Enhanced Microbial Sulfate Removal Through a Novel Electrode-Integrated Bioreactor

A THESIS SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF MINNESOTA BY

DANIEL C TAKAKI

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

DR. CHAN LAN CHUN

JUNE 2018

Acknowledgements

Academic support came from my major advisor Dr. Chan Lan Chun, as well as my thesis research committee members, Dr. Daniel S. Jones and Dr. Adrian Hanson. Additionally, the faculty of the University of Minnesota Water Resources Science program and the

University of Minnesota – Duluth Civil Engineering Department.

Laboratory support for this project came from Tobin Deen, Tyler Untiedt, Adelle

Schumann, Sara Constantine, Thomas Vennemann, Jacob Daire, Kristofer Isaacson, and

Brock Anderson of the Chun Research Group, University of Minnesota – Duluth, and

Sophie LaFond-Hudson and Amber White of the Johnson Group, University of

Minnesota - Duluth.

This project would not have been possible without the financial support from a number of sources including the University of Minnesota College of Food, Agricultural, and Natural

Resources Science (CFANS) Diversity Scholars – Graduate Student Fellowship, the

United States Geological Survey (USGS) Water Resource Center Annual Grant

Competition, and the Mining Innovation Grant.

Technical support for this project was provided from the University of Minnesota

Genomics Center and Dr. Daniel S. Jones, University of Minnesota College of Science and Engineering, for assisting in microbial community genomic sequencing and data composition. Steven Johnson, University of Minnesota-Duluth, Natural Resources

Research Institute, helped to create the flow-through column reactors.

Dedication

This thesis is dedicated to my parents, Pam and Steve Takaki, and my siblings Jeff and

Marissa for their unyielding support and to my close friends who kept me humble and motivated while working on this project.

Abstract

In northeast Minnesota, elevated levels of sulfate in freshwater systems is a topic of great interest, due to potential adverse impacts to wild rice ecosystems. Sulfate may contribute to methylmercury production and eutrophication in certain conditions.

Increased interest has emerged for developing low cost and efficient technologies to treat high levels of sulfate in mining and industrial waste water. The use of biological sulfate reduction is a promising and economically viable plan for maintaining low levels of sulfate and sulfide, but its performance is highly variable.

This project developed a sediment bioelectrochemical batch reactor that used a low electrical potential to enhance and sustain biological sulfate reduction by continuously supplying electron donor substrates (electrolytic hydrogen) to sulfate reducing bacteria. The project aims to understand the effect of a low applied voltage on the efficacy of sulfate reduction and iron sulfide formation. Reactors contained creek sediment (Second Creek, MN) and an artificial mine water with a sulfate concentration of

~1000 ppm. The sulfur chemistry in the pore water of the reactors was assessed to determine sulfate reduction, resulting in over 90% reduction in porewater sulfate at the cathode in batch reactors, where electrolytic hydrogen gas was generated at a rate of 4.14 mmol/day. Simultaneously, ferrous iron was released into the reactor via iron electrodissolution and reacted with reduced sulfide ions to form iron sulfide precipitates.

This level of hydrogen generation was sustained over a 14-day period and successfully showed that the application of a low voltage to sediment bioreactors is a promising technology to treat sulfate contaminated waste waters.

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The microbial community structure and relative abundance of different species associated with sulfate reduction were also examined. It was shown that relative abundance of sulfate reducing bacteria, specifically Desulfovibrio, a genus of deltaproteobacteria positively associated with sulfate reduction, which utilize hydrogen as their preferred electron donor, increased throughout batch reactor operation when operated at 2V.

Finally, the sediment bioelectrochemical batch reactor served as a proof of concept for the application of low electrical potential to enhance and sustain biological sulfate reduction. The outcomes of this reactor operation laid the groundwork to develop a prototype flow-through bioelectrochemical reactor designed to handle larger volumes of waste water for an extended period of time. Preliminary results from this flow-through reactor demonstrated the ability to generate a constant supply of electrolytic hydrogen used by sulfate reducing bacteria. Through these experiments, recommendations have been made to improve efficacy of flow-through reactors.

Table of Contents Acknowledgements i Dedication ii Abstract iii Table of Contents v List of Tables vi List of Figures vii Chapter 1. Introduction 1.1 Sulfate in Freshwater Ecosystems 1 1.2 Treatments of Sulfate Contaminated Waste Waters 3 1.3 Biological Sulfate Reduction as a Treatment Method 5 1.4 Project Scope 6 1.5 Project Hypothesis and Objectives 8 Chapter 2. Sediment Bioelectrochemical Batch Reactor for Sulfate Treatment 2.1 Sediment Bioelectrochemical Batch Reactor Configuration and Design 9 2.2 Operation and Sampling 10 2.3 Chemical Analysis 11 2.3.1 Physiochemical Analysis 11 2.3.2 Sulfur Chemistry Analysis 12 2.4 Sediment Bioelectrochemical Batch Reactor Results 13 2.5 Summary 20 Chapter 3. Microbial Community Analysis of Sediment Bioelectrochemical Batch Reactor 3.1 Sediment Collection and DNA Extraction Methods 24 3.2 Quantification of SRB populations using qPCRs 24 3.3 Amplicon Sequencing and Bioinformatics 26 3.4 Results 27 3.4.1 Quantification of SRB populations using qPCR 27 3.4.2 Microbial Community Diversity Analysis 31 3.5 Discussion 40 Chapter 4. Flow-Through Bioelectrochemical Reactor for Sulfate Treatment 4.1 Packed-Bed Bioelectrochemical Reactor Design 43 4.2 Reactor Operation and Analysis 44 4.3 Results 45 4.4 Discussion 54 4.5 Recommendations 56 Bibliography 60 Appendix A. Sediment Microbial Community Taxa 64

List of Tables

Table 1-1. Summary of costs and efficiencies associated with sulfate treatment processes adopted from Bowell 2004 and MPCA 2017. 4

Table 2-1. Chemical composition of artificial mine water 10

Table 3-1. Primers sets targeting the dsrAB gene tested for use in polymerase chain reaction and quantitative polymerase chain reaction. 26

List of Figures

Figure 1-1. Sulfate concentrations in surface water in Minnesota. 2

Figure 2-1. Schematic diagram and photograph of sediment bioelectrochemical batch reactor. 9

Figure 2-2. Change of redox potential in porewater throughout the operation of sediment bioelectrochemical batch reactors operated at 2V and 0V. 14

Figure 2-3. Change of pH in porewater throughout the operation of sediment bioelectrochemical batch reactors operated at 2V and 0V. 14

Figure 2-4. Change of total iron concentration in porewater throughout the operation of sediment bioelectrochemical batch reactors operated at 2V and 0V. 15

Figure 2-5. Change in porewater sulfate concentration at the cathode throughout the duration of sediment bioelectrochemical batch reactors operated at 2V and 0V. 16

Figure 2-6. Black band iron sulfide formation developed at the cathode in sediment bioelectrochemical reactor after 14 days of 2V applied. 17

Figure 2-7. Solid phase sulfide concentration in sediment bioelectrochemical batch reactors after 14 days of operation at 2V and 0V. 18

Figure 2-8. SEM image and elemental mapping of Fe and S in black band precipitates in 2V reactors sediment after 14 days (1000x magnification). 19

Figure 2-9. SEM image and elemental mapping of Fe and S in untreated Second Creek sediment (1000x magnification). 19

Figure 3-1. Quantification of 16S rRNA gene and the dsrA gene in the sediment bioelectrochemical reactors as a function of reactor operation duration. 29

Figure 3-2. Ratio of dsrA to 16S rRNA gene copy numbers per gram of sediment in sediment bioelectrochemical batch reactors initially. 30

Figure 3-3. Ratio of dsrA to 16S rRNA gene copy numbers per gram of sediment in sediment bioelectrochemical batch reactors after 14 days. 30

vii

Figure 3-4. Distribution of the most abundant phyla relative OTUs in both the control and 2V sediment bioelectrochemical batch reactors near the cathode (sample port D). 32

Figure 3-5. Distribution of the most abundant class relative OTUs in both the control and 2V sediment bioelectrochemical batch reactors near the cathode (sample port D). 33

Figure 3-6. Changes in relative abundance of orders of sulfate reducing bacteria near the cathode, measured as a function of Relative Observed OTUs versus reactor operation time. 34

Figure 3-7. Change in relative abundance of Desulfovibrionales over time near the cathode in relation to the change in sulfate concentration near the cathode in 2V reactor. 35

Figure 3-8. Changes in relative abundance of different taxa near the cathode, measured as a function of Relative Observed OTUs versus batch reactor operation time. 36

Figure 3-9. Relative abundance of sulfate reducing bacteria, iron oxidizing bacteria, sulfide oxidizing bacteria, and methanogenic bacteria on day 14 of batch reactor operation. 37

Figure 3-10. Principle Coordinate Analyses (PCoA) ordinations of diversity of microbial communities within sediment bioelectrochemical batch reactors based on treatment method. 39

Figure 3-11. Principle Coordinate Analyses (PCoA) ordinations of diversity of microbial communities within sediment bioelectrochemical batch reactors based on operation time. 40

Figure 4-1. Schematic diagram of packed-bed bioelectrochemical reactor. 44

Figure 4-2. Change in current density through stainless steel mesh electrode in flow through bioelectrochemical reactors. 48

Figure 4-3. Redox potential (V) in the porewater of flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. 49

Figure 4-4. Change in porewater pH in flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. 50

Figure 4-5. Change in porewater sulfate concentration (ppm) in flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. 51

viii

Figure 4-6. Change in total dissolved iron in flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. 52

Figure 4-7. Black band precipitate formation developed at the cathode in flow- through bioelectrochemical reactor during reactor operation. 53

Figure 4-8. Proposed series of electrodes connected via titanium wiring for flow- through bioelectrochemical reactors. 57

Chapter 1. Introduction 1.1 Sulfate in Freshwater Ecosystems Sulfate anions are commonly found in aquatic systems. While more abundant in marine aquatic ecosystems, sulfate is often found in low concentrations in freshwater ecosystems. Although there are natural sources, elevated levels of sulfate in freshwater ecosystems frequently result from anthropogenic sources such as waste-water treatment plants, industrial (e.g. mining and manufacturing) waste streams, agricultural run-off, or mining landscape run-off. When sulfate is introduced in excess to naturally low-sulfate freshwater systems, those systems often experience adverse impacts on ecosystem health.

