s t-test was conducted to compare treatments. A p value less than 0.05 was considered statistically significant. Plots were generated in OriginLab 9.1.

2.5. Results and discussion

2.5.1. Methane production rates and methane recoveries

The impact of particle amendments on two performance metrics – maximum methane

production rates (Qmax) and methane recovery (rCH4) – was investigated in this study. Since the

-1 initial TCOD varied considerably (4.7 ± 1.5 g L ) across the three sample collection dates, Qmax

and rCH4values were normalized to the average of the two no-particle controls from each batch, unless indicated otherwise. This approach allowed to draw conclusions on improvements or reductions in the two performance metrics relative to the unamended reactors.

2.5.1.1. Graphite

Graphite amendments yielded larger Qmax values than the no-particle controls for all sizes and loadings (based on averages) (Figure 2.1A), although none of these increases were

-1 -1 statistically significant (0.16 < p < 0.93). The absolute Qmax of 25 ± 2.2 mL (g VSseed) d (data not shown; 23 ± 11% higher than the no-particle control, p = 0.17) obtained with the P-Med- graphite amendment was the highest Qmax of all particle amendments in this study. This result is consistent with a prior report of Qmax increasing 19 – 45% (relative to the no-particle control) in

-1 9 ethanol-fed reactors amended with 1.7 g graphite (g VSseed) . In general, Qmax was larger for P- graphite than G-graphite at each particle loading, but the differences were not significant (p =

0.91, 0.93 and 0.82 for Low, Med and High particle loadings, respectively). Moreover, Qmax did not correlate strongly with either G-graphite or P-graphite loadings, because at higher loadings, a saturation-like behavior occurred for Qmax (Figure A.3.1A-B). Graphite surface area was

2 positively correlated with Qmax (R = 0.73, p = 0.019) (Figure 2.1B), indicating that particle surface area and reactor kinetics were closely related.

Methane recovery with the graphite amendments followed a trend similar to Qmax

(Figures 2.2 and A.3.1C-D). The highest absolute rCH4 of 63 ± 4.1% (data not shown; an increase of 28 ± 8.4% relative to the no-particle control, p = 0.098) was obtained with the P-

High-graphite amendment. Decreasing the graphite loading (for both G and P sizes) decreased

2 rCH4. Methane recoveries correlated well (R = 0.74, p = 0.018) with particle surface area, which was similar to the Qmax results.

2.5.1.2. Biochar

The impact of biochar amendments on Qmax depended on particle size and loading. G- biochar amendments yielded Qmax values greater than the no-particle controls, whereas P-biochar amendments produced lower Qmax values than the controls (Figure 2.1A). The highest absolute

-1 -1 Qmax of 21 ± 1.1 mL (g VSseed) d (data not shown), which was 12 ± 6.1% higher than the no- particle control (p = 0.34), was obtained with the G-Low-biochar amendment. Increasing G- biochar loadings did not result in a clear trend in Qmax (Figure A.3.1A); however, increasing P- biochar loadings decreased Qmax, reaching a minimum of 57 ± 3.0% lower than the no-particle control (p = 2.8E-03) (Figure A.3.1B). A linear relationship between Qmax and biochar surface area did not exist, but in general, Qmax decreased with increasing surface area (Figure 2.1B).

Interestingly, even though the biochar surface area was several orders of magnitude larger than graphite, the normalized Qmax values obtained from G-biochar-amended reactors were similar to those from graphite-amended reactors. This result suggests that material properties other than surface area enhanced Qmax in the graphite-amended reactors. Prior studies have also observed conflicting impacts of biochar treatments on Qmax. In some cases, increasing the biochar loading or using P-biochar instead of G-biochar increased Qmax by up to 71% compared with the no- particle controls.11,12,18 In other reports, P-biochar amendments at high particle loadings

18 produced lower Qmax. For example, Sunyoto et al. reported a 14% drop in Qmax when the P- biochar (3.5 – 26 μm diameter) loading was doubled from 16.6 g L-1 to 33.3 g L-1, which is consistent with our results. The biochar types and substrates used in those studies were different from the ones in this study. Differences in adsorption capabilities of biochar (discussed later) may be one explanation for some of the observed discrepancies.

Methane recoveries with biochar amendments were larger with G- than P-biochar, but did

not yield a clear trend with biochar loadings (Figures 2.2A and A.1C-D). The lowest rCH4 of 59

± 3.1% below the no-particle control (p = 8.2E-03) was obtained with the P-High-biochar

amendment. Similar to the Qmax results, rCH4 was negatively affected by increasing particle surface area, although the correlation was weak (Figure 2B). Other studies have also noted a decrease in methane recovery with increased biochar loading. For example, in one case, methane recovery (relative to the no-particle control) changed from 9.6% to ‒11% when the biochar loading increased from 8.3 g L-1 to 33.3 g L-1.18

2.5.1.3. Activated carbon

The lowest Qmax values in this study were obtained with the AC amendments. Rates lower than the no-particle controls were observed for all AC sizes and loadings except the G-

2 High-AC treatment (Figure 2.1A). Although increasing G-AC loadings increased Qmax (R =

2 0.90), increasing P-AC loadings decreased Qmax (R = 0.71) (Figure A.3.1A-B). The P-High-AC amendment resulted in a 76 ± 0.46% reduction of Qmax compared with the no-particle control (p

= 4.6E-05). This was the lowest normalized Qmax obtained across all particle types, sizes and loadings. Providing a larger surface area of AC did not translate into larger Qmax values, although the correlation was insignificant (Figure 2.1B). In other studies, AC amendments have been

10,13,14,46 reported to increase Qmax, and the improvements were largely attributed to properties other than conductivity, such as the ability of AC to alleviate acid buildup and ammonia inhibition.

All AC amendments resulted in rCH4 values below those of the no-particle controls, and

reached a minimum rCH4 = ‒78 ± 0.26% from P-High-AC amendments (p = 3.3E-03) (Figure

2 2.2A). P-AC loading correlated moderately (R = 0.71) with rCH4, while G-AC did not follow a

clear trend (Figure A.3.1C-D). Increased AC loadings led to reduced rCH4 in a similar fashion as biochar, but the decreases were even larger. Our findings agree with a previous report of AC-

amended reactors fed with waste activated sludge, in which rCH4 values were reduced by 40 –

45% with G-AC amendments.14 However, AC amendments have also been reported to increase

rCH4. For example, rCH4 values ranged from ‒10% to 64% (relative to the no-particle controls) when the organic loading rates were increased.10 The reasons for these discrepancies with AC are unclear, but may be due to a combination of material properties and wastewater composition

(discussed below).

2.5.1.4. Glass

The amendments of glass particles showed no significant impact on Qmax compared with the no-particle controls (0.15 < p < 0.93). P-glass amendments only yielded slightly higher Qmax values than the no-particle controls, while G-glass amendments resulted in lower Qmax values

(Figure 2.1A). None of the changes in Qmax were statistically different (p > 0.05). Moreover, glass did not yield any significant impact of surface area on Qmax (Figure 2.1B), which suggests that the surface area alone cannot explain the improvement in Qmax in graphite-amended reactors.

Other material properties, such as electrical conductivity, surface roughness, or hydrophobicity may have contributed to the rate differences.

Methane recoveries varied depending on glass particle sizes and loadings, but to a lesser extent than was observed for the other particle types (Figure 2.2A). Similar to the Qmax results,

P-glass amendments slightly increased rCH4, and G-glass amendments decreased rCH4 (except at the highest particle loading), and none of the changes were significantly different compared with

the no-particle controls (0.29 < p < 0.80). Glass surface area did not have a significant impact on

rCH4, which is consistent with the Qmax results (Figure 2.2B). In summary, the glass particle results indicate that surface area is not the sole factor leading to variations in Qmax across the different materials.

Figure 2.1. Maximum methane production rates (Qmax) normalized to each respective batch no- particle control average as a function of particle type [graphite, biochar, activated carbon (AC), glass], size (granular or powdered) and (A) particle loading or (B) particle surface area. The x- axis labels in (A) refer to the three particle loadings tested (L – Low; M – Medium; H – High). Error bars represent the range of replicate experiments (n = 2). P-values indicate the statistical significance of the slopes.

Figure 2.2. Methane recoveries (rCH4) normalized to each respective batch no-particle control average as a function of particle type [graphite, biochar, activated carbon (AC), glass], size (granular or powdered) and (A) particle loading or (B) surface area. The x-axis labels in (A) refer to the three particle loadings tested (L – Low; M – Medium; H – High). Error bars represent the range of replicate experiments (n = 2). P-values indicate the statistical significance of the slopes.

2.5.2. Particle electrical conductivity

Since electron conduction through PCMs is a proposed mechanism of DIET,6,7,41 the relationship between PCM particle conductivity and Qmax was examined. In this study, it was hypothesized that if differences in Qmax were largely controlled by conductivity, a positive correlation would exist. This study suggested that particle conductivity was poorly correlated with Qmax (Figure 2.3). AC- and graphite-amended reactors produced a trend consistent with an increase of Qmax with conductivity. At G-Low, G-Med and all the P particle loadings, AC amendments exhibited a sharp drop in Qmax relative to biochar amendments at the same particle loadings. This resulted in the lack of a relationship between Qmax and conductivity across all particle types. Another reason for the poor correlation in G-Low and G-Med amendments was that the least conductive material (biochar, 80-fold less conductive than graphite) produced high

Qmax values. Unlike graphite, the enhancement with biochar amendments was diminished with

the amendments of smaller particles (Figures 2.1A and A.1B). The G-High treatment might have a synergistic effect of multiple particle properties including conductivity.

Our findings add to the growing body of literature indicating that there is no clear trend between reactor kinetics and particle conductivity. Chen et al.7 compared three biochars with conductivities of 2.11, 4.33, and 4.41 μS cm-1. Amending reactors with the least conductive biochar accelerated ethanol degradation rates to the same degree as the most conductive biochar, with both reaching a ca. 6-fold higher rate compared to the no-particle control. Adding G-AC, which had a significantly higher conductivity (3,000 ± 327 μS cm-1) did not further increase ethanol degradation rates beyond those obtained with the least conductive biochar.6,7 The biochar and AC amendment results in this study are in agreement with these prior reports. Even though the AC used in this study had a conductivity almost 6-fold larger than the biochar, Qmax with AC amendments were 20% lower on average than with the biochar amendments. It is important to note that it is difficult to draw conclusions regarding the influence of conductivity when different material types are used because other inherent properties may have a larger effect on reaction rates. In the next section, the role of some of these properties on the performance metrics was further explored.

Figure 2.3. Maximum methane production rates (Qmax; normalized to each respective batch no- particle control average) as a function of particle electrical conductivity. G – granular; P – powdered; Low, Med, and High refer to particle loadings. Error bars represent the range of replicate experiments (n = 2).

2.5.3. Adsorptive properties of particles

29,47 Since biochar and AC adsorb organics, the impact of this property on Qmax and rCH4 was investigated. During the biotic experiments, biochar and AC amendments removed up to 20

± 3.1% (G-High-biochar; p = 0.032) and 17 ± 1.0% (G-High-AC; p = 0.015) more TCOD compared with the no-particle controls (Figure 2.4A). Despite the additional TCOD removal,

rCH4 from reactors amended with P-biochar, G-AC and P-AC at all particle loadings were lower than those from the no-particle controls, ranging from −11 ± 9.8% (G-Med-AC; p = 0.71) to −78

± 0.26% (P-High-AC; p = 0.0033) (Figure 2.2A). This result indicates that the additional TCOD removed was not directed towards methane generation.

In abiotic tests, in which particles were added to sterile swine wastewater (at both G-Low and P-Low treatments), biochar and AC amendments decreased the wastewater supernatant

TCOD (Figure 2.4B). Compared to the no-particle controls, the highest abiotic TCOD removal

occurred with the G-Low-AC (30 ± 2.7%, p = 7.9E-03) and P-Low-AC (34 ± 0.22%, p = 4.2E-

05) amendments. G-Low- and P-Low-biochar amendments reduced TCOD under abiotic conditions at 14 ± 0.44% (p = 9.8E-04) and 28 ± 0.88% (p = 9.8E-04), respectively. Abiotic

TCOD removal was positively correlated, although not significantly, with biochar and AC surface area at the lowest particle loading (R2 = 0.76, p = 0.084; data not shown).

VFA analysis at the end of the abiotic tests supported the TCOD sorption findings

(Figure A.3.2). The VFA n-valerate was reduced by up to 29 ± 0.49% and hexanoate by more than 25% with AC amendments. No significant adsorption of short chain fatty acids (C2 – C4) was observed. These findings are consistent with both theoretical predictions and prior experimental reports. The Duclaux-Traube rule states that VFAs with longer –CH2 chains, and hence more hydrophobic, are more likely to be adsorbed onto nonpolar AC surfaces.48 This rule has been confirmed in experimental tests, where it has been shown that AC exhibits decreasing affinities in the order butyric, propionic, and acetic acid.48,49

In contrast to biochar and AC, little to no TCOD or VFA adsorption to the graphite and glass particles occurred. Their lower affinity for TCOD resulted in a larger proportion of the

removed TCOD resulting in methane gas, as confirmed by the higher rCH4 values. The enhanced

rCH4 with graphite may therefore have been due to not only its conductive nature, but also its ability to minimize adsorption. Support for this hypothesis can be taken from a comparison of the

TCOD removals and rCH4 between graphite and biochar/AC treatments. In the biotic tests, P- biochar, G-AC and P-AC amendments removed 11 – 17% more TCOD than the no-particle

controls, but resulted in rCH4 reductions of 11 – 78%. Conversely, graphite amendments removed only 0 – 7.4% more TCOD compared with the no-particle controls, but recovered 6.0 – 48% more methane from the consumed TCOD.

Figure 2.4. (A) TCOD removals (normalized to the no-particle control average) in reactors amended with graphite, biochar, activated carbon (AC), or glass particles. The x-axis labels refer to the three particle loadings tested (L – Low; M – Medium; H – High). (B) TCOD removals in -1 sterilized swine wastewater [particle loading of 2.2 g particles (g VSseed) ]. All bottles started with a TCOD equal to the no-particle control (empty columns). The final supernatant TCOD was recorded after a 19-day incubation (diagonal lines). G – granular; P – powdered. Error bars represent the range of replicate experiments (n = 2).

To confirm that the particles themselves did not contribute to COD nor did they

significantly adsorb methane (both of which would have affected rCH4), two additional control experiments were conducted. First, an abiotic COD test of the particles in deionized water was conducted. The COD values ranged from 8 to 23 mg L-1, which was less than 2% of the total

COD in the wastewater (Table A.2.2) and thus could be ignored. Second, methane gas

adsorption onto the particles was studied in a separate abiotic test. Adsorption in these tests with either 100% or 10% methane in the headspace yielded minimal decreases after 24 hours (Figure

A.3.3), likely due to the low solubility of methane in water.

2.5.4. Electron balances

To better quantify the fate of COD in the reactors, electron balances based on TCOD,

VFA and methane measurements were determined. The electrons added as initial TCOD

(TCODi) were categorized into the four product categories, and the total number of electrons associated with each category is depicted as a percentage of the TCODi normalized to the no- particle controls. Graphite was the only amendment that consistently converted TCODi to methane at higher percentages than the no-particle controls (Figure 2.5A). The highest control- normalized TCODi-to-CH4 conversion with graphite amendments reached 14 ± 3.0% (P-High- graphite; p = 0.046). Higher TCODi-to-CH4 conversions occurred with the G-biochar amendment, but the conversions decreased for all the P-biochar treatments compared with no- particle controls (Figure 2.5B). In these treatments, TCODi-to-solids conversions increased, suggesting that adsorption-based removal was a major electron sink as particle surface area increased. The lowest TCODi-to-CH4 conversion was observed with AC amendments and was dependent on particle size and loading (Figure 2.5C). Roughly 28 ± 0.11% fewer electrons as methane were obtained relative to the control for the P-High-AC amendment (p = 1.5E-03).

Instead of methane generation, the majority of TCODi in all AC treatments resulted as

TCODsolids. It is hypothesized that this finding is largely driven by the adsorptive nature of AC, which is consistent with the results of the abiotic TCOD and VFA removal tests reported above.

The amendment of graphite, which had a lower adsorptive capacity for organics, resulted in a higher TCODi-to-CH4 conversion.

Adsorption can reduce the bioavailability of organics, which is one possible explanation

for why rCH4 decreased in the P-biochar- and AC-amended reactors. In soils and sediments, the adsorption of organic contaminants (e.g., polycyclic aromatic hydrocarbons) onto biochar and

AC decreased their degradation by microorganisms.32,50,51 However, predicting bioavailability is not straightforward and depends largely on the inherent properties of adsorbents (e.g., wood source, pyrolysis temperature), the properties of adsorbates (e.g., functional groups) as well as surrounding conditions (e.g., pH, temperature).21 For example, it has been suggested that bioavailability is related to desorption efficiency, which can vary across adsorbents.21 Thermally activated carbon, which was used in this study, has been shown to have higher adsorption efficiency and lower desorption efficiency compared with chemically activated carbon.30 Further studies are required to better understand the impact of adsorption and its relationship to methane production.

An additional hypothesis that was not explored here is that biochar and/or AC accumulated electrons from microbial respiration. Biochar has been shown to accept and store electrons from microorganisms.45 Based on the electron balances (Figure 2.5), it is plausible that biochar and AC were charged with electrons derived from microorganisms, especially at higher particle loadings. Abiotic measurements of biochar electron storage have shown that capacitance depends on feedstock type, pyrolysis temperature, and pyrolysis gas composition. Using an electrochemical method, Sun et al. determined that biochar pyrolyzed at 400 – 600 ºC had greater electron storage capacities than those treated at 700 – 800 ºC.52 The biochar used in this study was produced at 980 ºC. Its ability to store electrons may have been limited. Further

investigation of microbially-mediated biochar and AC electron charging is needed to determine the role of conduction versus capacitance in driving microbial redox transformations.

Figure 2.5. Electron balances in reactors amended with (A) graphite, (B) biochar, (C) activated carbon (AC) and (D) glass. Balances are based on the COD distributions at the end of the incubation relative to the initial TCOD (TCODi). All values are normalized to the no-particle controls. Values to the right of zero show an increase relative to the control, while those to the left indicate a decrease. G – granular; P – powdered. Low, Med, and High refer to particle loadings. Error bars represent the range of replicate experiments (n = 2).

2.5.5. The influence of biochar and AC mediated pH changes

Carbonaceous sorbents can affect solution pH depending on their functional group chemistry.53,54 This metric is important because it can impact many biological processes in

digesters, including hydrolysis, acidogenesis, and methanogenesis.55 In the presence of P-High-

AC, the initial pH of the swine wastewater increased from 7.6 ± 0.04 to 8.1 ± 0.03 (Table A.2.2).

To determine if this pH change could explain some of the observed differences in Qmax among particle types, a separate test in the absence of any particles but with the pH adjusted from 7.2 to

8.1 by the addition of NaOH was performed. Qmax and rCH4 at the elevated pH were 18 ± 5.3% (p

= 0.18) and 22 ± 2.1% (p = 0.011) lower, respectively, than the no-particle control at pH 7.2

(Figure 2.6). Based on this result, the bulk pH increase alone cannot explain why Qmax and rCH4 dropped by over 70% when the P-High-AC particles were added to the reactors (Figures 2.1A and 2.2A).

Changes in pH also impact the distribution of inorganic nitrogen species, such as free

56,57 ammonia (NH3), which at high concentrations can inhibit methanogenesis. In this study, the

-1 -1 initial TAN concentrations were 220 ± 36 mg N L , corresponding to 2.8 ± 0.46 mg NH3-N L

-1 58 at pH 7.2 and 20 ± 3.3 mg NH3-N L at pH 8.1 (30 °C). These values are substantially lower

56,57 than the NH3-N concentrations that have been reported to inhibit methanogenesis. Therefore,

NH3 was unlikely to inhibit methane production in this study, even in the most extreme case of the P-High-AC amendment that changed bulk pH to 8.1.

+ Biochar and AC have been extensively studied as NH4 adsorbents, and the adsorption is

59–63 + + pH-dependent. High pH drives NH4 to NH3, reducing NH4 adsorption, but it can also

+ 61,62 deprotonate surface-based acidic functional groups, which favors NH4 chemisorption.

