ELECTROCHEMICAL STIMULATION of DENITRIFICATION in WOODCHIP BIOREACTORS and WETLANDS by Kevin Ramrattan a Thesis Submitted To

ELECTROCHEMICAL STIMULATION OF DENITRIFICATION IN

WOODCHIP BIOREACTORS AND WETLANDS

Kevin Ramrattan

A thesis submitted to the faculty of the University of Minnesota in partial

fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Sebastian Behrens

October 2020

Kevin Ramrattan

Abstract

Elevated concentrations of nitrate in agricultural runoff can contribute to nutrient enrichment in coastal environments and when ingested can result in methemoglobinemia, a potentially fatal condition in infants. Woodchip bioreactors are ditches constructed at the edge of fields to create anaerobic environments replete with woodchips as carbon and electron sources to promote the growth of denitrifying bacteria to reduce nitrate to inert nitrogen gas. In the Midwest snow melt coincides with fertilizer application, creating a large volume of water with a high nitrate load that limits the efficiency of woodchip bioreactors. The lower temperatures also restrict microbial nitrate-reducing metabolism.

One avenue for improving denitrification is the implementation of current-carrying electrodes to supply electron donors to the bacteria. Pyrogenic carbon (Biochar) has been demonstrated to sorb the water-soluble nitrate and acts as an electron shuttle. It is unknown how these two variables in tandem affect denitrification rates, and the microbes associated with these substrates remain uncharacterized. Here, we use batch bioreactors to test the nitrate removal capabilities of woodchip reactors amended with biochar and electrochemical stimulation. Aliquots were collected to measure nitrate removal using a continuous flow analyzer and bacterial communities were characterized based on 16S rRNA gene analysis. Electrode-containing reactors were significantly (p-value < 0.05) less efficient at treating nitrate during the first 8 hours following nutrient injection, but by

24 hours all reactors performed comparably with respect to nitrate removal. Electrode

i biofilms also had less α-diversity and in terms of β-diversity. Microbiome samples under the influence of electrodes were different from those that were not exposed to any electrochemical stimulation. The difference is bacterial community and the performance lag during the first 8 hours of operation may be a result of increased oxygen concentration in the reactors as a result of O2 evolution during electrolysis in the electrode-containing bioreactors. Biochar had no discernable effect on nitrate removal and did not have a significantly different microbiome from woodchips and water.

Electrode biofilm samples were found to enrich Cyanobacteria while the biofilm on the biochar enriched Acidobacteria. Using the results of our experiment we propose the construction of a benchtop-scale electrochemically stimulated constructed wetland reactor.

Acknowledgements

I would like to thank members of the Behrens lab for their support. In particular, Aaron

Pauleon for operating the woodchip bioreactors and Julie Johnston for submitting samples to UMGC. I would especially like to thank Dr. Sebastian Behrens for his guidance and revisions to this work. I would also like to thank Dr. Satoshi Ishii and Dr.

Jeffrey Strock for graciously serving as members of my committee. Finally, I would like to thank Vijay Marupudi for his efforts in helping make clean figures. Funding from

MnDRIVE made this work possible.

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Contents

List of Figures and Figure Descriptions…………………………………………………...v List of Tables and Table Descriptions…………………………………………………..viii

1. INTRODUCTION ...... 1 1.1 Eutrophication, Nitrogen Pollution, Water Quality ...... 1 1.2 Denitrification ...... 4 1.3. Best Management Practices ...... 5 1.4. Electrochemical Stimulation ...... 7 1.5 Objectives ...... 9 2. METHODS ...... 10 2.1. Reactor Construction and Performance Analysis ...... 10 2.2. Microbiome Analysis ...... 12 3. RESULTS ...... 14 3.1. Reactor Performance Analysis ...... 14 3.2. Microbiome Analysis ...... 18 4. DISCUSSION ...... 38 4.1. Denitrifying Bioreactors ...... 38 4.2. Bacteria ...... 44 5. OUTLOOK ...... 48 5.1. Constructed Wetlands ...... 48 5.2. Operational Considerations ...... 49 5.3. Electrode Design ...... 54 5.4. Electron Transfer ...... 56 5.5 Reactor Configuration ...... 57 5.6. Concluding Remarks ...... 59 References ...... 61 Appendix……………………………………………………………………………...….78

List of Figures and Figure Descriptions Figure 2.1. Batch bioreactors were constructed and operated in conditions with and without biochar and with and without electrodes. Reactors were operated in the dark with a current of 5.5mA, later increased to 9.5mA. In the microbiome analysis, duplicates were taken from each of the reactor’s water, woodchip, biochar, and electrode biofilms. The naming convention established here (Reactor 1 = -biochar/-electrode, Reactor 2 = +biochar/-electrode, Reactor 3 = -biochar/+electrode, and Reactor 4 = +biochar/+electrode) is used throughout this document………………………………………………………………………………………11

Figure 3.1. Absolute NOx removal recorded on 6/27, 7/10, 7/25, 8/6, 9/17, 9/21, 10/10, 10/17, 10/27, and 10/31. Data points correspond to percent NOx concentrations at 2, 4, 6, 8, 24, and 48 hours (where applicable) across the 4 reactors. On 9/21 the current was increased from 5.5mA to 9.5mA. NOx measurements in reactors 3 and 4 tend to be higher after nutrient injection. Most nitrate removal occurs within the first 2 hours. Error bars are not included but error from the SEAL AA3 would be <1% (Dafner, 2015)………………………………………………….…….………………………………….14

Figure 3.2. Relative NOx removal recorded on 6/27, 7/10, 7/25, 8/6, 9/17, 9/21, 10/10, 10/17, 10/27, and 10/31. Data points correspond to percent NOx removal at 2, 4, 6, 8, and 24 hours across the 4 reactors. On 9/21 the current was increased from 5.5mA to 9.5mA. Within the first 8 hours, electrode-containing reactors trail the performance of non-electrode reactors. This gap was exacerbated by the increase in current density. Error bars are not included but error from the SEAL AA3 would be <1% (Dafner, 2015). ………………………….……..…………….15

Figure 3.3. Average NOx removal across reactors 1-4 (R1, R2, R3, R4) along with the corresponding standard deviation reported for each time interval in hours post injection (HPI) of nutrients……………………………………………………………………………..……....17

Figure 3.4. Abundances by phylum across reactors sorted by sampling source. Samples taken from biochar in reactor 2 were exceptionally low in read count thus do not form a visible stacked bar when comparing the number of sequence reads……………………………………………………………………………………...…….20

Figure 3.5. (a) Reactor microbial community composition by phylum, after filtering the data. Only the 19 most abundant phyla are shown. Proteobacteria represent the dominant phylum. (b) The breakdown by the 37 most abundant classes. In cases where no class resolution could be determined, the phylum name is instead used. Numbers represent relative OTU sequence abundance with red highest (100%) to blue lowest (0%)…………………………..…………………………………………………………….….21

Figure 3.6. Alpha diversity from Table 3.2 visualized. On the left is the Chao1 calculation which counts observed species and likely number of unobserved species. The Shannon index is on the right. Each boxplot corresponds to a reactor number with the sampling site. Alpha diversity using rarefaction as a means of normalization is presented in Figure S4.………………………………………………………………………………...……………24

Figure 3.7. Rarefaction curve organized by reactor of origin (R1, R2, R3, R4) and colored by sampling source (woodchip, water, biochar, electrode). Electrodes appear to be well sampled while water and woodchip samples are not sequenced with enough depth…………………………………………………………………………………………...... 25

Figure 3.8. Principal coordinate analyses to visualize the dissimilarity in β-diversity. Samples are labeled according to their reactor of origin as well as sampling source. Along the axes are the percentages of variance each axis explains.(a) PCoA plot generated from Bray-Curtis distances, and (b) PCoA plot using unweighted Unifrac distances…………………………………………26

Figure 3.9. A clustering hierarchy calculated using unweighted Unifrac data matrix. Water and woodchip samples of the same reactor cluster closely together, and water/woodchip samples are separate from the biochar and electrode samples. One water sample from reactor 1 clusters with the biochar samples while the other duplicate clusters with water samples from reactor 2……...27

Figure 3.10. Log2 fold change when (a) Reactor 4 is compared to reactor 3, with respect to significantly different OTU sequence abundance………………………………………………...32

Figure 3.10. (cont.). Log2 fold change when Reactor 4 is compared to (b) reactor 2, and (c) reactor 1 with respect to relative OTU sequence abundance……………………………………………………………………………………...…33

Figure 3.10. (cont). (cont.) Log2 fold change when Reactor 3 is compared to (d) reactor 2, or (e) reactor 1 with respect to significantly different OTU sequence abundances………………………………………………………………………………………..34

Figure 3.11. Log2 fold change when biochar is compared to (a) electrodes, or (b) woodchips with respect to significantly different OTU sequence abundances……………………………………………………………………………………..…35

Figure 3.11. (cont.) Log2 fold change among significantly different OTU abundances comparing (c) biochar to water, (d) and electrodes to water samples………………………………………………………………………………..……….…36

Figure 3.11 (cont.) Log2 fold change of significantly different OTU abundance comparing (e) electrodes to woodchips, (f) and woodchips to water samples……………………………………………………………………………………..…….37

Figure 4.1. Water samples were taken from each reactor and centrifuged resulting in a biomass pellet. From left to right: reactor 1, reactor 2, reactor 3, reactor 4. There is a difference in coloration between the electrode containing and electrode-free reactors……………………………………………………………………………………...…….43 Figure 5.1. Setup of denitrifying bioreactor. Two such reactors have been constructed, one open- circuit and one closed-circuit and reactor performance and microbial community and activity will be monitored while reactors are operated………………………………….………………………………………………..………53

Figure S1. (a) The NOx removal rate (proportion NOx removal/hour) up to 24 hours after nutrient injection. Reactors 3+ and 4+ are the performances of the reactors 3 and 4 after the current was increased……………………………………………………………………………..78

Figure S2. (a) Sample microbial community composition by phylum, according to reactor source. Only the 19 most abundant phyla are shown. (b) The breakdown by the 37 most abundant classes. In case no class could be determined, the phylum name is listed instead. Numbers represent percent relative sequence abundance of the respective community with red being closer to 100% and blue closer to 0%...... 79