Excess sulfate contamination in freshwater can cause mercury methylation in wetland sediments (Gilmour et al. 1992), the release of phosphate from sediment to induce eutrophication in water bodies (Lamers et al. 1998), and adverse aquatic organism impacts (Soucek 2007).

In northern Minnesota, the elevated levels of sulfate result in impacts on the health and growth of wild rice (Pastor et al. 2017 and MPCA 2016). Due to natural microbial activities, sulfate is biologically reduced to hydrogen sulfide, which inhibits the growth of wild rice in certain conditions. In Minnesota, wild rice has both ecological and cultural importance. Ecologically, wild rice has many inherent values including supplying food and habitats for a variety of aquatic organisms (MNDNR 2008), improving water quality through nutrient cycling (Pastor and Walker 2006), and it may also reduce sediment resuspension (David 2013). From a cultural viewpoint, not only is wild rice the state grain of Minnesota (MPCA 2014), but for many indigenous groups in the region, it is a sacred crop (Vennum 1991).

In northern Minnesota, sulfate is historically and geologically present at low concentrations (~10 ppm; Figure 1-1). Discharge from industrial facilities including, waste water treatment plants and mining operations contribute to the increased concentration of sulfate in surface water, according to the work conducted by the

Minnesota DNR (MNDNR). Due to on-going taconite mining operations and non-ferrous mines, the surrounding watersheds in the region, including the St. Louis River watershed, which flows into Lake Superior, are impacted for the elevated level of sulfate. (Bavin and

Berndt 2014). Though currently under discussion, the standard for sulfate in Minnesota for wild rice waters is very low (10 mg/L) compared to the national standard for drinking water (250 mg/L) set by the Environmental Protection Agency (MPCA 2016).

Figure 1-1. Sulfate concentrations in surface water in Minnesota (MPCA 2017).

In general, a common problem associated with mine water is acid mine drainage, due to the production of sulfuric acid from weathering and leaching processes of sulfide minerals, which leads to a higher metal content. In this region, however, the naturally occurring presence of Fe-, Ca-, or Mg- bearing carbonate minerals (e.g. those in Biwabik

Iron Formation) play a significant role in neutralizing sulfuric acid from mining activities. Consequently, the water tends to have a circumneutral pH, and the main containment of concern is sulfate (Bavin and Berndt 2014). Thus, there is increased interest in developing low cost and efficient ways to treat sulfate contaminated waters from abandoned, operating, and future mines.

1.2 Treatments of Sulfate Contaminated Waste Waters

Bowell (2004) has reviewed and compared different treatment options for removing sulfate contamination from mine waste waters. In this analysis, capital and operation costs were assessed, along with sulfate removal efficiencies, sludge production, and various advantages and disadvantages between the methodologies. Table 1-1 summarizes efficiencies, costs, and sludge production of sulfate treatment processes. The most known technologies for sulfate removal are reverse osmosis, electrodialysis, and membrane filtration. While these processes are effective, they often have high costs associated with them. Table 1-1 shows a comparison between those three options and the use of bioreactors to treat sulfate. In this study, bioreactors were reported to remove more than 90% of sulfate from waste water and the other three options were able to remove over 95% of sulfate.

Bioreactors had the lowest associated capital cost and the operating costs of bioreactors and membrane filtration were much lower than reverse osmosis and

3 electrodialysis. When comparing the main disadvantages, reverse osmosis, electrodialysis, and membrane filtration all are hindered by short membrane life, which means they have high maintenance associated with their operations. One major disadvantage bioreactors have experienced is a high carbon and energy cost, which this project aims to address. The main advantage reverse osmosis, electrodialysis, and membrane filtration seem to have over bioreactors is that they improve drinking water quality (Bowell 2004). However, in the context of this research, that concern may not be necessary to address.

Table 1-1. Summary of costs and efficiencies associated with sulfate treatment processes adopted from Bowell 2004 and MPCA 2017. Bioreactor Reverse Electrodialysis Membrane Osmosis Filtration Sulfate Removal >90% >99% >99% >95% Capital Cost $0.32 M per $0.82 M per $0.69 M per $0.50 M per 103 m3 / day 103 m3 / day 103 m3 / day 103 m3 / day Operating Costs $0.30 /m3 $0.87 /m3 $0.48 /m3 $0.27 /m3 Sludge Low- Low Low Moderate-high production moderate Maintenance Moderate High High High Monitoring Moderate- Low-moderate Low-moderate Moderate-high high

Several studies have reported the potential of bioreactors as a low-cost method of bioremediation of sulfate contaminated waste water. In both active and passive treatment systems, this method has been successfully applied (Johnson and Hallberg 2005). Active treatment methods include both off-line and low powered bioreactors and passive treatment include constructed wetlands and permeable reactive barriers (Muyzer and

Stams 2008, Miao et al. 2012, and Bowell 2014). Most biological treatment methods

-2 utilize sulfate reducing bacteria who can metabolize sulfate (SO4 ) within the system to hydrogen sulfide ions (HS-) (Kumar et al. 2013).

1.3 Biological Sulfate Reduction as a Treatment Method

Certain anaerobic microorganisms gain energy through respiration of sulfate via the process known as dissimilatory sulfate reduction. In this process, they convert sulfate to a more reactive reduced form of sulfur, hydrogen sulfide. This process, therefore, can be leveraged to remove sulfate from waste streams. Biological sulfate removal typically occurs in two steps. First, sulfate is reduced to sulfide by dissimilatory sulfate reduction

(1).

2− + − 4퐻2 + 푆푂4 + 퐻 → 퐻푆 + 4퐻2푂 (1)

The electron donors used to “feed” sulfate reducing microorganisms are either hydrogen or organic carbon (commonly acetate, lactate, or ethanol). The sulfide produced by

Reaction 1 is then immobilized, either by adding iron to precipitate out as solid iron sulfide (2), or by partially oxidizing the sulfide to form solid elemental sulfur (3).

− 2+ + 퐻푆 + 퐹푒 → 퐹푒푆(푠) + 퐻 (2)

− + 0 2퐻푆 + 푂2 + 2퐻 → 2푆(푠) + 2퐻2푂 (3)

Reaction 2 is abiotic; however, Reaction 3 is ideally facilitated by sulfide oxidizing microorganisms that accelerate sulfide oxidation orders of magnitude over its abiotic rates (Millero et al. 1987, Vannini et al. 2008). Therefore, successful sulfate bioremediation depends on both the reduction and the precipitation steps.

The delivery of electron donor substrates to sulfate-reducing microorganisms is vital to stimulate and sustain Reaction 1. Typically, there is a deficiency in these substrates to promote the metabolic process within bacteria in anoxic condition (Garcia-

Saucedo 2008). As with electron donor substrates, a supply of iron or oxidants is required to promote Reactions 2 and 3, respectively. Methods to supply substrates into bioreactors or passive systems include direct gas addition techniques (sparging processes and membrane-supplied gas; Gibson et al. 1998, Ma et al. 2003) or the addition of liquid and solid chemicals (dissolved organic carbon substrates, iron species, or hydrogen-releasing compounds; Nebe et al. 2009). However, these approaches require periodic reapplication or constant feeding to sustain the treatment’s effect because the chemical compounds and gases are rapidly consumed. Because these delivery systems can be costly, a new approach that can provide a continuous source at a controlled rate is needed.

This research is aiming to develop an alternative biological treatment approach in which a low electrical potential is used to sustain biological sulfate reduction by continuously supplying electron donor substrates. The use of an electric potential can create desired electron flows that indirectly and directly stimulate microbial sulfur transformations.

1.4 Project Scope

The goal of this project was to develop sediment bioelectrochemical reactors that use the principles of biological sulfate reduction coupled with oxidative iron dissolution as a mechanism to treat sulfate contaminated waters. These reactors are designed to stimulate native microbial communities for biological sulfate reduction by supplying a low electrical potential as a means to continuously supply electron donor substrates to the

6 bacteria. In this process, electrolytic hydrogen is used by sulfate reducing bacteria to carry out sulfate reduction. Concurrently, sulfide ion precipitation will occur using metal ions found in the sediment as well as dissolved metal ions from the electrodes used in the reaction.

A crucial role of the bioelectrochemical reactors is to provide a constant supply of electron donor substrates. As all organisms do, sulfate reducing bacteria need both electron donors and electron acceptors to carry out their metabolic processes. In this process, the bacteria use sulfate as their terminal electron acceptor and hydrogen as their primary electron donor to reduce sulfate to hydrogen sulfate (Reaction 1) (Muyzer and

Stams 2008).

Often, when this principle is applied in natural and engineered systems, there is a deficiency in availability of electron donors. Electron donors often come in the form of organic carbon such as lactate or acetate or they come in the form of hydrogen gas. When a low electrical potential is applied to the bioreactor, electrolytic hydrogen evolution can be coupled with the oxidation of zero-valent iron, which supplies a continuous flow of hydrogen for the microbes (4) and (5).

+ − 2퐻 + 2푒 → 퐻2(푔) (4)

− − 2퐻2푂 + 2푒 → 퐻2 + 2푂퐻 (5)

- 2- Sulfide species (H2S, HS , and S ) produced by microbial sulfate reduction are more toxic. In this process, the sulfide will be immobilized by supplying iron species (mostly

Fe2+) to the reactors via electrodissolution of iron electrodes. The electrodes used in the

7 reactors will be stainless steel and this iron supply will result in the precipitation of solid iron sulfide (Reaction 2).

The benefits of producing an iron sulfide precipitate is that the solids may concentrate in one location, thus making removal more targeted. Additionally, by using electrolytic hydrogen gas, the use of acetate and lactate will not be required in the system.

1.5 Project Hypothesis and Objectives

Supplying a low electrical potential to indigenous microbial communities in sulfate contaminated waters will enhance and sustain biological sulfate reduction by increasing sulfate reducing bacteria populations and capture the resulting sulfide through metal precipitation.