+ 25,26,62 Although debatable, adsorption can reduce the microbial accessibility of NH4 . The abiotic

TAN adsorption test (Figure A.3.4) showed that the highest TAN removal was 16 ± 0.0% with

G- and P-biochar amendments (0.97 mg TAN g-1; p = 5.9E-03) and 18 ± 2.4% with the G-High-

AC amendment (1.12 mg TAN g-1; p = 0.022). The removals with biochar and AC via adsorption

are consistent with results from other studies.60,64 Although the low percentages of adsorbed

+ TAN suggest that NH4 adsorption might not play a major role in the observed reactor

+ performance, it is still possible that NH4 became less bioavailable due to biochar and AC amendments in the swine wastewater reactors. Further investigation of pH fluctuations due to particle amendments and their influence on ammonia speciation is needed.

Figure 2.6. Cumulative methane production using a non-pH adjusted wastewater (pH = 7.2) and a sample adjusted to pH 8.1 using NaOH. No particles were added. Error bars represent the range of replicate experiments (n = 2).

2.5.6. Particle surface properties

Since surface roughness and porosity impact microbial attachment and growth, these two characteristics were qualitatively examined with SEM (Figure 2.7). Both G- and P-graphite consisted of three-dimensional, sheet-like structures with a high degree of roughness (Figure

2.7A-B). The glass particles were smooth and lacked any distinguishable surface structures

(Figure 2.7G-H). Rougher surfaces are believed to reduce shear forces and provide more favorable sites for bacterial adhesion.44,65 The roughness of graphite relative to glass likely

improved bacterial attachment. This is one plausible explanation for the higher Qmax and rCH4 values from the graphite-amended reactors and for the stronger correlation between Qmax and graphite surface area than those observed in glass-amended reactors.

Particle porosity can influence microbial access to protection and nutrients, and may have played a role in some of the amendments. G-biochar and G-AC had visible pore structures, with the G-biochar pores being noticeably larger than those of G-AC (Figure 2.7C and 2.7E). The G- biochar pores ranged from ca. 5 – 100 μm in diameter, while the G-AC pores were generally less than 10 μm. These pore structures were not observed on P-biochar and P-AC surfaces (Figure

2.7D and 2.7F), likely due to the destructive grinding and sieving when the powder was made.

Pores can harbor microbes, induce the formation of extracellular polymeric substances and provide protection from toxins.11,46 In addition, cells growing in the pores can be exposed to higher substrate concentrations, which can benefit reaction kinetics.62 Pore sizes at least 2 – 3 times larger than the cells have been reported to enable microbial colonization.25,66 Considering that rod-shaped methanogens (e.g., Methanosaeta) can have lengths of 1.2 – 120 μm,67,68 it may be difficult for some microorganisms to fully penetrate the smaller pores of AC, rendering some of the measured surface area inaccessible.24,44,46 Adsorption of organics and minerals may further decrease the available pore space for microorganisms.25 The large pores on G-biochar in this study may have been more accessible to microorganisms than the small pores on P-biochar or

AC, even though adsorption occurred in all of these treatments (Figure 2.4B). After these large pores were destroyed during the grinding process to make P-biochar, TCODi-to-CH4 conversion decreased while adsorptive removal of TCODi increased to levels comparable with AC. These results suggest that adsorption, especially within small pores, is detrimental for both Qmax and

rCH4.

Figure 2.7. Scanning electron micrographs of (A) G-graphite, (B) P-graphite, (C) G-biochar, (D) P-biochar, (E) G-AC, (F) P-AC, (G) G-glass and (H) P-glass. Images show new particles that were not added to the reactors. White scale bar represents 50 µm. G – granular; P – powdered.

2.6. Conclusions

The overall objective was to determine the impact of pyrogenic carbonaceous material

(PCM) amendments on the methane production rate (Qmax) and methane recovery (rCH4) from wastewater-fed, short-term anaerobic batch reactors. To better understand variation in these two metrics across the materials tested, the role of several physical and chemical properties was investigated. Graphite amendments were the only treatment that yielded Qmax values greater than the no-particle controls at all sizes and loadings. Increasing granular biochar loadings decreased

Qmax values, and powdered biochar amendments consistently resulted in Qmax values below the controls. Activated carbon (AC) amendments generally underperformed relative to the no- particle controls, and decreased Qmax values by up to ca. 80% relative to the controls. Material conductivity was a poor predictor of Qmax, largely due to a sharp drop in Qmax with AC amendments relative to biochar amendments despite AC having five times greater conductivity.

Organic matter adsorption was found to be a key material property that impacted reactor

performance, especially rCH4. Electron mass balances showed that as biochar and AC loadings increased or their particle size decreased, wastewater TCOD was largely adsorbed to particle surfaces rather than being converted into methane. Graphite, which had little to no adsorptive behavior, resulted in larger conversions of TCOD into methane than the other materials, for almost all graphite loadings and sizes.

In conclusion, material properties other than conductivity can play an important role when PCMs are added to short-term batch reactors. The adsorptive nature of PCMs has largely been overlooked in discussions of using these materials to stimulate methane generation, but these findings show that it deserves consideration. The impact of PCM amendments on long- term, continuously-fed anaerobic digesters warrants investigation to verify the short-term results

in this study. The bioavailability of trace metals (e.g., Fe, Ni and Co) may be affected by adsorption and pH changes as well. Considering they are important growth factors for bacteria and methanogens, determining their fate in the presence of adsorbents such as biochar and AC should be examined. Depending on the feedstock and pyrolysis conditions, PCMs may contain constituents that are toxic to microorganisms (e.g., heavy metals, polycyclic aromatic hydrocarbons). The identity and concentration of these species should be explored in future studies as they can potentially inhibit microbial activity and methane production.

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Chapter 3. Developing microbial communities containing a high abundance of exoelectrogenic microorganisms using activated carbon granules

3.1. Abstract

Microorganisms that can transfer electrons outside their cells are useful in a range of wastewater treatment and remediation technologies. Conventional methods of enriching exoelectrogens are cost-prohibitive (e.g., controlled-potential electrodes) or lack specificity (e.g., soluble electron acceptors). Here we report a low-cost and simple approach to enrich exoelectrogens from a mixed microbial inoculum. After validating the method using the exoelectrogen Geobacter sulfurreducens, we subjected native microorganisms from a pilot-scale biological activated carbon (BAC) filter to incubations in which acetate was provided as the electron donor and granular activated carbon (GAC) as the electron acceptor. The BAC-derived community oxidized acetate and reduced GAC at a capacity of 1.0 mmol e- (g GAC)-1. After three transfers, acetate oxidation rates increased 4.3-fold, and microbial morphologies and GAC surface coverage became homogenous. Although present at < 0.01% in the inoculum, Geobacter species were significantly enriched in the incubations (up to 96% abundance), suggesting they were responsible for reducing the GAC. The ability to quickly and effectively develop an exoelectrogenic microbial community using GAC may help initiate and/or maintain environmental systems that benefit from the unique metabolic capabilities of these microorganisms.

3.2. Significance

This work proposes a low-cost method to enrich exoelectrogens from mixed cultures, with activated carbon as the sole electron acceptor. It is the first microbial characterization of an

activated carbon-reducing microbial community derived from a drinking water biological activated carbon system. This work also suggests that long-range electron transfer in activated carbon might occur. This work has been submitted to Science of the Total Environment and is under review.

3.3. Introduction

Exoelectrogenic bacteria, such as Geobacter and Shewanella, are unique in that they can transfer electrons outside their cells. They play important roles in water/wastewater treatment and remediation.1,2 Many of them utilize insoluble metals, such as Fe(III) and Mn(III, IV), as terminal electron acceptors.3,4 They can also reduce electrodes in microbial electrochemical technologies (METs) while converting organic matter into electrical current, methane, or other products.5 The presence and activity of exoelectrogens are closely related to the performance of

METs and other treatment systems.6–10 Some of these microorganisms participate in direct interspecies electron transfer, which has been hypothesized to improve methane generation during anaerobic digestion via electron conduction from bacteria to methanogens.8,11–13

Exoelectrogens such as Geobacter are commonly detected or stimulated during bioremediation of contaminants.9,14,15 Techniques that can enrich and maintain exoelectrogenic cultures are therefore useful for a wide variety of water, wastewater, and remediation processes.

Approaches to obtain exoelectrogenic cultures suffer from cost and specificity limitations. The most common approach uses electrodes, either fixed to a specific potential or allowed to float.16 Electrodes have been used to enrich exoelectrogens from water, sediment, and soil.17,18 Electrodes, specialized electrochemical cells, and potentiostats used to fix potentials can be cost-prohibitive, especially when considering larger-scale treatment and remediation

applications. Using non-electrode based electron acceptors is another approach to grow exoelectrogens. Geobacter species, for example, can respire on fumarate and insoluble/soluble iron,19,20 and some of these electron acceptors have been used to stimulate exoelectrogen growth.21–23 The challenge with using these electron acceptors is that they lack specificity for exoelectrogens, and can support growth of a wide range of microorganisms.24–26

Recently it has been shown that exoelectrogens can respire on pyrogenic carbonaceous materials (PCMs), including activated carbon (AC) and biochar.27–29 Traditionally, PCMs are viewed as non-reactive sorbents of chemical and biological contaminants.30–32 The reactivity of these materials, driven by their redox properties and electrical conductivity, has been hypothesized to enable electron exchange with microorganisms.33–37 Mixed-culture studies have observed enrichments of putative exoelectrogens when PCMs are supplemented to reactors such as anaerobic digesters.15,33,38–40 Since AC and biochar are low-cost materials and are commonly used in the treatment and remediation industries, they may provide an affordable method with high specificity to enrich exoelectrogenic communities for applications ranging from wastewater treatment to bioremediation.

The purpose of this study was to establish and evaluate the use of PCMs to enrich exoelectrogens from mixed microbial communities. We first validated our approach using the exoelectrogenic bacterium G. sulfurreducens. Then, as a proof of concept, we obtained microorganisms from a pilot-scale biological activated carbon (BAC) filter at a drinking water treatment facility. We provided acetate as the electron donor and carbon source, and new granular activated carbon (GAC) identical to that used in the facility as the electron acceptor.

Our findings show that this method can enrich a high relative abundance (> 95%) of putative

exoelectrogenic Geobacter species from a microbial community containing less than 0.01%

Geobacter species in the inoculum.

3.4. Materials and methods

3.4.1. PCM preparation

Several different PCMs were used in this study (Table B.2.1). Upon receipt, the PCMs were sieved to different size ranges (Table B.2.1), and then air oxidized in continuously aerated deionized water 50 g PCM (L water)-1 for 72 hours.41 During this procedure, the water was changed every 24 hours, and the rinsate collected and filtered (0.2 µm, polyethersulfone) for dissolved organic carbon (DOC) analysis. The oxidized PCMs were oven dried at 105 °C for 12 hours and stored under a gas phase of N2 before use. Among the PCMs, the coconut shell-based

GAC (csGAC) was identical to the GAC used to start the pilot-scale BAC filter and was the primary focus of this study (see Appendix B.1.1 for GAC characterization methodology).

3.4.2. Validation of GAC respiration using Geobacter sulfurreducens

G. sulfurreducens PCA (DSM 12127 / ATCC 51573) was grown from a frozen stock (see

Appendix B.1.2 for culturing methodology). The incubations with GAC were conducted in 250 mL glass serum bottles, each containing 100 mL sterile, anoxic ATCC 1957 medium. The air oxidized csGAC (40 g L-1) served as the electron acceptor, and acetate (10 mM) as the electron donor. The total number of bioavailable electrons in acetate were 80 mM, assuming that all carbon could be oxidized to CO2. Three incubations were set up, each in triplicate: (1) acetate +

GAC + cells, (2) acetate + cells, and (3) acetate only. The inoculum (0.2 mL) was injected into each biotic incubation, and sterile buffer solution (0.2 mL) into each abiotic incubation. All

bottles were incubated at 30 °C. Liquid (1 mL) was periodically withdrawn from each bottle with a sterile, anaerobic syringe, diluted to 5 mL with deionized water, filtered (0.2 µm, polyethersulfone), and stored under 4 °C for acetate analysis. After acetate consumption reached completion, GAC surface colonization by G. sulfurreducens cells was visualized with scanning electron microscopy (SEM) (see Appendix B.1.3 for SEM methodology).

3.4.3. BAC culture source and preparation

The BAC culture was collected from a csGAC-based BAC filter at the Tampa Bay

Regional Surface Water Treatment Plant (Tampa, FL). Upstream of the filter, the water was ozonated to improve the biodegradation of natural organic matter (Appendix B.1.4 and Figure

B.3.1). BAC samples were collected in sterile 50 mL centrifuge tubes two hours after the filter was backwashed, shipped in a cooler with ice packs, and immediately kept under 4 °C for less than 24 hours before use.

The BAC culture was separated into eight sterile centrifuge tubes (4 – 5 g tube-1), each filled with 20 mL sterile buffer solution containing (per liter) 0.31 g NH4Cl, 2.5 g

NaH2PO4·H2O, 4.6 g Na2HPO4, and 0.13 g KCl. To remove cells from the BAC solids, each tube was shaken by hand (two minutes), vortexed (two minutes at 3200 rpm, Vortex-Genie 2, MO

BIO Laboratories, USA), and subjected to low-energy sonication in a water bath for two minutes

(FS30H, Fisher Scientific, Waltham, MA, USA).42,43 No intense, high-energy sonication was performed in order to avoid cell damage, which was observed in previous studies.42,44 After each two-minute treatment, large csGAC particles were allowed to settle and the suspension collected in a separate sterile container. Fresh buffer solution (20 mL) was then added to each tube for

each subsequent treatment. All suspensions were combined and centrifuged at 4000 rpm for 15 min and resuspended in 20 mL sterile medium for inoculation.

3.4.4. BAC culture incubations with GAC as the electron acceptor

The BAC culture incubations used an experiment setup similar to the G. sulfurreducens incubations, with a few modifications. The medium contained (per liter) 0.31 g NH4Cl, 2.5 g

NaH2PO4·H2O, 4.6 g Na2HPO4, 0.13 g KCl, and 10 mL each of Wolfe’s vitamin and mineral solutions. The pH was adjusted to 7.0 with 3.0 M NaOH. Acetate (10 mM) and csGAC (40 g L-1) were provided as the electron donor and acceptor, respectively. Six types of acetate-fed incubations [(1) acetate + GAC + cells, (2) acetate + GAC, (3) acetate only, (4) acetate + cells,

(5) acetate + O2, and (6) acetate + O2 + cells] were set up, each in triplicate. All bottles were purged with N2 and incubated at 30 °C. Acetate concentrations were monitored over time, and headspace gas (200 μL) samples were periodically taken with a gas-tight syringe to measure methane and hydrogen gases.

At the end of the first incubation, two additional incubations were performed (see

Appendix B.1.5 for detailed incubation methodology). Briefly, csGAC from the first incubation were treated with the same shaking-vortexing-sonicating method as described above, and cells were collected for DNA extraction and inoculating bottles in the second incubation. The same approach was used to start the third incubation. Only three treatments [(1) acetate + GAC + cells,

(2) acetate only, and (3) acetate + cells] were used in the second and third incubations.

3.4.5. DNA extraction and microbial community analysis

DNA was extracted with a DNeasy PowerSoil Kit (QIAGEN Inc., Germantown, MD,

USA). The 16S V3 and V4 regions were PCR-amplified with the forward primer of 5'-

TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG, and reverse primer of 5'-

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC, according to the manufacturer’s protocol.45 Next-generation sequencing was performed using the

Illumina MiSeq platform with a paired-end sequencing of 300 base pairs length. The sequences were further analyzed with QIIME 2 (see Appendix B.1.6 for community analysis methodology).

3.4.6. Analytical methods

Acetate was quantified using a Dionex ICS-5000+ Ion Chromatography system with a conductivity detector and a Dionex IonPac AS11-HC column (Thermo Scientific, Waltham, MA,

USA). Soluble chemical oxygen demand (sCOD) was measured using Hach Method 8000 with a

DR/890 Portable Colorimeter (Hach, Loveland, CO, USA). Gas composition was analyzed using a gas chromatograph (Model 310; SRI Instruments, Torrance, CA, USA) equipped with a thermal conductivity detector (TCD) and a six-foot S.S. Molecular Sieve 5A column. The TCD current was 80 mA, and argon was the carrier gas with a flow rate of 19 mL min−1. The detection limits were 0.10% for methane and 0.020% for hydrogen. DOC in the GAC rinsate was analyzed with a TOC-VCSN Total Organic Carbon Analyzer (Shimadzu Scientific Instruments, Inc.,

Columbia, MD, USA).

3.4.7. GAC electron accepting capacity calculations

The electron exchange capacity (EEC, a measurement of total redox-active moieties) of

DOC in the rinsate from each washing step was calculated using the carbon content (64%) and the EEC of the Leonardite Humic Acid Standard [4.7 mmol e- (g humic acid)-1].46,47 The microbially-accessible electron accepting capacity (mEAC) of csGAC was calculated based on the amount of electrons associated with the acetate consumed during incubations:28

!C V # C (V # xV ) # ∑x (C V ) # m % φ mEAC [mmol e- (g GAC)-1] = 0 0 x 0 i i=1 i i sink (3.1) mGAC M

-1 where C0 is the initial acetate concentration (mg L ), V0 is the initial volume (L), Cx is the final

-1 th acetate concentration (mg L ), x is the total number of samples taken, Vi is the volume of the i

th -1 sample taken (L), Ci is the acetate concentration of the i sample taken (mg L ), msink is the acetate loss in the controls (mg), φ is the number of electron equivalents per mmol of acetate (8

- -1 mmol e mmol , assuming the end product of acetate oxidation is CO2), mGAC is the mass of csGAC per bottle (g), M is the molar mass of acetate (59 mg mmol-1). This equation takes into consideration the abiotic acetate removal, biotic acetate accumulation, and sampling volume.

3.5. Results and discussion

3.5.1. Preparation and characterization of the GAC

We first describe the preparation of csGAC through a multi-step aeration and rinsing process. This method, which was adapted from previous work on biochar 28,41, helps remove soluble impurities, such as DOC, that could serve as an electron donor or acceptor. After one rinse cycle, the DOC was 0.59 ± 0.27 mg L-1 in the rinsate (Figure 3.1). Assuming a working

GAC concentration of 40 g L-1, the estimated EEC of the DOC was 1.1E-04 ± 4.9E-05 mmol e-

(g GAC)-1, which is negligible compared to the electron equivalents in acetate [2 mmol e- (g

GAC)-1] provided in the incubations. Conducting two additional rinsing cycles did not result in a significant decrease (p > 0.05) in DOC. We repeated this process with a different type of GAC

(lignite-coal based) to confirm that it consistently yielded low DOC values (Figure B.3.2). These tests indicate that at least one rinse cycle is needed to ensure that DOC derived from GAC will be not be a major electron source or sink in the incubations.

Figure 3.1. Dissolved organic carbon (DOC) in the rinsate from the coconut shell-based GAC (csGAC) rinse cycles. The data represent the average of triplicates ± one standard deviation (n = 3). EEC: electron exchange capacity.

We then used elemental composition analysis and X-ray photoelectron spectroscopy

(XPS) to characterize putative electron accepting moieties in the csGAC. We quantified the oxygen content and oxygen-containing functional groups because prior studies have shown positive correlations with the abundance of surface C=O groups and EAC.48,49 In addition, PCMs with O/C molar ratios over 0.09 were found to strongly charge and discharge electrons, suggesting that oxygen-containing functional groups imparted a battery-like function 50. Based on elemental analysis (Table B.2.2), csGAC had a relatively low oxygen content [4.2 ± 0.17 mmol (g GAC)-1] and O/C ratio (0.054 ± 0.0024) compared with literature values.48,49,51 Using

the bulk oxygen content and surface C=O atomic percentage (Figure B.3.3 and Table B.2.3) and assuming the relative abundance of C=O groups remained constant throughout the interior of the csGAC, we estimated an EAC of 1.2 mmol e- (g GAC)-1 (see Appendix B.1.1 for calculation).

While there are other functional groups and mechanisms by which GAC may accept and store electrons, our analysis here shows that estimating C=O group abundance can yield EAC values comparable to the literature.41,49,51

In addition to oxygen, we also measured the metal content, since metals (e.g., iron and manganese) can be potential electron sinks.49,50 Based on elemental analysis of Fe and Mn in the csGAC, these metals could accept up to 8.1E-04 ± 5.3E-05 mmol e- (g GAC)-1 [Table B.2.2, assuming that Fe(III) and Mn(IV) are reduced to Fe(II) and Mn(II)]. Therefore, metals, unlike

C=O groups, were unlikely to serve as major electron acceptors within the csGAC.