Figure S3. (a) Sample microbial community composition by phylum, according to sampling source. Only the 19 most abundant phyla are shown. (b) The breakdown by the 37 most abundant classes. In case no class could be identified, the phylum name is given instead. Numbers represent percent relative OTU sequence abundance with red being closer to 100% and blue closer to 0%...... 80

Figure S4. Sampling depth can impact species richness making normalization a consideration in microbiome studies. Rarefaction was used as a means for normalization of Figure 3.6. This process can lead to the loss of data. The alpha diversity trends across samples with respect to source and reactor were preserved………………………………………………………………..81

Figure S5. Even depth rarefaction curves restricted the number of reads from Figure 3.7 to the lowest number present (slightly under 2000 reads). At this depth, the electrode biofilms appear well sampled unlike the water, woodchip, and biochar biofilms…………………………………82

vii

List of Tables and Table Descriptions

Table 3.1. Average NOx removal across reactors 1-4 (R1, R2, R3, R4) along with the corresponding standard deviation reported for each time interval in hours post injection (HPI) of nutrients…………………………………………………………………………...17

Table 3.2. Diversity indices for each of the 21 samples listed by source (woodchip, water, biochar, electrode), reactor of origin (R1, R2 ,R3, R4) and sample replicate number (1 or 2)…………………………………………………………………………………………....22

Table S1. Coarse grit walnut shells (~4.76mm) purchased from Hammon’s Product Company, MO was charred for 3 hours (1hr heating, 2hr cooling) by pyrolysis by Char Energy LLC, MN in a mobile downdraft gasifier. Chemical characterization was completed by Eurofins Scientific Product Testing Lab in Hamburg, Germany using thermogravimetry (Krider, 2018)………………………………………………………………………………83

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1. INTRODUCTION

1.1 Eutrophication, Nitrogen Pollution, Water Quality

Nitrate contamination of water is an urgent and pervasive environmental and public health concern (Ashok and Hait, 2015). Sources of nitrate in surface and ground water include agricultural runoff and municipal wastewater, as well as urban runoff, leaching of waste, and pesticides (Tyagi et al., 2018). When it comes to human health, the role of nitrate is complex. Dietary nitrate, which can be obtained through leafy green vegetables and cured meats, promotes vascular health through inhibiting platelet aggregation, enhancing endothelial dysfunction, and improving exercise performance in both healthy individuals and peripheral arterial disease patients (Zhai et al., 2017). However, an excess of nitrates has been linked to hypertension, spontaneous abortion, and congenital defects.

Though nitrate itself is not carcinogenic, populations ingesting excessive nitrates may develop gastric cancer when nitrate is reduced to nitrite in the body and reacts with amines to form nitrosamines, which have been identified as potential carcinogens (Dan-

Hassan et al., 2012).

While some studies identify nitrate as a progenitor for health risks, congeneric studies have reached differing conclusions (Zhai et al., 2017), meaning nitrate’s implication in health will continue to be debated. Infants (0-6 mo.) are a subpopulation particularly vulnerable to the detrimental impact of nitrate. Infants fed formula mixed with water containing high levels of nitrate are at risk for developing methemoglobinemia, a

1 potentially fatal condition. Nitrate-reducing bacteria convert ingested nitrate to nitrite which binds to hemoglobin to form methemoglobin which interferes with the ability of the molecule to carry oxygen (Ward et al., 2018). Infants are particularly vulnerable as they have a higher percentage of fetal hemoglobin than adults, drink more water per body weight, and have fewer enzymes to catalyze the conversion of methemoglobin to hemoglobin (Fossen Johnson, 2019). The resulting hypoxia and subsequent cyanosis have lent credence to the colloquial term “blue baby syndrome”. Alternative explanations in the literature claim production of nitric oxide as an immune response during gastroenteritis in response to bacteria-contaminated water as the causative agent of methemoglobinemia (Bryan and van Grinsven, 2013). In light of this health risk, the

World Health Organization determined the maximum permissible concentration of nitrate

- (NO3 ) to be 50 mg/L (11.3 mg NO3-N) with the European Union adopting the same benchmark (Górski et al., 2019). In the United States, the EPA, emboldened by the Safe

Drinking Water Act, set the maximum contamination level even lower to 10 mg NO3-N/L

(Pennino et al., 2017).

While a consensus on nitrate’s role in human health continues to take shape, the pernicious effect on aquatic environments has been well studied. To meet the challenge of feeding billions, soils have been amended by the application of nitrogen-containing fertilizers globally. Commercial N-fertilizers are primarily manufactured as ammonia gas

(NH3) which may be further manufactured as N-containing granular or liquid products

+ (McIsaac, 2003). Reacting with water in the soil converts ammonia to ammonium (NH4 )

+ which is absorbed onto soil particles. Energy-rich NH4 is used by soil bacteria which 2

- decompose it to nitrate (NO3 ), a form of nitrogen most used by plants for growth

(McIsaac, 2003). Nitrates, which are readily water-soluble, percolate through the soil or are transported through runoff (Khan and Mohammed, 2014). Enrichment of limiting nutrients in water bodies constitutes eutrophication. The introduction of nitrogen, typically fixed in a biologically usable form by diazotrophs, is problematic in estuarine and coastal environments. The increased primary production is marked by turbid algal blooms. The decay of detritus consumes oxygen resulting in anoxic zones unable to support fish and aquatic invertebrates, such as those created at the convergence of the

Mississippi River and the Gulf of Mexico (Beutel et al., 2016). Since the second half of the twentieth century, eutrophication has dramatically increased, with models predicting

18-39 days of harmful cyanobacterial blooms by 2090 in the United States, more than double the average of seven days today (Huisman et al., 2018). Climate change has the power to compound nitrate-driven eutrophication as changes in precipitation patterns result in a 19% nitrogen load increase in riverine N as a result of runoff in the United

States, requiring a 33% reduction in inputs to offset (Sinha et al., 2017). In addition to producing odors and tastes that foul water quality, cyanobacteria can produce toxins that directly impact human and environmental health. Apart from disrupting regional ecology, eutrophication also has economic consequences: fisheries decline, and tourism recedes.

Finally, chronic nitrate exposure has been shown to act as an endocrine disruptor in fish

(Kellstock, et al., 2018), an important consideration in watershed management of healthy fauna.

1.2 Denitrification

Denitrification is part of the nitrogen biogeochemical cycle concerned with returning biologically available nitrogen to atmospheric nitrogen through a sequence of reductions

- from nitrate or nitrite (NO2 ) to inert dinitrogen gas (N2). This process is mediated through bacterial respiration using four reductases: nitrate reductase (NAR), nitrite reductase, (NIR) nitric oxide reductase (NOR), and nitrous oxide reductase (NOS). NAR

- - is encoded by the nas, nar, and nap operons and reduces NO3 to NO2 (Levy-Booth et al.,

2014) with napA and narG being among the best studied genes. NIR is encoded by the convergently evolved nirK or nirS (copper-containing enzyme and cytochrome cd1- containing enzyme, respectively) and produces nitric oxide (NO). NOR, a cytochrome- containing enzyme, converts NO to N2O. Finally, NOS performs the final reduction from

N2O to N2. Organisms lacking the nosZ gene produce N2O as a terminal product and contribute to climate change through the emission of this exceptionally potent greenhouse gas (Graf et al., 2014).

Wastewater treatment plants capitalize on microbial respiration to efficiently and cost-effectively remove nitrogen from water. Most of the microbes used are facultative anaerobes, using nitrogen species as electron acceptors in place of oxygen and over 60 genera have thus far been identified, accounting for up to 5% of the soil community

(Groffman, 2012). The electron donors can be organic or inorganic in the case of heterotrophic and autotrophic denitrifiers. (Lu et al., 2014) The chemolithoautotrophs use diverse reduced inorganic compounds such as ferrous iron, hydrogen gas, and sulfur-

4 reduced compounds as electron donors by fixing inorganic carbon (bicarbonate or carbon dioxide). This has two distinct advantages over use of heterotrophs in denitrification: limited biomass and reduced biofouling, and no addition or cost of organic substrate additives such as methanol, glucose, and acetate to serve as electron donors and carbon sources (Di Capua et al., 2015).

Dissimilatory nitrate reduction to ammonium (DNRA) competes with denitrification and results in the accumulation of ammonium rather than dinitrogen gas; it is estimated 4-

10% of nitrate removed is attributed to DNRA (Wang and Chu, 2016) in batch bioreactors using different insoluble carbon sources. This, however, is not necessarily a divergent path from denitrification. On the contrary, some microbes oxidize ammonium to N2 gas by coupling the reaction to the reduction of nitrite with water as a byproduct in a process known as anaerobic ammonium oxidation, or anammox (Kartal et al., 2012).

1.3. Best Management Practices

The excess of nitrogen-containing fertilizers applied to agricultural settings contributes to water quality degradation when the nitrates runoff into surface water or percolate through the soil to contaminate groundwater (Ruidisch et al., 2013).

Fortunately, the development of nutrient reduction strategies termed “best management practices” (BMPs) can mitigate the load of nitrate in water. There are remedial strategies including controlled drainage, wetland remediation, and denitrification bioreactors, as well as preventative strategies that encompass reduced fertilizer application, changing

5 from fall application to spring, split application of fertilizer, cover crops, and crop rotation (Christianson et al., 2013).

Chief among preventative strategies is simply the application of less fertilizer. Winter cover crops (CC), such as forage brassicas or winter fallow, used after harvest and before the next growing season uptake N thereby reducing nutrient leaching (Fraser et al., 2013).

On average, N-scavenging nonleguminous cover crops reduce nitrate leaching into freshwater by 56% compared to controls with no CC (Thapa et al., 2018). Precipitation, soil drainage, and timing of planting exert control on CC effectiveness. In crop rotation, legume crops can furnish the soil with mineralized nitrogen when the symbiotic bacteria in their nodules perform nitrogen fixation, reducing the demand for fertilizer application

(Tribouillois et al., 2016).

Remedial strategies are designed to treat the nitrate in situ. Subsurface or tile draining is a practice to capture drainage and lower the water table in a controllable manner.

Compared to free drainage, this results in lower N loadings to surface waters (Williams et al., 2015). Engineered structures intersecting tile drainage are known as bioreactors (Law et al., 2018), taking advantage of biological denitrification to remove nitrate. Inevitably, some nitrate is removed due to biological assimilation. These bioreactors are designed to optimize heterotrophic denitrification by generating an anaerobic setting with abundant carbon, typically woodchips, and nitrate (Pluer et al., 2016). Warmer temperatures, longer hydraulic retention times, and high nitrate concentrations are associated with greater nitrate removal rates (Addy et al., 2016).