The specific objectives of this work are:

1) Develop a proof of concept bioelectrochemical reactor that successfully reduces sulfate via sulfate reducing bacteria enhanced by a low electrical potential applied through a batch reactor and a flow-through column.

2) Evaluate the efficacy of sulfate reduction and iron sulfide precipitation along with the physiochemical changes in the bioelectrochemical reactors.

3) Determine microbial communities associated with biological sulfate reduction and how they change in response to the application of low electrical potential using 16S rRNA amplicon sequencing and quantitative polymerase chain reaction (qPCR).

Chapter 2. Sediment Bioelectrochemical Batch Reactor for Sulfate Treatment The sediment bioelectrochemical batch reactor is developed to determine the efficacy of electrolysis of water and/or iron to enhance and sustain microbial sulfate reduction and sulfur recovery from high sulfate waste streams. This chapter includes the configuration of sediment bioelectrochemical reactors, physiochemical changes in the reactors, and the performance of the reactors in terms of biological sulfate reduction and its sulfide capture.

2.1 Sediment Bioelectrochemical Batch Reactor Configuration

The batch reactors were constructed from polycarbonate containers with a volume of 800 mL (0.87 quarts). Each reactor had two stainless steel mesh electrodes (20 Mesh

T304 0.016 36” wide) that were uniformly cut to 6 cm wide by 4.75 cm tall. Figure 2-1 shows the dimensions of the reactors along with an actual reactor image.

Figure 2-1. Schematic diagram and photograph of sediment bioelectrochemical batch reactor. The image on the left shows the dimensions of the reactor along with the placement of electrodes and porewater sampling locations. The image on the right shows the benchtop set-up of the reactor connected to an external power supply. Sample ports are referenced to as A, B, C, and D starting from the left side of the image, 1 cm on either side of the electrodes.

Each reactor was filled with 400 mL of creek sediment collected from Second Creek,

Hoyt Lakes, MN, which is impacted by a high sulfate concentration (400-600 ppm). The creek is downstream of a proposed Cu-Ni mining project. The sediment only contained indigenous bacteria without any bacterial augmentation. Each reactor was filled with 400 mL of an artificial mine water with a sulfate concentration of ~1000 ppm and a circumneutral pH (pH=7). The composition of the artificial mine water, Table 2-1, was based on the chemical composition of taconite tailings basins in northeastern Minnesota

(Hudak et al., 2017).

Table 2-1. Chemical composition of artificial mine water. Chemical Concentration (mM) Sodium Sulfate 10.399 Magnesium Chloride 10.289 Calcium Carbonate 1.871 Sodium Chloride 3.044 Potassium Chloride 1.543 Aluminum Sulfate 5.660 pH 7.4 Alkalinity 187.1 mg/L as CaCO3 Conductivity 9 mS/cm

2.2 Operation and Sampling Batch reactors were operated to examine the effect of electrolysis on biological sulfate reduction and iron sulfide formation as a function of the applied voltage: 1.2 V,

1.6 V, and 2 V. For each experiment, there were four reactors used, two control reactors with an open circuit and two reactors with a closed circuit set at one of the three specified voltages.

During the operation, both pore water and sediment samples were periodically collected through all reactors. Porewater samples (1 mL) were collected at four sampling

10 locations in each reactor using 5cm Rhizon CSS samplers with 1 mL sterilized plastic syringes. The four sampling locations, shown in Figure 2-1, were set at 1 cm on either side of each electrode. These water samples were immediately deposited into 2 mL microcentrifuge tubes and promptly analyzed for pH, oxidation-reduction potential

(ORP), iron, and sulfur chemistry. Sediment samples were collected from the same locations using 1 mL sterilized plastic syringes and stored in sterilized 2 mL microcentrifuge tubes at -20°C for sulfur chemistry and microbial community analysis.

2.3 Chemical Analysis

2.3.1 Physiochemical Analysis

Porewater samples were analyzed for redox potential, pH, total dissolved iron, and sulfate concentration immediately after sampling. The redox potential and pH of porewater were measured using ORP microelectrode (MI-800, Microelectrode Inc.) and micro-combination pH Probe (MI-4145, Microelectrode Inc.), respectively. Zobell solution was used to verify the performance of the ORP electrode. Total dissolved iron in porewater was measured using the Ferrozine method described by Gibbs (1976). The method was modified for 200 µL of sample volume. Briefly, 200 µL of each sample was added to 300 µL of reducing agent (1.4 M hydroxylamine amine in 2 M hydrochloric acid). 300 µL of 2.0 mM Ferrozine solution was added to solution for the complexation, followed by 300 µL of 10M ammonium hydroxide (pH 9.5). This solution was mixed and samples were pipetted in triplicate into a 96-well plate and the absorbance was measured in a spectrophotometer at 562 nm (Thermo Multiskan Spectrum). Calibrations were prepared using ferric chloride solution ranging from to 1.468 µM to 1036 µM.

2.3.2 Sulfur Chemistry Analysis

For the sulfur chemistry analysis, both porewater and solid phases samples were analyzed. Sulfate concentration in the porewater was measured using a turbidimetric method (EPA method 9038). Samples were filtered using two-micron syringe filters and a 5% ZnCl solution was added to the filtered sample to capture sulfide in porewater. The solution was centrifuged at 10,000 g for 1 min for ZnS precipitates and 100 µL of supernatant was added to 900 µL of DI water in a 1:10 dilution. A small scoop of barium chloride (BaCl2 · 2H2O) was added in the diluted solution with a conditioning reagent, and the turbidity of BaSO4 was measured at 450 nm using the spectrophotometer

(Thermo Multiskan Spectrum). Sodium sulfate solutions ranging from 0.03 mM to 5 mM were used for calibration standards.

Sediment samples were analyzed for solid phase sulfide concentration. Acid volatile sulfide (AVS) was used to characterize the sulfide present in the solid phase following the protocol outlined in US EPA method 376.3 (National Environmental

Methods Index). This method was performed in an anoxic chamber environment and used a calibration curve based on solid phased sulfides ranging from 0.004 mM to 16 mM. Additionally, a scanning electron microscope coupled with Energy Dispersive X-ray

Spectroscopy (SEM-EDX; Hitachi 3030 Plus) was used to image surface topography and obtain qualitative elemental composition of iron and sulfur species present in the solid phase. For the SEM-EDX, sediment samples were flash-frozen with liquid nitrogen and transferred to the cold-stage chamber (Deben Eltra Coolstage, -25°C) attached to the

SEM. The temperature (-25°C) was maintained during sample observation and EDX analysis. The samples were observed at wide ranges of magnification and elemental

12 composition and mapping were conducted using Bruker Quantax 70. The analysis was used to confirm the presence of iron sulfide precipitates in the sediment.

2.4 Sediment Bioelectrochemical Batch Reactor Results

Selection of an optimal voltage to stimulate anaerobic sulfate reduction activities and iron electrodissolution was critical since the applied voltage affects physiochemical conditions such as redox potential and pH in the reactor. Based on the potential required for electrolytic hydrogen generation and iron oxidation, the sediment bioelectrochemical reactors were operated at three applied voltages: 1.2 V, 1.6 V, and 2 V, each using open circuit reactors as a control. The average current densities in the reactors ranged from

0.00295 to 0.357 mA/cm2 and under the specified voltages, hydrogen was produced at

0.034 to 4.14 mmol/day based on Faraday’s Law and the measured current flowing through the system. Overall, sulfur chemistry and physiochemical results indicated greater than 90% sulfate reduction in the reactors operated at 2.0 V, while it was negligible in those operated at 1.2 and 1.6 V.

While the reactors were operated at 2V for 14 days, porewater and sediment samples were collected on 1, 5, 8, 11, and 14 days. Redox potential and pH of the porewater samples were measured and the results are presented in Figures 2-2 and 2-3.

Over time, the redox potential decreased near the cathode, where H2(g) was generated, while it remained relatively constant near the anode, where Fe0 was oxidized to Fe2+.

Accordingly, the pH near the anode, where the total dissolved iron increased, gradually decreased to pH 4, while the pH near the cathode increased to pH 10 as the proton is consumed to generate H2 (g). The decrease of pH near the anode is likely due to other oxidation reactions, in addition to iron oxidation.

0.5 Day 1 Control 0.45 Day 5 Control 0.4 Day 8 Control 0.35 Day 11 Control 0.3 Day 14 Control 0.25 Day 1 2V 0.2 Day 5 2V 0.15

Redox Potential SHE vs (V) Day 8 2V 0.1 Day 11 2V 0.05 Day 14 2V 0 1.0 Anode 1.0 1.0 Cathode 1.0 Distance From Electrode (cm)

Figure 2-2. Change of redox potential vs standard hydrogen electrode in porewater throughout the operation of sediment bioelectrochemical batch reactors.

12 Day 1 Control

10 Day 5 Control Day 8 Control

8 Day 11 Control

Day 14 Control

6 pH Day 1 2V

4 Day 5 2V Day 8 2V

2 Day 11 2V

Day 14 2V 0 1.0 Anode 1.0 1.0 Cathode 1.0 Distance From Electrode (cm)

Figure 2-3. Change of pH in porewater throughout the operation of sediment bioelectrochemical batch reactors.

Porewater samples collected throughout the experiment were also analyzed for total dissolved iron concentration to determine the dissolution of the iron electrode using the Ferrozine method. In Figure 2-4, the porewater total iron concentration is shown at each of the four sampling locations in the reactor throughout the experiment. While open circuit reactors showed that iron was present at concentrations less than 0.091 mM in the sediment porewater, porewater dissolved iron concentrations in the reactor near the anode, where iron dissolution occurred, increased upto 1.96 mM over 14 days of operation. Until Day 14, porewater iron near the cathode remained.

2.5 Day 1 Control

Day 5 Control 2 Day 8 Control

Day 11 Control 1.5 Day 14 Control

Day 1 2V 1 Day 5 2V

Total Dissolved Iron (mM) Day 8 2V 0.5 Day 11 2V

Day 14 2V 0 1.0 Anode 1.0 1.0 Cathode 1.0 Distance From Electrode (cm)

Figure 2-4. Change of total iron concentration in porewater throughout the operation of sediment bioelectrochemical batch reactors.