3.5.2. Validation of GAC as a terminal electron acceptor for exoelectrogen respiration

To validate that GAC can accept electrons from an exoelectrogenic microorganism, we conducted a test with G. sulfurreducens, acetate as the electron donor, and csGAC as the sole electron acceptor. We selected G. sulfurreducens because it is a model bacterium for the study of microbial reduction of electrodes, PCMs, and humin (a PCM analogue), and it is often used in bioremediation.2,26,29,52–54 After inoculating the bottles, acetate consumption began after 1.1 days

(Figure 3.2A). The maximum acetate oxidation rate of 5.9 ± 0.74 mg acetate (g GAC)-1 day-1 occurred between days 1.9 – 2.3, and acetate consumption ended by day 6. Based on total acetate removed at the end of the incubation, the mEAC was 0.91 ± 0.0069 mmol e- (g GAC)-1. This mEAC is slightly lower than the estimated EAC value calculated in Section 3.5.1, and is comparable to values reported in the literature [0.8 – 0.85 mmol e- (g PCM)-1].28,33,35 To

determine if growth on csGAC occurred, we examined the csGAC surface at the end of the cycle using SEM. G. sulfurreducens heavily colonized the csGAC and formed biofilm structures with what appeared to be abundant extracellular polymeric substances (EPS) (Figure 3.2B). EPS is typically generated when Geobacter cells colonize surfaces and form biofilms, and is usually seen when they respire on electrodes.55,56

Figure 3.2. (A) Acetate consumption by G. sulfurreducens when csGAC was provided as the terminal electron acceptor. Acetate concentrations (mg L-1) at different time points were normalized to the initial concentrations at Day 0. Error bars represent one standard deviation of triplicate experiments (n = 3). (B) Scanning electron micrograph of G. sulfurreducens on the csGAC surface at the end of incubation. White scale bar represents 10 µm.

We also assessed if other PCMs, including biochar, could be used to grow G. sulfurreducens. For all incubations, acetate was consumed, but the mEACs varied, ranging from

0.55 to 1.08 mmol e- (g PCM)-1 (Figure B.3.4). Acetate was not removed in abiotic controls, which is consistent with the poor adsorption of acetate to PCMs reported in the literature.28,35

The differences across mEACs are likely due to variations in functional group type and abundance and degree of electrical conductivity.49,50,57 The mEAC values calculated using COD removals during the incubations, which is a more affordable and faster option than IC analysis,

agreed well with those based on acetate measurements (Figures B.3.4 and B.3.5). These results indicate that the incubation method can support the growth of the exoelectrogen G. sulfurreducens on csGAC in a matter of days.

3.5.3. Enrichment of exoelectrogens from a mixed culture community

3.5.3.1. Acetate oxidation rates and mEACs

After validating the method using G. sulfurreducens, we next determined if it could selectively enrich exoelectrogens from a mixed microbial community. We obtained native cultures growing on the csGAC in a pilot-scale BAC filter. Unlike wastewater and sediments,

BAC filters have not been explored as a source of exoelectrogens, although some studies have reported their presence using 16S amplicon sequencing.58,59 After shearing cells off the BAC and providing them with acetate as the electron donor and fresh, unused csGAC as the electron acceptor, acetate consumption started after six days (Figure 3.3A). By Day 13, 56 ± 4.3% (n = 3) of the initial acetate was consumed. The maximum degradation rate during the first incubation was 1.3 ± 0.13 mg acetate (g GAC)-1 day-1, which is consistent with microbial PCM reduction rates reported in other studies [0.48 – 2.0 mg acetate (g PCM)-1 day-1].28,35,53 Acetate was not consumed in the abiotic controls or biotic incubations lacking an electron acceptor (Figures 3.3A and B.3.6), indicating that acetate did not adsorb to csGAC or bioaccumulate. No organic substrates other than acetate were identified with ion chromatography. After subtracting background acetate removals measured in the controls, the mEAC was 1.0 ± 0.049 mmol e- (g

GAC)-1, which is comparable to the value determined above with G. sulfurreducens. When oxygen was supplied as the electron acceptor (no GAC present), acetate was depleted within three days (minimum rate of 215 ± 27 mg acetate L-1 day-1), indicating that aerobic acetate

degraders were active in the BAC inoculum. The aerobic treatment was included to ensure that the culture was viable and to compare microbial community development with the BAC cultures

(see Section 3.5.3.3).

Figure 3.3. (A) Acetate consumption in the first incubation, and (B) in the Acetate + GAC + cells reactors in all three incubations when acetate and csGAC or oxygen (O2) were provided to the BAC culture. Acetate concentrations (mg L-1) at different time points were normalized to the initial concentrations at Day 0. Error bars represent one standard deviation of triplicate experiments (n = 3).

To determine if the culture could maintain or improve rates of GAC reduction, we sheared cells off the csGAC at the end of the first incubation, and transferred them to new bottles containing fresh csGAC and acetate (Figures 3.3B and B.3.6). In the second incubation, acetate consumption started after a shorter lag phase (two days vs. six days) and approached the same final mEAC more rapidly (seven days vs. 13 days) than the first incubation. We repeated the transfer process an additional time. In the third incubation, acetate degradation was faster, but the difference in rates between the second and third incubations was much smaller than between the first and second incubations, indicating that the culture was approaching a maximum acetate oxidation rate. The acetate consumption rate in the third incubation was 5.3 ± 0.023 mg acetate

(g GAC)-1 day-1 (measured between days 1.7 – 2.7), which was 4.3-fold higher than the rate in the first incubation. The estimated mEACs based on acetate removal in the second and third incubations were 1.0 ± 0.0043 and 1.0 ± 0.0067 mmol e- (g GAC)-1, respectively, indicating that despite kinetic differences, in all incubations the csGAC had a similar capacity for electrons. In summary, these experiments show that microorganisms obtained from the BAC system could couple acetate oxidation with GAC reduction.

We also explored the possibility that electrons from acetate resulted in hydrogen (e.g., hydrogenases60) or methane (i.e., acetoclastic methanogens61) gases. Neither gas was detected in any of the incubations (below detection limits), indicating that these gases could account for no more than 0.72% of the electrons available in acetate. The lack of methane generation was likely due to the aerobic conditions of the BAC system, which should inhibit or slow the growth of methanogens. If anaerobic cultures (e.g., anaerobic digester sludge) are used as inocula, it is important to carefully monitor methane as a proxy for acetoclastic methanogenesis; however, acetoclasts generally grow much more slowly than exoelectrogens such as Geobacter species

(0.1 – 0.3 vs. 0.34 – 2.8 day-1).62–64

3.5.3.2. Microbial surface coverage of GAC

Microbial colonization of the csGAC surface at the end of each incubation was consistent with the acetate consumption results. Using SEM, we observed cells on the original BAC

(Figure B.3.7) and csGAC at the end of each incubation (Figure 3.4). Cell morphologies were highly diverse on the original BAC surface, and there was an abundance of what appeared to be

EPS. At the end of the first incubation, there was a dramatic shift in morphology towards rod- shaped cells. Surface coverage was not complete; there were many areas where raw GAC surface

was visible, including large and small pore-like structures (Figure 3.4B). At the end of the second incubation, the csGAC had a more homogenous coverage of cells, almost all of which appeared rod-shaped (Figure 3.4C). At the end of the third incubation, the csGAC was almost entirely covered with rod-shaped cells (Figure 3.4D). One notable difference across the incubations was the greater abundance of EPS-like structures surrounding the cells in the second and third incubations, which were similar to those observed in the G. sulfurreducens test (Figure

3.2B). The presence of EPS in the last two incubations suggests that the cells better adapted to attaching and growing on the csGAC surface. These images show a clear relationship between the GAC respiration results and cell growth, wherein higher respiration rates led to more thorough cell colonization of the GAC.

Figure 3.4. Scanning electron micrographs of (A) raw csGAC with no cells, (B) csGAC after the first incubation, (C) csGAC after the second incubation, and (D) csGAC after the third incubation. White scale bar represents 10 µm.

3.5.3.3. Taxonomic distribution of microorganisms in the GAC incubations

At the end of each incubation, we collected DNA from the csGAC particles and suspension to characterize the composition of the microbial communities (Figure 3.5). DNA extracted from the csGAC particles in the first incubation was too low (~0.3 ng µL-1) to be accurately sequenced, so discussion of that sample is not included. For the second and third incubations, we used additional steps to shear the cells off the GAC (see Methods), which improved DNA recovery.

Figure 3.5. Taxonomic distribution of microorganisms at the genus level in the BAC inoculum, on the csGAC surface and in suspension when csGAC was the electron acceptor. Abundances are based on averages of biological triplicates. Genera with relative abundances of less than 2% and unclassified genera were grouped into the “Others” category. GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors; 1, 2 and 3 represent the incubation cycle.

The microbial community in the inoculum was highly diverse, but became significantly less diverse with each incubation. In the inoculum, the most abundant genus belonged to the family Entotheonellaceae (8.6 ± 1.2%). The community richness and evenness decreased

dramatically after the first incubation and continued to decrease as measured by the Chao1,

Simpson, Shannon and Pielou’s evenness indexes (Table B.2.4). In general, fewer OTUs were detected in the incubations with csGAC (24 – 113 from the incubations vs. 1754 in the inoculum,

Table B.2.4 and Figure B.3.8). At the end of the first incubation, the composition of the suspension shifted dramatically to primarily Geobacter species (75 ± 3.0%) followed by

Azoarcus species (22 ± 5.3%). In each subsequent incubation, the abundance of Geobacter species increased. The relative abundance of Geobacter present on the csGAC surface was 92 ±

0.87% in the second incubation and 96 ± 0.40% in the third incubation. Geobacter sequences also predominated the suspended communities in the second (89 ± 0.93%) and third incubations

(93 ± 0.16%). The significant enrichment of Geobacter species in these incubations is quite remarkable considering that the BAC inoculum contained only 0.0020 ± 0.0022% Geobacter.

Although BAC is generally regarded as an oxic system, anaerobic bacteria, including Geobacter, have been found in BAC filters at abundances up to 20%,59 indicating the possible existence of hypoxic zones inside BAC filters that may support anaerobes.

The genera Azoarcus and Anaerospora were also detected in the incubations, but at much lower abundances than Geobacter species. The Azoarcus genus accounted for 22 ± 5.3% of the total genera recovered from the suspension in the first incubation, but its abundance decreased in the subsequent incubations to 0.22 ± 0.049% on the csGAC and 0.27 ± 0.015% in the suspension of the third incubation. Azoarcus has been detected in anode biofilms in METs and is capable of respiring on electrodes, but at much slower rates than Geobacter.65,66 Competition with

Geobacter may explain the decrease of Azoarcus and increase of Geobacter in the second and third incubations. Anaerospora was found in some samples, but the abundances were generally low (< 5%). Anaerospora have been isolated from electricity-generating anodes.67 It remains

unknown if microorganisms within this genus can reduce GAC with using similar mechanisms as Geobacter species. Pure-culture studies are needed to verify their ability to respire on GAC.

To better visualize community differences across the inoculum and incubations, we created a principal coordinates analysis (PCoA) plot (Figure B.3.9A). The inoculum, which was farthest from all the incubations, was located in its own quadrant (Quadrant III). Communities from the csGAC incubations, especially those from the second and third incubation, closely clustered together in Quadrant IV due to the high similarity of the microbial composition of those samples (i.e., high abundances of Geobacter). When oxygen was the electron acceptor, samples growing on acetate fell in Quadrants II, with a high abundance of Pseudomonas species

(77 ± 4.3%) in the bottles (Figure B.3.10).

To determine if the community shifts were significant, we conducted PERMANOVA tests (Figure B.3.9B). The BAC inoculum was significantly different from the communities that developed on csGAC (p = 0.010) and in suspension (p = 0.004), indicating that providing csGAC as an electron acceptor strongly shifted the community structure. No significant differences were observed between the communities on csGAC versus in suspension (p = 0.100). One possible explanation for their similarity is that after the maximum mEAC was reached, cells detached from the surface to search for alternative electron acceptors. This mechanism has been suggested for Geobacter species when they are growing on insoluble electron acceptors such as iron oxide.68,69 The oxygen-supplemented communities with acetate were not significantly different from the inoculum (p = 0.089). This result was somewhat expected because the BAC system is not maintained under anaerobic conditions; thus, many of the microorganisms are likely well adapted to aerobic growth. Overall, the microbial community results provide strong support that

the Geobacter species were the primary microorganisms reducing the csGAC and show the effectiveness of the method for enriching exoelectrogens.

3.5.4. Mechanisms of GAC reduction

The ability of csGAC to accept electrons from microorganisms was likely due to the presence of electron accepting functional groups and electrical conductivity. As discussed in

Section 3.5.1, previous studies have shown strong correlations between the oxygen content and/or abundance of certain oxygen-containing groups (e.g., C=O) and EACs. However, microorganisms, such as Geobacter, cannot make direct contact with all the csGAC surface, as micropores (< 2 nm) contribute to roughly 94% of the csGAC surface in our study (Table B.2.2) and are smaller than typical Geobacter cells (0.5 by 2 – 4 μm) and their electron-conducting filaments (3 – 5 nm in diameter).1,19,20,70,71 Electrons from Geobacter were likely conducted through csGAC until they reduced an oxidized functional group in the GAC interior. One possibility is that the polyaromatic structures present in csGAC allowed microorganisms that were sterically hindered by small pores to reduce C=O groups in the GAC interior. The near unity double bond equivalent and aromaticity index values in csGAC (Table B.2.2) are indicative of abundant condensed aromatic structures.49,72 The XPS results further confirm the high aromaticity of csGAC, with the majority of carbon-containing groups belonging to aromatic

C=C (Table B.2.3). These structures, which were likely generated during the high temperature

(1000 °C) steam activation process, resulted in a relatively high electrical conductivity of 2.3 ±

0.23 S cm-1 (Table B.2.2). Although the impact of PCM conductivity on electron charging and discharging of surface functional groups is unclear, a high or more continuous conductivity might enable delocalized electron transfer from microorganisms to the functional groups, as

proposed in a recent study.57 To better understand what PCM properties enable microbial respiration of PCMs, deconstructing PCM properties and studying their effect individually, as was recently done to study PCM reduction of halogenated compounds,57 is needed.

3.5.5. Implications

The incubation method described here provided a low-cost and simple approach to enrich an exoelectrogenic community rich in Geobacter species. Surprisingly, this method could rapidly select for Geobacter species even when their abundance was initially low. In the BAC inoculum,

Geobacter abundance was only 0.0020%, but increased significantly to 96% by the end of the third incubation. It is uncommon for poised electrodes to obtain this level of abundance of

Geobacter species unless specific potentials are used or METs are operated over long time scales.6,7,73 This level of enrichment was obtained without expensive electrodes, custom-built reactors, or specialty equipment, such as potentiostats.16,74,75 Providing soluble (e.g., fumarate, ferric citrate) or insoluble (e.g., ferrihydrite) electron acceptors is another method that has been used to culture exoelectrogens; however, those electron acceptors can support the growth of non- exoelectrogens and they may interfere with electrode reactions (e.g., electrode-driven iron oxidation/reduction).26,76,77

The enrichment approach described here may be useful for a variety of applications other than METs. Geobacter species have been amended to treatment systems and contaminated sediments/groundwater to stimulate bioremediation (i.e., bioaugmentation), as they can degrade or render harmless many environmental contaminants, including aromatic hydrocarbons (e.g., benzene, toluene) and inorganics (e.g., arsenic, manganese).15,20,78,79 Supplementing Geobacter to anaerobic digesters has been shown to accelerate methane production by enhancing

Geobacter-methanogen syntrophic interactions.8 Techniques that can increase the abundance and/or activity of Geobacter might contribute to enhanced contaminant removal and biogas recovery. Our findings suggest that enriching Geobacter on GAC may provide a readily available inoculum source for these environments and applications. Moreover, amending GAC directly in these environments as a low-cost terminal electron acceptor and acetate as an electron donor may facilitate contaminant transformations mediated through PCMs.13,15,38–40,80

3.6. Conclusions

The overall objective of our work was to develop a low-cost method to enrich exoelectrogenic microorganisms. Our findings demonstrate that providing granular activated carbon (GAC) as the sole electron acceptor and acetate as the electron donor to mixed culture communities can lead to highly enriched exoelectrogenic communities. Using a culture obtained from a pilot-scale biological activated carbon (BAC) system, we show that operational taxonomic units matching to Geobacter species can increase dramatically in relative abundance from 0.0020% in the inoculum to 96% after three incubations. This method provides an alternative to costly electrode-based enrichments and non-exoelectrogenic specific techniques

(e.g., soluble electron acceptors).

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Chapter 4. Structures and functions of landfill microbial communities exposed to elevated temperatures

4.1 Abstract

There have been reports of a few municipal solid waste (MSW) landfills with temperatures up to 80 – 100 °C. These landfills may experience elevated odor emission, increased leachate quantity and strength, and reduced methane production. Little research has focused on the response of landfill microbial communities to elevated temperatures. The purpose of this study was to evaluate the impact of temperature on the microbial community composition and functionality in samples of waste excavated from a section of landfill exhibiting elevated temperatures. Samples were collected from two landfills (LFA and LFB) at multiple depths as inocula, fed with synthetic MSW, and incubated under a range of temperatures (47.5 – 82.5 °C).

Methanothermobacter, which reached abundances as high as 100% relative to total archaeal populations, likely played a key role in methanogenesis under thermophilic conditions (52.5 –

67.5 °C). Operational taxonomic units affiliated with fermentative microorganisms were abundant in samples over 67.5 °C. Sustained volatile fatty acid generation above 67.5 °C and cessation of methane production indicate that fermentative microorganisms could withstand higher temperatures than methanogens. Using Piphillin to estimate functionality within the communities, we found that gene markers associated with methane metabolism were negatively associated with the incubation temperature, while those associated with carbohydrate metabolism

(e.g., cellulose degradation) remained abundant at temperatures as high as 82.5 °C. The results of this study show the significant impact of incubation temperature on the structures and functions of landfill microbial communities and imply the key microorganisms associated with landfills.

4.2 Significance

This work constitutes the first study to comprehensively investigate the response of landfill microbial communities to elevated temperatures. It correlates microbial community structures and functions with measurable methane and fatty acid production, and suggests key microorganisms associated with these processes. This study provides insights into the behavior of landfills experiencing elevated temperatures. Sections of this chapter were published in the following article. The article was a collaboration with work done by Sierra Schupp as part of her

Master’s thesis in 2020 (Schupp, S.; de la Cruz, F.; Cheng, Q.; Call, D. F.; Barlaz, M. A.

Evaluation of the temperature range for biological activity in landfills experiencing elevated temperatures. ACS ES&T Engg. Published online at https://doi.org/10.1021/acsestengg.0c00064).

4.3 Introduction

Landfills are the primary disposal method for municipal solid waste (MSW). Typical landfills have a temperature range of 20 – 65 °C, which is favorable for many mesophilic and thermophilic methanogens, as well as fermenting bacteria that decompose complex organic wastes and produce methanogenic precursors.1 However, it has been reported that the waste temperatures in a few MSW landfills has increased to 80 – 100 °C, which exceeds the range exhibited in typical landfills.1,2 These landfills, named elevated temperature landfills (ETLFs), usually experience spatial and temporal variations of temperatures due to microbial activities

(e.g., aerobic and anaerobic degradation of MSW) and chemical reactions (e.g., ash hydration and metal corrosion).1,3 This can potentially lead to elevated odor emission, increased leachate quantity and strength, reduced methane production and damage to landfill infrastructure.2,4

Understanding the impact of temperature fluctuation on the distribution and activity of microorganisms in ETLFs can improve the understanding of landfill performance. MSW landfilled per year can potentially produce > 500 m3 min-1 methane for a continuous period of 20

– 30 years, and serves as the third largest anthropogenic source of methane emission in the

United States.5,6 Despite the key role that microorganisms play in the degradation of MSW, no comprehensive study has been conducted to investigate the response of landfill microbial communities to elevated temperatures. Methanogenic activity in anaerobic digesters (engineered systems that also degrade organic wastes into methane) has been extensively characterized and correlated with temperature;7,8 however, it is difficult to translate these results to landfills due to differences in substrates and operation. Therefore, the purpose of this study was to systematically evaluate the impact of temperature on microbial community composition and activity using the microbial populations in excavated waste samples. Landfill samples were collected from two landfills (LFA and LFB) at multiple depths as inocula. They were fed with synthetic municipal solid waste and incubated under a range of temperatures (47.5 – 82.5 °C). The microbial communities under different temperatures were analyzed with the 16S rRNA gene sequencing, which has been used to determine the microbial community structure in landfills and anaerobic digesters treating landfill leachate.9–14 Multiple 16S-based metagenomic approaches were utilized to characterize the impact of temperatures on the composition and functionality of landfill communities.

This study was a collaboration with Sierra Schupp (MS, 2020). She was in charge of running reactors and measuring gas and acid compositions, while I was in charge of analyzing and interpreting the DNA sequence data.