Constructed wetlands (CW) collect surface and drainage runoff and provide an environment for microbial denitrification and phytoremediation and can remove up to

90% of nitrate (Tournebiz et al., 2017) with other processes such as mineralization, sorption and burial contributing to removal. Though they have a larger area footprint than other BMPs, CWs adjoining intensely managed agricultural areas greatly reduce riverine nitrate and improve downstream water quality (Hansen et al., 2018).

While local geographic considerations determine which BMP options are most promising, there are also considerations of an economic dimension governing which

BMP best suited in a certain situation. BMPs must be simple and low-cost to encourage agricultural producers or local communities to adopt them. Pressed by the growing urgency to reduce nitrate pollution, Minnesota has outlined nutrient reduction targets to improve future water quality. To protect the integrity of water in the Gulf of Mexico and the Great Lakes, the Minnesota Nutrient Reduction Strategy anticipates a 20% reduction in nitrogen by 2025 as a target as well as a 45% reduction goal for nitrogen in the

Mississippi River by 2040 (Anderson et al., 2016). A combination of BMPs will be needed to attain these goals.

1.4. Electrochemical Stimulation

There are several active areas of research to improve our capability of remediating nitrate pollution through denitrification. Nitrate concentrations often exceed 10 mg/L

NO3-N in drainage ditches in agricultural areas of southern Minnesota (Krider, 2018).

Though woodchip bioreactors are simple, low-maintenance, effective, cost-effective, and 7 have small area footprints, there are concerns stemming from the weather changes in the

Midwest; fertilizer application coincides with snow melt in early spring resulting in high nitrogen loading at high flow rates (Hackshaw, 2018). In Minnesota, 50% of the average annual runoff occurs before 11 May when daily air temperatures are 5°C (Feyereisen et al., 2016). The low temperature also presents a limitation to nitrate removal, likely a result of low metabolic activity of denitrifiers (Jang et al., 2019). To improve the efficiency of woodchip bioreactors during fast flow regimes, introducing electrons via an electrode has been proposed. Reactors demonstrated higher denitrification efficiencies when 500 mA of current was supplied, thereby providing a readily available electron source as an alternative to obtaining electrons from the metabolism of woodchips (Law et al., 2018). Low electrical potential has been demonstrated to accelerate denitrification by electroactive subsoil heterotrophs, like Geobacter and Pseudomonas, which typically oxidize dissolved organics to obtain electrons (Qin et al., 2017). Autotrophs can also benefit from the introduction of electrodes. Electrolysis of water produces hydrogen gas which can be used as an electron donor for autotrophic denitrification. Some autotrophs such as Thiobacillus denitrificans are capable of directly accepting electrons from the electrode (Yu et al., 2015). The gradient of dissolved oxygen declines closer to the electrode surface creating a micro-environment conducive to nitrate reduction. Both heterotrophs and autotrophs can be used in the same system, as carbon dioxide produced by heterotrophic respiration serves as a carbon source for autotrophs. Even in this dynamic system, heterotrophs can outnumber autotrophs by two orders of magnitude

(Dehghani et al., 2018).

Previous studies have identified another variable that may improve efficiency: biochar, a persistent, sorptive, and carbon-rich solid produced by pyrolysis of biomass.

Pore spaces are increased more than 1000-fold during pyrolysis. Many pores are remnants of cell wall structures of the original biomass feedstock and produce favorable habitats for microbes (Schmalenberger, and Fox, 2016). Biochar can also affect bacterial communities through sorption to signaling molecules (Masiello et al., 2013) and acting as electron shuttles (Chen et al., 2015). In bioreactors amended with biochar, the relative sequence abundances of complete denitrifiers and N2O reducers were increased (Harter et al., 2016). Prior to this work, the influence biochar and electrochemical stimulation exerted in tandem on denitrification had not been characterized.

1.5 Objectives

This study aims to shed light on the bacterial community by constructing batch woodchip bioreactors with and without both biochar and electrodes, as well as couple the communities to the nitrate reduction capacities of their respective bioreactors via 16S rRNA gene sequence data. In this approach, we hope to elucidate whether certain taxa are associated with biochar and electrodes, and whether these variables enrich certain microbes to affect local diversity. Biochar plays a role in mediating electron transport between an electrode surface and bacteria. In addition, biochar has a large surface area for microbial attachment. We hypothesize that the addition of biochar increases electron distribution in bioreactors and allows microbial colonization thereby improving the efficiency of nitrate removal. We also hypothesize the electrodes will improve

9 denitrification rates by serving as a source of readily available electrons. Furthermore, we aim to apply the results and literature reports to propose an up-flow, benchtop-scale, electrochemically stimulated constructed wetland utilizing the nitrate-remediating power of autotrophic denitrifiers.

2. METHODS

2.1. Reactor Construction and Performance Analysis

Four denitrifying batch bioreactors were constructed using plastic “Really Useful” boxes (1.5L volume, Length x Width x Height = 6.25” x 4.5” x 3.75”, US patent:

7159733, EU Patent: 1301412). Woodchips (mixed hard-wood, The Mulch Store) were cut to 0.25-0.5” width and soaked in DI water mixed with soil, obtained from agricultural areas in Minnesota, for 3 days prior to placement in the reactors. Woodchips were added to all reactors. In addition to woodchips, reactor 2 had biochar while reactor 3 housed electrodes (type and construction see below). Reactor 4 contained both biochar and electrodes. No additions were made to the woodchips in reactor 1. Woodchips occupied

1L of total volume in the reactors. Where relevant, a 900mL woodchip/100ml biochar mixture occupied 1L of reactor volume. Walnut shell biochar (Hammon’s Product

Company, MO; charred by Char Energy LLC, MN at 600°C for 3 hours in mobile downdraft gassifier) that passed #10 sieve (2.00 mm) but not #60 sieve (0.250) were mixed with woodchips to form a 10% v/v mixture. Each reactor was filled with 800mL of

DI water.

The reactor’s electrode setups consisted of two graphite cylinders (10cm x 0.5cm radius) at one end of the reactor that acted as cathodes, with a submerged surface area of

25.13cm2 each. In each reactor a stainless-steel sheet (10cm x 0.005cm x 3.25cm) served as anode with a submerged surface area of 49.3cm2 placed at the other end of the plastic box. The cathodes were placed 6.5cm from each other, and 9cm from the anode.

Insulated tin wire was soldered to the anode and applied to the cathode using silicone sealant to protect the graphite/wire junction. A FisherBrand™ FB300 Power Supply

(Fisher Scientific, Waltham MA) provided a current of 5.5 mA (1.1A/m2) for the first four months of reactor operation which was later increased to 9.5mA (1.93A/m2) for the following two months. Consequently, the voltage readings increased from 4-5V to 7V.

Figure 2.1. Batch bioreactors were constructed and operated in conditions with and without biochar and with and without electrodes. Reactors were operated in the dark with a current of 5.5mA, later increased to 9.5mA. In the microbiome analysis, duplicates were taken from each of the reactor’s water, woodchip, biochar, and electrode biofilms. The naming convention established here (Reactor 1 = -biochar/-electrode, Reactor 2 = +biochar/-electrode, Reactor 3 = -biochar/+electrode, and Reactor 4 = +biochar/+electrode) is used throughout this document.

Reactors were operated for six months in the dark at room temperature with periodic additions of nitrate/phosphate concentrated stock solution. Media was injected into the reactors to bring nitrate and phosphate concentrations to 22.21mg/L and 0.33mg/L, respectively, every 2-3 days. Aliquots of water were removed from the stagnant batch reactors for chemical analyses. Using separate 3mL syringes, 1.5mL samples were obtained immediately before and after each nutrient addition at 2, 4,6,8, 24, and 48-hour intervals and were subsequently centrifuged and stored at -20°C. NOx concentrations in the reactor water samples were measured using the SEAL AutoAnalyzer 3 (Seal

Analytical, Meqon WI), a continuous segmented flow analyzer. All nitrate was reduced to nitrite prior to colorimetric detection following the manufacturer’s protocol for nitrate quantification.

2.2. Microbiome Analysis

Duplicate samples of woodchip, water, biochar, and electrode (anode and cathode samples were combined for analysis) biofilms were collected from each reactor where applicable after the six month bioreactor operating period ended, and DNA was extracted using the DNeasy PowerSoil® kit (Qiagen) in accordance with the manufacturer’s protocols. DNA concentration in each extract was quantified with a Qubit Fluorimeter

(Fisher Scientific, Waltham MA) using the dsDNA High Sensitivity Assay Kit. DNA extracts were subsequently stored at -20°C until further use. DNA extracts were submitted to the University of Minnesota Genomics Center (UMGC) for amplicon sequencing using the V1 – V3 variable region of the 16S rRNA gene on the Illumina

MiSeq system (Illumina, San Diego CA) using the 2 × 300 bp MiSeq Reagent Kit v3.

UMCG protocols previously described (Gohl et al., 2016) were utilized including DNA quality assessment, PCR product purification, and library preparation. PCR water controls were used to verify reagent purity and to identify potential contamination.

Primers, which were optimized for activated sludge microbiome analysis (Albertsen et al., 2015), were selected from the Earth Microbiome Project. The data released from

UMGC were free of adaptors and de-multiplexed using MiSeq Reporter Software v2.5.1.3.

The online Nephele (v2.4.0) platform allowed for Operational Taxonomic Unit

(OTU)-picking using QIIME (v1.9.1) as well as sequence data quality control (removing singletons and chimeric sequences) using the default and recommended settings. OTUs act as proxies for species and cluster sequences, in this case, at a 97% similarity threshold in an open-reference manner using the SILVA 97 (v132) database. Downstream analysis was performed on R (v3.5.1) using the phyloseq and ampvis2 packages to visualize alpha and beta diversity, as well as differential OTU abundances. OTUs with fewer than 2 sequences, those that failed to align with PyNAST, and those without an assigned phylum were excluded from analyses. Alpha diversity was tested for significance using the

Wilcox rank sum test, while beta diversity was tested using a PERMANOVA test, followed by a pairwise multilevel comparison PERMANOVA using the pairwiseAdonis package.