In this reactor, sulfate reduction occurred at the cathode where electrolytic hydrogen gas was produced. Figure 2-5 shows the concentration of sulfate decreasing at the cathode in the 2V reactor over a two-week period by 93.6%, decreasing from 1246.5 ppm sulfate to 80.3 ppm sulfate, while there is no significant decrease of sulfate concentration in the control reactor (0V). The sulfate reduction in the 2V reactors presumably results from the electrical stimulation on microbial sulfate reduction activities.

1800

1600

1400

1200

1000 2V Cathode 800 Control Cathode

600 Sulfate Sulfate Concentration (ppm) 400

200

0 0 2 4 6 8 10 12 14 16 Operation Time (Days)

Figure 2-5. Change in porewater sulfate concentration at the cathode throughout the duration of sediment bioelectrochemical batch reactors operated at 2V and 0V.

The sulfide produced from biological sulfate reduction appeared to be captured as iron sulfide as black precipitates shown by Figure 2-6. We also conducted acid volatile sulfide to quantify the sulfide in sediment to confirm if the black precipitates are iron

16 sulfide (Figure 2-7). The solid phase sulfide was concentrated in the proximity of the cathode and the concentration profile of sulfide has a good agreement with the location of black band precipitates. On the other hand, sulfide in the control reactor remained constant across the reactors.

Figure 2-6. Black band precipitate formation developed at the cathode in sediment bioelectrochemical reactor after 14 days of 2V applied to system to enhance microbial sulfate reduction.

300

250

200

150 2V Applied

Control 100

umol umol sulfide/g dry mass sediment 50

1.0 Anode 1.0 1.0 Cathode 1.0 Distance From Electrode (cm)

Figure 2-7. Solid phase sulfide concentration in sediment bioelectrochemical batch reactors after 14 days of operation at 2V and 0V.

In addition to AVS, solid phase iron sulfide precipitation in the reactors were observed using the SEM-EDX. Elemental mapping of iron and sulfur in the sediment using EDX showed larger particles of iron sulfide precipitates in the sediment collected from black band formations, shown in Figure 2-8 in comparison that the sediment collected from the control reactors in Figure 2-9. Iron and sulfur mapping of sediment in the control reactor shows that the sediment contains similar amounts of iron and sulfur distributed throughout the sediment, but none has formations similar to those found in the black band precipitations.

Figure 2-8. SEM image and elemental mapping of Fe and S in black band precipitates in 2V reactors sediment after 14 days (1000x magnification).

Figure 2-9. SEM image and elemental mapping of Fe and S in untreated Second Creek sediment (1000x magnification).

2.5 Summary Sediment bioelectrochemical reactors were operated at three different voltages:

1.2 V, 1.6 V, and 2 V. The batch reactors operated at 2 V maintained a steady current density of 0.357 mA/cm2, which proved sufficient in generating hydrogen gas within the sediment. The electrolytic hydrogen delivered was used as a continuous source of electron donors for sulfate reducing bacteria present in the sediment. Due to increased metabolic activity of sulfate reducing bacteria driven by the supply of hydrogen, the reactors were successful in reducing sulfate from 1200 ppm to 80 ppm in the vicinity of cathode and capturing the reduced sulfate as iron sulfide.

Under the 1.2 V and 1.6 V applied conditions, reactors were ineffective in reducing sulfate, as were the control reactors. This may be due to low current flowing through the reactors operating at these voltages (0.00295 mA/cm2 and 0.0048 mA/cm2, as current density, respectively), suggesting the generation of electrolytic hydrogen may have been too low to stimulate biological sulfate reduction. The low current density under these conditions is likely related with high internal resistance of the reactor because of hydrolytic conductivity and the concentrations of electrolytes. In our creek sediment condition, an applied voltage of 2V (an average current density of 0.357 mA/cm2) was required to stimulate biological sulfate reduction.

While the continuous application of voltage to the reactors supplied hydrogen to sulfate reducing bacteria as electron donors, it also created physiochemical gradients throughout the reactor via oxidation reactions at the anode. The primary reaction at the anode was electrodissolution of stainless steel mesh electrodes (Fe0) to ferrous iron

(Fe2+), which reacted with sulfide ions to form, iron sulfide precipitates.

Considering the electron balances of the coupled reactions of biological sulfate reduction with electrolytic hydrogen (8 electrons) and iron electrodissolution (2 electrons), there was excess production of ferrous iron in the system. Additionally, the pH of the porewater near the anode was low (pH ~4), which likely resulted from iron electrodissolution from the anode accompanied by some oxidation reactions: the oxidation of organic and inorganic material in the sediment and oxygen production from water electrolysis.

AVS and SEM-EDX characterization of sulfide in sediment indicated that the application of voltage (2 V) induced biological sulfate reduction, localizing reduced sulfide as iron sulfide precipitates (black band precipitation) at one location, near the cathode. Though we did not determine the specific mineral form of iron sulfide, it was assumed that the solid phase sulfide was present as a form of pyrrhotite (Fe1−xS (x = 0 to

0.2)), which is a relatively stable sulfide mineral, near circumneutral pH levels. The control reactors showed high concentrations of solid phase sulfide, which means the sediment collected from Second Creek naturally had a high concentration of sulfide present as well.

Overall, the sediment bioelectrochemical batch reactors operated at 2V were able to successfully enhance microbial sulfate reduction and simultaneously capture sulfide as iron sulfide by iron species released from iron electrodissolution. Since the application of an electric current to an aquatic sediment created the physiochemical gradients within the reactors, including redox potential, pH and also increased generation of electron donor/acceptor substrates across the reactors, microbial communities within the reactors may respond differently based on open vs closed circuit reactors and based on certain

21 locations from electrodes. As such, an in-depth characterization of the microbial communities in the reactors is discussed in Chapter 3.

As this was a proof of concept bioelectrochemical reactor, the results of this experiment will be used as a baseline to develop a flow-through bioelectrochemical reactor system for practical application (Chapter 4).

Chapter 3. Microbial Community Analysis of Sediment Bioelectrochemical Batch Reactor This chapter discusses the changes of microbial community and structure in the sediment bioelectrochemical reactors to understand the function and activities of the requisite microbes involved in sulfate biotransformation and response to electrical stimulation. While chemical analysis allows us to evaluate the performance of the sulfate transformation in the sediment bioelectrochemical reactors, studying the changes of microbial communities in the reactor allow us to understand why the reactors perform well or not. Since the application of an electric current to an aquatic sediment created the physiochemical gradients presented in Chapter 2, including redox potential, pH and also electron donor/acceptor substrates across the reactors, microbial communities may respond differently depending on open vs closed circuit reactors and on certain proximity from electrodes. Particularly, it was expected microbial populations and metabolisms may change in the vicinity where electron donors are provided (hydrogen for sulfate reducing bacteria in the reactors). The changes in microbial communities were assessed through two different methods. 16S rRNA amplicon sequencing was used to characterize the total microbial communities in the sediment, showing changes in the relative abundance of microbial taxa including clades associated with sulfate reduction, sulfide oxidation, methanogenesis, and other energy metabolisms. Secondly, quantitative polymerase chain reactions (qPCR) was used to quantify sulfate reducing bacteria populations by targeting dissimilatory sulfide reductase subunit A (dsrA). Both methods were used to examine the changes in populations over the course of experiments conducted using sediment bioelectrochemical batch reactors.

3.1 Sediment Collection and DNA Extraction

Sediment collected throughout batch reactor operations was stored in 2 mL microcentrifuge tubes and stored in -20°C. Samples were collected using 1 mL sterile slip tip syringes from the locations specified in Chapter 2.1. Microbial DNA was isolated using PowerSoil DNA Isolation Kit (Mo Bio Laboratories). Before DNA extraction, all samples were frozen then thawed three times to improve DNA yield. Extraction was performed following the manufacturers protocols. Extracted DNA were stored at -20°C for later analysis. The concentration of nucleic acids was determined using a Qubit fluorometer and the Qubit dsDNA HS Assay (Qubit 4, Thermo Fisher Scientific).

3.2 Quantification of SRB populations using qPCRs

To measure the change in sulfate-reducing bacteria (SRB) population throughout the experiments, quantitative polymerase chain reaction (qPCR) was used. TaqMan PCR assay was adapted for quantification of the bacterial abundance through targeting of the

16S rRNA gene and SRB through quantification of dissimilatory sulfate reductase (dsrA) gene during the operation of the bioreactors (StepOnePlusTM Real-Time PCR System,

Thermo Fisher Scientific). The assay incorporated the universal primers 331f (5′‐TCCT

ACGGGAGGCAGCAG T‐3′), 797r (5′‐GGACTACCAG GGTATCTAATCCTGTT‐3′) and the probe BacTaq ((6-FAM)-5’-CGTATTACCGCGGCTGCTGGCAC-

3’(TAMRA)), targeting almost all bacterial phyla (Nadkarni et al., 2002). Several primer sets were tested to target a subset of the dsrAB sequences identified from SRB using agarose gel electrophoresis (Table 3-1). The dsr1F-dsrAR primer set was used for the quantification of sulfate-reducing bacteria population. For qPCR, DNA isolation samples were diluted to 0.5 ng/µL using DNA-free PCR grade water and the iTaq™ Universal

SYBR® Green Supermix protocol (Bio-Rad Laboratories, Inc.) was followed using the following cycling parameters: 50°C for 2 min, 95°C for 2 min, followed by 40 cycles of

95°C for 15 sec and 65°C for 1:30 min (Leloup et al., 2007; Kondo et al., 2008). Plasmid standards were created by cloning the target gene from PCR product amplified from anaerobic cultures enriched with sulfate using the Strataclone PCR kit (Stratagene, Santa

Clara, CA). Purified plasmid DNA was quantified by the Qubit 1.0 fluorometer

(Invitrogen, Grand Island, NY) before preparation of six 10-fold dilutions for qPCR standards ranging from 30 to 300,000 copies per 5 µL. Each run contained triplicate reactions of standards, reactions of non-transcript controls, and reactions of samples. The limit of detection was 45 copies of target per reaction tube for all qPCR assays.