4.4 Materials and methods

4.4.1 Experimental setup and operation

Details about sampling, reactor setup and operation can be found in Sierra Schupp’s thesis and publication.15,16 Briefly, landfill samples were collected from two landfills (LFA and

LFB) in the southeastern United States at multiple depths. The fines fraction from each of these samples (termed excavated samples) was utilized as inoculum, fed with synthetic MSW and incubated under a variety of temperatures (Figure C.2.1). The volatile solids (VS), and cellulose

(C), hemicellulose (H), and lignin (L) concentrations were determined for excavated samples.

The gas volume and composition (e.g., hydrogen and methane) as well as volatile fatty acid

(VFA) concentrations were periodically analyzed for incubated samples. Methane and VFA yields were normalized to the maximum value detected for each excavated sample.

4.4.2 DNA extraction and microbial community analysis

Each excavated sample was blended with anaerobic phosphate buffer (23.7 mM) and centrifuged in 50 mL tubes at 3220 g for 5 min to form a pellet for DNA extraction. Each incubated sample was mixed with hand shaking, poured in four 1.7-mL tubes and centrifuged at

6000 g for 10 min to form pellets for DNA extraction. The supernatant after centrifugation was decanted and the pellets stored at -80 °C. DNA was extracted from the pellets with a DNeasy

PowerSoil Kit (QIAGEN Inc., Germantown, MD, USA) with a few modifications to minimize the high contamination levels.15,16 Following extraction, the 16S V3 and V4 regions were PCR- amplified with the modified 341F (5’-CCTAYGGGRBGCASCAG) and 806R (5’-

GGACTACNNGGGTATCTAAT) universal primers for both archaea and bacteria. Next-

generation sequencing was performed using the Illumina MiSeq platform with a paired-end sequencing of 300 base pairs length.

QIIME 2 (version 2020.2)17 was used to process raw DNA sequences. The DADA2 plugin18 was used to filter and merge the forward and reverse reads. The merged reads were then mapped to the Silva 132 99% Operational Taxonomic Units (OTUs) Database for microbial community composition analysis. A classifier was trained with the forward and reverse primers used in this study. The abundances of microbial communities in each sample were exported at the genus level, and their relative abundances were visualized using the R ggplot2 package

(version 3.3.0).19 Genera with low relative abundances (< 5%) were grouped as “Others”. OTUs that contributed to the differences between temperatures were identified with the simper function in the R vegan package (version 2.5-6).20 The core microbiota were classified using the R microbiome package (version 1.9.97).21

Alpha and beta diversities were analyzed using the R phyloseq package (version

1.30.0).22 Unless otherwise noted in figure captions, the rarefaction depths for LFA and LFB samples were 10,106 and 23,907, respectively, which allowed rarefaction curves for the samples to reach plateaus with minimal removal of samples. The pairwise t-test was performed to compare alpha diversity metrics between samples using the Benjamini-Hochberg (BH) adjustment method. The beta diversity was illustrated in a non-metric multidimensional scaling

(NMDS) plot based on the Bray-Curtis distance. The ellipses were generated assuming multivariate t-distribution. The permutational multivariate analysis of variance

(PERMANOVA) was performed with the R vegan package (version 2.5-6)20 to statistically characterize the differences between each pair of incubation temperatures, using the Benjamini-

Hochberg (BH) adjustment method. The distance-based redundancy analysis (db-RDA) was

performed using the capscale function in the R vegan package (version 2.5-6)20 based on the Bray-Curtis distance, and the extent of OTUs explained by multiple environmental factors was summarized in tables.

The OTU abundance tables and representative sequence files exported from QIIME 2 were submitted to the Piphillin Server for functional prediction.23,24 The representative sequences of OTUs were searched against a database using USEARCH version 8.1.1861. The Kyoto

Encyclopedia of Genes and Genomes (KEGG) database was selected as the reference database, with the percent identify cutoff set as 97%. The relative abundances of pathways and KEGG

Orthologs (KOs) of interest were calculated by normalizing their absolute abundances to the abundances of all the pathways and KOs detected in each sample. The one-way analysis of means (not assuming equal variances) was conducted to characterize the impact of temperatures on community functionalities. The correlation between distance matrices based on phylogenetic diversity and microbial community compositions were determined using the Mantel’s test with

999 permutations.20 For all the statistical tests described in this section, a p-value less than 0.05 was considered significant.

4.5 Results and discussion

4.5.1 Methane and volatile fatty acid production during incubation

Within the tested temperature range, methane production decreased as the incubation temperature increased (Figure 4.1). Methane generation peaked at 47.5 °C, and the relative methane yields remained over 78% of the maximum when the incubation temperatures were below 57.5 °C. The maximum absolute methane yields from LFA and LFB were 37 ± 14 and 72

± 54 mL reactor-1, respectively. However, methane yield dropped sharply as the temperature

increased, and reached < 10% at 72.5 °C. Low levels of methane (< 0.005 mL reactor-1) were produced at the highest temperature tested (82.5 °C). These results demonstrate that methane production was dependent on the incubation temperature, and it decreased as temperatures increased beyond 57.5 °C. This trend is in good agreement with a previous study on thermophilic anaerobic digestion, which reported a gradual decrease in methane recoveries from 55 to 80 °C.25

Samples excavated at different temperatures exhibited similar methane production behavior under the same incubation temperature, indicating that methane production was independent of the excavation temperature. In contrast to methane yields, VFA generation continued under thermophilic conditions (Figure 4.1). VFA relative yields remained above 75% up to 77.5 °C, but dropped to 35 ± 16% at 82.5 °C, suggesting that the microorganisms responsible for fermentation were more tolerant of higher temperature than methanogens.

Figure 4.1. Methane yields from LFA and LFB and VFA production from LFB at multiple incubation temperatures. The relative yield values were normalized to the maximum yield recorded for each excavated sample. Error bars represent one standard deviation of 16 reactors (n = 16).

4.5.2 Microbial community composition in the excavated and incubated samples

In LFA excavated samples, Methanothermobacter was the predominant methanogen (62

– 100% of the total archaeal populations) at all excavation temperatures (Figure 4.2).

Methanothermobacter is a genus of Methanobacteriaceae that grows optimally from 55 – 70 °C and up to 74 °C with hydrogen and carbon dioxide as the electron donor and acceptor, respectively.26 This genus has been detected in landfill leachate as well as in thermophilic anaerobic digesters.10,12,15,27,28 The bacterial genus Bacillus had the highest relative abundance within bacteria in samples with excavation temperatures of 58, 63, 67 and 70 °C (Figure 4.2).

Many Bacillus species are spore-forming, cellulolytic and heat-tolerant, and they have been found to predominate in landfills.9,29–31 In addition, Tepidibacillus (76%) predominated in the sample with the excavation temperature of 52 °C, while Herbinix (40%) predominated at 55 °C.

Lysinibacillus was found abundant when the excavation temperatures were 64 °C (71%) and

75 °C (79%). The predominance of these non-Bacillus genera could be due to environmental factors other than excavation temperature. For example, Lysinibacillus is known for its tolerance and biosorption of heavy metals,32,33 and Tepidibacillus is a moderate thermophile capable of anaerobic and microaerophilic fermentation.34–36 These genera may be more competitive than

Bacillus species in environments with high levels of cellulose and perhaps a sample with elevated metal content.

Figure 4.2. Microbial community composition in excavated samples from LFA. No replicate measurements were performed for excavated sample at each temperature (n = 1). Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category.

After incubation at 47.5 °C, Methanosarcina and Methanosaeta became abundant in the

LFA samples (Figure 4.3). Methanosarcina could be enriched to as high as 86% (excavation temperature 55 °C), while Methanosaeta reached up to 58% (excavation temperature 52 °C). At

52.5 °C, Methanosarcina and Methanosaeta were detected, but their relative abundances decreased to < 59% and < 35%, respectively. Methanosarcina can produce methane via the hydrogenotrophic and acetoclastic pathways, while all known Methanosaeta species are strict acetoclasts.37,38 Methanosarcina and Methanosaeta typically predominate in high- and low- acetate concentration environments, respectively, and both are frequently detected in anaerobic digesters.39,40 The maximum temperatures for known Methanosarcina and Methanosaeta species

are roughly 55 – 60 °C,37,38 which could explain their absence when the incubation temperature exceeded 57.5 °C. At 57.5 – 62.5 °C, Methanothermobacter predominated the archaeal communities with relative abundances of 42 – 100%. At 67.5 °C, a gradual increase in the relative abundance of uncultured Thermoprotei archaeon was observed, which was further enriched up to 88% at 72.5 °C. Little information is available on this non-methanogenic genus, but its predominance was likely due to its heat-tolerance.41,42 These results indicate that a thermophilic community capable of methane production may be present in MSW landfills and greatly affected by the incubation temperature. The predominance of Methanothermobacter at temperatures above 52.5 °C suggests that the hydrogenotrophic methanogenesis played an important role in methane generation at elevated temperatures.

Figure 4.3. Archaeal community composition in LFA samples incubated at multiple temperatures. Abundances are based on average of biological duplicates or single sample when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category.

Considerable shifts were observed in the bacterial communities after incubation (Figure

4.4). Defluviitoga and Hydrogenispora were abundant at lower incubation temperatures (47.5 –

52.5 °C). These genera include thermophilic fermenters that have been detected in thermophilic anaerobic digesters.43,44 The Hydrogenispora genus contains spore formers and has been added to anaerobic reactors to shift bacterial communities towards hydrogen production.45,46 The high abundance of these genera might explain the enrichment of hydrogenotrophic and acetoclastic methanogens, since they could produce hydrogen and acetate during fermentation and develop syntrophic relationships with methanogens. When the incubation temperature increased to 57.5 –

62.5 °C, Hydrogenispora remained in high relative abundance, and an unclassified genus named anaerobic digester metagenome (affiliated with the class Clostridia) became enriched (up to

55%). At higher incubation temperatures (67.5 – 72.5 °C), multiple putative thermophilic and hyperthermophilic fermenters were observed in high abundance, including Caldicoprobacter,

Pseudothermotoga, Thermotoga and Acetothermia clone OPB14.47–52

Figure 4.4. Bacteria community compositions in LFA samples incubated at multiple temperatures. Abundances are based on averages of biological duplicates or single samples when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category.

Microbial communities in excavated samples from LFB (Figure 4.5) shared similar structures compared with those from LFA. Methanothermobacter accounted for 23 – 92% of the total archaeal populations in LFB samples excavated at 52 – 79 °C. The samples excavated at 67 and 70 °C contained abundant Methanofollis, which was not observed in any LFA sample.

Methanofollis is a hydrogenotrophic methanogen that has been detected in landfill leachate.10,13,14

With respect to bacteria, Bacillus was ubiquitously present in all the excavated samples from

LFB (18 – 68%), consistent with the results from LFA. An uncultured genus belonging to the family Methylococcaceae was found at 52 °C, with a relative abundance of 23%. This family contains methanotrophs, suggesting that methane oxidation could have occurred in the landfill.53

Anaerobic methane oxidation in landfills would be important and warrants further study.

Samples with the excavation temperatures of 70 and 71 °C contained 15% and 18% Garciella, respectively, which is classified as an anaerobic, thermophilic fermenting bacterium.54

Figure 4.5. Microbial community compositions in excavated samples from LFB. No replicate measurements were performed for excavated sample at each temperature (n = 1). Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category.

After incubation, Methanothermobacter predominated (up to 100%) in the majority of the archaeal populations in LFB samples (Figure 4.6), although little methane was produced at high temperatures. Methanosarcina was relatively abundant in samples with low incubation (47.5 °C) and excavation temperatures (52 and 61 °C). Methanoculleus, which was observed in low abundance (< 23%) in LFA samples incubated at 47.5 °C, was enriched to up to 45% in LFB samples at the same temperature. It utilizes the hydrogenotrophic pathway for methanogenesis, and is frequently detected in landfill leachate and anaerobic digesters.10–14,28,30 Unlike LFA, the uncultured Thermoprotei archaeon was absent in most LFB samples incubated at > 62.5 °C.

Future investigation is needed to reveal its function under high temperatures. In terms of 86

bacterial populations (Figure 4.7), the predominant genera at low incubation temperatures (47.5

– 52.5 °C) in LFB were Defluviitoga (up to 37%) and Hydrogenispora (up to 72%). As the temperature increased to 52.5 – 62.5 °C, the genus anaerobic digester metagenome became the most abundant (up to 65%). These findings are in good agreement with the LFA microbial community compositions under a similar temperature range. Besides these genera,

Coprothermobacter was more frequently seen in LFB when the incubation temperatures were moderately high (57.5 – 67.5 °C). This thermophilic fermenter has been reported to establish a syntrophic relationship with hydrogenotrophic archaea (e.g., methanogens) in anaerobic digesters.55 At 67.5 – 77.5 °C, Caldicoprobacter and Thermotoga became enriched, which was also observed in LFA samples under similar incubation temperatures (67.5 – 72.5 °C). A new genus named Caldanaerobacter was found in most LFB samples incubated at 72.5 – 77.5 °C. It contains thermophilic fermenting bacterial species that can grow at 80 °C.56 At the highest temperature tested in this study (82.5 °C), an unexpected genus Pseudomonas was enriched when the excavation temperatures of the samples were high (74 and 79 °C). This genus was present with low abundances (< 2.9%) in all the LFB excavated samples. Although most

Pseudomonas species are mesophilic, multiple lines of evidence have suggested that some

Pseudomonas species can grow in thermophilic environments.57–59 The significance and function of this genus in landfills require further study.

Figure 4.6. Archaeal community compositions in LFB samples incubated at multiple temperatures. Abundances are based on average of biological duplicates or single sample when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category.

Figure 4.7. Bacteria community compositions in LFB samples incubated at multiple temperatures. Abundances are based on average of biological duplicates or single sample when duplicates were not available. Genera with relative abundances of less than 5% and unclassified genera were grouped into the “Others” category.

The archaeal and bacterial population distributions could correspond to hydrogen and

VFA production profiles observed in LFA and LFB. At low incubation temperatures (47.5 –

52.5 °C), the acetoclastic methanogens Methanosarcina and Methanosaeta were relatively abundant, possibly leading to no VFA accumulation at the end of incubation. Methanosarcina might also be responsible for hydrogen utilization.38 As the temperature increased to 57.5 –

67.5 °C, Methanothermobacter predominated the archaeal populations and could scavenge hydrogen, resulting in no hydrogen accumulation. The lack of VFA accumulation after incubation under these temperatures suggests that the syntrophic acetate oxidation (SAO) might occur, i.e. acetate was transformed into hydrogen and carbon dioxide. This phenomenon has been observed in many thermophilic anaerobic digestion studies, where syntrophic acetate oxidazing bacteria (SAOB) formed syntrophic relationships with hydrogenotrophic methanogens.60–62 Putative SAOB such as Coprothermobacter, Thermotoga and genera affiliated with the class Clostridia (e.g., Caldicoprobacter), were identified in LFA and LFB samples incubated under this temperature range. The genus anaerobic digester metagenome, also affiliated with Clostridia, was present in high abundance and might participate in SAO and contribute to VFA degradation. The potential role of this genus in SAO requires future investigation. When the temperature further increased to > 70 °C, methanogenesis was substantially inhibited and hydrogen was accumulated, which could result in unfavorable thermodynamics for VFA degradation and VFA buildup in the reactors.

Some of the microorganisms mentioned above were shared across samples (i.e. with high prevalence), as revealed by the core microbiota analysis (Figures C.2.2 and C.2.3). For example, five OTUs, which belong to four genera (Pseudomonas, Methanothermobacter,

Bacillus and Herbinix), were most frequently detected in LFA excavated samples (Figure

C.2.2A). At least 50% of these samples contained the five OTUs with their relative abundances of > 0.1%. After lab-scale incubation, three other genera (anaerobic digester metagenome, uncultured Thermoprotei archaeon and Coprothermobacter) emerged, indicating a community shift caused by incubation (Figure C.2.2B). Only Methanothermobacter was prevalent in all

LFA samples (before and after incubation), suggesting its essential role in methane generation from LFA. Compared with LFA, LFB excavated samples had more prevalent OTUs (20 vs 5), and the majority of them fall within the Pseudomonas, Bacillus and Tepidimicrobium genera

(Figure C.2.3A). Interestingly, no OTUs in the Methanothermobacter genus could be identified as prevalent, although Methanothermobacter was present in all the LFB excavated samples. This is likely due to the low abundances of this genus under high excavation temperatures (e.g.,

0.076% of all archaeal and bacterial OTUs at 74 °C). After incubation, only six OTUs (belonging to five genera) were identified as prevalent in LFB samples, and three of them (anaerobic digester metagenome, Methanothermobacter and Coprothermobacter) were also recognized as prevalent in LFA incubated samples (Figure C.2.3B), suggesting convergence of microbial communities after incubation, regardless of sampling sites. The OTUs ubiquitously present in the incubated samples can potentially endure a wide range of temperatures, making them more adaptive to temperature fluctuations in ETLFs.

4.5.3 Microbial diversity and redundancy analyses

We performed alpha and beta diversity analyses to investigate the impact of incubation on the microbial community composition. Alpha diversity indices (e.g., Observed, Chao1,

Shannon and Simpson) measure the “within sample” diversity. The LFA communities became significantly less diverse after incubation and with increasing incubation temperatures (Figure

4.8A and Table C.1.1). Microbial communities in excavated samples were the most diverse 90

among all sample groups, as evidenced by the largest alpha diversity values. When the incubation temperature increased, microbial communities had fewer species (i.e. low richness), and a small number of species were highly abundant (i.e. low evenness). This is in agreement with the community composition analysis, which suggests the presence of dominant species at high incubation temperatures. The LFB communities were significantly less diverse after incubation (similar to LFA), yet only a few significant differences could be observed between incubated samples (Figure 4.8B and Table C.1.2). While Observed and Chao1 values decreased as the incubation temperature increased, Shannon and Simpson indices at high incubation temperatures (> 67.5 °C) were comparable to those at low temperatures (< 62.5 °C). Observed and Chao1 indices take into consideration only species richness, while Shannon and Simpson indices include both richness and evenness. Therefore, it is likely that at high temperatures, fewer microbial species were present, but they had similar relative proportions (i.e. high evenness). The excavation temperature, however, did not correlate with alpha diversity in either landfill system.

Figure 4.8. Alpha diversity in excavated and incubated samples from (A) LFA and (B) LFB. Excav on the x axis represents excavated samples (not incubated), and the numbers represent the temperatures used during the incubations. The Observed index counts the number of distinct OTUs present in a sample. The Chao1 index estimates the number of distinct OTUs by giving more weight to rare OTUs. The Shannon index takes into account both abundance and evenness of OTUs in a sample and assumes that all OTUs are represented and randomly sampled. The Simpson index measures the probability that two reads randomly selected from a sample belong to different OTUs. A p-value of less than 0.05 was considered significant.

We then explored the beta diversity (a measure of “between sample” dissimilarity) in order to visualize differences between communities under different excavation and incubation conditions. We found that LFA and LFB excavated communities were significantly different from each other, and a larger dispersion (i.e. larger ellipse) of the LFA samples was seen compared to LFB (Figure C.2.4). This indicates that LFA was more diverse than LFB in terms of microbial community composition. After incubation, the communities diverged significantly from the excavated communities (p < 0.05) with regard to composition (Tables C.1.3 and

C.1.4). This result was expected because of the differences between lab- and field-based physical and chemical properties. In each landfill, samples incubated at the same incubation temperature closely clustered together, indicating that they had high similarity in microbial composition

(Figure 4.9). Statistically, the community shifts from one incubation condition to another

(including Excav) were significant (Tables C.1.4 and C.1.5). There were only two exceptions to this trend in LFB, when the incubation temperature increased from 72.5 to 77.5 °C, and from

77.5 to 82.5 °C. It should be noted that LFB communities exposed to the highest incubation temperatures (72.5 – 82.5 °C) had higher variance than samples at lower temperatures (Figure

4.9B), which could result in statistical insignificance. No significant differences were found between samples varying in excavated temperatures (similar to the alpha diversity results), indicating the limited impact of the excavation temperature.

Figure 4.9. Non-metric multidimensional scaling (NMDS) illustrating beta diversity in excavated and incubated samples from LFA (A) and LFB (B) based on Bray-Curtis dissimilarity (a quantitative measure of community differences). Excav in legends represents excavated samples (not incubated), and numbers represent the temperatures during the incubations. The ellipses represent 95% confidence intervals around their centroids. The stress values of 0.16 and 0.23 provide a fair representation in reduced dimensions.