3. RESULTS

3.1. Reactor Performance Analysis

NOx concentration (mg/ml) concentration NOx 5

2 8 4 0 6 2 8 0 4 2 8 0 6 4 4

48 24 24

-5 24

6_27 9_17

10_17 10_27 Time (Date/hours post nutrient injection)

Reactor 1 Reactor 2 Reactor 3 Reactor 4

Absolute nitrate removal (referred to as NOx since the SEAL AA3 automatically reduced nitrate to nitrite) is presented in Figure 3.1. Spikes indicate the nutrient injections at a given date with removal measured 2, 4, 6, 8, and 24 hours later. Across all four reactors, greater than 85% nitrate/nitrite was removed 8 hours after injection, and more than 95% was removed 24 hours post-injection with a similar reading at the 48-hour interval (Figure 3.2, 3.3). While all reactors were successful in removing NOx, the removal of NOx significantly (p-values < 0.05) differed across reactors within the first 8 hours (Figure 3.2). The average removal 2 hours post injection was 71%, 68%, 51%, and

60% for reactors 1, 2, 3, and 4, respectively (Table 3.1). The same measure for the

Reactor 1 Reactor 2 Reactor 3 Reactor 4 100%

90%

80%

70%

60%

50%

40%

30%

20% removed NOx Percent

10% (Standardized to T0 = 22.21 mg/mL) 22.21 = T0 to (Standardized

4 4 4 8 2 6 8 4 2 6 2 6 2 6 2 6 4 8 8

48 24 24 24 24

8_6

6_27 7_25 10_31 Time (date & hours post nutrient injection) 10_27

15 reactors at 4 hours is 88%, 87%, 69%, and 78% removal and improves to 95%, 94%,

83%, and 82% two hours later. Though it appears reactor 4 performs slightly better than reactor 3, Student’s T-tests performed at these time intervals show no significant differences in performance.

An ANOVA test found the reactor performance to be significantly different (p-value

= 0.0198) with reactors 1 and 2 significantly outperforming reactors 3 and 4 (Student’s T- test p-values < 0.05). This trend of non-electrode reactors outperforming electrode reactors continues to the 8-hour interval where the ANVOVA p-value was calculated to be 0.0757. Through the first 8 hours, the performances of reactors 1 and 2 did not statistically differ (p-values > 0.05). Beyond 8 hours, the reactor performances were comparable (Figure 3.3 shows NOx removal, Figure S1 shows NOx removal rate). The current supplied to reactors was initially 5.5 mA (1.1mA/m2) but was increased to 9.5mA

(1.93mA/m2) on 9/21/2018 to get closer to the desired current density of 2mA/m2, a benchmark proposed by Law et al. (2018) for effective denitrification in a similar double cathode/anode batch bioreactor. The subsequent readings after this adjustment show a more pronounced performance gap between the non-electrode and electrode-containing reactors during the first 8 hours (Figure 3.2). NOx removal remained comparable 24 hours post injection across all four reactors when the current was increased. Though the presence of electrodes seems to cause a lag in NOx removal, the presence of biochar had no discernable effect.

Table 3.2. Average NOx removal across reactors 1-4 (R1, R2, R3, R4) along with the corresponding standard deviation reported for each time interval in hours post injection (HPI) of nutrients.

HPI R1 R1 R2 R2 R3 R3 R4 R4

AV STDEV AV STDEV AV STDEV AV STDEV

2 71% 10% 68% 14% 51% 16% 60% 12% 4 88% 7% 87% 5% 69% 13% 78% 9% 6 95% 3% 94% 3% 83% 8% 82% 10% 8 96% 3% 95% 4% 89% 7% 89% 9% 24 98% 3% 97% 3% 97% 3% 98% 3% 48 96% 0% 96% 1% 96% 1% 96% 0%

(Hours post nutrient injection)

Figure 3.3. Average NOx removal across reactors 1-4 (R1, R2, R3, R4) along with the corresponding standard deviation reported for each time interval in hours post injection (HPI) of nutrients.

3.2. Microbiome Analysis

One of the most widely adopted Next-generation sequencing methodologies is targeted amplicon sequencing of the 16S rRNA gene used for the identification and quantification of microbial populations. This ubiquitous prokaryotic gene has both high conserved and variable regions, with the conserved regions serving as amplification targets for PCR universal primers to select the species-specific variable regions (Patuzzi et al., 2019) to allow for taxonomic classification. Clustering sequences fragments (“reads”) according to similarity yields operations taxonomic units (OTUS) with the similarity threshold being

97% (Ramiro-Garcia et al., 2018). Reference-based OTU clustering allows sequences to be mapped to curated sets of gene sequences provided by databases such as Greengenes

(DeSantis et al., 2006), and SILVA (Quast et al., 2013). These databases tend to be biased such that clinically relevant microorganisms are overrepresented and might not effectively assign taxonomy to environmental or non-human environmental samples

(Pollock et al., 2018). Reference-based OTU picking will discard samples not in the database. Therefore, these environmental samples were clustered in an open-reference approach where OTUs were assigned by the database and those not recognized were clustered de novo. In our case, the SILVA 97 (v132) database was selected for OTU picking using the QIIME v1.9.1 (Caporaso et al., 2010) pipeline provided by Nephele v2.4.0 (Weber et al., 2018), an NIH-based analysis platform. The Nephele platform also provides quality control for samples by filtering samples with low quality read. A minimum phred score of 19 was set and a maximum of 10 consecutive low-quality base calls were permitted when joining reads. The pipeline trimmed primers as well as 18 identified and removed chimeric sequences. Data analysis was performed in R using the phyloseq package (McMurdie and Holmes, 2011). After excluding OTUs with fewer than

2 sequences and sequences that fail to align with PyNAST, 28,000 OTUs were detected.

Filtering OTUs with no phylum assignment resulted in 18,061 OTUs remaining.

Comparing the relative sequence abundance of OTUs at the phylum level across all reactors, the four reactors are dominated by Proteobacteria, Acidobacteria, Firmicutes, and Bacteroidetes (Figure 3.4). All reactor communities contained greater than 50%

Proteobacteria. Acidobacteria relative abundance in reactors 1, 2, 3, and 4 was 5.1%,

7.1%, 10.4% and 12.5%, respectively (Figure S2 a). Firmicutes abundance was 4.7%,

9.5%, 8.8%, and 7.7% while Bacteroidetes represented 9.2%, 7.6%, 8.2% and 6.5% of all sequences in Reactors 1, 2, 3, and 4, respectively. Reactors 3 and 4 contained relatively high numbers of Cyanobacteria (R1 = 0.4%, R2 = 0.4%, R3 = 1.7%, R4 = 2.2%) while

Ignavibacteriae (R1 = 1.4%, R2 = 2.2%, R3 = 0%, R4 = 0%) and Chloroflexi (R1 =

2.2%, R2 = 4.5%, R3 = 0.2%, R4 = 0.5%) were far more abundant in reactors 1 and 2.

(Number of sequence reads) sequence of (Number

Figure 3.5. (a) Reactor a microbial community composition by phylum, after filtering the data. Only the 19 most abundant phyla are shown. Proteobacteria represent the dominant phylum. (b) The breakdown by the 37 most abundant classes. In cases where no class resolution could be determined, the phylum name is instead used. Numbers represent relative OTU sequence abundance with red highest (100%) to blue lowest (0%)

Table 3.2. Diversity indices for each of the 21 samples listed by source (woodchip, water, biochar, electrode), reactor of origin (R1, R2 ,R3, R4) and sample replicate number (1 or 2).

Removing samples with fewer than 1000 reads as a form of quality control resulted in the loss of both biochar samples from reactor 2, as well as one of the woodchip samples taken from reactor 2. A heatmap was tabulated using the ampvis2 package (Andersen et al., 2018) to better visualize the percent sequence abundance of taxa present in each sample (Figure 3.5). Interestingly, electrode samples contained more

22 cyanobacteria (6.3%, second most represented phylum after Proteobacteria at 75.6%) than other samples (Figure S3 a) while selecting against Acidobacteria. Biochar samples contained 48.2% Proteobacteria, and 29.6% Acidobacteria, more than twice the fraction of woodchips at 10.7%, but is 0% Bacteroidetes and only 0.2% Verrucomicrobia, which were far lower in relative abundance than in the electrode, water and woodchip samples.

Water samples contained 65.5% Proteobacteria, 8.5% Acidobacteria, 7.8% Firmicutes, while woodchip communities comprised 38.8% Proteobacteria, 16.4% Bacteroidetes, and

10.7% Acidobacteria.

Alpha diversity metrics were calculated using the phyloseq package and presented in Table 3.2 & Figure 3.6. The Chao1 and Shannon indices both indicate water and woodchip samples were highly diverse with greater than 5000 estimated species compared to the biochar and electrode samples which had less than half and one-fifth estimated species, respectively. The water sample from reactor 1 also had low diversity.

This is probably caused by one of the duplicates that has lower diversity (Table 3.2) and does not cluster with the other (Figure 3.9). Pairwise comparisons using the Wilcox rank sum test for significance found that only the alpha diversity between electrode and water, and electrode and woodchips were significantly different (p-value < 0.05). Rarefaction curves were calculated to determine how comprehensive the diversity of each sample had been covered by our sequencing approach (Figure 3.7). The sequencing depth (reads) was insufficient to describe the total diversity of woodchip and water samples across all four reactors, while the microbial community on the electrode surfaces appeared to be sufficiently sampled. This holds true when sequencing depth is equalized (Figure S5). 23

Principal coordinate analyses were performed to assess the beta diversity between

Figurethe microbial 1.5. Alpha communities diversity from of the Table samples 1 visualized. using bothOn the Bray left- isCurtis the Chao1 and unweighted calculation which counts observed species and likely number of unobserved species. The Shannon index is on the right.Unifrac Each distances. boxplot correspondsUnifrac distances to a reactor account number for withphylogeny the sampling and evolutionary site. distances when calculating dissimilarity and the unweighted nature of this calculation allows lower abundance OTUs to have a greater impact on distance. Woodchip and water bacterial communities tend to cluster together, with the reactor 1 and 2 samples forming discrete clusters away from the reactor 3 and 4 samples (Figure 3.8). The biochar samples from reactor 2 were excluded from this analysis due to too few reads.