Table 3-1. Primers sets targeting the dsrAB gene tested for use in polymerase chain reaction and quantitative polymerase chain reaction. Size is number of base pairs. Primer Set Forward Reverse Size Source dsrA- CGGCGTTGCGCA GTGGMGCCG 140 Pereyra et 280F/RH3- TTTYCAYACVVT TGCATGTT al. 2010 dsr-R’ dsrA- CGGCGTTGCGCA GCCGGACGATGCA 390 Pereyra et 280F/dsrA- TTTYCAYACVVT GHTCRTCC al. 2010 660R TGRWA dsr1F- ACSCACTGGAAG GTGGMRCCGTGC 221 Bourne et dsrAR CACGGCGG AKR TTG G al. 2011 dsr2060- CAACATCGTYCAY TGTAGCAGT 350 Foti et al. dsr4R ACCCAGG TACCGCA 2007 dsr1F-dsr4R ACSCACTGGAAGC TGTAGCAGTTACC 1900 Wagner et ACGGCGG GCA al. 1998

dsr190F1- CACTGGAARCAYG NNATRCARTGCAT 726 Pelikan dsr916R1 GYG RCA 2015 dsr1762F1- CAYACCCAGGGNT CAGTTDCCRCART 331 Pelikan dsr2107R1 GG ACAT 2015

3.3 Amplicon Sequencing and Bioinformatics

16S rRNA amplicon sequencing was used to characterize the total microbial community, in the bioreactors, in order to evaluate changes in the abundance of clades associated with sulfate reduction, sulfide oxidation, methanogenesis, and fermentation.

DNA samples were submitted to the University of Minnesota Genomics Center for high- throughput sequencing using universal primers: 515f (5’-

GTGCCAGCMGCCGCGGTAA-3’) and 806r (5’-GGACTACHVGGGTWTCTAAT-3’) targeting the V4 region of the 16S rRNA gene. Sequencing was performed on the

Illumina MiSeq platform using a 2x300-bp paired end, dual indexing protocol (Gohl et al., 2016). All fastq files (n=522), were generated from four MiSeq runs.

The 16S sequence were sorted, trimmed, verified, and aligned (Jones et al., 2017) to a taxonomic database (Ribosomal Database Project; RDP ver. 11) for assessment of phylogenetic diversity using the MOTHUR software program (Schloss et al., 2013).

Briefly, raw sequences were filtered and trimmed based on quality through Sickle, and a phylogenetic library of the bacteria present within the sediment during reactor operation was created (Jones et al., 2017). Open-reference operational taxonomic units (OTUs) were clustered at a 3% dissimilarity cutoff using UCLUST and compared against the

SILVA v.128 16S rRNA database using PyNast (Caporaso et al. 2010b; Edgar 2010;

Quast et al. 2013).

Libraries were rarefied by random subsampling, to 12580 sequences per sample for statistical analysis. Alpha diversity measures were calculated using Good’s coverage,

Chao1, and Shannon indices. In order to statistically evaluate similarities or dissimilarities of bacterial communities among samples, NMDS and PCoA ordinations were computed in mothur using Bray-Curtis dissimilarity.

3.4 Results

3.4.1 Quantification of SRB populations using qPCR

A total of 196 samples were collected from the batch reactors operated at 2 V during 14 days. To assess the effect of the electrical potential on population size of all bacteria and sulfate-reducing bacteria in the reactors, the gene copies of the 16S rRNA as well as the dsrA genes were quantified using qPCR, respectively. Figure 3-1 shows the

27 trend of 16S rRNA presence in sediment samples collected from both 0V and 2V sediment bioelectrochemical batch reactor operation over a two-week period collected 1 cm from the cathode where hydrogen was generated. qPCR analyses of the 16S rRNA gene indicated that the copy number of this gene increased over 10-fold between days 0 and 7 and then became steady between days 7 and 14, suggesting the population size of total bacteria increase regardless of the application of voltage. Interestingly, the population size of sulfate-reducing bacteria, as the copy number of dsrA gene, had the same trends in that of all bacteria (16S rRNA gene) in both the 0V and 2V reactor, at the cathode, as shown in Figure 3-1.

Figures 3-2 and 3-3 show the ratios of dsrA to 16S rRNA gene copy numbers as a function of position across the reactor. Figure 3-2 shows that there is a higher ratio of dsrA gene copies to 16S rRNA gene copies initially compared to Day 14 in both the 2V and control reactors. The ratio of dsrA gene copies to 16S rRNA gene copies is twice as high at the cathode on Day 14 in the 2V reactor compared to the control reactor.

Figure 3-1. Quantification of 16S rRNA gene and the dsrA gene in the sediment bioelectrochemical reactors as a function of reactor operation duration. Samples are collected from the sediment next to the cathode. Quantification, represented as copy number per gram of sediment, are resulting from qPCR. The “cathode” is signified as being 1 cm from the electrode (Sample port D in Figure 2-1)

0.06

0.05

0.04

0.03

0.02

16S rRNA gene copy ratio dsrA/ 0.01 Day 1 2V

Day 1 Control 0 1.0 Anode 1.0 1.0 Cathode 1.0 Distance From Electrode (cm)

Figure 3-2. Ratio of dsrA to 16S rRNA gene copy numbers per gram of sediment in sediment bioelectrochemical batch reactors initially.

0.0007

0.0006

0.0005

0.0004

0.0003

16S rRNA 16S rRNA gene copy ratio 0.0002 dsrA/ Day 14 2V 0.0001 Day 14 Control 0 1.0 Anode 1.0 1.0 Cathode 1.0 Distance From Electrode (cm)

Figure 3-3. Ratio of dsrA to 16S rRNA gene copy numbers per gram of sediment in sediment bioelectrochemical batch reactors after 14 days.

3.4.2 Microbial Community Diversity Analysis

The composition and structure of microbial communities were characterized to examine how specific microbial taxa responded to an applied low electric potential in the reactor over time using 16S rRNA amplicon sequencing analysis. High-throughput DNA sequencing of 196 samples generated ~13 million paired-end reads and 54% of sequences passed the quality filtration step.

A mean of 3,835 ± 176 OTUs were identified among the samples of initial sediment (t=1 days), with the classification to 414 orders and 94 phyla. An average of

1.71% of sequence reads could not be classified to any phyla among samples. The bacterial community composition of sediment in the reactors at t = 1 days was diversely comprised of members of the phyla Proteobacteria (mean relative abundance of 38.4%),

Chloroflexi (9.52%), Bacteroidetes (9.78%), Acidobacteria (9.25%), Cyanobacteria

(4.35%), Actinobactria (6.41%), Planctomycetes (2.89%), Verrucomicrobia (2.81%)¸

Aminicenantes (0.78 %), Nitrospirae (1.27 %), and Firmicutes (1.49 %) (Figure 3-4). All other phyla accounted for a mean of <13.1%.

Generally, both species richness and evenness of the microbial community were relatively similar over time, regardless of an applied electrical potential. An average of

3676 ± 306 OTUs were identified among the samples in 2V reactors, which is comparable in the initial sediment. This is in agreement with the Shannon biodiversity indices (average of 7.14 in the 2V reactor and 7.33 in the control reactor). Interestingly,

Simpson’s indices (a measure of dominance) showed greater numbers (upto a 10-fold increase) in the 2V reactors, particularly near the cathode in comparison to those in the

31 control reactors (an average of 0.0026). This suggests the microbial community shift occurred in the vicinity of the cathode, where hydrogen was produced.

As Figure 3-5 shows changes in relative OTUs at the class-level, there is a clear increase over time in the class Clostridia, while also decreases in class

Betaproteobacteria. These changes are driven first, by increases in relative abundance of the genus Alkaliphilus, which contains members commonly associated with halophilic environments and a family of anaerobic sulfate reducing thermophiles, SRB2.

Figure 3-4. Distribution of the most abundant phyla relative OTUs in both the control and 2V sediment bioelectrochemical batch reactors near the cathode (sample port D).

Figure 3-5. Distribution of the most abundant class relative OTUs in both the control and 2V sediment bioelectrochemical batch reactors near the cathode (sample port D).

Sediments contain diverse microbial communities that included multiple taxa associated with sulfate-reducing fermentation and methanogenic lifestyles. Bacterial taxa that likely represent sulfate-populations generally made up to 8% of the microbial community, with the most abundant genera including Desulfovibrio, Desulfobacter, and

Desulfobulbus. Five specific Deltaproteobacteria were sorted at the order level and one

Clostridia was sorted to the family level: Desulfovibrionales, Desulfarculales,

Desulfobacterales, Desulfurellales, and Desulfuromonadales and SRB2 (Class

Clostridia). Figure 3-6 shows change in relative abundance of these clades near the

33 cathode where sulfate reduction occurred over the duration of the experiment. While the relative abundance of the other four clades of sulfate reducing bacteria slightly decreased over time, Desulfovibrionales and SRB2 increased at an exponential rate.

Figure 3-6. Changes in relative abundance of orders of sulfate reducing bacteria (Appendix A) near the cathode, measured as a function of Relative Observed OTUs versus reactor operation time.

The order of sulfate reducing bacteria that increased most with an applied voltage was Desulfovibrionales. This change was likely due to hydrogen generation within the reactor to stimulate the metabolic activity of these bacteria. Figure 3-7 shows the change in relative abundance over time of Desulfovibrionales as compared to the change in

34 sulfate concentration in the porewater near the cathode over time. As the

Desulfovibrionales relative abundance increases, the sulfate concentration decreases in turn.

1800 0.008

1600 0.007

1400 0.006

1200 0.005 1000 0.004 800 0.003

600 Relative OTUs Relative Observed Sulfate Concentration (ppm) Sulfate 0.002 400

200 0.001

0 0 0 2 4 6 8 10 12 14 16 Sulfate ConcentrationReactor at Cathode Operation Time (Days)Sulfate Concentration (Control) Desulfovibrionales at Cathode Desulfovibrionales (Control)

Figure 3-7. Change in relative abundance of Desulfovibrionales over time near the cathode in relation to the change in sulfate concentration near the cathode in control and 2V reactors.

Because the sediment used was a native creek sediment, it is interesting to determine how the low electrical potential applied to the batch system would impact other bacteria besides sulfate reducing bacteria taxa. Figure 3-8 below shows the relative abundance of sulfate reducing bacteria compared to iron oxidizing bacteria, sulfide oxidizing bacteria, and methanogens (See Appendix A for specific genus represented in each taxa). Over the batch reactor operation time, overall relative abundance of sulfate

35 reducers did not vary much near the cathode, while the relative abundance of iron oxidizers, sulfide oxidizers, and methanogens all decreased.