We next identified OTUs that contributed strongly to the differences between incubation temperatures by calculating similarity percentages (SIMPER).63,64 Those OTUs were likely to be temperature-sensitive and thus may require careful investigation during landfill operation. We observed that many abundant species reported in Section 4.5.2 could largely explain these

differences. In LFA samples (Table C.1.6), OTUs belonging to Methanothermobacter,

Defluviitoga and anaerobic digester metagenome were frequently seen to explain > 5% of the variation and contribute significantly to the Bray-Curtis dissimilarity between each comparison

(p < 0.05). At high incubation temperatures (67.5 – 72.5 °C), OTUs that belong to Thermotoga,

Pseudothermotoga and uncultured Thermoprotei archaeon contributed significantly to the dissimilarity between LFA samples, which is in agreement with their unique presence under thermophilic and hyperthermophilic conditions. In LFB samples (Table C.1.7), a few more

OTUs (e.g., in Coprothermobacter, Caldanaerobacter and Pseudomonas genera) were identified, while the uncultured Thermoprotei archaeon was absent, which is in accordance with the observed differences in the community compositions of two landfills as previously described.

We further investigated the impact of several measurable environmental factors, including excavation temperature, incubation temperature, and parameters other than temperature (e.g., CH/L and VS of excavated samples), on the microbial community composition of incubated samples. VS is a rough estimate of the organic portion of excavated samples, taking into account both microorganisms (inocula), organic substrates and plastics. CH/L is the ratio of the major biodegradable (cellulose, hemicellulose) to non-biodegradable (lignin) constituents, which can serve as an indicator of the extent of decomposition. These factors were expected to impact microbial communities because they are closely associated with substrate availability and microbial activity. The ordination constrained with these four factors (Figure 4.10) only explained 26% and 15% of the total variation observed in LFA and LFB microbial communities, respectively, indicating that the majority of the microorganisms in these two landfills did not show a relationship with these factors. This was somewhat expected considering the complexity of landfill systems (e.g., local variation, uneven moisture content and substrate distribution) that

may affect the properties of excavated samples. Future investigation is needed to identify other factors that can help explain microbial community structure shifts in landfills.

Among the four environmental factors, the incubation and excavation temperatures explained most of the variance in LFA and LFB incubated samples, as reflected by their long vector projection lengths on RDA1 and RDA2, respectively (Figure 4.10). The constrained axis

RDA1 accounted for the majority of explainable variance (> 60%) while RDA2 was less than

20%, suggesting that the incubation temperature could explain more dissimilarity than the excavation temperature. On the contrary, CH/L and VS had limited impact on the variance (short vector projections on both axes), which was somewhat expected since these values did not vary as much across samples as the two temperature variables. Furthermore, samples with high relative methane yields (red and orange points, Figure 4.10A-B) were negatively related to incubation temperatures, while samples with low relative methane (blue points, Figure 4.10A-B) and high relative VFA yields (red points, Figure 4.10C) were characterized by high incubation temperatures. This observation is consistent with our findings showing low abundances of methanogens and insignificant methane yields at high incubation temperatures. Meanwhile, VFA production was consistently observed at 72.5 – 82.5 °C accompanied by limited methanogenesis, indicating active fermentation in these samples under hyperthermophilic conditions. These results are consistent with earlier studies showing the heat-resistant nature of some fermentative microorganisms.55,65 Note that as we did not measure VFA production from samples incubated at lower temperatures, the VFA profile is not complete, and a more thorough sampling is required to better illustrate the VFA production shifts across incubations.

Figure 4.10. Distance-based redundancy analysis (db-RDA) showing the impact of incubation temperature, excavation temperature, volatile solid concentration (VS) and ratio of cellulose to lignin content (CH/L) on microbial compositions in incubated (A) LFA and (B-C) LFB samples. The plots (A) and (B) were colored with relative methane yields, and (C) with relative volatile fatty acid (VFA) concentrations. The gray dots in each plot represent OTUs. Samples with the excavation temperature of 70 °C were not included in (A) analysis due to lack of VS and CH/L data. Samples with no VFA measurements were not shown in (C).

We also determined how OTUs could be predicted by these environmental factors

(Tables C.1.8 and C.1.9). Most OTUs in LFA incubated samples were associated with the incubation temperature (17 out of 37), followed by the excavation temperature (11 out of 37).

The other two variables (CH/L and VS) were related to a smaller number of detected OTUs. A similar trend was also observed for OTUs in LFB incubated samples. More specifically, multiple

OTUs belonging to Methanothermobacter, Methanosarcina, Hydrogenispora, Acetomicrobium,

Defluviitoga, Pseudothermotoga and Thermotoga were found highly correlated with the incubation temperature in both landfills. Two OTUs in Methanothermobacter and

Pseudothermotoga could be related to the excavation temperature. Two OTUs identified in

Hydrogenispora and Coprothermobacter genera were mostly affected by the CH/L ratio, and one

OTU in the Syntrophomonadaceae family was mostly predicted by VS. Overall, these results provide strong support that microorganisms in LFA and LFB were mostly impacted by the incubation temperature. However, as mentioned earlier, the fewer OTUs predicted by CH/L and

VS could result from lack of variance in these values, and future research is required to fully interpret their role in shifting microbial community structures.

4.5.4 Prediction of microbial functionalities

We predicted the microbial functional potential of LFA and LFB microbial communities incubated at different temperatures, using the open-source pipeline Piphillin.23,24 It can predict metagenome functions with DADA2-corrected 16S rRNA sequences, and yield functional profiling results comparable to those from the whole genome shotgun (WGS) sequencing.23,24 At a cutoff of 97%, less than 10% of the OTUs were assigned to the reference genomes by Piphillin. which is not a large proportion of the overall microbial community to be used in the prediction of

metagenomes. However, there was a fairly high and significant correlation (Mantel's R2 = 0.46 for LFA and 0.50 for LFB, p < 0.001 for both) between the distance matrices calculated from phylogenetic and predicted metagenomic compositions of LFA and LFB incubated samples.66,67

Therefore, we believe the predicted metagenomes could capture the potential metabolic changes across incubations.

We first examined genes associated with methane production and cellulose degradation, as these processes are of particular interest in landfill systems. Piphillin results suggest that the relative abundances of genes mcrA, mcrB, mcrC, mcrD and mcrG, which encode enzymes involved in methanogenesis,68,69 were negatively correlated with the incubation temperature, and could decrease to near zero at > 62.5 °C in both landfills (Figure 4.11). This result corresponds well with the methanogen abundance decrease along with the incubation temperature increase. In addition, Piphillin detected the presence of multiple glycoside hydrolase family 5 protein- encoding genes, which could function as an indicator of cellulose degradation.27 The relative abundance of two genes associated with this process in LFA incubated samples increased significantly as the incubation temperature was elevated (Figure 4.12A). The trend was not as clear in LFB samples, but high relative abundances of these genes could still be observed at elevated temperatures (> 67.5 °C, Figure 4.12B). It could be possible that some cellulosedegrading microorganisms were more heat-tolerant and active relative to other species (e.g., methanogens) at high incubation temperatures, which is consistent with a higher temperature limit of VFA production relative to methane production.

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Figure 4.12. Relative abundances of cellulose degradation-related genes in (A) LFA and (B) LFB samples incubated at various temperatures, predicted by Piphillin. Each gene was normalized to the number of total genes detected in a sample. A p-value of less than 0.05 was considered significant. K01179: gene encoding endoglucanase; K01181: gene encoding endo- 1,4-beta-xylanase; K19355: gene encoding mannan endo-1,4-beta-mannosidase.

In addition, the predicted pathways involved in carbohydrate, energy and lipid metabolism significantly correlated to the incubation temperature (p < 0.05) in both landfills

(Figures C.2.5 and C.2.6). Carbohydrate and lipid metabolic pathways were positively correlated with the incubation temperature in terms of their relative abundances, and the energy metabolic pathways negatively correlated. The amino acid metabolism, including pathways such as cysteine and methionine metabolism, showed no clear connection with the incubation

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temperature in LFA and LFB incubated samples. Although the incubation temperature has been shown to significantly affect protein degradation,70 the protein content in the MSW substrate

(1.6% N by weight) might be too low to cause a stable and pronounced impact.

The carbohydrate metabolism accounted for 7.1 – 12% of the total predicted pathways

(32 – 52% of the total metabolic pathways) in LFA incubated samples, suggesting that microorganisms from LFA actively digested/synthesized carbohydrates under all tested temperatures. Similarly, carbohydrate metabolism in LFB samples was higher in relative abundances at higher temperatures (up to 12% of the total predicted pathways and 53% of the total metabolic pathways). This is in accordance with the previous results showing higher relative abundances of cellulose-degrading genes in samples exposed to high temperatures

(Figure 4.12). With regard to specific pathways, the starch and sucrose metabolic pathway could be positively characterized by the incubation temperature (Figure C.2.7), suggesting that these processes remained vigorous at high temperatures (57.5 – 77.5 °C). As the incubation temperature further increased to 82.5 °C, starch and sucrose metabolisms became inhibited, which corresponds to the decrease in VFA production monitored at this temperature.

The energy metabolism, including oxidative phosphorylation, photosynthesis, carbon fixation and metabolisms of methane, nitrogen and sulfur, explained 16 – 35% of the total metabolic pathways in LFA (Figure C.2.5) and 15 – 36% in LFB (Figure C.2.6), with methane metabolism being the most influential process and being strongly correlated with the incubation temperature (p < 0.05) (Figure C.2.7). This observation is in accordance with the decreases in methane yields and methanogen abundances under elevated incubation temperatures (Figures

4.1, 4.3 and 4.6). The lipid metabolism accounted for a small portion of metabolic pathways in both landfills, but correlated well with the incubation temperature (p < 0.05). This could be

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caused by production of rigid long-chain lipids that are necessary for stabilizing cell membranes in thermophilic environments.65

While Piphillin can produce preliminary predictions for environmental samples, it should be noted that functional prediction based on 16S rRNA gene sequence data can be biased since it relies heavily on existing reference genomes.24,71 Additionally, the 16S sequencing methods and data processing can potentially affect the reconstruction of metagenomes.23 Transcriptomic and proteomic functional profiling are more robust options to accurately describe the metabolic processes in landfills and evaluate their significance associated with different environmental factors.

4.5.5. Implications

Our study has implications for landfill operation and maintenance. The wellhead gas temperature is required to be frequently monitored at full-scale landfills as an indicator of landfill performance. As described in the New Source Performance Standards (NSPS), when the wellhead temperature increases above 55 °C, concern rises as there may be a subsurface fire and inhibition of methanogenesis that impedes anaerobic decomposition.72 However, the wellhead gas temperature can be ~10 – 20 °C lower than the waste temperature,2,73 indicating an actual waste temperature of 65 – 75 °C. Based on our findings this temperature range may already shut down methanogen activities while allowing for hydrogen and fatty acid accumulation. Therefore, caution may be taken before the wellhead gas temperature reaches 55 °C, when the landfills are operated for maximum methane production.

In addition, our study improves the understanding of landfill ecology and explores the possibility of utilizing landfill microbial communities as microbial resources. For example, we identified multiple microorganisms associated with cellulose degradation in landfill samples, 103

especially under thermophilic conditions. Previous studies have suggested that cellulosedegrading mixed communities can produce abundant cellulolytic enzymes that effectively break down recalcitrant biomass.74,75 Therefore, the landfill microbial communities may serve as a renewable resource of these enzymes with potential applications in the biomass-to-biofuel technology.

4.6 Conclusions

The overall objective of this study was to determine the impact of temperature on microbial community structure and functionality in landfill samples exhibiting elevated temperatures. Microbial communities in the original excavated samples from two landfills (LFA and LFB) and samples after lab-scale incubation were investigated using the 16S rRNA amplicon sequencing. The incubation temperature significantly changed the community composition and diversity, while the excavation temperature had limited impact.

Methanothermobacter was the major methanogen present in both landfills that might be responsible for methane production, and multiple thermophilic fermenters (e.g., Defluviitoga,

Hydrogenispora) and acetate-oxidizing bacteria (e.g., Coprothermobacter) might form syntrophic relationships with it by producing hydrogen gas. Besides, some fermenters were capable of producing volatile fatty acids (VFAs) at high incubation temperatures (e.g., > 70 °C) where methanogenesis was inhibited. With regard to functionality, Piphillin predicted the shifts in methanogenesis activities and fermentation under various temperatures, which is in good agreement with the measured methane and VFA yields. In conclusion, this study reveals the key role of incubation temperature in landfill microbial communities and profiles the community distribution in landfills operating under thermophilic conditions and above.

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(55) Gagliano, M. C. C.; Braguglia, C. M. M.; Petruccioli, M.; Rossetti, S. Ecology and Biotechnological Potential of the Thermophilic Fermentative Coprothermobacter Spp. FEMS Microbiology Ecology. 2015, 91, fiv018.

(56) Fardeau, M. L.; Bonilla Salinas, M.; L’Haridon, S.; Jeanthon, C.; Verhé, F.; Cayol, J. L.; Patel, B. K. C.; Garcia, J. L.; Ollivier, B. Isolation from Oil Resorvoirs of Novel Thermophilic Anaerobes Phylogenetically Related to Thermoanaerobacter Subterraneus: Reassignment of T. Subterraneus, Thermoanaerobacter Yonseiensis, Thermoanaerobacter Tengcongensis and Carboxydibrachium Pacificum to Caldanaerobacter Subterraneus Gen. Nov., Sp. Nov., Comb. Nov. as Four Novel Subspecies. Int. J. Syst. Evol. Microbiol. 2004, 54 (2), 467–474.

(57) Droffner, M. L.; Brinton, W. F.; Evans, E. Evidence for the Prominence of Well Characterized Mesophilic Bacteria in Thermophilic (50-70°C) Composting Environments. Biomass and Bioenergy 1995, 8 (3), 191–195.

(58) DeCicco, B. T.; Noon, K. F. Thermophilic Mutants of Pseudomonas Fluorescens. Arch. Mikrobiol. 1973, 90 (4), 297–304.

(59) Manaia, C. M.; Moore, E. R. B. Pseudomonas Thermotolerans Sp. Nov., a Thermotolerant Species of the Genus Pseudomonas Sensu Stricto. Int. J. Syst. Evol. Microbiol. 2002, 52 (6), 2203–2209.

(60) Lü, F.; Bize, A.; Guillot, A.; Monnet, V.; Madigou, C.; Chapleur, O.; Mazéas, L.; He, P.; Bouchez, T. Metaproteomics of Cellulose Methanisation under Thermophilic Conditions Reveals a Surprisingly High Proteolytic Activity. ISME J. 2014, 8 (1), 88–102.

(61) Dyksma, S.; Jansen, L.; Gallert, C. Syntrophic Acetate Oxidation Replaces Acetoclastic Methanogenesis during Thermophilic Digestion of Biowaste. Microbiome 2020, 8 (1), 105.

(62) Mosbæk, F.; Kjeldal, H.; Mulat, D. G.; Albertsen, M.; Ward, A. J.; Feilberg, A.; Nielsen, J. L. Identification of Syntrophic Acetate-Oxidizing Bacteria in Anaerobic Digesters by Combined Protein-Based Stable Isotope Probing and Metagenomics. ISME J. 2016, 10 (10), 2405–2418.

(63) Clarke, K. R. Non-Parametric Multivariate Analyses of Changes in Community Structure. Aust. J. Ecol. 1993, 18 (1), 117–143.

(64) Warton, D. I.; Wright, S. T.; Wang, Y. Distance-Based Multivariate Analyses Confound Location and Dispersion Effects. Methods Ecol. Evol. 2012, 3 (1), 89–101.

(65) Siliakus, M. F.; van der Oost, J.; Kengen, S. W. M. Adaptations of Archaeal and Bacterial Membranes to Variations in Temperature, PH and Pressure. Extremophiles. 2017, 21 (4), 651–670.

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(66) Poret-Peterson, A. T.; Albu, S.; McClean, A. E.; Kluepfel, D. A. Shifts in Soil Bacterial Communities as a Function of Carbon Source Used During Anaerobic Soil Disinfestation. Front. Environ. Sci. 2019, 6 (JAN), 160.

(67) Ibekwe, A. M.; Ors, S.; Ferreira, J. F. S.; Liu, X.; Suarez, D. L.; Ma, J.; Ghasemimianaei, A.; Yang, C. H. Functional Relationships between Aboveground and Belowground Spinach (Spinacia Oleracea L., Cv. Racoon) Microbiomes Impacted by Salinity and Drought. Sci. Total Environ. 2020, 717, 137207.

(68) Ermler, U.; Grabarse, W.; Shima, S.; Goubeaud, M.; Thauer, R. K. Crystal Structure of Methyl-Coenzyme M Reductase: The Key Enzyme of Biological Methane Formation. Science. 1997, 278 (5342), 1457–1462.

(69) Ferry, J. G. Enzymology of One-Carbon Metabolism in Methanogenic Pathways. FEMS Microbiol. Rev. 1999, 23 (1), 13–38.

(70) Kim, H. W.; Nam, J. Y.; Shin, H. S. A Comparison Study on the High-Rate Co-Digestion of Sewage Sludge and Food Waste Using a Temperature-Phased Anaerobic Sequencing Batch Reactor System. Bioresour. Technol. 2011, 102 (15), 7272–7279.

(71) Douglas, G. M.; Maffei, V. J.; Zaneveld, J. R.; Yurgel, S. N.; Brown, J. R.; Taylor, C. M.; Huttenhower, C.; Langille, M. G. I. PICRUSt2 for Prediction of Metagenome Functions. Nat. Biotechnol. 2020, 38 (6), 685–688.

(72) US EPA. Standards of Performance for Municipal Solid Waste Landfills (40 CFR Part 60) ; 2016.

(73) Martin, J. W.; Stark, T. D.; Thalhamer, T.; Gerbasi-Graf, G. T.; Gortner, R. E. Detection of Aluminum Waste Reactions and Waste Fires. J. Hazardous, Toxic, Radioact. Waste 2013, 17 (3), 164–174.

(74) Hess, M.; Sczyrba, A.; Egan, R.; Kim, T. W.; Chokhawala, H.; Schroth, G.; Luo, S.; Clark, D. S.; Chen, F.; Zhang, T.; Mackie, R. I.; Pennacchio, L. A.; Tringe, S. G.; Visel, A.; Woyke, T.; Wang, Z.; Rubin, E. M. Metagenomic Discovery of Biomass-Degrading Genes and Genomes from Cow Rumen. Science. 2011, 331 (6016), 463–467.

(75) Ransom-Jones, E.; McCarthy, A. J.; Haldenby, S.; Doonan, J.; McDonald, J. E. Lignocellulose-Degrading Microbial Communities in Landfill Sites Represent a Repository of Unexplored Biomass-Degrading Diversity. mSphere 2017, 2 (4).

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Chapter 5. Limitations and future research

Anaerobic microbial processes are essential in the field of wastewater and solid waste treatment. As these processes are often limited by the range of contaminants that can be targeted and the long timescales that are often required, it is necessary to explore more efficient microbial pathways for contaminant degradation and energy production, and to develop microbial communities suitable for these purposes. This dissertation provides insight on this area of research with experimental evaluation of mediating capabilities of pyrogenic carbonaceous materials (PCMs) and metagenomic characterization of mixed communities. Nevertheless, some limitations should be noted.

Chapter 2 focuses on the impact of amending pyrogenic carbonaceous materials (PCMs) on methane production from anaerobic bioreactors treating swine wastewater. In this work, organic matter adsorption was found to be a key material property that impacted methanogenesis in PCM-mediated reactors. However, this chapter only investigated short-term batch bioreactors not the long-term, continuously-fed anaerobic digesters that are also very frequently utilized.

Besides, due to the complex nature of wastewater, the findings may not directly apply to other types of waste streams, and the adsorption effect should be re-evaluated based on the wastewater type. Moreover, this study evaluated a combined role of multiple PCM physicochemical properties, which may vary significantly depending on the PCM feedstock and pyrolysis conditions. Therefore, caution should be taken when PCMs generated under various conditions are amended in anaerobic digesters.

Chapter 3 proposes a simple yet effective method to enrich exoelectrogenic communities by providing PCMs as the sole electron acceptor and acetate as the sole electron donor to a

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mixed microbial community. Although this work isolated the electrical property from other properties and investigated its role for microbial processes, it remains unclear how PCM’s electrical conductivity and redox activity collaborate (or compete) with each other to mediate acetate degradation by Geobacter. In addition, the microbially accessible electron accepting capacities (mEACs) of PCMs were calculated based on acetate consumption, which overestimated the true number of electrons entering PCMs, as some acetate could be utilized for biomass synthesis.1

Chapter 4 describes the structures and functions of microbial communities present in landfills exposed to elevated temperatures, and reveals the key role of incubation temperature in shifting landfill communities. However, it should be noted that the community analysis was only performed for end-of-incubation samples (i.e. when the reactions were complete), and the results could not reflect timely the community structures during incubation. Therefore, the shifts in microbial community structures, especially when methane, hydrogen and volatile fatty acid yields fluctuated, remain unclear. Furthermore, the functional prediction by Piphillin was based solely on the 16S results, which can vary substantially from the shotgun metagenomics sequencing, and the construction of functional network may be biased towards microorganisms with completely characterized genomes.