Figure 2.7. Rarefaction curve organized by reactor of origin (R1, R2, R3, R4) and colored by sampling source (woodchip, water, biochar, electrode). Electrodes appear to be well sampled while water and woodchip samples are not sequenced with enough depth.

Using a PERMANOVA test for β-diversity significance, we found that the sample typesFigure (woodchip, 3.6. Rarefaction biochar, curve water, organized or electrode) by reactor and of origin reactors (R1, were R2, R3,significantly R4) and colored different. by sampling source (woodchip, water, biochar, electrode). Electrodes appear to be well sampled while water and woodchip samples are not sequenced with enough depth. In order to identify which samples and reactors were significantly different, a pairwise multilevel comparison PERMANOVA

Figure 3.8. a Principal coordinate analyses to visualize the a dissimilarity in β-diversity. Samples are labeled according to

their reactor of

2 2 origin as well as sampling source. PC Along the axes are the percentages of variance each axis explains.(a) PCoA plot generated from Bray-Curtis distances, and (b) PCoA plot PC 1 using b unweighted Unifrac distances. b

2 2

PC 1 26 was calculated using the pairwiseAdonis package (Arbizu, 2020) and unweighted Unifrac distances. Mirroring the trend we observed with α-diversity, the microbial communities associated with the electrode biofilm were significantly different from the communities associated with the woodchips (p-value = 0.030, R2 = 0.27), as well as the reactor water microbial communities (p-value = 0.024, R2 = 0.23). Though it would be tempting to suggest electrodes drive significant changes in microbial communities based on the clustering in Figure 3.8, the p-value for the comparison of reactors 4 and 2 (biochar- amended reactors with and without electrodes, respectively) was 0.072 (R2 = 0.17), and

Figure 3.9.. A clustering hierarchy calculated using unweighted Unifrac data matrix. Water and woodchip samples of the same reactor cluster closely together, and water/woodchip samples are separate from the biochar and electrode samples. One water sample from reactor 1 clusters with the biochar samples while the other duplicate clusters with water samples from reactor 2. 27

the p-value for reactors 3 and 1 (biochar-free reactors with and without electrodes) was

0.162. These findings are also supported by the sample clustering hierarchy observed in the unweighted Unifrac tree shown in Figure 3.9. However, when aggregate samples subjected to electrodes were compared to aggregate samples not exposed to electrode presence, these were found to be significantly different (p-value = 0.02, R2 = 0.11). In this case samples were not broken down by source or reactor, but simply whether electrodes were in the same reactor from where the samples were taken.

In the following we compare differences between OTU abundances in the different reactors, the different sample sources, and the 12 different sample type/reactor combinations using QIIME. The statistical tests confirmed that OTU abundances were not different across all conditions when p-values were adjusted for sample number.

However, when comparing differential abundances between just two possible variables

(whether reactor groups, or sample sources) several OTUs were observed to vary in abundance between samples. This rather descriptive exploratory analysis was performed using the phyloseq package and DESeq2 in R and results have been plotted in Figures

3.10 and 3.11.

Testing differences at the OTU level between reactors and between sampling sources revealed OTUs that were either significantly (p-value<0.05) more abundant or less abundant. When reactor 4 was compared to reactor 3 (Figure 3.10a), the genus

Magnetospirillum was found to be more abundant in reactor 4 (log2 fold = 36.764168) while OTUs of the genus Zooglea were found to be significantly less abundant in reactor

4 (log2 fold = -5.393271 to -7.998207). Comparisons between reactor 4 and reactor 2

(Figure 3.10b) found greater abundances of Opitutus (log2 fold = 29.8304) in reactor 4, along with less abundance of Lentimicrobium (log2 fold = -12.80215) and 2 OTUs representing Ruminococcaceae UGC-010 (log2 fold = -7.704275, -10.535586). Two

OTUs of Flavobacterium were also detected to be different, however one was more abundant in reactor 4 while the other was less abundant in that reactor. OTUs that were classified as Acidobacteria and were significantly different in abundances seemed to be more abundant in reactor 4, while the Ignavibacteriae phylum was more abundant in reactor 2 by comparison. When compared to reactor 1 (Figure 3.10c), reactor 4 housed 15

Firmicutes OTUs that were more abundant (log2 fold = 6.699948 to 9.033806). Two

OTUs each of Opitutus and Magnetospirillum were detected with one being more abundant in reactor 4 and the other being less abundant for both genera. Significantly more abundant in reactor 4 than in reactor 1 were several OTUs corresponding to genera like Burkholderia-Paraburkholderia, Christensenellaceae R-7 group, and

Telmatospirillum while OTUs in genera like Denitratisoma, Flavobacterium,

Caulobacter and, Rhizobium were less abundant in reactor 4. In comparisons at the OTU level between reactor 3 and reactor 2 (Figure 3.10d) the following genera were more abundant in reactor 3: Opitutus, Flavobacterium, Acidovorax (log2 fold = 23.45092,

9.939212, 10.177690, respectively) while the following were less abundant in reactor 3:

Magnetospirillum, Denitratisoma, Candidatus Koribacter (log2 fold = -8.1655212, -

34.18264, -7.693188, respectively). When reactor 3 was compared to reactor 1 (Figure

3.10e), Opitutus (log2 fold = 15.51148) and Flavobacterium (log2 fold = 11.93814, and

9.33929) stood out as more abundant along with OTUs in genera like Acidovorax,

Anaerospore, Burkholderia-Paraburkholderia, and Sporomusa. Less abundant in reactor

3 than reactor 1 were 2 OTUs of Denitratisoma (log2 fold = -7.789083, and -8.455043),

Mangetospirillum (log2 fold = -5.056044) and Ruminoclostridium 1 (log2 fold = -

6.980051).

This analysis of differences at the OTU level was extended to the sampling sources. Genera more abundant (log2 fold >5) in the biochar biofilm than the electrode biofilm (Figure 3.11a) included genera like Cellulomonas, Clostridium sensu stricto 13,

Gemmatimonas, Mangeospirillum, Paenibacillus, Rhizomicrobium, Rhodoplanes and

Telmatospirillum. Genera less abundant (log2 fold < -10) in the same comparison include

Anaerosporomusa, Caulobacter, Chryseobacterium, Flavobacterium,

Hydrocarboniphaga, Ralstonia, and Terrimicrobium. Zoogloea was also found to be less abundant in the biochar compared to electrodes samples (log2 fold < -5) . When the biochar samples were compared to the woodchips (Figure 3.11b), Burkholderia-

Paraburkholderia, Christensenellaceae R-7 group, Magnetospirillum, Pedomicrobium, and Telmatospirillum were found to be more abundant in the biochar (log2 fold >5), and

Anaerosporomusa, Caulobacter, Flavobacterium, Lentimicrobium, Opitutus,

Sediminibacterium and Terrimicrobium were found to be less abundant (log2 fold < -15) in the biochar compared to woodchip samples. Figure 3.11c compares the OTU differences between biochar and water samples to illustrate greater abundances (log2 fold

>5) of Cellulomonas, Christensenellaceae R-7 group, Magnetospirillum,

Rhizomicrobium, and Telmatospirillum in the biochar and lesser abundances (log2 fold < 30

-15) of Caulobacter, Chryseobacterium, Ferruginibacter, Flavobacterium,

Hydrocarboniphaga, Ruminoclostridium 1, Sediminibacterium, and Terrimicrobium compared to the water microbiome. The electrode biofilm showed greater abundances

(log2 fold >5) of Flavobacterium, Hydrocarboniphaga, Paludibacter, Ralstonia,

Sphingomonas, and Zoogloea and fewer abundances (log2 fold < -20) of Cnuella,

Flavobacterium, Lentimicrobium, Opitutus, Paenibacillus, Ruminococcaceae UCG-010 compared to water samples (Figure 3.11d). OTU differences between electrode biofilms and the microbes inhabiting woodchips (Figure 3.11e) showed electrode biofilms were more abundant (log2 fold >5) in Ralstonia, Sphingomonas, Sphingopyxis, and Zoogloea than woodchip microbiomes while at the same time less abundant (log2 fold < -25) in

Cnuella, Flavobacterium, Lentimicrobium, Opitutus, Paenibacillus, and

Ruminococcaceae UCG-010. Finally, woodchip samples compared to water samples showed greater abundances (log2 fold >5) of Opitutus and Paludibacter with no OTUs registering as significantly less abundant in woodchips versus water (Figure 3.11f).

Figure 3.10. Log2 fold change when (a) Reactor 4 is compared to reactor 3, a with respect to significantly different OTU sequence abundance.

Uncultured OTU

b OTU

OTU

b c

Uncultured OTU

OTU

Figure 3.10. (cont.). Log2 fold change when Reactor 4 is compared to (b) reactor 2, and (c) reactor 1 with respect to relative OTU sequence abundance. 33

d Uncultured OTU OTU

OTU

Uncultured OTU

OTU

Figure 3.10. (cont). (cont.) Log2 fold change when Reactor 3 is compared to (d) reactor 2, or (e) reactor 1 with respect to significantly different OTU sequence abundances.

Uncultured OTU

OTU

Uncultured OTU

OTU

Figure 3.11. Log2 fold change when biochar is compared to (a) electrodes, or (b) woodchips with respect to significantly different OTU sequence abundances. 35

Uncultured OTU

OTU

Uncultured OTU

OTU

Figure 3.11. (cont.) Log2 fold change among significantly different OTU abundances comparing (c) biochar to water, (d) and electrodes to water samples. 36

Uncultured OTU

37 Figur e 3.11. (cont.) Log2 fold change of significantly different OTU abundance comparing (e) electrodes to woodchips, (f) and woodchips to water samples.