Figure 3-8. Changes in relative abundance of different taxa near the cathode, measured as a function of Relative Observed OTUs versus batch reactor operation time. Relative abundance of sulfate reducing bacteria stayed increased slightly overall while, iron oxidizing bacteria, sulfide oxidizing bacteria, and methanogens all decreased over the 14- day batch reactor operation (See Appendix A).

Additionally, the iron sulfide precipitates shown in Chapter 2 had a lower biodiversity and distinct microbial communities comparing to the sediment in the reactors. Figure 3-9 shows the relative abundance of the same four taxa of interest: sulfur reducing bacteria, iron oxidizing bacteria, sulfur oxidizing bacteria, and methanogens.

Comparatively between the cathode of the 2V reactor and the control reactor, the four

36 taxa of interest were more prevalent. Both sulfate reducing bacteria and methanogenic bacteria showed an increase in relative abundance within the black band iron formation.

Particularly, sulfate reducing bacteria in the class Clostridia showed a nearly 9% increase of relative abundance between the sediment near the cathode in the 2V reactor on day 14 and in the black band precipitates from the same day.

0.16

0.14

0.12

0.1

0.08

0.06 Methanogens

Relative Relative Observed OTU Sulfide Oxidizing Bacteria 0.04 Iron Oxidizing Bacteria Sulfate Reducing Bacteria 0.02

0 Control 2V Applied Black Band Iron

Figure 3-9. Relative abundance of sulfate reducing bacteria, iron oxidizing bacteria, sulfide oxidizing bacteria, and methanogenic bacteria on day 14 of batch reactor operation. Samples collected in both Control and 2V Applied reactors were collected near the cathode.

While the dominant taxonomic groups were generally consistent among the reactors, the microbial community structure was dynamic and evolved significantly with operation time and exposure to electrical current. Ordination analyses (PCoA and

NMDS) demonstrated that all replicates for each sample were generally consistent, separating distinctly with time and exposure to an electric current. PCoA produced

37 coordinate axes with eigenvalues of 14.39 and 6.88 and NMDS produced ordinations with a stress level of 0.274 and an R-squared value of 0.51. Neither of the two produced significant dissimilarity profiles. Figure 3-10 shows the change in diversity based on treatment options. There are two distinct clusters that formed based on black band iron and the control reactor, while a third set of data shows a larger dissimilarity within the data set, based on the 2V applied reactors. When compared to Figure 3-11, which shows the change in diversity of microbial communities over time, this subset of the data from the 2V reactor correlated to the later days in reactor operation.

Figure 3-10. Principle Coordinate Analyses (PCoA) ordinations of diversity of microbial communities within sediment bioelectrochemical batch reactors. Eigenvalues associated with ordination were 14.39 and 6.88. Qualitative analysis of data shows clusters of data near control reactor, black band iron formations. A portion of data from the batch reactor operated at 2V shows increased variance from the control cluster. Data points are differentiated by treatment method.

Figure 3-11. Principle Coordinate Analyses (PCoA) ordinations of diversity of microbial communities within sediment bioelectrochemical batch reactors. Eigenvalues associated with ordination were 14.39 and 6.88. Data points are differentiated based on time and treatment within the 2V and control reactors. The circled set of data points indicate a variance in data from the beginning of 2V reactor and the control reactor experiments.

3.5 Discussion Overall, the application of a low electrical potential influenced the microbial community diversity and activity in the sediment bioelectrochemical batch reactors. The changes of the microbial community were apparent near the cathode in the reactor. Total relative abundance of sulfur reducing bacteria did not change much in the batch reactors near the cathode. However, the hydrogen-utilizing sulfate reducing bacteria were enriched in the reactor since electrical current created environmental conditions such as a lower redox potential and the supply of hydrogen as electron donors, which were

40 favorable for such organisms. Within the sulfate reducing taxa, members of the genus

Desulfovibrio have been commonly associated with using hydrogen as their preferred electron donor substrate. The exponential increase in relative abundance also corresponds with the sulfate reduction that occurred within the reactor, meaning the order

Desulfovibrionales were able to outcompete other sulfate reducing bacteria for resources

(sulfate and hydrogen). Conversely, the relative abundance of sulfur oxidizing bacteria, iron oxidizing bacteria, and methanogens all decreased.

Within the black band iron formation, methanogens saw an increase in relative abundance. There is concern that sulfate reduction to hydrogen sulfide can increase methylation within aquatic sediments. In the case of this reactor however, as with the captured iron sulfide, methanogen and sulfate reducing bacteria relative abundance increase was localized to one location within the reactor.

While both NMDS analyses and PCoA were performed, neither produced results that showed strong data correlations. NMDS ordination had a stress level close to 0.3, which often will mean there is no strong correlation of data points, or in this case, that there was not a strong similarity between microbial communities. PCoA developed more compelling trends within the analysis, despite having very low eigenvalues. In general, the diversity of the microbial communities in control reactors was very similar throughout the experiment, which was to be expected. In the 2V reactors the diversity of communities increased and the populations became less similar as the reactors operated for longer. This was noted especially in the black band iron formation and sediment samples collected on days 11 and 14. These trends aided to confirm that the application

41 of a low electrical potential did influence the diversity of communities present within the sediment in batch reactors.

As expected, qPCR results showed an increase in overall microbial populations throughout batch reactor operation. In general, however, there were no distinct differences in overall population increases between control and 2V reactors. While this overall population increase may have been aided by the low electrical potential applied to the system, more likely the population increase is due to the reactors operating at room temperature (25°C), which is a warmer temperature than the temperature in Second

Creek, thus creating a more suitable environment for microbial activity.

By applying a low electrical potential to the sediment bioelectrochemical reactors, the relative abundance of Desulfovibrionales increased exponentially as sulfate reduction decreased. This correlation indicated that the treatment successfully was able to enhance and sustain biological sulfate reduction, while also creating an environment that allowed the Desulfovibrionales family to outcompete other sulfate reducing bacteria that generally require organic carbon as their primary form of electron donors. Furthermore, the sulfur reducing population outcompeted bacteria for resources within the batch reactors, despite overall bacterial communities increasing throughout reactor operation.

Chapter 4. Flow-Through Bioelectrochemical Reactor The goal of the flow-through bioelectrochemical reactor was to develop a bench scale, practical reactor that was designed to treat larger volumes and flows of contaminated wastewater. This work will develop a lab scale prototype of the packed-bed bioelectrochemical reactor system. The results of this experiment will be used to examine other variables and operation parameters for these bench scale reactors. Long term, these flow-through column reactors have the potential to be applied to a larger scale system.

The scaled-up version of this reactor may be used in currently existing treatment systems and it may also be used in operating remote large-scale bioreactors. Preliminary operation parameters of the flow through columns were based on results of the proof of concept sediment bioelectrochemical batch reactors.

4.1 Packed-Bed Bioelectrochemical Reactor Design

The flow-through columns were made from polycarbonate tubing (5.5 cm inner diameter) and each reactor was 36 cm tall. The reactors were partitioned into three discrete parts and an electrode was placed between each section upon assembly, which had the same diameter as the inner diameter of the column. Figure 4-1 shows the full dimensions of the column. The column was packed with a fine grain sand (Quikrete) and sulfate-impacted creek sediment mixture (4:1 ratio by volume) and saturated with artificial mine water. The column was saturated with artificial mine water (Table 2-1) using a peristaltic pump (Lead Fluid, Model: BT100F-1). Each reactor had a stainless- steel mesh electrode as the cathode and a titanium mesh electrode as the anode, both cut to fit the cross-sectional area of the column (diameter of 5.5 cm). There were nine ports in the reactor spaced evenly across the column for porewater sampling and electrical

43 potential measurement. Rhizon porewater samplers were built into the sampling port to collect porewater samples throughout the duration of the experiment for reactor performance monitoring. Four of these ports were used during the sampling process based on proximity to electrodes (see Figure 4-1). An Ag/AgCl reference electrode was also built into the one of the ports to measure electric potential of the electrodes in the column. At either end of the columns, sterilized foam pieces were used to act as diffusers in the column to minimize channeling in the sediment.

Figure 4-1. Schematic diagram (left) and photograph (right) of packed-bed bioelectrochemical reactor. The image on the left shows the dimensions of the reactor along with the placement of electrodes and porewater sampling locations. The image on the right shows the benchtop set-up of the reactor connected to an external power supply.

4.2 Reactor Operation and Analysis The column reactors were operated in duplicate, adjacent to an open circuit flow- through reactor as the control. The reactors were connected to a programmable DC power

44 supply (BK Precision 9130B). The applied voltage was set initially at 2 V and then throughout the experiment increased to 3 V then 4 V to improve the performance.

Artificial mine water was pumped into the system initially at a flow rate of 108 mL/day and then decreased to 30.2 mL/day (hydraulic retention time of 8.5 days) through a multichannel peristaltic pump. The current passed through the reactor was monitored by measuring voltage across a 10 Ω external resistor using a digital multimeter/data acquisition system (Keithley Model 2700).

During operation, porewater samples were collected from four sampling ports in the proximity of the electrodes along with the effluent using 1 mL sterilized plastic syringes and placed in 2 mL microcentrifuge tubes and promptly analyzed for the sulfate concentration, total iron concentration, pH and redox potential, using the same methods as described in Chapter 2.3. Sediment samples were collected at both the beginning and end of the experiment for sulfur chemistry and microbial community analyses.

Additionally, each column had a removable 0.22-micron filter in the effluent line to capture any bacteria flowing out of the system, which were changed every week. Effluent water flowed into Kynar gas sampling bags, equipped with on/off valves.