The work presented in this dissertation can be expanded by considering the limitations described above. For instance, the synthesized PCM-like materials with precisely controlled properties may be utilized to better illustrate the impact of individual property on microbial reactions described in Chapters 2 and 3. This method has been applied to reveal the mechanism of reductive degradation of trichloronitromethane mediated by PCMs.2 Besides, the stable isotope labeling (e.g., 13C) can help better trace the electron flux into PCMs from acetate

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oxidation and more accurately estimate mEACs. For the landfill study, it may be necessary to sample and analyze DNA periodically to investigate how the microbial communities were developed over time and how the community shifts correspond to changes in gas and acid production. The stable isotope labeling technique can also help identify reactions under different temperatures. Furthermore, other “meta-omics” technologies, such as metatranscriptomics, metaproteomics and metabolomics, may enable us to better interpret the observations in this work and understand more deeply the functions of landfill microbial communities.

This work can also be expanded to other related areas of research. PCMs are naturally present in nature, and have been widely applied to soils, sediments and aquatic environments as sorbents to remove contaminants.2,3 However, little is known about whether or not microorganisms naturally present in these environmental systems can transfer electrons to/from

PCMs. The microorganisms may utilize PCMs as either electron sinks for their growth, or as

“batteries” to power reductive degradation of contaminants, which extends the benefits of PCM amendments. Future research can be conducted to screen these microorganisms from PCM- amended environments (other than biologically activated carbon filter) and evaluate the importance of this pathway. It is also possible to tune these systems to facilitate the microbial electron exchange with PCMs for environmental purposes of interest.

Another area of potential future research is to recover resources from landfilled waste with microbial processes. Besides biogas, landfills generate high-strength leachate, where energy, nutrients and metals can be recovered using bioelectrochemical systems.4 Moreover, microbial resources such as cellulases can be discovered and extracted, as described in Chapter

4. With the assistance of “omics” technologies, microorganisms and enzymes that catalyze more efficient reactions of interest could be discovered and applied in the future.

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References

(1) Esteve-Núñez, A.; Rothermich, M.; Sharma, M.; Lovley, D.; Esteve-Nunez, A.; Rothermich, M.; Sharma, M.; Lovley, D.; Esteve-Núñez, A.; Rothermich, M.; Sharma, M.; Lovley, D.; Esteve-Nunez, A.; Rothermich, M.; Sharma, M.; Lovley, D.; Esteve- Núñez, A.; Rothermich, M.; Sharma, M.; Lovley, D. Growth of Geobacter Sulfurreducens under Nutrient-Limiting Conditions in Continuous Culture. Environ. Microbiol. 2005, 7 (5), 641–648.

(2) Li, Z.; Mao, J.; Chu, W.; Xu, W. Probing the Surface Reactivity of Pyrogenic Carbonaceous Material (PCM) through Synthesis of PCM-Like Conjugated Microporous Polymers. Environ. Sci. Technol. 2019, 53 (13), 7673–7682.

(3) Ren, P.; Liu, Y.; Shi, X.; Sun, S.; Fan, D.; Wang, X. Sources and Sink of Black Carbon in Arctic Ocean Sediments. Sci. Total Environ. 2019, 689, 912–920.

(4) Iskander, S. M.; Brazil, B.; Novak, J. T.; He, Z. Resource Recovery from Landfill Leachate Using Bioelectrochemical Systems: Opportunities, Challenges, and Perspectives. Bioresource Technology. 2016, 201, 347–354.

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APPENDICES

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Appendix A. Supplementary information for Chapter 2

A.1. Supplementary methods

A.1.1. Soluble COD of particle amended deionized water (Table A.2.1)

Deionized water (20 mL) was mixed with particles in 50 mL tubes, and the tubes were shaken by hand and then allowed to stand for 24 hours (25 °C). Supernatant was filtered through polyethersulfone syringe filters (pore size 0.2 μm; VWR International, Radnor, PA, USA) and soluble COD (SCOD) was measured using Hach Method 8000 (DR/890 Portable Colorimeter;

Hach, Loveland, CO, USA). The highest particle-to-working volume ratio (3.4 g particles per

100 mL) was used.

A.1.2. pH of particle-amended deionized water and swine wastewater (Table A.2.2)

Deionized water (20 mL) or the swine wastewater mixture [seed:feed = 1:30 (v:v); seed and feed collected on May 17, 2016] was mixed with particles in 50 mL tubes, and the tubes were shaken by hand and then allowed to stand for 5 min (25 °C). Supernatant pH was measured using an Orion 3-Star benchtop pH meter equipped with an Orion ROSS Ultra Refillable pH/ATC Triode (Thermo Scientific, Waltham, MA, USA). The highest particle-to-working volume ratio (3.4 g-particles per 100 mL) was used.

A.1.3. Abiotic volatile fatty acid (VFA) adsorption in sterile swine wastewater amended with particles (Figure A.3.2)

The swine wastewater mixture [seed:feed = 1:30 (v:v); seed and feed collected on May

17, 2016] and amended particles were autoclaved before use. The sterile swine wastewater mixture (100 mL) was mixed with sterile particles in 125 mL serum bottles. The bottles were

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incubated on shakers at 30 °C for 19 days. Supernatant VFAs were determined using a Dionex

ICS-5000+ Ion Chromatography system with a conductivity detector and Dionex IonPac AS11-

HC column (Thermo Scientific, Waltham, MA, USA). The lowest particle loading (2.2 g- particles per g-VSseed before sterilization) was used.

A.1.4. Abiotic methane adsorption in deionized water amended with particles (Figure

A.3.3)

Gas containing either 100% or 10% methane gas was directly injected into 20 mL of deionized water in 25 mL serum bottles amended with particles (headspace pressure balanced with air during injection). The highest particle-to-working volume ratio (3.4 g particles per 100 mL) was used. The bottles were incubated at 30 °C and the gas composition analyzed after 24 hours with a gas chromatograph (Model 8610C; SRI Instruments, Torrance, CA, USA) equipped with a thermal conductivity detector (TCD) and CTR I Column.

A.1.5. Abiotic total ammonia nitrogen (TAN) adsorption in NH4Cl solutions amended with particles (Figure A.3.4)

-1 NH4Cl solutions (20 mL; initial TAN = 220 mg L ) were mixed with particles in 25 mL bottles and the bottles incubated on shakers at 30 °C for 19 days. The highest particle loading

(3.4 g particles per 100 mL) was used. The initial pH of the mixture was adjusted to 7.0 with 6 M

HCl. The final TAN concentrations in the supernatant were measured using Hach Method 10031

(DR/890 Portable Colorimeter; Hach, Loveland, CO, USA).

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A.2. Supplementary tables

Table A.2.1. Soluble COD of particle-amended deionized water.

Particle Activated Graphite Biochar Glass size carbon SCOD G 13 ± 7 8 ± 2 11 ± 5 13 ± 1 (mg L-1) P 21 ± 7 16 ± 4 23 ± 17 17 ± 5 Average ± range (n = 2). G – granular; P – powdered. A loading of 3.4 g particles per 100 mL was used.

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Table A.2.2. pH of particle-amended deionized water and swine wastewater.

Particle Activated No- Graphite Biochar Glass size carbon particle Deionized G 5.2 ± 0.03 8.5 ± 0.02 9.3 ± 0.02 9.1 ± 0.02

water P 5.3 ± 0.01 9.0 ± 0.01 9.4 ± 0.03 9.0 ± 0.02 Swine G 7.3 ± 0.01 7.7 ± 0.02 8.0 ± 0.02 7.8 ± 0.03 7.6 ± 0.04 wastewater P 7.4 ± 0.02 7.8 ± 0.01 8.1 ± 0.03 7.7 ± 0.03 Average ± range (n = 2). G – granular; P – powdered. A loading of 3.4 g particles per 100 mL was used.

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A.3. Supplementary figures

Figure A.3.1. Maximum methane production rates as a function of particle loadings from (A) granule-amended reactors and (B) powder-amended reactors. Methane recoveries as a function of particle loading from (C) granule-amended reactors and (D) powder-amended reactors. Error bars represent the range of replicate experiments (n = 2). Correlation coefficients greater than 0.7 are shown.

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Figure A.3.2. Volatile fatty acid (VFA) adsorption in sterile swine wastewater amended with particles. The minimum detection limit was 20 mg L-1. Concentrations below this limit are not shown. The lowest particle loading (2.2 g particles per g VSseed before sterilization) was used. G – granular; P – powdered. Error bars represent the range of replicate experiments (n = 2).

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Figure A.3.3. Abiotic methane adsorption in deionized water amended with particles. Blue diagonal lines represent the methane concentrations after adsorption with 100% methane initially injected. Yellow horizontal lines represent the methane concentrations after adsorption with 10% methane initially injected. The highest particle-to-working volume ratio (3.4 g particles per 100 mL; n = 1) was used. A 24-hour incubation was used. G – granular; P – powdered.

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Figure A.3.4. Total ammonia nitrogen (TAN) adsorption in NH4Cl solutions amended with particles under abiotic conditions. The highest particle-to-working volume ratio (3.4 g particles per 100 mL) was used. A 19-day incubation was used. G – granular; P – powdered. Error bars represent the range of replicate experiments (n = 2).

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Appendix B. Supplementary information for Chapter 3

B.1. Supplementary methods

B.1.1. Characterization of csGAC

The physical properties and chemical composition of the csGAC are summarized in

Table B.2.2. Brunauer-Emmett-Teller (BET) surface area and pore distribution were characterized using N2 adsorption (Autosorb-1; Quantachrome Instruments, Boynton Beach, FL,

USA). The ash content was determined by heating the samples under 500 °C for 12 hours and quantifying the residuals. For elemental composition, carbon (C), hydrogen (H) and nitrogen (N) content was determined with a CHN analyzer (Model 2400, PerkinElmer Inc., Waltham, MA,

USA). Sulfur (S), iron (Fe) and manganese (Mn) content was analyzed with an ion-coupled plasma spectrometer (Model 8000, PerkinElmer Inc., Waltham, MA, USA). Oxygen (O) content was calculated based on mass balance, which is O% = 100% – C% – H% – N% – S% – ash%.1–3

The O/C and H/C molar ratios were calculated based on the elemental composition. The electrical conductivity of GAC was determined as previously described.4

The double-bond equivalent (DBE, expressed on a per-carbon basis) and aromaticity index (AI) of the csGAC were calculated based on the following equations, assuming negligible phosphorus (P) present in GAC.5,6 Both parameters measure the density of aromatic structures, but AI takes into consideration the π-bonds by heteroatoms and thus is more conservative.5

DBE > 0.7 and AI > 0.67 indicate the presence of condensed aromatic rings.5

1 + [C]!0.5[H]"0.5[N]"0.5[P] DBE = (B.1) [C]

1 + [C]![O]![S]!0.5[N]!0.5[H] AI = (B.2) [C]![O]![S]![N]![P]

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X-ray photoelectron spectroscopy (XPS) was conducted using a SPECS FlexMod XPS system with a PHOIBOS 150 Analyzer. Mg Kα (1254 eV) was used as the X-ray source.

Photoelectrons were collected using a take-off angle of 90º relative to the sample surface (~ 3 mm in diameter), with the pass energy 24 eV for the survey spectrum and 20 eV for high- resolution spectra. The X-ray incidence angle was set to ~ 30° from the sample surface and X- ray source to the analyzer ~ 60°. The working pressure in the analysis chamber was ~ 10-10 mbar.

Binding energy was calibrated by referencing to C (1s) at 284.5 eV. Deconvolution of C (1s) and

O (1s) spectra was conducted using the CasaXPS software (version 2.3.22), after Shirley background subtraction. The spectra were fitted to mixed Gaussian-Lorentzian lineshapes

(GL30), with the same FWHM (1 – 2) for all fitted peaks. Quantification was carried out using the O (1s) sensitivity factor of 2.85. The surface functional groups were identified according to the different binding energies described elsewhere.7–14 The peak fitting is illustrated in Figure

B.3.3. The functional groups, corresponding binding energies and their atomic percentages are shown in Table B.2.3. The assignment for Peaks 2-4 in the O (1s) region remains debatable, but the exact assignment of these features is not the focus of this study.

The XPS survey spectrum of csGAC (Figure B.3.3) shows a strong C (1s) peak at 284.5 eV and a relatively weak O (1s) peak at 532.5 eV. No clear peaks for phosphorus (~ 133 eV and

~ 190 eV),15,16 nitrogen (~ 400 eV)9,16 and sulfur (~ 160 – 170 eV)17 can been observed, indicating no significant quantities of functional groups associated with these elements. The atomic O (1s) / C (1s) ratio was calculated to be 0.18, which is 3.3 times higher than that calculated from elemental composition analysis. According to literature, the exterior surface of pyrogenic carbonaceous materials (PCMs) is more vulnerable to oxidation than the interior.18–21

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Therefore, the aeration of csGAC might only increase the surface oxygen content and result in a higher O/C ratio detected with XPS.

To date, most PCM studies have reported a positive correlation between electron accepting capacities (EACs) and C=O (especially quinone) content. Therefore, we assumed that the microbially accessible electron accepting capacities (mEACs) in our study could be associated with C=O content in the csGAC. Here we referred to a method previously described in the literature6 and calculated the bulk C=O content based on our XPS and elemental analysis results.

According to C (1s) peak assignments [Table B.2.3, assuming that in O–C=O (Peak 5), each C corresponds to two O]:

Peak 4 C=O !mmol (g GAC)-1" = × bulk O content Peak 3 + Peak 4 + (Peak 5) × 2

= 1.2 mmol (g GAC)-1

According to O (1s) peak assignments (Table B.2.3):

Peak 1 C=O !mmol (g GAC)-1" = × bulk O content Peak 1 + Peak 2 + Peak 3 + Peak 4 + Peak 5 + Peak 6

= 0.69 mmol (g GAC)-1

Assuming that each mol of C=O can accept one mol of electrons, and therefore C=O could potentially contribute to 1.2 or 0.69 mmol e- (g GAC)-1, depending on which XPS spectrum was used for calculation. We selected 1.2 mmol e- (g GAC)-1 due to the higher quality of C (1s) peak (with stronger peak signal and less noise). This value indicates that C=O content in the bulk csGAC might be sufficient to explain the mEACs [0.91 – 1.0 mmol e- (g GAC)-1] observed in our study. Nevertheless, microorganisms might need to utilize GAC’s conductive matrix to conduct electrons into the GAC interior and reduce the functional groups beyond reach.

Another assumption in our calculation was that the percentage of C=O was uniform throughout

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the GAC interior, which might be inaccurate due to less oxidized GAC in the interior that could have fewer C=O bonds.18–21

B.1.2. Growth of Geobacter sulfurreducens from frozen stock

G. sulfurreducens PCA (DSM 12127 / ATCC 51573) was grown from a frozen stock

(−80 °C) in 125 mL glass serum bottles, each containing 50 mL sterile, anoxic ATCC 1957 medium with 10 mM sodium acetate as the electron donor and 50 mM sodium fumarate as the electron acceptor. The bottles were incubated on a shaker (230 rpm) at 30 °C. After cell growth approached the stationary phase (OD600 = 0.36), the bottles were stored under 4 °C (fridge stock) for future use. To prepare the inoculum for the GAC reduction experiment, cell suspensions from the fridge stock (1 mL) were re-inoculated into 100 mL fresh medium in 250 mL bottles. After cell growth reached the early stationary phase (OD600 = 0.30), cells (1 mL) were harvested, centrifuged at 8000 g, and resuspended in 0.2 mL medium without any electron acceptor and donor as the inoculum.

B.1.3. Scanning electron microscopy of GAC

To prepare samples for imaging, the csGAC particles were first fixed in glutaraldehyde solution (2.5% in 50 mM phosphate buffer, pH = 7.2) at 4 °C for 12 hours, and then washed with the same phosphate buffer under room temperature for three times (two minutes each), dehydrated in an ethanol/water gradient of 50%, 70%, 90% and 100% (two minutes each) and air-dried under 30 °C for at least two hours. The samples were sputter coated with gold and visualized with a Hitachi S3200N variable pressure scanning electron microscope (VPSEM), equipped with an Everhart-Thornley secondary electron detector (Hitachi High Technologies

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America, Schaumburg, IL, USA). The raw csGAC with no cell colonization was visualized without treatment.

B.1.4. Operation of the Tampa Bay Regional Surface Water Treatment Plant

The Tampa Bay Regional Surface Water Treatment Plant is a 120 million gallon per day

(MGD) facility. The treatment processes include Actiflo™ flocculation and sedimentation, ozone disinfection, biofiltration [48 inches of coconut-shell based granular activated carbon (csGAC) and six inches of sand], and final disinfection with chlorination and ammonia addition. The biofiltration is operated in downflow mode, and backwashed routinely twice a week. The processes are illustrated in Figure B.3.1.

B.1.5. Cell transfer method for the second and third incubations

At the end of the first incubation, two additional incubations were performed. For each of triplicate reactors, GAC was collected when acetate degradation rates approached zero (first incubation), and 5 – 10 pieces of GAC particles were chemically fixed for SEM. The remaining

GAC was treated with the same shaking-vortexing-sonicating method as described in the Method section in the main text, and the liquid generated from each treatment combined. The combined liquid (5 mL) was centrifuged at 4000 rpm for 15 min to collect cells for DNA extraction. These cells were regarded as cells attached to the carbon surface [GAC (C)]. The suspension (40 mL) was poured into a sterile tube and centrifuged to collect microorganisms for DNA extraction.

These cells were regarded as the suspended fraction [GAC (S)]. The remaining suspension and liquid from all triplicate reactors were combined, centrifuged and resuspended in sterile medium as the inoculum for a new incubation cycle (second incubation). The same approach was used to

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start the third incubation. All cell transfers were performed in an anaerobic glovebox [N2/H2

(95:5) atmosphere], and all reactors were purged with nitrogen gas to remove the residual hydrogen gas.

B.1.6. Microbial community analysis with QIIME 2

QIIME 2 (version 2019.1) was used to analyze the sequencing data.22 The DADA2 plugin in QIIME 2 was used to filter and merge the forward and reverse reads 23. Several alpha diversity metrics for community richness and evenness, including number of OTUs, Chao1 index, Simpson's index, Shannon's diversity index, Good’s coverage and Pielou's evenness, were computed. The weighted UniFrac distance was calculated to show beta diversity, and was illustrated in a principal coordinates analysis (PCoA) plot. Permutational multivariate analysis of variance (PERMANOVA) analysis was conducted to examine the statistical differences in microbial compositions between samples, and p-values less than 0.05 were considered significant. The sequences were mapped to the Silva 132 99% OTUs Database. A classifier was trained with the forward and reverse primers used in this study. The relative abundance of microbial communities in each sample was exported at the genus level. Barplots were created for the average abundance values, and the communities with low relative abundances (< 2%) were grouped as “Others”.

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B.2. Supplementary tables

Table B.2.1. PCMs used in this study.

PCM type Abbreviation Feedstock Size (mm) Source Coconut shell Kindly offered by Tampa Coconut granular activated csGAC 0.85 – 2.4 Bay Regional Surface Water shell carbon Treatment Plant, Tampa, FL Lignite coal Kindly offered by Dempsey Lignite granular activated lcGAC 1.7 – 4.8 E. Benton Water Treatment coal carbon Plant, Garner, NC Bituminous coal Kindly offered by Tampa Bituminous granular activated bcGAC 0.85 – 2.4 Bay Regional Surface Water coal carbon Treatment Plant, Tampa, FL Kindly offered by Dr. Rice husk biochar rhBC Rice husk 0.25 – 0.42 Joshua Kearns at NCSU Hardwood Kindly offered by Dr. Hardwood biochar hwBC 0.25 – 0.42 chips Joshua Kearns at NCSU Pine wood Purchased from Waste To Pine wood biochar pwBC 0.25 – 0.42 sawdust Energy Inc., Slocomb, AL

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Table B.2.2. Specifications of the csGAC (after air oxidation) used in this study.