4. DISCUSSION

4.1. Denitrifying Bioreactors

The performance with respect to nitrate removal of reactors with electrodes and reactors lacking electrodes were the same. Both types of reactors removed on average at least 97% of NOx 24 hours after nutrient injection. Differences in efficiency during the first 8 hours of performance were detected between electrode-containing and electrode- free reactors. The electrode-containing reactors significantly underperformed (p-value <

0.05) compared to electrode-free reactors. This was unexpected as prior studies in the literature point to electrochemical stimulation as improving denitrification rates by up to

76% compared to controls (Jiang et al., 2018). The setup employed by Jiang et al. compared denitrification rates between a biofilm with an applied current and a normal biofilm in a two-chamber batch bioreactor with carbon electrodes, however oxygen concentrations were low due to reduction reactions at the electrode. No differences in performance were found between reactors 1 and 2, and between reactors 3 and 4.The lag between reactors 1 & 2, and 3 & 4 widened after the current was increased. This points towards electrolysis of water generating oxygen that diffused too easily throughout the batch bioreactor thereby disrupting nitrate reduction and leading to less effective denitrification. Interestingly, the color of water samples between the electrode-free and electrode-containing reactors was also found to be quite different. Water samples from reactors 1 and 2 were amber, likely colored by tannins from the woodchips, while water from reactors 3 and 4 was clearer (Figure 4.1.). This phenomenon may be a result of high dissolved oxygen concentrations enabling microbes to oxidize tannins thus affecting 38 water turbidity and coloration. The pH of each reactor was 6.90, 6.85, 6.42, 6.98 in reactors 1, 2, 3, and 4, respectively. The lessons from this experiment are important for designing better denitrification systems (explored in section 5) and will be taken into consideration for future studies.

One major environmental parameter driving bacterial community diversity in soil is pH, with greater bacterial diversity in neutral soils and less diversity in acidic soils

(Fierer and Jackson, 2006). Biochar applications can increase soil pH to promote bacterial diversity, especially copiotrophic organisms. There are conflicting reports in the literature on whether biochar increases or decreases soil respiration and whether it changes microbial biomass (Jenkins et al., 2017). Microbial responses to biochar depend on the biochar (feedstock, and pyrolysis temperature) edaphic conditions of the soil, land use, vegetation, and the original microbial community; additionally, many taxa show variation in response (Jenkins et al., 2017). Adding low-temperature pyrolysis biochar to forest soils decreased microbial diversity, while soils amended with high-temperature biochar exhibited increased diversity (Khodadad et al., 2011). The effect has been described as a result of differential carbon accessibility (labile vs refractory) in low vs high temperature biochars. While natural biochar buried for between 100 to 350 years and artificially inoculated fresh biochar demonstrated extensive microbial colonization, field-applied biochar in the short term (3 years) was only sparsely colonized (Quilliam et al., 2013). This may be a result of mineral salt concentrations and PAHs in fresh biochar, in combination with a nutrient deficiency (lower available C in biochar versus surrounding soil) make biochar a challenging substrate to colonize. 39

Though the biochar samples, consisting of the biofilm on biochar particles, in our experiment seems to have lower diversity than either the woodchip or water samples, this difference was not found to be significant. In the literature, biochar most significantly changes bacterial groups at the genus level though increases in relative abundance of

Actinobacteria, Bacteroidetes, Firmicutes, Planctomycetes, and Gemmatimonadetes as well as decreases in Acidobacteria and Chloroflexi in bulk soil amended with biochar

(Sheng and Zhu, 2018). Here, we report on the charosphere (analogous to rhizosphere) itself. The relative OTU sequence abundance of Acidobacteria and Bacteroidetes stand out from the other sampling sources (Figure S3). Though most biochar is known to be alkaline a high proportion of Acidobacteria were found to inhabit the medium (29.6% relative OTU sequence abundance). This may be explained by a chemical characterization revealing the walnut shell biochar to be slightly acidic (pH = 6.6) (Table

S1). It is important to note that not all Acidobacteria are acidophiles and some subgroups in the phyla prefer neutral or alkaline conditions (Dedysh and Damsté, 2018). Previous research by Dai et al. (2016) revealed biochar-amended soil increases the abundance of high nutrient requirement copiotrophs like Bacteroidetes and decreases the abundance of low nutrient requirement oligotrophs like Acidobacteria. While Dai et al. (2016) found biochar to enrich soil biota with Bacteroidetes and decrease abundance of Acidobacteria, here we demonstrate the conditions in biochar itself (charosphere) to not support growth of Bacteroidetes and promote proliferation of Acidobacteria. Cyanobacteria and

Bacteroidetes, were both found to have 0% relative OTU sequence abundance in the biofilm on biochar. It is possible the biochar used in our study had a low fraction of easily

40 mineralizable carbon leading to oligotroph proliferation. The walnut shell biochar used in this experiment was charred by slow pyrolysis at 600°C for 3 hours and biochars produced at lower temperatures tend to have more microbially-available carbon. Luo et al. (2013) found biochar prepared at 350°C contributed up to 20% of microbial biomass

C, while biochar prepared at 700°C constituted less than 2% of microbial biomass C.

Though biochar has been reported to influence microbial communities in the literature, there have so far been no reports of consistent functional changes due to biochar (Noyce et al., 2016), likely owing to functional redundancy. Filtering low-read samples resulted in the loss of biochar samples from reactor 2. It is possible the biochar microbiome would differ from that of reactor 4 due to the chemical conditions induced by the electrodes. The two available biochar samples rarefaction curve indicated more OTUs are likely if sequencing depth is increased, and alpha diversity analysis showed no difference in diversity from the water and woodchip samples. This, however, may be a result of a low number of replicates. The two biochar samples cluster closely together (Figure 3.8a, 3.8b,

3.9) and are somewhat distinct from other samples, however, the differences are not supported by statistical significance because of the low sample number (n=2).

Biochar also had no discernable effect on the performance of the woodchip batch bioreactors. Reactor 2 performed similarly to reactor 1 and reactor 4 performed similarly to reactor 3, indicating the electrodes, rather than the biochar impacted reactor performance. Again, the absence of any observable biochar effect may be due to the low number of samples impacting significance testing. There are nevertheless compelling reasons to use biochar in soil. Overuse of nitrogen fertilizers contributes to acidification 41 of soil. The application of biochar can restore soil pH and improve microbial diversity, as well as increase plant growth (Xu et al., 2014). Biochar amended soils also increase N2O reduction thereby mitigating the emissions of this greenhouse gas (Harter et al., 2014).

Electrode microbiomes were found to be well sampled (indicated by the rarefaction curves) and possessed significantly less alpha diversity compared to water and woodchip microbiomes (p-value = 0.018 in both comparisons). In beta diversity analysis, the electrode samples tend to cluster together suggesting the environmental conditions created by electrodes enriched for similar taxa. This idea is further reinforced by the clustering hierarchy showing electrode samples together. It is important to note the biofilms scraped off electrodes were not segregated into cathode and anode samples but were simply grouped as electrode samples. The method of sampling also favored bacteria capable of producing a biofilm on a graphite/biochar surface potentially profiting physiologically from direct electron transfer. The electrode biofilm community was found to be significantly different from the water and woodchip communities. Pairwise multilevel comparison PERMANOVA between reactors 1 and 3, and between reactors 2 and 4 were not found to be significant. However, samples taken from reactors 3 and 4 are significantly different (p-value = 0.02) from those taken from reactors 1 and 2. Electrodes may facilitate changing the microbiome by supplying electrons, altering pH, or changing dissolved oxygen concentrations in batch bioreactors. Electrons supplied through electrodes have been shown to significantly increase the relative abundances of typical soil electroactive nitrate-reducing microbes such as Alcaligenaceae and

Pseudomonadaceae families (Qin et al., 2019) as well as the genus Geobacter (Qin et al., 42

2017). In our reactors, the genus Zoogloea was found to be more abundant in the electrode microbiome. This genus produces a gelatinous biofilm and our sampling methods of scraping the electrode biofilm could have favored collecting these OTUs.

They are obligate aerobes, and the electrolysis could have provided them with oxygen as anaerobes like Rhodoplanes, Sporobacter, and Telmatospirillum were less abundant on electrodes. The poorly characterized cyanobacterial class MJ635-21, now called

Sericytochromatia (Soo et al., 2017), was also found on electrodes. These cyanobacteria do not fix carbon or perform phototrophy. It is unknown what this association indicates, but it warrants further investigation. Electrodes supported high proportions of

Proteobacteria (Alphaproteobacteria and Betaproteobacteria, mainly) and few OTUs corresponding to Acidobacteria.

Figure 4.1. Water samples were taken from each reactor and centrifuged resulting in a 43 biomass pellet. From left to right: reactor 1, reactor 2, reactor 3, reactor 4. There is a difference in coloration between the electrode containing and electrode-free reactors. 4.2. Bacteria

An uncultured genus of Acidobacteriaceae was present in significantly higher abundance in reactor 4 (Figure 3.10a, b, c) when compared to any other of the reactors (1,

2 or 3). This family is acidophilic and abundant in soils and members have diverse physiological features (Campbell et al., 2014). Beijerinckiaceae is another family abundant in reactor 4. This diazotrophic alphaproteobacterial family is metabolically diverse and includes obligate methanotrophs and chemoorganoheterotrophs (Marín and

Arahal, 2014). Other taxa found to be more abundant in reactor 4 belong to the family

Acetobacteraceae. This family comprises species that are strictly aerobic chemoorganotrophs (Hommel, 2014) They also fall into two subgroups: an acetous group and an acidophilic group (Komagata and Yamada, 2014).

Compared to reactors 2 and 4, the relative sequence abundance of the genus

Magnetospirillum in reactor 3 is significantly lower (Figure 3.10 a, d). Known representatives of this genus are ubiquitous in freshwater environments and possess a helical morphology with a flagellum at each end of the cell. These organisms conduct anoxic or microoxic denitrification using short-chained fatty acids as a carbon source

(Dziuba et al., 2020). Most notably, members of the Magnetospirillum genus biomineralize magnetosomes which are crystals of magnetite (Fe3O4) encircled by a lipid bilayer conferring a permanent magnetic dipole moment and allowing cells to align with extracellular magnetic fields (Le Nagard et al., 2018). It is thought the magnetosome organelles, along with aerotactic sensing, enable magnetotactic bacteria to navigate along

44 oxygen gradients in aquatic sediment (Lefèvre et al., 2014); reducing conditions were found to select for magnetosome production (Olszewska-Widdrat et al., 2019). Opitutus, anaerobic chemoorganotrophs capable of reducing nitrate to nitrite (Janssen, 2015) was found to be more abundant in reactors 3 and 4. The soil and freshwater Flavobacterium genus is mostly aerobic using oxygen as a terminal electron acceptor, though about half of the species can reduce nitrate to nitrite and one species is capable of complete denitrification (Bernardet and Bowman, 2015).