4.3 Results

The packed-bed bioelectrochemical reactors were initially operated to stimulate anaerobic sulfate reduction based on the key parameters obtained from the batch reactors discussed in Chapter 2: 2 V of applied voltage in the system enriched with sulfate- impacted creek sediment and artificial mine water. Unlike the batch reactors, in this system, flow rate was a critical operation parameter because it may impact a few things: overall internal resistance of the bioreactors, the physiochemical conditions, and the

45 concentration of electron donors and acceptors related to microbial kinetics. Because the production of hydrogen was determined by the current in the reactors, it was important to maintain a sufficient current in the system. Table 4-1 shows each operation setting and the corresponding current density and hydrogen production. At the initial operation condition, the measured cathodic potential was 0.6 V where the generation of hydrogen should be thermodynamically possible. However, the hydrogen concentration calculated by Faraday’s law with measured current was too low to stimulate biological sulfate reduction. To improve performance, the applied voltage and flow rate were adjusted from

2 V to 4 V and from 108 mL/day to 30.2 mL/day, respectively. When reactors were operated at 4 V applied and 108 mL/day created the greatest current density and hydrogen production. Figure 4-2 shows the change in current density throughout the reactor operation as well as change in applied voltage and flow rate.

Table 4-1. Average current density and daily hydrogen production in flow through bioelectrochemical reactors operating at a range of applied voltage (2V to 4V) and flow rates (30.2 mL/day to 108 mL/day). Applied Flow Rate Current Density Hydrogen Production Voltage (V) (mL/day) (mA/cm2) (mM/day) 2 108 0.00137 0.0156 3 108 0.00378 0.0432 4 108 0.0215 0.248 4 30.2 0.0182 0.21

The performance of the reactors was monitored by measuring physiochemical parameters and sulfate concentration during 80-day operation. Redox potential and pH of porewater samples were measured and the results are presented in Figures 4-3 and 4-4.

Though samples were collected at all voltages and flow rates, the data presented below represents the samples collected when the reactors operated at 4 V and 30.2 mL/day. In

46 the columns, redox potential decreased near the cathode over time where hydrogen gas was generated, while it stayed relatively constant near the anode. pH increased near the cathode to 9.1 and decreased dramatically near the anode, reaching a pH of 2.85. This suggests potential anodic oxygen production in the system.

Hydrogen production in flow through reactors was much lower than the batch reactors (0.248 mM/day compared to 4.14 mM/day), and as such, there was less sulfate reduction in the pore water. The largest reduction in sulfate occurred near the cathode on day 54 when the sulfate concentration was 115 ppm from the influent artificial mine water with concentration of 1300 ppm sulfate. The profile of reduced sulfate concentration shifted upwards through the column nearer to the anode on days 60 and 67, where sulfate concentrations were 770 and 869 ppm, respectively. Figure 4-5 shows the change in sulfate concentration throughout the column when 4V was applied to the system with a flow rate of 30.2 mL/day.

Total dissolved iron concentration was monitored since the creek sediment mixed in the sand are naturally rich in iron (initially solid phase). Figure 4-6 shows the change in total dissolved iron concentration throughout the reactor. Total dissolved iron concentrations were low near the and increased to 2.68 mM in the area around the anode at its peak. Moreover, iron sulfide, as black precipitates, was observed in the vicinity of the cathode (Figure 4-7). Presumably, the increase in the dissolved iron concentration is likely due to the microbial iron reducing activities across the reactor. The dissolved iron species near the cathode were consumed by the precipitation reaction with sulfide formed from biological sulfate reduction.

Figure 4-2. Change in current density through stainless steel mesh electrode in flow-through bioelectrochemical reactors. Data points were collected every 30 minutes throughout reactor operation via Keithley Nanovoltmeter. Change in applied voltage is noted on the right axis with dashed line and times of significance including change of flow rate and sampling days are signified with vertical dotted lines. R3 and R4 are duplicate packed-bed reactors operating at the same applied voltage and flow rate.

Titanium Anode 20

10 Distance Distance Through Reactor (cm)

Stainless Steel Cathode

Flow Direction

0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 Redox Potential vs SHE (V) Day 46 Day 54 Day 60 Day 67

Day 46 Control Day 54 Control Day 67 Control

Figure 4-3. Redox potential (V) vs Standard Hydrogen Electrode in the porewater of flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. Samples collected on day 46 were the first samples collected after the flow rate decreased from 108 to 30.2 mL/day.

Titanium 20 Anode

10 Distance Distance Through Reactor (cm)

Stainless Steel Cathode

Flow Direction

0 0 2 4 6 8 10 pH Day 46 Day 54 Day 60 Day 67

Day 46 Control Day 54 Control Day 67 Control

Figure 4-4. Change in porewater pH in flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. Samples collected on day 46 were the first samples collected after the flow rate decreased from 108 to 30.2 mL/day.

Titanium 20 Anode

10 Distance Distance Through Reactor (cm)

Stainless Steel Cathode

Flow Direction

0 0 200 400 600 800 1000 1200 1400 1600 1800 Sulfate Concentration (ppm) Day 46 Day 54 Day 60 Day 67

Day 46 Control Day 54 Control Day 67 Control

Figure 4-5. Change in porewater sulfate concentration (ppm) in flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. Samples collected on day 46 were the first samples collected after the flow rate decreased from 108 to 30.2 mL/day.

Titanium Anode

10 Distance Distance Through Reactor (cm)

Stainless Steel Cathode

Flow Direction

0 0 0.5 1 1.5 2 2.5 3 Iron Concentration (mM) Day 46 Day 54 Day 60 Day 67

Day 46 Control Day 54 Control Day 67 Control

Figure 4-6. Change in total dissolved iron in flow-through bioelectrochemical bioreactors operated at 4V and 30.2 mL/day. Samples collected on day 46 were the first samples collected after the flow rate decreased from 108 to 30.2 mL/day.

Figure 4-7. Black band precipitate formation developed at the cathode in flow-through bioelectrochemical reactor during reactor operation. Precipitation began to form day after 46 days of reactor operation.

4.4 Discussion Flow-through bioelectrochemical reactors were operated for 80 days at varying applied voltages: 2V, 3V, and 4V. Initially, reactors were operated at 2V based on results from the sediment bioelectrochemical batch reactors and then increased to 3V after 7 days. After 30 days, the applied voltage was increased to 4V. The increase in applied voltage was due to the low current in the reactor not generating sufficient quantities of hydrogen.

A second major variable that was considered when operating reactors was the flow rate. A low flow rate of 108 mL/day was initially selected because it was anticipated a high flow rate would not allow for sulfate reducing bacteria in the sediment to utilize generated hydrogen for microbial sulfate reduction. After 14 days of reactors operating at

4V and 108 mL/day, the flow rate decreased to 30.2 mL/day, allowing for a retention time of greater than 8 days in consideration of sulfate reducing biokinetics

Operating at 4V and 30.2 mL/day, porewater redox potential and pH in the reactor deviated from control reactor results. The decrease in redox potential at the cathode as the pH simultaneously increased indicated that there was increased microbial activity within the reactor. A decrease in redox potential was significantly related to the production of cathodic hydrogen. The lower flow rate likely allowed for the hydrogen concentration within the reactor to be more available to sulfate reducing bacteria. Such sulfate reduction was observed near the cathode on day 54.

In correspondence to the reduced sulfate in the porewater at the cathode, there was the formation of a black band precipitation, akin to that formed in sediment bioelectrochemical batch reactors (Figure 2-6). There was no supplementary addition of

54 iron into the reactors, so all dissolved iron in the system was present in the system from creek sediment. The low concentration of total dissolved iron at the cathode coupled with the high concentration at the anode were attributed to two factors. The first is that there was dissolved ferrous iron that precipitated out with sulfide to form the black band formation. The second is that conditions as the cathode stimulated iron reducing bacteria metabolisms. As such, solid phase iron reduced to dissolved iron and the iron that did not bind to reduced sulfide ions continued to flow through the reactors, towards the anode.

Current density, which directly related to hydrogen production, was a function of a number of different variables, including applied voltage, flow rate, surface area of electrode, and resistance. Figure 4-2 shows that at 2V and 3V the current density was relatively minute, but after the applied voltage increased to 4V, the current density increased by over a factor of 5. This increase maintained constant until the flow rate decreased, which caused the current density to decrease slightly, though it remained constant at 0.0182 mA/cm2. May 10th (day 72 of operation), there was a power outage, which stopped both the flow and the applied voltage in the reactor for an unspecified time, causing the current to decrease significantly.

Overall, the first prototype of the flow-through bioelectrochemical reactor formed a foundation for future applications of this technology. Though the sulfate reduction as measured in the porewater was not as low as anticipated, and did not follow consistent trends, the results of physiochemical analyses based on redox potential and pH tell a lot about the potential for this work. A relatively low oxidation-reduction potential at the cathode corresponded with an increase in current and hydrogen production, the crucial aspect of this technology to enhance and sustain microbial sulfate reduction. In addition

55 to the redox potential, another important aspect of these reactors was the ability to maintain a constant current under specific conditions. The constant current created conditions suitable for the constant generation of hydrogen in the reactor, a key electron donor substrate used for biological sulfate reduction. A crucial factor for future operations with this technology should be to increase the current, which would in turn, increase hydrogen production.

4.5 Recommendations

Given what was learned from sediment batch and packed-bed bioelectrochemical reactors, there are a few key recommendations on design and operation parameters of bioelectrochemical reactors for sulfate treatment: electrode configuration (ratio of electrode surface area to the reactor size, electrode spacing, and materials), electron balance, optimal flow rate per current, and packing material. These component parameters are interconnected towards the generation of electrolytic hydrogen and its coupled oxidation reactions, which are critical to the stimulation of microbial sulfate reduction and subsequent sulfide capture.

Both batch and packed-bed reactors’ results showed that the amount of cathodic hydrogen generated was crucial to stimulate biological sulfate reduction. The amount of cathodic hydrogen, depends on total current flowing through the system. The current passed through the reactors is a function of electrode potential, internal resistance, and the surface area of electrodes per reactor size. Aside from increasing the applied voltage to the system, there are other options by which the current across the system can be increased. For instance, if the current density is maintained, as in above reactors, increased surface area of electrodes will allow for more current to flow through the

56 electrode, i.e. if current density is held constant, and surface area is increased, total current will in turn increase. A higher current will correspond to increased hydrogen production. The surface area of electrode can be increased creating a series of electrodes connected to each other in the reactors, depicted in Figure 4-8. In addition to the increase of current passed through the reactors, a series of electrodes may provide a larger reaction area through the reactor given that the most sulfate reduction occurs in the proximity of the cathode in the batch and packed-bed reactors.