Parameter Unit GAC Branda Aquasorb Feedstocka Coconut shell Mesh sizea 8 × 20 Apparent densitya g mL-1 0.45 – 0.55 BET surface areab m2 g-1 1089 ± 30 Micropore areab 1020 ± 27 Mesopore areab 68 ± 3.3 Micropore volumeb cc g-1 0.41 ± 0.011 Carbon (C)b mmol (g GAC)-1 77 ± 0.16 Hydrogen (H)b 3.1 ± 0.55 Nitrogen (N)b 0.14 ± 0.018 Oxygen (O)b 4.2 ± 0.17 Sulfur (S)b 0.064 ± 0.0050 Iron (Fe)b 7.2E-04 ± 5.1E-05 Manganese (Mn)b 4.5E-05 ± 1.1E-06 Ash contentb wt% dry basis 0.22 ± 0.019 O/Cb 0.054 ± 0.0024 H/Cb 0.040 ± 0.0071 DBEb 0.99 ± 0.0034 AIb 0.99 ± 0.0036 Conductivityc S cm-1 2.3 ± 0.23 a Information provided by the manufacturer. b Average ± range (n = 2). c Average ± one standard deviation (n = 10).

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Table B.2.3. Deconvolution results of C (1s) and O (1s) regions from XPS analysis of csGAC (after air oxidation).

Region Peak Binding energy (eV) Assignment Atomic % C (1s) 1 284.5 ± 0.3 C=C (sp2) 45 2 285.2 ± 0.3 C–C (sp3) and C–H 27 3 286.0 ± 0.3 C–O 8.5 4 287.4 ± 0.3 C=O 7.7 5 289.1 ± 0.3 O–C=O 5.3 6 290.6 ± 0.3 π–π* transitions in aromatic rings 4.5 7 292.5 ± 0.3 Plasmon band 1.5 O (1s) 1 531.1 ± 0.3 C=O in quinones and carbonyl groups 16 2 532.2 ± 0.3 O–C=O and –OH 34 3 533.1 ± 0.3 C–O–C 26 4 534.0 ± 0.3 COOH and COOC 13 5 535.1 ± 0.3 Chemisorbed water or oxygen 6.6 6 536.3 ± 0.3 Physisorbed water 1.8 7 537.0 ± 0.3 Plasmon band 2.6

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Table B.2.4. Alpha diversity metrics. Calculations are based on subsampling of 102,018 sequences (i.e., the size of the smallest library). The data presented represent the average of biological triplicates ± one standard deviation (n = 3). GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors; 1, 2 and 3 represent the incubation cycle.

Sample # OTUs Chao1 index Simpson's Shannon's Good’s coverage Pielou's index diversity index evenness Inoculum 1754 ± 149 1810 ± 168 0.99 ± 0.00089 8.6 ± 0.10 100 ± 0.031% 0.80 ± 0.0019 Acetate + GAC (S)_1 76 ± 20 81 ± 19 0.66 ± 0.039 1.8 ± 0.19 100 ± 0.0018% 0.29 ± 0.035 Acetate + GAC (C)_2 51 ± 26 55 ± 29 0.51 ± 0.025 1.6 ± 0.074 100 ± 0.0069% 0.30 ± 0.040 Acetate + GAC (S)_2 113 ± 43 131 ± 55 0.52 ± 0.037 1.8 ± 0.10 100 ± 0.016% 0.26 ± 0.020 Acetate + GAC (C)_3 44 ± 7.6 49 ± 9.6 0.27 ± 0.0084 0.88 ± 0.027 100 ± 0.0029% 0.16 ± 0.0055 Acetate + GAC (S)_3 24 ± 3.1 24 ± 3.4 0.28 ± 0.015 1.0 ± 0.032 100 ± 0.00050% 0.22 ± 0.017 Acetate + O2 115 ± 21 116 ± 21 0.59 ± 0.033 2.4 ± 0.12 100 ± 0.00072% 0.35 ± 0.022

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B.3. Supplementary figures

Figure B.3.1. Schematic diagram of the Tampa Bay Regional Surface Water Treatment Plant. The biologically active carbon (BAC) filter samples were collected from the biofiltration unit shown in the diagram.

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Figure B.3.2. Dissolved organic carbon (DOC) in the rinsate of lignite coal-based GAC (lcGAC) during the preparation steps. The data represent the average of triplicates ± one standard deviation (n = 3). EEC: electron exchange capacity.

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Figure B.3.3. (A) XPS survey spectrum, (B) C (1s) spectrum, and (C) O (1s) spectrum of csGAC.

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Figure B.3.4. The mEAC values of multiple PCMs calculated from acetate degradation by Geobacter sulfurreducens. Error bars represent one standard deviation of triplicate experiments (n = 3) on mEAC estimations from acetate for csGAC and lcGAC, and error bars represent range of duplicate experiments (n = 2) on other mEAC estimations. rhBC: rice husk-based biochar; hwBC: hardwood-based biochar; pwBC: pine wood-based biochar; csGAC: coconut shell-based GAC; lcGAC: lignite coal-based GAC; bcGAC: bituminous coal-based GAC.

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Figure B.3.5. Correlation between COD and acetate measurement in bottles with Geobacter sulfurreducens medium and acetate.

139

Figure B.3.6. Acetate consumption in the abiotic and biotic controls in the (A) second and (B) third incubations. Acetate concentrations (mg L-1) measured at different time points were normalized to the initial concentrations at Day 0. Error bars represent one standard deviation of triplicate experiments (n = 3).

140

Figure B.3.7. Scanning electron micrograph of the original BAC culture (from the pilot-scale system) attached to csGAC. White scale bar represents 10 µm.

141

Figure B.3.8. Alpha rarefaction curves based on observed OTUs. The sampling depth was 102,018, which was the size of the smallest library of all samples. GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors; 1, 2 and 3 represent the incubation cycle. Rarefaction curves for all samples approached saturation, indicating that the sampling depth fully covered the diversity of microbial communities.

142

Figure B.3.9. (A) Weighted unifrac distance-based PCoA plot displaying the distances between community compositions and (B) pair-wise PERMANOVA results showing the significance of distances between each pair of samples. A p-value of less than 0.05 was considered significant. The numbers 1, 2 and 3 and arrows in (A) represent the incubation cycle. GAC (C): communities present on the carbon surface of csGAC; GAC (S): communities present in the suspended fraction of the reactors.

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Figure B.3.10. Taxonomic distribution of microorganisms in the BAC inoculum and when oxygen (O2) was the electron acceptor. Genera with the relative abundance of less than 2% and unclassified genera are grouped into the “Others” category. The abundances represent averages of biological triplicates.

144

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Appendix C. Supplementary information for Chapter 4

C.1. Supplementary tables

Table C.1.1. Pairwise t-test results showing the significance of alpha diversity metrics between each pair of LFA samples. A p-value of less than 0.05 was considered significant. “Excav” represents excavated samples.

Observed Chao1 Shannon Simpson Excav vs 47.5 °C 0.046 0.074 0.63 0.51 Excav vs 52.5 °C 2.6E-04 8.7E-04 0.15 0.52 Excav vs 57.5 °C 5.5E-04 2.6E-03 2.0E-03 0.87 Excav vs 62.5 °C 2.6E-04 1.3E-03 1.9E-08 1.2E-04 Excav vs 67.5 °C 3.2E-06 2.7E-05 9.5E-08 0.010 Excav vs 72.5 °C 5.0E-07 4.6E-06 6.0E-09 3.3E-05 47.5 vs 52.5 °C 0.020 0.033 0.21 0.95 47.5 vs 57.5 °C 0.043 0.098 8.8E-04 0.27 47.5 vs 62.5 °C 0.019 0.046 1.4E-09 5.6E-07 47.5 vs 67.5 °C 1.7E-04 8.7E-04 6.0E-09 1.0E-04 47.5 vs 72.5 °C 1.0E-05 6.2E-05 1.4E-09 5.6E-07 52.5 vs 57.5 °C 0.68 0.53 0.041 0.39 52.5 vs 62.5 °C 0.91 0.88 6.1E-08 5.6E-07 52.5 vs 67.5 °C 0.067 0.12 6.2E-07 1.3E-04 52.5 vs 72.5 °C 2.3E-03 6.2E-03 2.5E-08 6.2E-07 57.5 vs 62.5 °C 0.64 0.61 5.8E-05 7.8E-06 57.5 vs 67.5 °C 0.027 0.036 4.7E-04 2.9E-03 57.5 vs 72.5 °C 7.5E-04 1.6E-03 3.7E-06 7.8E-06 62.5 vs 67.5 °C 0.087 0.11 0.63 0.15 62.5 vs 72.5 °C 3.0E-03 5.6E-03 0.060 0.19 67.5 vs 72.5 °C 0.067 0.098 0.031 0.014

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Table C.1.2. Pairwise t-test results showing the significance of alpha diversity metrics between each pair of LFB samples. A p-value of less than 0.05 was considered significant. “Excav” represents excavated samples.

Observed Chao1 Shannon Simpson Excav vs 47.5 °C 0.12 0.090 0.26 0.87 Excav vs 52.5 °C 8.1E-03 4.4E-03 0.063 0.71 Excav vs 57.5 °C 1.6E-03 8.9E-04 1.4E-03 0.13 Excav vs 62.5 °C 1.6E-03 8.9E-04 1.1E-05 8.2E-05 Excav vs 67.5 °C 1.6E-03 8.9E-04 1.4E-03 0.049 Excav vs 72.5 °C 1.6E-03 8.9E-04 2.9E-03 0.13 Excav vs 77.5 °C 0.067 0.036 0.067 0.13 Excav vs 82.5 °C 9.0E-03 4.7E-03 0.15 0.66 47.5 vs 52.5 °C 0.34 0.30 0.29 0.80 47.5 vs 57.5 °C 0.18 0.17 0.013 0.12 47.5 vs 62.5 °C 0.17 0.15 9.9E-05 2.2E-05 47.5 vs 67.5 °C 0.13 0.12 0.011 0.037 47.5 vs 72.5 °C 0.12 0.086 0.032 0.13 47.5 vs 77.5 °C 0.93 0.91 0.33 0.31 47.5 vs 82.5 °C 0.27 0.24 0.53 0.71 52.5 vs 57.5 °C 0.93 0.92 0.20 0.23 52.5 vs 62.5 °C 0.93 0.91 5.9E-03 8.2E-05 52.5 vs 67.5 °C 0.82 0.88 0.15 0.087 52.5 vs 72.5 °C 0.77 0.72 0.26 0.23 52.5 vs 77.5 °C 0.55 0.58 0.90 0.50 52.5 vs 82.5 °C 0.93 0.91 0.87 0.87 57.5 vs 62.5 °C 0.93 0.92 0.11 4.3E-03 57.5 vs 67.5 °C 0.93 0.92 0.83 0.62 57.5 vs 72.5 °C 0.93 0.88 0.87 0.97 57.5 vs 77.5 °C 0.30 0.33 0.15 0.66 57.5 vs 82.5 °C 0.93 0.92 0.20 0.41 62.5 vs 67.5 °C 0.93 0.92 0.20 0.053 62.5 vs 72.5 °C 0.93 0.91 0.093 7.3E-03 62.5 vs 77.5 °C 0.27 0.30 2.8E-03 1.4E-03 62.5 vs 82.5 °C 0.98 0.94 0.013 2.3E-03 67.5 vs 72.5 °C 0.97 0.92 0.70 0.65 67.5 vs 77.5 °C 0.21 0.24 0.11 0.31 67.5 vs 82.5 °C 0.93 0.96 0.16 0.21 72.5 vs 77.5 °C 0.18 0.17 0.20 0.66 72.5 vs 82.5 °C 0.93 0.92 0.26 0.41 77.5 vs 82.5 °C 0.39 0.38 0.91 0.66

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Table C.1.3. Pairwise permutational multivariate analysis of variance (PERMANOVA) results showing the significance of distances between each pair of samples. A p-value of less than 0.05 was considered significant.

LFA_incubated LFB_excavated LFB_incubated LFA_excavated 2.0E-03 2.0E-03 1.5E-03 LFA_incubated 1.5E-03 1.5E-03 LFB_excavated 1.5E-03

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Table C.1.4. Pairwise permutational multivariate analysis of variance (PERMANOVA) results showing the significance of distances between each pair of LFA samples. A p-value of less than 0.05 was considered significant. “Excav” represents excavated samples.

52.5 °C 57.5 °C 62.5 °C 67.5 °C 72.5 °C Excav 47.5 °C 4.4E-03 1.5E-03 1.5E-03 1.5E-03 2.6E-03 1.5E-03 52.5 °C 1.5E-03 1.5E-03 1.5E-03 1.5E-03 1.5E-03 57.5 °C 1.5E-03 1.5E-03 2.6E-03 1.5E-03 62.5 °C 3.5E-03 3.5E-03 1.5E-03 67.5 °C 8.4E-03 1.5E-03 72.5 °C 1.0E-03

151

Table C.1.5. Pairwise permutational multivariate analysis of variance (PERMANOVA) results showing the significance of distances between each pair of LFB samples. A p-value of less than 0.05 was considered significant. Numbers in yellow represent p-values higher than or close to 0.05. “Excav” represents excavated samples.

52.5 °C 57.5 °C 62.5 °C 67.5 °C 72.5 °C 77.5 °C 82.5 °C Excav 47.5 °C 0.032 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 52.5 °C 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 57.5 °C 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 1.3E-03 62.5 °C 6.0E-03 1.3E-03 1.3E-03 0.0093 1.3E-03 67.5 °C 1.3E-03 1.3E-03 0.0093 1.3E-03 72.5 °C 0.94 0.057 1.3E-03 77.5 °C 0.044 1.3E-03 82.5 °C 5.0E-03

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Table C.1.6. SIMPER analysis results showing OTUs in LFA incubated samples that contributed to the differences between each pair of incubation temperatures. Only the top five OTUs with highest contribution percentages between each pair of temperatures were listed. A p-value of less than 0.05 was considered significant.

Pair Taxonomy Percentage p-value D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 9.0% > 0.05 ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.7% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon 47.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methanosarcinales;D_4__ 6.1% > 0.05 52.5 °C Methanosarcinaceae;D_5__Methanosarcina D_0__Bacteria;D_1__Hydrothermae;D_2__uncultured bacterium;D_3__uncultured 4.9% > 0.05 bacterium;D_4__uncultured bacterium;D_5__uncultured bacterium;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 4.8% 0.0035 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 12.6% 0.0004 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.6% 0.0004 Methanothermobacteraceae;D_5__Methanothermobacter 47.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 8.3% 0.0019 57.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methanosarcinales;D_4__ 4.9% 0.0004 Methanosarcinaceae;D_5__Methanosarcina D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 3.5% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 22.5% 0.0005 Methanothermobacteraceae;D_5__Methanothermobacter 47.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 8.0% 0.0005 62.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 7.0% 0.0007 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome

153

Table C.1.6 (continued).

D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methanosarcinales;D_4__ 4.6% 0.0007 47.5 vs Methanosarcinaceae;D_5__Methanosarcina 62.5 °C D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 3.3% 0.0004 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 13.1% 0.0006 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 8.0% 0.0029 ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone 47.5 vs OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 6.7% > 0.05 67.5 °C OPB14;D_6__Acetothermia clone OPB14 D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 4.8% 0.0125 gaceae;D_5__Pseudothermotoga D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methanosarcinales;D_4__ 4.5% 0.0007 Methanosarcinaceae;D_5__Methanosarcina D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 20.5% 0.0036 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 11.2% > 0.05 OPB14;D_6__Acetothermia clone OPB14 47.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 7.9% 0.0134 72.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 6.6% 0.0305 archaeon;D_6__uncultured Thermoprotei archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methanosarcinales;D_4__ 4.5% 0.0134 Methanosarcinaceae;D_5__Methanosarcina

154

Table C.1.6 (continued).

D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 13.0% 0.0004 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 11.5% 0.0005 ae;D_5__Defluviitoga;D_6__uncultured bacterium 52.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.1% 0.0064 57.5 °C Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 4.9% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Heliobacteriaceae 4.1% 0.0007 ;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 21.4% 0.0008 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 10.9% 0.0004 ae;D_5__Defluviitoga;D_6__uncultured bacterium 52.5 vs D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 6.9% 0.0103 62.5 °C metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 4.6% 0.0018 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Heliobacteriaceae 3.7% 0.0012 ;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 11.8% 0.0036 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 10.4% 0.0007 ae;D_5__Defluviitoga;D_6__uncultured bacterium 52.5 vs D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone 67.5 °C OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 7.1% 0.0149 OPB14;D_6__Acetothermia clone OPB14 D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 5.1% 0.0214 gaceae;D_5__Pseudothermotoga

155

Table C.1.6 (continued).

D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei 52.5 vs archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 4.6% > 0.05 67.5 °C archaeon;D_6__uncultured Thermoprotei archaeon D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 20.9% 0.0020 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 11.4% 0.0217 OPB14;D_6__Acetothermia clone OPB14 52.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 9.8% 0.0098 72.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 6.5% > 0.05 archaeon;D_6__uncultured Thermoprotei archaeon D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 4.5% 0.0062 gaceae;D_5__Pseudothermotoga D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 20.4% 0.0014 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 10.9% 0.0289 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Heliobacteriaceae 57.5 vs 4.5% 0.0022 ;D_5__Hydrogenispora;D_6__uncultured bacterium 62.5 °C D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Heliobacteriaceae 4.2% 0.0367 ;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Hydrothermae;D_2__uncultured Thermotogae bacterium;D_3__uncultured Thermotogae bacterium;D_4__uncultured Thermotogae 3.6% > 0.05 bacterium;D_5__uncultured Thermotogae bacterium;D_6__uncultured Thermotogae bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 12.8% 0.0016 57.5 vs metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 67.5 °C D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.8% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter

156

Table C.1.6 (continued).

D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 8.2% 0.0103 OPB14;D_6__Acetothermia clone OPB14 57.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 5.7% 0.0492 67.5 °C gaceae;D_5__Pseudothermotoga D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 5.3% > 0.05 archaeon;D_6__uncultured Thermoprotei archaeon D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 21.2% 0.0005 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 12.2% 0.0086 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone 57.5 vs OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 11.6% 0.0143 72.5 °C OPB14;D_6__Acetothermia clone OPB14 D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 9.4% 0.0089 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 6.4% > 0.05 archaeon;D_6__uncultured Thermoprotei archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 17.6% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 10.2% > 0.05 62.5 vs OPB14;D_6__Acetothermia clone OPB14 67.5 °C D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 9.2% 0.0134 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 6.8% > 0.05 archaeon;D_6__uncultured Thermoprotei archaeon

157

Table C.1.6 (continued).

62.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 6.4% > 0.05 67.5 °C gaceae;D_5__Pseudothermotoga D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 23.7% 0.0105 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 22.6% 0.0043 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone 62.5 vs OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 12.3% > 0.05 72.5 °C OPB14;D_6__Acetothermia clone OPB14 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 7.7% 0.0103 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 6.1% > 0.05 archaeon;D_6__uncultured Thermoprotei archaeon D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 23.7% 0.0149 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 15.4% 0.0120 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone 67.5 vs OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 14.2% > 0.05 72.5 °C OPB14;D_6__Acetothermia clone OPB14 D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei archaeon;D_5__uncultured Thermoprotei 8.6% > 0.05 archaeon;D_6__uncultured Thermoprotei archaeon D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 5.9% > 0.05 gaceae;D_5__Pseudothermotoga

158

Table C.1.7. SIMPER analysis results showing OTUs in LFB incubated samples that contributed to the differences between each pair of incubation temperatures. Only the top five OTUs with highest contribution percentages between each pair of temperatures were listed. A p-value of less than 0.05 was considered significant.

Pair Taxonomy Percentage p-value D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 12.0% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 7.6% 0.0050 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 47.5 vs 7.5% > 0.05 ae;D_5__Defluviitoga;D_6__uncultured bacterium 52.5 °C D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__unidentified thermophilic eubacterium ST12;D_5__unidentified thermophilic eubacterium 3.1% > 0.05 ST12;D_6__unidentified thermophilic eubacterium ST12 D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 3.0% > 0.05 obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 10.9% 0.0008 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.0% 0.0004 Methanothermobacteraceae;D_5__Methanothermobacter 47.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 9.1% 0.0008 57.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 7.8% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.9% 0.0241 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 16.8% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 47.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 12.9% 0.0082 62.5 °C Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 9.2% 0.0004 ae;D_5__Defluviitoga;D_6__uncultured bacterium

159

Table C.1.7 (continued).