An OTU of the family Rhodocyclaceae was found to be more abundant in reactor 3 than in other reactors. These organisms have widely different lifestyles and without further taxonomic resolution, it is difficult to infer any links between reactor performance and the metabolic capabilities of these OTUs. Another OTU belonging to the phylum

Saccharibacteria was found to be less abundant in reactor 3. We found 36 OTUs in reactor 1 that had a significantly different abundance in any of the other three reactors

(Figure 3.10 c, e). In the case of reactor 2, 14 OTUs were detected that were of significantly higher abundance when compared to the other three reactors.

Electrode biofilms in reactors 3 and 4 (Figure 3.11 a, d, e) appeared to be enriched in

Sericytochromatia, which lacks the genes for phototrophy or carbon-fixation. Only a few

Sericytochromatia genomes have been sequenced meaning the ecological niche and the full metabolic repertoire of these cyanobacteria remain unknown. Four OTUs corresponding to the genus Zoogloea were also uniquely associated with electrodes.

These organisms are obligately aerobic, oxidize organic carbon, and produce a

45 characteristic gelatinous matrix when unperturbed in aquatic environments. Zoogloea is ubiquitous in aquatic environments and can be found adhering to solid objects (Dugan et al., 2006). Strains of Telmatospirillum were found to be less abundant on electrode surfaces compared to the other microbiomes. These species grow chemoorganotrophically on organic acids and glucose under anoxic conditions, with some autotrophic growth also detected on mixtures of H2:CO2 (Sizova et al., 2007).

Telmatospirillum are diazotrophic organisms, isolated from acidic wetlands. They are closely related to Magnetospirillum and possibly oxidize sulfur compounds too

(Hausmann et al., 2018). The order Gemmatimonadetes although ubiquitous in many environments is only represented by four cultivated strains, including two belonging to the genus Gemmatimonas. Another study did not find a Gemmatimonas isolate to utilize nitrate, nitrite, or N2O under anaerobic conditions although the presence of nosZ genes in the genome should have allowed for the reduction of N2O under anaerobic conditions

(Chee-Sanford et al., 2019). In another experiment in which the biosurfactant rhamnolipids were added to a denitrifying biofilter, the abundance of Gemmatimonas did increase. Gemmatimonas can perform nitrogen-fixation, as well as aerobic denitrification for greater overall nitrogen removal (Peng et al., 2019). However, this OTU was found to be more abundant in non-electrode samples.

Obligately anaerobic chemoorganotrophic Sporobacter is more abundant in non- electrode samples. Rhodoplanes are photoheterotrophic organisms that grow under anoxic conditions using simple organics as carbon and electron sources. Like

Sporobacter, these organisms were also more abundant in non-electrode samples. In the 46 dark, they can grow chemotrophically and can perform denitrification in the presence of nitrate (Hiraishi and Imhoff, 2015). In addition, two other family-level OTUs were identified as different in abundance in electrode samples: Comamonadaceae (more abundant on electrodes compared to the other samples) and Acetobacteraceae (less abundant on electrodes compared to the other samples). At the higher taxonomic rank of orders, two OTUs belonging to Ignavibacteriales and one OTU belonging to the

Rhizobiales, an order known to include nitrogen-fixing bacteria, were found to be less abundant on the electrode surface than in any other sample. One Saccharibacteria OTU was also identified as being less abundant in the electrode samples.

Biochar-associated OTUs are listed in Figure 3.11 a, b, c. One OTU at the genus-level found to be more abundant in the biochar samples was Magentospirillum. Strangely, also

OTUs belonging to the Acidobacteriaceae were found to be more abundant in biochar than in the other samples, although biochar environments tend to be more alkaline and less acidic. Finally, two OTUs corresponding to the order Micrococcales were found to be more abundant in biochar samples compared to the other samples.

Caulobacter is a chemoorganotrophic genus that can be isolated from municipal and freshwater. Caulobacter species can use oxygen as the terminal electron acceptor but can also reduce nitrate to nitrite (Poindexter, 2015). The genus Caulobacter was found to be less abundant in biochar when compared to woodchips, water, or electrode samples.

Biochar also seemed deficient with respect to the genus Anaerosporomusa. A characterized strain of Anaerosporomusa has been described as heterotrophic and

47 growing anaerobically by fermentation (Choi et al., 2016). Sediminibacterium,

Ferruginibacter, along with three other OTUs in the Chitinophagaceae family were also found to be relatively low in abundance in the biochar samples. Terrimicrobium was found to be more abundant in non-biochar samples. All these organisms are facultative anaerobes and are capable of fermentation but are generally unable to use extracellular electrons acceptors other than oxygen (Qiu et al., 2014). Anaerobic Ruminiclostrodium that ferment plant matter (Yutin and Gaperin, 2013), as well as aerobic, heterotrophic hydrocarbon-degrading Hydrocarboniphaga (Liu et al., 2011), and chemoorganotrophic

Chryseobacterium (Matu et al., 2019), were also found to be less abundant in the biochar samples compared to the woodchips, water, or electrode samples.

The bacteria were introduced to the bioreactor through the woodchips which were inoculated in a mixture of soil and DI water. It is possible this selected for organisms that are present in conditions with limited nutrients such as iron and potassium.

Environmental parameters in the bioreactors such as dissolved oxygen, pH, conductivity, and redox potential were not measured but may yield data demonstrating these factors to influence microbial population in future studies.

5. OUTLOOK

5.1. Constructed Wetlands

In addition to in-field management and edge-of-field structures, constructed/vegetative wetlands are recognized as a reliable and robust water treatment technology. Wetlands are ecosystems that support shallow standing or slow-flowing 48 water and have conditions that restrict aeration. These ecosystems offer ecological services such as wastewater treatment and nutrient removal. Water passing through a wetland is treated physically through sedimentation, filtration, and UV radiation, chemically, through precipitation and sorption, and volatilization, and biologically through microbial and phytic transformation. The most important criteria for constructed wetlands are hydrology (subsurface or open water), flow path (horizontal or vertical), and macrophyte species (Vymazal, 2010). The advantages of constructed wetlands are low maintenance and cost-effectiveness; however, the area footprint presents a drawback.

One venue for reducing the area required is by enhancing the performance of constructed wetlands with microbial electrochemical technology.

The outlook section of this document provides parameters for the development of an electrochemically stimulated denitrifying bioreactor as a model system for wetland water treatment scenarios (Figure 5.1). Our hypothesis is that this design will increase denitrification while lowering the required current intensity for electrochemical stimulation. The project will make use of a Seal AA3 segmented flow analyzer to quantify nitrate concentrations and 16S rRNA gene amplicon sequencing for the analyses of the performance and microbial community composition in the wetland reactors.

5.2. Operational Considerations

Bio-electrochemical systems (BES) use microbes to catalyze electrochemical reactions through interactions with electrodes (Mook et al., 2013). The microbes exchange electrons with the electrodes by either donating or accepting electrons. In 49

Microbial Fuel Cells, anaerobic microbes donate electrons to electrodes to generate an electrical current by oxidizing organic substrates. In recent years microbial electrosynthesis, microbial production of reduced compounds, in a current-consuming

BES have been studied intensively. One such application is in denitrifying bioreactors.

Heterotrophic denitrification requires organic substrates as carbon and electron sources such as acetate, ethanol, or insoluble carbon sources like wheat straw. This creates additional expenditure, promotes biomass growth that may clog the reactor (Liang and

Feng, 2018), and allows non-target microbes to proliferate (Nguyen et al., 2016). Thus, the use of autotrophic denitrifiers in bioelectrochemical systems with either electrons or hydrogen supplied as electron donors for microbial denitrification might obviate some of these observed problems.

Hydrogen gas produced at the cathode can serve as electron donors for autotrophic denitrification according to the following overall reaction:

− + 2NO3 + 5H2 + 2H → N2 + 6H2O. A constant electrical current supplied by a power source avoids accumulation of denitrification intermediates, which can be a result of competition among denitrifying enzymes for electrons (Mook et al., 2013). The electrical current supplied to the cathode is proportional to the amount of hydrogen evolution.

Hydrogen evolution potential at a pH of 7 is -611mV, meaning a more negative cathode potential is required for autotrophic denitrifiers (Feleke et al., 1998). However, the greater amount of gas effervescence at higher currents can induce dry spaces that reduce biofilm formation and contact between microbes and nitrate thereby reducing reactor performance (Szekeres et al., 2001). Additionally, higher current also promotes oxygen at 50 the anode which can decrease hydrogenotrophic denitrification. There are, however, disadvantages in relying solely on hydrogenotrophs; the energy expense of generating H2 is high, hydrogen is poorly soluble in water leading to a low utilization efficiency and the proliferation of planktonic cells (Chen et al., 2017). This necessitates generating opportunities for also using autotrophic denitrifiers that are directly capable of accepting electrons from an electrode such as Thiobacillus or Sulfurimonas. Gregory et al. (2004) have demonstrated that cathodes can act as electron donors. Chen et al. (2017) demonstrated that a potential of -500 mV successfully allowed the cathode to serve as the sole electron source for denitrifiers. Different currents have been reported as effective in other research ranging from 15-40 mA, likely arising from different experimental conditions, reactor designs, and electrodes (He at al., 2016).

Using a column denitrifying BES reactor Pous et al. (2017) found decreasing hydraulic retention time (HRT) increased nitrate consumption rate and recommended using low volume reactors connected in series with a high HRT to reduce nitrate in larger scale systems. Their results indicated that at a high HRT the presence of higher quantities of nitrate inhibited bacteria although such a mechanism has not yet been described, however inhibition of denitrification by an accumulation of secondary metabolites has been previously described (Bardon et al., 2014). In the literature, reactors designed to resemble constructed wetlands have been operated at a 24-hour HRT (Xu et al., 2017).

Designing an up-flow reactor, where influent flows vertically starting from the bottom of the reactor, can reduce construction costs and maintain nitrate removal efficiency (Xu et al, 2017). 51

Cecconet et al. (2019) found biocathodes buried in sand and in gravel led to the accumulation of N-intermediates suggesting incomplete denitrification compared to control biocathodes without the presence of either sand or gravel. Additionally, these reactors saw a 20-36% decrease in nitrate removals compared to the control reactors.