7 Figure 4-8. Proposed series of electrodes connected via titanium wiring for flow-through bioelectrochemical reactors. The central electrode has a diameter of 5.5 cm and the outer four electrodes have a diameter of 4 cm.

A second recommendation to improve the performance of the reactor is to reduce system resistance of the reactor, which can influence both current generation and efficiency. The system resistance is composed of the bulk resistance, primarily related to

57 the electrical resistance between the electrodes, the internal resistance caused by the contact of active materials with the electrode (gas bubble formation), and the resistance to flow in the pores of the sediment (determined by the pore structure and the solution conductivity). In a flow system with an electrochemical reaction, such as these reactors, resistances to ionic transfer across the packed materials and to ionic transfer within the solution based on its conductivity should be considered in the design and operation of the reactors. The surface area of electrodes to the reactors, optimal flow rate to the current density, and the type of packing materials may decrease the system resistance, therefore increase electrical current in the system.

In the first prototype packed-bed bioelectrochemical reactor, the sand with a creek sediment was used primarily for inert and homogenous packing materials with a natural biological inoculum. The creek sediment indigenous microbial community, specifically the sulfur reducing bacteria communities, was used based on their positive response to the application of a low electric potential. The packing material is another important design component which may improve the performance of the reactor, through larger surface area and affinity for biofilm formation and pore structure in relation to the solution. As a recommendation example, a mixture of fine grain sand (Quikrete) and with a bacterial carrier material such as granular bioAPT (American Peat Technology, LLC) would be beneficial to sustain and enhance microbial growth within the reactor. To maintain the use of native microbial communities, media generated from Second Creek sediment and phosphate buffer saline solution (PBS) may be used to inoculate the sand/bioAPT mixture. The use of bioAPT and sand will decrease the internal resistance

58 due to ionic transfer across the membrane, because the porosity in the column will increase from the sediment and sand mixture.

The application of such recommendations also depends on the nature of the waste stream since the water chemistry (e.g. alkalinity, sulfate concentration, conductivity, organic content) of different waste streams interplays with the dynamics of the reactor

(resistance, flow rate, electron balance of redox reaction). For instance, a pulp and papermill industrial wastewater has different characteristics from artificial mine water used in this work. This waste water may have a lower sulfate concentration (400-600 ppm), but it will have a higher alkalinity and conductivity. This increased conductivity will increase the flow of electrons through the solution, which will aid in decreasing the resistance in the reactor.

These recommendations are designed to improve aspects of the reactor that cannot be altered once operation has begun. Changing specific operating parameters, such as applied voltage and flow rate may need to be addressed as the experiments operate. Increasing surface area of electrodes, as well as decreasing internal resistance should all be utilized to increase the current in the system. The increase in current should in turn, increase hydrogen gas production, which will enhance and sustain microbial sulfate reduction in the packed-bed bioelectrochemical reactors. Using this technology could prove extremely beneficial in mitigating costs while maintaining efficiencies in larger scale bioreactors to treat sulfate contamination due to both the low cost of operation and the potential for increased sulfate reduction in waste waters.

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Appendix A. Selected Taxa from Sediment Microbial Communities

Bacterial Taxa Associated with Sulfate Reduction Class Order Family Genus Deltaproteobacteria Desulfarculales Desulfarculaceae Desulfarculus Deltaproteobacteria Desulfarculales Desulfarculaceae Desulfatiglans Deltaproteobacteria Desulfarculales Desulfarculaceae Desulfocarbo Deltaproteobacteria Desulfarculales Desulfarculaceae unclassified Deltaproteobacteria Desulfarculales Desulfarculaceae uncultured

Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfatibacillum Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfatirhabdium Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfatitalea Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfobacter Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfobacterium Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfobacula Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfobotulus Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfococcus Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfoluna Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfonatronobacter Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfonema Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulforegula Deltaproteobacteria Desulfobacterales Desulfobacteraceae SEEP-SRB1 Sva0081_sediment_ Deltaproteobacteria Desulfobacterales Desulfobacteraceae group Deltaproteobacteria Desulfobacterales Desulfobacteraceae unclassified Deltaproteobacteria Desulfobacterales Desulfobacteraceae uncultured Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulfobulbaceae_ge Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulfobulbus Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulfocapsa Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulfopila Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulfoprunum Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulforhopalus Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulfotalea Deltaproteobacteria Desulfobacterales Desulfobulbaceae Desulfurivibrio Deltaproteobacteria Desulfobacterales Desulfobulbaceae MSBL7 Deltaproteobacteria Desulfobacterales Desulfobulbaceae unclassified Deltaproteobacteria Desulfobacterales Desulfobulbaceae uncultured Deltaproteobacteria Desulfobacterales unclassified unclassified

Deltaproteobacteria Desulfovibrionales Desulfomicrobiaceae Desulfobaculum

Deltaproteobacteria Desulfovibrionales Desulfovibrionaceae Desulfomicrobium Deltaproteobacteria Desulfovibrionales Desulfovibrionaceae Desulfovibrio Deltaproteobacteria Desulfovibrionales Desulfovibrionaceae unclassified Deltaproteobacteria Desulfovibrionales unclassified unclassified

Deltaproteobacteria Desulfurellales Desulfurellaceae H16 Deltaproteobacteria Desulfurellales Desulfurellaceae G55

Thermoprotei Desulfurococcales Desulfurococcaceae Thermogladius

Deltaproteobacteria Desulfuromonadales Desulfuromonadaceae Desulfuromonas Deltaproteobacteria Desulfuromonadales Geobacteraceae Geobacter Deltaproteobacteria Desulfuromonadales Geobacteraceae Geothermobacter Deltaproteobacteria Desulfuromonadales Geobacteraceae Geoalkalibacter Deltaproteobacteria Desulfuromonadales MA-28-I98C MA-28-I98C_ge Deltaproteobacteria Desulfuromonadales unclassified unclassified Deltaproteobacteria Desulfuromonadales uncultured uncultured_ge

Deltaproteobacteria Syntrophobacterales Syntrophaceae Desulfobacca Deltaproteobacteria Syntrophobacterales Syntrophaceae Desulfomonile Deltaproteobacteria Syntrophobacterales Syntrophaceae Smithella Deltaproteobacteria Syntrophobacterales Syntrophaceae Syntrophus Deltaproteobacteria Syntrophobacterales Syntrophaceae uncultured Deltaproteobacteria Syntrophobacterales Syntrophobacteraceae Desulforhabdus Deltaproteobacteria Syntrophobacterales Syntrophobacteraceae Desulfovirga Deltaproteobacteria Syntrophobacterales Syntrophobacteraceae Syntrophobacter Deltaproteobacteria Syntrophobacterales Syntrophobacteraceae unclassified Deltaproteobacteria Syntrophobacterales Syntrophobacteraceae uncultured Deltaproteobacteria Syntrophobacterales unclassified unclassified Deltaproteobacteria Desulfarculales Desulfarculaceae Desulfarculus Deltaproteobacteria Desulfarculales Desulfarculaceae Desulfatiglans

Clostridia Thermoanaerobacterales SRB2 SRB2_ge

Bacterial Taxa Associated with Sulfide Oxidation Class Order Family Genus Epsilonproteobacteria Campylobacterales Helicobacteraceae Sulfuricurvum Epsilonproteobacteria Campylobacterales Helicobacteraceae Sulfurimonas Epsilonproteobacteria Campylobacterales Helicobacteraceae Sulfurovum

Betaproteobacteria Hydrogenophilales Hydrogenophilaceae Sulfuricella Betaproteobacteria Hydrogenophilales Hydrogenophilaceae Sulfuriferula

Betaproteobacteria Hydrogenophilales Hydrogenophilaceae Sulfurirhabdus Betaproteobacteria Hydrogenophilales Hydrogenophilaceae Thiobacillus

Bacterial Taxa Associated with Iron Oxidation Class Order Family Genus Betaproteobacteria Nitrosomonadales Gallionellaceae Candidatus_Nitrotoga Betaproteobacteria Nitrosomonadales Gallionellaceae Ferriphaselus Betaproteobacteria Nitrosomonadales Gallionellaceae Gallionella Betaproteobacteria Nitrosomonadales Gallionellaceae Gallionellaceae_ge Betaproteobacteria Nitrosomonadales Gallionellaceae Sideroxydans Betaproteobacteria Nitrosomonadales Gallionellaceae uncultured

Betaproteobacteria Hydrogenophilales Hydrogenophilaceae Ferritrophicum Betaproteobacteria Nitrosomonadales Gallionellaceae Candidatus_Nitrotoga

Bacterial Taxa Associated with Methanogenesis Class Order Family Genus Methanobacteria Methanobacteriales Methanobacteriaceae Methanobacterium Methanobacteria Methanobacteriales Methanobacteriaceae Methanobrevibacter Methanobacteria Methanobacteriales Methanobacteriaceae Methanosphaera

Methanomicrobia Methanocellales BS-K-E9 BS-K-E9_ge Methanomicrobia Methanocellales Methanocellaceae Methanocella Methanomicrobia Methanocellales Methanocellaceae Methanocella Methanomicrobia Methanocellales Methanocellaceae Rice_Cluster_I

Methanomicrobia Methanomicrobiales Methanocorpusculaceae Methanocorpusculum Methanomicrobia Methanomicrobiales Methanomicrobiaceae uncultured Methanomicrobia Methanomicrobiales Methanoregulaceae Methanolinea Methanomicrobia Methanomicrobiales Methanoregulaceae Methanoregula Methanomicrobia Methanomicrobiales Methanospirillaceae Methanospirillum Methanomicrobia Methanomicrobiales Rice_Cluster_II Rice_Cluster_II_ge

Candidatus_ Methanomicrobia Methanosarcinales GOM_Arc_I Methanoperedens Methanomicrobia Methanosarcinales Methanosaetaceae Methanosaeta Methanomicrobia Methanosarcinales Methanosarcinaceae Methanolobus Methanomicrobia Methanosarcinales Methanosarcinaceae Methanomethylovorans Methanomicrobia Methanosarcinales Methanosarcinaceae Methanosarcina Methanomicrobia Methanosarcinales Methermicoccaceae Methermicoccus