D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.5% 0.0004 47.5 vs Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon 62.5 °C D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.3% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 9.1% 0.0008 ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 9.0% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 47.5 vs D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 7.2% > 0.05 67.5 °C obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.6% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.6% 0.0064 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 9.6% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 8.7% 0.0015 ae;D_5__Defluviitoga;D_6__uncultured bacterium 47.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 6.8% 0.0237 72.5 °C gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.3% 0.0027 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.0% 0.0113 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 8.7% 0.0004 ae;D_5__Defluviitoga;D_6__uncultured bacterium 47.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 8.7% 0.0114 77.5 °C Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.2% 0.0009 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon

160

Table C.1.7 (continued).

D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 4.6% 0.0113 47.5 vs gaceae;D_5__Thermotoga;D_6__uncultured bacterium 77.5 °C D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.0% 0.0014 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 9.0% 0.0039 ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 8.5% 0.0403 Methanothermobacteraceae;D_5__Methanothermobacter 47.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.4% 0.0076 82.5 °C Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseudomonadales;D_ 4.9% 0.0069 4__Pseudomonadaceae;D_5__Pseudomonas D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 4.1% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 11.9% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 10.8% 0.0116 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 52.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.7% 0.0015 57.5 °C Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 8.1% 0.0008 ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.9% 0.0381 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 15.5% 0.0055 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 52.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 13.8% > 0.05 62.5 °C Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 11.0% 0.0097 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon

161

Table C.1.7 (continued).

D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 8.1% 0.0005 52.5 vs ae;D_5__Defluviitoga;D_6__uncultured bacterium 62.5 °C D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.8% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 10.7% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.1% 0.0164 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon 52.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 7.6% 0.0011 67.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 7.5% > 0.05 obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 7.2% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 9.7% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 9.5% 0.0091 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon 52.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 7.2% 0.0029 72.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 6.9% 0.0393 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 5.7% 0.0022 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 9.7% 0.0112 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon 52.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 8.8% > 0.05 77.5 °C Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 7.0% 0.0006 ae;D_5__Defluviitoga;D_6__uncultured bacterium

162

Table C.1.7 (continued).

D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 5.3% 0.0008 52.5 vs metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 77.5 °C D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 4.6% 0.0188 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.3% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 8.9% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter 52.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D_4__Petrotogace 7.5% 0.0052 82.5 °C ae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 5.9% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseudomonadales;D_ 5.2% 0.0264 4__Pseudomonadaceae;D_5__Pseudomonas D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 16.4% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 14.1% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 57.5 vs 13.4% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium 62.5 °C D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.3% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__unidentified thermophilic eubacterium ST12;D_5__unidentified thermophilic eubacterium 4.5% 0.0004 ST12;D_6__unidentified thermophilic eubacterium ST12 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 13.5% > 0.05 57.5 vs metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 67.5 °C D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.6% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter

163

Table C.1.7 (continued).

D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 8.1% > 0.05 obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter 57.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.8% > 0.05 67.5 °C Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.1% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 11.0% 0.0008 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.1% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter 57.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 7.4% 0.0057 72.5 °C gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.8% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.4% 0.0233 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 11.1% 0.0004 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.5% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter 57.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.7% > 0.05 77.5 °C Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 5.0% 0.0026 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.4% 0.0015 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.6% > 0.05 57.5 vs Methanothermobacteraceae;D_5__Methanothermobacter 82.5 °C D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 9.7% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome

164

Table C.1.7 (continued).

D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 7.3% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium 57.5 vs D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseudomonadales;D_ 5.9% 0.0030 82.5 °C 4__Pseudomonadaceae;D_5__Pseudomonas D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 4.3% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured archaeon D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 19.1% 0.0380 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 13.8% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter 62.5 vs D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 8.1% > 0.05 67.5 °C obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 7.3% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__Caldicoprobactera 2.3% 0.0315 ceae;D_5__Caldicoprobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 19.1% 0.0004 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 11.4% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter 62.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 7.6% 0.0203 72.5 °C gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 6.0% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.5% > 0.05 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 19.0% 0.0004 62.5 vs metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 77.5 °C D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 12.1% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter

165

Table C.1.7 (continued).

D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 5.9% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium 62.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 5.0% 0.0094 77.5 °C gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.5% 0.0082 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 17.6% 0.0140 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 12.6% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter 62.5 vs D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 7.8% > 0.05 82.5 °C Methanothermobacteraceae;D_5__Methanothermobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseudomonadales;D_ 6.0% 0.0054 4__Pseudomonadaceae;D_5__Pseudomonas D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 3.2% > 0.05 obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.3% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 9.1% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 67.5 vs D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 7.7% > 0.05 72.5 °C obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 7.2% 0.0302 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.2% > 0.05 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.3% > 0.05 67.5 vs Methanothermobacteraceae;D_5__Methanothermobacter 77.5 °C D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 8.8% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome

166

Table C.1.7 (continued).

D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 7.6% 0.0233 obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter 67.5 vs D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 4.9% 0.0142 77.5 °C gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.2% 0.0206 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 9.9% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 9.7% > 0.05 metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome 67.5 vs D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D_3__Coprotherm 7.8% > 0.05 82.5 °C obacterales;D_4__Coprothermobacteraceae;D_5__Coprothermobacter D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseudomonadales;D_ 5.3% 0.0362 4__Pseudomonadaceae;D_5__Pseudomonas D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 2.8% > 0.05 gaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 12.5% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 10.1% > 0.05 gaceae;D_5__Thermotoga;D_6__uncultured bacterium 72.5 vs D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Famil 6.1% > 0.05 77.5 °C y III;D_5__Caldicellulosiruptor;D_6__Caldicellulosiruptor saccharolyticus DSM 8903 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 5.7% > 0.05 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Famil 2.7% > 0.05 y III;D_5__Caldicellulosiruptor;D_6__Caldicellulosiruptor saccharolyticus DSM 8903 D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 10.7% > 0.05 72.5 vs Methanothermobacteraceae;D_5__Methanothermobacter 82.5 °C D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 7.8% > 0.05 gaceae;D_5__Thermotoga;D_6__uncultured bacterium

167

Table C.1.7 (continued).

D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseudomonadales;D_ 5.3% > 0.05 4__Pseudomonadaceae;D_5__Pseudomonas 72.5 vs D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anaerobic digester 4.5% > 0.05 82.5 °C metagenome;D_5__anaerobic digester metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.3% > 0.05 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanobacteriales;D_4__ 11.0% > 0.05 Methanothermobacteraceae;D_5__Methanothermobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogales;D_4__Thermoto 5.2% > 0.05 gaceae;D_5__Thermotoga;D_6__uncultured bacterium 77.5 vs D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseudomonadales;D_ 5.2% > 0.05 82.5 °C 4__Pseudomonadaceae;D_5__Pseudomonas D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Famil 4.3% > 0.05 y III;D_5__Caldicellulosiruptor;D_6__Caldicellulosiruptor saccharolyticus DSM 8903 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacterales;D_4__Ther 4.3% > 0.05 moanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultured bacterium

168

Table C.1.8. OTUs identified in LFA incubated samples explained by four environmental factors, including incubation temperature, excavation temperature, volatile solid concentration (VS) and ratio of cellulose to lignin content (CH/L). Numbers in the table represent the extent of explanation by a factor, and those with the largest absolute values are considered to be the predictor of an OTU. The plus sign indicates a positive correlation between the OTU abundance and factor, and the minus sign negative correlation. Samples with the excavation temperature of 70 °C were not included in this analysis due to lack of VS and CH/L data.

Incubation Excavation Taxonomy CH/L VS temperature temperature D_0__Bacteria;D_1__Hydrothermae;D_2__uncultured Thermotogae bacterium;D_3__uncultured Thermotogae bacterium;D_4__uncultured -0.10 0.09 -0.21 -0.54 Thermotogae bacterium;D_5__uncultured Thermotogae bacterium;D_6__uncultured Thermotogae bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.06 0.04 0.01 -0.26 Syntrophomonadaceae;D_5__uncultured;D_6__uncultured prokaryote D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei 0.48 0.37 0.23 0.17 archaeon;D_5__uncultured Thermoprotei archaeon;D_6__uncultured Thermoprotei archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanob 0.49 -0.06 -0.07 0.01 acteriales;D_4__Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanob acteriales;D_4__Methanothermobacteraceae;D_5__Methanothermobacter;D_6 -0.31 0.30 0.17 0.25 __uncultured archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methano -0.56 0.05 0.04 -0.06 sarcinales;D_4__Methanosarcinaceae;D_5__Methanosarcina D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.39 0.16 0.23 0.07 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.52 0.19 0.21 0.06 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Ruminococcaceae;D_5__uncultured Clostridium sp.;D_6__uncultured -0.49 0.05 -0.19 -0.30 Clostridium sp.

169

Table C.1.8 (continued).

D_0__Bacteria;D_1__Hydrothermae;D_2__uncultured bacterium;D_3__uncultured bacterium;D_4__uncultured -0.33 -0.28 0.29 0.06 bacterium;D_5__uncultured bacterium;D_6__uncultured bacterium D_0__Bacteria;D_1__Synergistetes;D_2__Synergistia;D_3__Synergistales;D -0.57 -0.18 -0.21 -0.21 _4__Synergistaceae;D_5__Acetomicrobium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D -0.62 -0.15 -0.27 -0.26 _4__Petrotogaceae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogale 0.54 0.27 0.09 0.03 s;D_4__Thermotogaceae;D_5__Pseudothermotoga D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogale 0.50 0.17 0.02 0.00 s;D_4__Thermotogaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__Caldicoprobacter 0.19 -0.08 -0.18 -0.05 guelmensis D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ 0.17 -0.14 -0.16 -0.12 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.30 -0.12 -0.28 -0.09 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacter ales;D_4__Thermoanaerobacteraceae;D_5__Gelria;D_6__uncultured 0.28 -0.06 -0.21 -0.05 bacterium D_0__Bacteria;D_1__Synergistetes;D_2__Synergistia;D_3__Synergistales;D -0.35 -0.23 -0.33 -0.11 _4__Synergistaceae;D_5__Acetomicrobium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methano sarcinales;D_4__Methanosaetaceae;D_5__Methanosaeta;D_6__Methanosaeta -0.34 -0.35 0.19 0.06 thermophila PT D_0__Bacteria;D_1__Acetothermia;D_2__Acetothermiia;D_3__Acetothermia clone OPB14;D_4__Acetothermia clone OPB14;D_5__Acetothermia clone 0.36 0.36 0.18 0.31 OPB14;D_6__Acetothermia clone OPB14 D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia -0.22 -0.38 0.08 0.03

170

Table C.1.8 (continued).

D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanob acteriales;D_4__Methanothermobacteraceae;D_5__Methanothermobacter;D_6 0.09 -0.29 0.10 0.05 __uncultured bacterium D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methano sarcinales;D_4__Methanosaetaceae;D_5__Methanosaeta;D_6__Methanosaeta -0.26 -0.33 0.14 0.13 thermophila PT D_0__Bacteria;D_1__Acidobacteria;D_2__Aminicenantia;D_3__Aminicenan tales;D_4__Aminicenantes bacterium clone OPB95;D_5__Aminicenantes -0.24 -0.39 0.13 0.14 bacterium clone OPB95;D_6__Aminicenantes bacterium clone OPB95 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured low G+C Gram- 0.18 -0.20 0.07 0.07 positive bacterium clone OPB54 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014 -0.22 0.43 0.18 0.29 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__unid entified thermophilic eubacterium ST12;D_5__unidentified thermophilic -0.13 0.41 0.20 0.25 eubacterium ST12;D_6__unidentified thermophilic eubacterium ST12 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__unid entified thermophilic eubacterium ST12;D_5__unidentified thermophilic -0.25 0.50 0.27 0.38 eubacterium ST12;D_6__unidentified thermophilic eubacterium ST12 D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogale s;D_4__Thermotogaceae;D_5__Pseudothermotoga;D_6__Pseudothermotoga -0.15 -0.29 0.11 -0.03 elfii D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__uncultured compost 0.18 -0.09 -0.19 -0.06 bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anae robic digester metagenome;D_5__anaerobic digester 0.16 -0.01 0.22 0.07 metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D _3__Coprothermobacterales;D_4__Coprothermobacteraceae;D_5__Coprother -0.08 -0.21 -0.27 -0.16 mobacter

171

Table C.1.8 (continued).

D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__uncultured compost 0.18 -0.09 -0.19 -0.06 bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.07 0.10 0.23 0.02 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ 0.09 -0.09 -0.19 -0.06 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.28 -0.10 -0.37 -0.26 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium

172

Table C.1.9. OTUs identified in LFB incubated samples explained by four environmental factors, including incubation temperature, excavation temperature, volatile solid concentration (VS) and ratio of cellulose to lignin content (CH/L). Numbers in the table represent the extent of explanation by a factor, and those with the largest absolute values are considered to be the predictor of an OTU. The plus sign indicates a positive correlation between the OTU abundance and factor, and the minus sign negative correlation.

Incubation Excavation Taxonomy CH/L VS temperature temperature D_0__Bacteria;D_1__Proteobacteria;D_2__Gammaproteobacteria;D_3__Pseu 0.25 0.16 0.01 -0.02 domonadales;D_4__Pseudomonadaceae;D_5__Pseudomonas D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanob 0.30 -0.12 -0.26 -0.22 acteriales;D_4__Methanothermobacteraceae;D_5__Methanothermobacter D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanob acteriales;D_4__Methanothermobacteraceae;D_5__Methanothermobacter;D_6 -0.48 0.18 0.11 0.23 __uncultured archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methano sarcinales;D_4__Methanosaetaceae;D_5__Methanosaeta;D_6__Methanosaeta -0.24 0.14 -0.16 -0.17 thermophila PT D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methano -0.16 -0.14 0.01 -0.12 sarcinales;D_4__Methanosarcinaceae;D_5__Methanosarcina D_0__Archaea;D_1__Euryarchaeota;D_2__Methanomicrobia;D_3__Methano microbiales;D_4__Methanomicrobiaceae;D_5__Methanoculleus;D_6__Metha -0.23 0.07 0.17 0.26 noculleus thermophilus D_0__Archaea;D_1__Crenarchaeota;D_2__Bathyarchaeia;D_3__uncultured Thermoprotei archaeon;D_4__uncultured Thermoprotei 0.15 -0.15 -0.10 -0.09 archaeon;D_5__uncultured Thermoprotei archaeon;D_6__uncultured Thermoprotei archaeon D_0__Archaea;D_1__Euryarchaeota;D_2__Methanobacteria;D_3__Methanob acteriales;D_4__Methanothermobacteraceae;D_5__Methanothermobacter;D_6 -0.04 -0.36 -0.04 -0.26 __uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Ruminococcaceae;D_5__uncultured Clostridium sp.;D_6__uncultured -0.36 -0.17 -0.06 -0.08 Clostridium sp.

173

Table C.1.9 (continued).

D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__uncultured compost 0.20 -0.11 0.08 -0.03 bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacter ales;D_4__Family III;D_5__Caldicellulosiruptor;D_6__Caldicellulosiruptor 0.22 0.15 -0.17 -0.17 saccharolyticus DSM 8903 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacter ales;D_4__Family III;D_5__Caldicellulosiruptor;D_6__Caldicellulosiruptor 0.24 0.15 -0.15 -0.13 saccharolyticus DSM 8903 D_0__Bacteria;D_1__Coprothermobacteraeota;D_2__Coprothermobacteria;D _3__Coprothermobacterales;D_4__Coprothermobacteraceae;D_5__Coprother -0.10 -0.01 0.11 0.10 mobacter D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Petrotogales;D -0.61 -0.05 0.11 0.18 _4__Petrotogaceae;D_5__Defluviitoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogale 0.19 -0.02 0.11 0.04 s;D_4__Thermotogaceae;D_5__Pseudothermotoga D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogale 0.31 0.00 -0.05 -0.03 s;D_4__Thermotogaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogale 0.25 0.03 -0.16 -0.07 s;D_4__Thermotogaceae;D_5__Thermotoga;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Thermoanaerobacter ales;D_4__Thermoanaerobacteraceae;D_5__Caldanaerobacter;D_6__uncultur 0.40 -0.01 0.12 -0.02 ed bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__unid entified thermophilic eubacterium ST12;D_5__unidentified thermophilic -0.45 0.21 -0.03 0.06 eubacterium ST12;D_6__unidentified thermophilic eubacterium ST12 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__unid entified thermophilic eubacterium ST12;D_5__unidentified thermophilic -0.44 0.22 0.05 0.16 eubacterium ST12;D_6__unidentified thermophilic eubacterium ST12 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.42 0.06 0.00 0.07 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium 174

Table C.1.9 (continued).

D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.58 0.13 0.05 0.18 TTA-B61 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.27 0.10 0.12 0.20 Heliobacteriaceae;D_5__Hydrogenispora D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014 -0.39 -0.04 -0.18 -0.14 D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__DTU014;D_4__anae robic digester metagenome;D_5__anaerobic digester -0.15 0.11 0.14 0.12 metagenome;D_6__anaerobic digester metagenome D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.44 0.05 -0.03 0.00 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ 0.22 0.10 0.10 0.19 Caldicoprobacteraceae;D_5__Caldicoprobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ 0.13 0.05 0.16 0.20 Caldicoprobacteraceae;D_5__Caldicoprobacter D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__uncultured compost 0.15 -0.21 0.04 -0.18 bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__uncultured compost 0.18 -0.13 0.11 -0.02 bacterium D_0__Bacteria;D_1__Thermotogae;D_2__Thermotogae;D_3__Thermotogale s;D_4__Thermotogaceae;D_5__Pseudothermotoga;D_6__Pseudothermotoga -0.10 -0.30 -0.23 -0.18 elfii D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.11 -0.26 -0.34 -0.21 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.06 -0.20 -0.17 -0.19 Heliobacteriaceae;D_5__Hydrogenispora;D_6__uncultured bacterium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__uncultured compost 0.07 -0.01 -0.10 -0.05 bacterium

175

Table C.1.9 (continued).

D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ Caldicoprobacteraceae;D_5__Caldicoprobacter;D_6__uncultured compost 0.04 0.03 0.11 0.19 bacterium D_0__Bacteria;D_1__Synergistetes;D_2__Synergistia;D_3__Synergistales;D -0.44 -0.15 -0.19 -0.19 _4__Synergistaceae;D_5__Acetomicrobium D_0__Bacteria;D_1__Firmicutes;D_2__Clostridia;D_3__Clostridiales;D_4__ -0.20 -0.20 -0.27 -0.30 Syntrophomonadaceae;D_5__uncultured;D_6__uncultured prokaryote D_0__Bacteria;D_1__Proteobacteria;D_2__Deltaproteobacteria;D_3__Therm odesulfobacteriales;D_4__Thermodesulfobacteriaceae;D_5__Thermodesulfob 0.23 -0.04 0.12 0.03 acterium D_0__Bacteria;D_1__Hydrothermae;D_2__uncultured bacterium;D_3__uncultured bacterium;D_4__uncultured -0.18 0.10 -0.12 -0.14 bacterium;D_5__uncultured bacterium;D_6__uncultured bacterium

176

C.2. Supplementary figures

Figure C.2.1. Experimental design of (A) LFA experiments and (B) LFB experiments. The tables show the excavation depth and temperature of each excavated sample, and the structure charts show the incubation temperatures for each excavated sample under laboratory conditions. Each incubation was performed in duplicate. DNA was extracted from each excavated sample (n = 1) and incubated sample in duplicate (n = 2).

177

Figure C.2.2. Core microbiota analysis showing genus names of the most prevalent OTUs in (A) excavated samples and (B) incubated samples in LFA.

178

Figure C.2.3. Core microbiota analysis showing genus names of the most prevalent OTUs in (A) excavated samples and (B) incubated samples in LFB.

179

Figure C.2.4. Non-metric multidimensional scaling (NMDS) illustrating beta diversity in excavated and incubated samples in LFA and LFB based on the Bray-Curtis dissimilarity (a quantitative measure of community dissimilarity). The rarefaction depth is 10,106. The ellipses represent 95% confidence intervals around their centroids. The stress value of 0.22 provides a fair representation in reduced dimensions.

180

Figure C.2.5. Relative abundances of four major metabolic pathways in LFA samples incubated at various temperatures, predicted by Piphillin. Each pathway was normalized to the number of total pathways detected in a sample.

181

Figure C.2.6. Relative abundances of four major metabolic pathways in LFB samples incubated at various temperatures, predicted by Piphillin. Each pathway was normalized to the number of total pathways detected in a sample.

182

Figure C.2.7. Relative abundances of the starch and sucrose metabolic pathway (part of carbohydrate metabolism) and methane metabolic pathway (part of energy metabolism) in (A) LFA and (B) LFB samples incubated at various temperatures, predicted by Piphillin. Each pathway was normalized to the number of total pathways detected in a sample. A p-value of less than 0.05 was considered significant.

183