Recirculation of water in the control (mediated by a magnetic stir bar) contributed to higher rates of denitrification and total nitrogen removal in the control reactors. The tight packing of sand caused a lower performance than the more open packing in the gravel reactors suggesting that less porous media restricts contact between the electrode biofilm and a reducible substrate. This concept is corroborated by findings showing greater surface area exposed to sand (greater burial) resulting in less effective biocathodes

(Nguyen et al., 2016).

Figure 5.1. Setup of denitrifying bioreactor. Two such reactors have been constructed, one open- circuit and one closed-circuit and reactor performance and microbial community and activity will be monitored while reactors are operated.

The optimal pH for denitrification in the literature ranges from 6.5 to 8.0 (Mook et al., 2013). The pH at the cathode must be adjusted as consumption of protons generates a higher pH (Xu et al., 2017) and dissolved carbon dioxide forms bicarbonate to provide this adjustment. Though bicarbonate ions are supplied in our synthetic agricultural runoff, the graphite anode can provide inorganic carbon as CO2 by reacting with oxygen produced from electrolysis (Hao et al., 2013). Studies have shown high ionic strength promotes nitrate removal in BES likely due to a decrease in internal resistance (which is the main reason nitrate removal in groundwater is incomplete) (Liang and Feng, 2018).

The greater conductivity also decreases the buildup of denitrification intermediates. The addition of exogenous electrolytes, like chloride ions (Cl-), is an effective way to increase ionic strength (Liang and Feng, 2018).

5.3. Electrode Design

Carbon, metal and composite (carbon/metal) materials have been tested as electrodes.

Carbon-based electrodes are biocompatible and resistant to corrosion; however, they are limited by low electrical conductivity, mechanical strength, and higher costs for wide- scale implementation (Guo et al., 2015). Metals, mainly stainless steel, have superior conductivity and strength but are poorly biocompatible. Additionally, noble metals (e.g. gold, platinum) have also been tested but are cost-prohibitive to implement. Composite materials combine the advantages of both carbon and stainless steel such as coating stainless steel with carbon to enhance biocompatibility (Guo et al., 2014). Electrodes can increase BES performance through the following modifications: increased surface area,

54 improved biocompatibility, enhanced electron exchange with bacteria and microbial cell adherence, and superior conductivity. Additionally, good electrodes should have limited environmental impact and low production costs.

Electrode structures are generally described as planar (mesh, plate, cloth) or three- dimensional (foam, felt, fiber brush) (Wei et al., 2011). Planar electrodes have a defined surface area and can easily allow biofilm visualization, which explains their popularity in research. Three-dimensional electrodes improve reactor performance as their high surface area promotes greater cell adherence and biofilm formation (Guo et al., 2015). Tight packing of granular, conductive material can also serve as a cathode, though potential clogging in long-term use may become an issue (Wei et al., 2011).

The low electron transfer rate between electrodes and microbes continues to be a limitation and there are numerous modifications that can be made to electrodes to increase electron transfer. The surfaces of most bacteria are negatively charged and consequently inducing a positive charge on electrode surfaces (through treatment with gaseous ammonia, chitosan, or melamine) has been shown to be favorable for biofilm formation (Liang and Feng, 2018). Coatings of carbon nanotubes provide additional surface area for bacterial colonization. Acid-treated carbon electrodes possess oxygen- containing functional groups that benefit bacterial adhesion (Chen et al., 2017). Finally immobilizing exogenous electron transport mediators on electrode surfaces has been suggested to increase reactor performance, although it is unknown if this is a stable long- term solution.

5.4. Electron Transfer

The ability of microbes to interact with external electron acceptors is central in BES applications. Under anaerobic conditions, certain microorganisms can reduce nitrate by directly accepting electrons from a cathode electrode. The rate of electron exchange between electrodes and microbes limits the reductive power of a BES (Karthikeyan et al.,

2019). Two broadly different mechanisms of external electron transfer (EET) are direct electron transfer (DET) and indirect electron transfer (IET). DET is mediated by direct contact with an insoluble substrate or electrode, or by cell appendages such as nanowire or pili (Tremblay et al., 2017). These appendages can be covered with conductive c-type cytochrome enzymes. In the two most studied model microorganisms for EET,

Shewanella and Geobacter, cytochromes embedded within β-barrels form porin- cytochrome complexes that are responsible for delivering electrons across the outer membrane. Cell-surface exposed cytochromes facilitate EET in both DET and IET.

Conductive nanowires are composed of type IV pilin proteins (Costa et al., 2018) and allow EET among bacteria beyond the initial cell monolayer of a biofilm on an electrode surface. In Shewanella, these membrane extensions involve the porin-cytochrome complex. Disrupting the chain of aromatic amino acids that make up the nanowire cytochrome complexes impairs the passage of electrical current suggesting these residues play a role in electron transport (Liu et al., 2018). These appendages are intrinsically conductive, likely owing to continuous interactions between aromatic side chains of amino acids.

IET relies on soluble redox active compounds to shuttle electrons between cell- surface proteins and the electron acceptor. These electron shuttles can be cell-excreted flavins, flavocytochromes and phenazines, or natural mediators such as humic or fluvic acids (Thrash and Coates, 2008). Exogenous chemical electron shuttles could be supplied to denitrifying bioreactors although some are considered to not be stable or particularly environmentally friendly (Abbas and Rafatullah, 2018).

5.5 Reactor Configuration

The design for a benchtop-scale denitrifying bioreactor to model electrochemically stimulated constructed wetlands is described in this section and illustrated in Figure 5.1.

We designed a continuous flow system rather than batch reactors such as the woodchip reactors used in the first part of this thesis because it better simulates flow in a wetland environment. The up-flow regime maintains nitrate removal at lower costs. For the reasons of biocompatibility and increased surface area for biofilm formation, carbon was selected as the material for our electrodes with a three-dimensional structure chosen rather than a planar structure. We opted for a BASi MF-2077 (BASi, West Lafayette IN) reticulated vitreous carbon electrode (50mm height x 20mm radius, 5 mm thickness, with

20 pores per inch), The BASi MF-2077 is a hollow cylinder electrode which was filled with biochar too. The cathode in our bioreactor design should be surrounded by a matrix of biochar. The biochar can act as a conductive media and electron shuttle, massively increasing the electrode surface area for biofilm formation. Additionally, the sediment was amended with biochar (10% v/v) to facilitate conductivity. A rod-shaped BASi MW- 57

4133 graphite foam electrode (100 pores per inch carbon foam, 10mm x 10 mm x 20 mm) served as the anode. At the anode is graphite, it can react with oxygen to provide carbon dioxide which serves as an inorganic carbon source and mediates the pH. No additional modifications were made to the electrodes used in this study. The anode in our reactor design was partially buried in sediment, but most of the length of the anode was exposed to the water column above the sediment layer.

The synthetic agricultural water proposed for use in this denitrifying bioreactor mimics the agricultural drainage in southern Minnesota. The major chemical constituents are as follows: nitrate (30mg/L), phosphate (0.5mg/L), calcium (55mg/L), chloride

(150mg/L), magnesium (20mg/L), and potassium (5mg/L) with no micronutrients added as there is no evidence suggesting they impact nitrate removal (Krider, 2018). Lower C/N ratios are associated with selecting for autotrophs (Liang and Feng, 2018), and our reactors are to be run without additional carbon. Heterotrophs will proliferate as the organic carbon in the sediment is consumed, and autotrophs will establish themselves after the sediment organics have been exhausted. We aim to enrich the reactor with hydrogenotrophic autotrophs, or those that can participate in EET with the cathode directly. A variety of current densities and HRTs have been described in the literature.

We could evaluate the performance of the reactor at different currents and residence times to better characterize its performance. The reactor described in this section operates at a benchtop scale and does not yet support macrophytic growth. Krider et al. (2018) used fox sedge (Carex vulpinoidea), dark green bulrush (Scirpus atrovirens), and rice cutgrass (Leersia oryzoides) in denitrifying troughs as these are native wetland plants in 58

Southern Minnesota and across the Midwest. If this reactor were to be scaled up, these plants could be considered.

5.6. Concluding Remarks

The snow melt in early spring transports the season's fertilizer application into nearby water bodies. To mitigate the amount of fertilizer entering waterways, the denitrification capabilities of woodchip bioreactors can be enhanced through exogenous electron donors provided through current-bearing electrodes. In this pilot project we operated batch bioreactors with electrodes, as well as electron-shuttling biochar and observed denitrification rates. We found electrodes to create environmental conditions that may drive microbiome differences. These conditions may have included greater dissolved oxygen which disrupted denitrification leading to lower denitrification efficiencies within the first eight hours of operation. The electrodes themselves were associated with aerobic and biofilm-forming species as well as a cyanobacteria class that remains poorly characterized. Biochar was found to not impact denitrification rates and was associated with Acidobacteria OTUs.

The results of this study do not dismiss the contributions of electrodes and biochar in improving denitrification but rather suggest batch bioreactors are not an appropriate design for testing their efficiency. A flow-through bioreactor may better mimic the conditions in the field and prevent an accumulation of evolved gases. Dissolved oxygen is a design parameter we will closely monitor in future denitrifying bioreactors.

Optimizing bioreactor as model systems for constructing electrochemically stimulated 59 wetlands allows for treating nitrate pollution not just from agricultural runoff, but also waste water discharge and groundwater. Continuing research on denitrification systems is necessary to ensure future human and environmental health.

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Appendix:

Figure S3. (a) Sample a microbial community composition by phylum, according to sampling source. Only the 19 most abundant phyla are shown. (b) The breakdown by the 37 most abundant classes. In case no class could be identified, the phylum name is given instead. Numbers represent percent relative OTU sequence abundance with red being closer to 100% and blue closer to 0%.

Table TableS1. Coarse S1. Coarse grit walnut grit walnut shells shells(~4.76mm) (~4.76mm) purchased purchased from Hammon’s from Hammon’s Product Company,Product MO Company, was charred MO for was 3 hours charred (1hr for heating, 3 hours 2hr (1hr cooling) heating, by 2hr pyrolysis cooling) by by Char Energypyrolysis LLC, MN by in Char a mobile Energy downdraft LLC, MN gasifier. in a mobile Chemical downdraft characterization gasifier. Chemical was completed by Eurofinscharacte Scientificrization Productwas completed Testing by Lab Eurofins in Hamburg, Scientific Germany Product using Testing thermog Labravimetry in (Krider,Hamburg, 2018). Germany using thermogravimetry (Krider, 2018).