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8-29-2017 Enhancing the Performance of Anaerobic Bioreactors for the Biological Degradation of Volatiles Organic Compounds in Groundwater Using Alternate Electron Acceptors Leslie Michele Pipkin Louisiana State University and Agricultural and Mechanical College, [email protected]
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Recommended Citation Pipkin, Leslie Michele, "Enhancing the Performance of Anaerobic Bioreactors for the Biological Degradation of Volatiles Organic Compounds in Groundwater Using Alternate Electron Acceptors" (2017). LSU Doctoral Dissertations. 4106. https://digitalcommons.lsu.edu/gradschool_dissertations/4106
This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. ENHANCING THE PERFORMANCE OF ANAEROBIC BIOREACTORS FOR THE BIOLOGICAL DEGRADATION OF VOLATILE ORGANIC COMPOUNDS IN GROUNDWATER USING ALTERNATE ELECTRON ACCEPTORS
A Dissertation
Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy
in
The Department of Civil and Environmental Engineering
by Leslie Michele Pipkin B.S., Florida State University, 2010 M.S., Florida State University, 2012 December 2017
ACKNOWLEDGEMENTS
I would like to recognize those who have encouraged, directed, and supported me during my studies at Louisiana State University leading to my doctoral degree and this dissertation. I am forever grateful for my major advisor Dr. John Pardue for providing me the opportunity to work on this research project and for his profound ideas, exceptional direction, beneficial recommendations, and inspiration throughout my graduate academic career at LSU. I would also like to thank the members of my advisory committee, Dr. William Moe, Dr. Clinton Willson, and
Dr. John White for providing instrumental feedback, guidance and encouragement during my dissertation. I’m grateful for the funding provided for the study by the Louisiana Board of
Regents Fellowship and The Governor’s Biotechnology Initiative.
I would also like to thank Mr. Frederick Symmes of Weston Solutions Inc. for his assistance in granting me access and allowing sampling trips to take place at the Re-Solve, Inc.
Superfund Site in Massachusetts.
Many thanks to my fellow colleagues and associates at Louisiana State University who have provided assistance and support over the years: Dr. Vijai Elango, Dr. Christopher Akudo,
Trent Key, Davis Lofton, Michael Cheatham, Yasmin Mohammad, Autumn Westrick, David
Curtis, Mathew Rodrigue, Matt Decell, Barry Breaux, and Jacob Vigh.
Lastly I would like to thank my family and close friends for their continued love and support during this endeavor. Specifically I would like to recognize my parents, Nancy and
David, my brother Andrew, my cousin Sabrina Rodriguez, and my love Nicholas Smith, as well as my close friends Shannon Haltom, Brandon and Lanie White, and Payton Dupree for their constant encouragement.
ii TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...... ii
ABSTRACT ...... v
CHAPTER 1: INTRODUCTION ...... 1 1.1 General Introduction ...... 1 1.2 Purpose of Study ...... 2 1.3 Research Objectives ...... 3 1.4 Organization of Dissertation ...... 4
CHAPTER 2: LITERATURE REVIEW ...... 6 2.1 A Brief History Into the Volatile Organic Compounds of Interest ...... 6 2.2 Biodegradation of BTEX ...... 9 2.3 Biodegradation of Chlorinated Solvents ...... 15 2.4 Development of ABRs for Treatment of VOCs ...... 19 25 Enhancement of ABR Treatment Design and Approach ...... 24
CHAPTER 3: A SUSTAINABLE BIOREMEDIATION APPROACH FOR BTEX- CONTAMINATED GROUNDWATER UNDER METHANOGENIC AND SULFATE-REDUCING CONDITIONS ...... 26 3.1 Introduction ...... 26 3.2 Materials and Methods ...... 28 3.3 Results and Discussion ...... 34 3.4 Design Implications ...... 63
CHAPTER 4: COMPLETE DEGRADATION OF CHLORINATED ETHANES IN SEQUENTIAL BIOREACTORS OPERATED UNDER VARYING REDOX CONDITIONS ...... 64 4.1 Introduction ...... 64 4.2 Materials and Methods ...... 66 4.3 Results and Discussion ...... 75 4.4 Design Implications ...... 105
CHAPTER 5: ANALYSIS OF SYSTEM PERFORMANCE OF FULL-SCALE ANAEROBIC BIOREACTOR TREATMENT BEDS ...... 106 5.1 Introduction ...... 106 5.2 Materials and Methods ...... 111 5.3 Results and Discussion ...... 121 4.4 Design Implications ...... 159
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS ...... 160
REFERENCES ...... 165
iii APPENDIX ...... 190
VITA ...... 225
iv ABSTRACT
Engineered anaerobic bioreactors (ABR) constructed from mixed media and sand can biodegrade high concentrations of volatile organic compounds (VOCs). Sustainable treatment of benzene, toluene, ethylbenzene, and xylenes (BTEX), chlorinated ethenes and ethanes were used to investigate the use of engineered ABRs, while also analyzing the system performance of a full-scale ABR system. Experimentations conducted on environmental and biological factors that affect ABR system performance was conducted through serum bottles, columns, and reactor studies.
Column and reactor ABR systems were designed to treat VOCs via biodegradation in groundwater with varying terminal electron acceptors (TEAs). Laboratory-scale column ABRs were constructed to treat BTEX compounds while simultaneously adjusting the TEA processes in the bed between methanogenic and sulfate-reducing conditions. Analysis of the groundwater showed that amending the contaminated water with sulfate lead to higher percentages of degradation of BTEX. Experimentations showed that sizing of a full-scale ABR treatment bed would be controlled by benzene due to the lower removal efficiency and lower required discharge limits.
A two-phase pilot-scale ABR system was operated through the use of sequential anaerobic/aerobic ABRs to reductively dechlorinate 1,1,1-trichloroethane (TCA) to chloroethane
(CA), followed by aerobic oxidation of CA. Two duplicate reactors operated in parallel in an up- flow mode were prepared for aerobic treatment studies of CA. Pure oxygen was supplied through a porous silastic tube under pressure with a pure oxygen gas cylinder. Investigations concluded that complete mineralization of chloroethane was observed in the effluents from the aerobic zone of the ABR system. During both studies, TEA changes were confirmed by
v using a 16S rRNA assessment of microbial populations within the ABR beds, which indicated a shift in bacteria and/or archaea to a more effectual degrading community structure.
Two full-scale ABR beds were analyzed for system performance through two sampling trips to the ReSolve, Inc. Superfund Site. Groundwater samples were collected along the length of both ABRs for analysis of chlorinated ethenes and ethanes, while suspended and attached bacteria samples were collected via groundwater and core sampling. Data collected from the
Site determined that the ABRs displayed consistent losses of chlorinated ethenes and ethanes, with the exception of minor CA accumulation in the ABRs in 2014, with CA degradation occurring in 2015, showing that the system performance improved over the course of the year.
vi CHAPTER 1. INTRODUCTION
1.1 General Introduction
Clean groundwater is crucial to meet the demands of public, agricultural, and industrial entities and is among the most important natural resources. Groundwater contamination of
Volatile Organic Compounds is considered one of the many key sources for deterioration of water quality in the United States (El-Naas et al., 2014). Until the 1970s, companies both deliberately (through agricultural applications or improper disposal) or unknowingly polluted adjacent groundwater aquifers due to lack of knowledge and non-existent environmental laws at the time (Boudreau, 2013). After events of the discovery of toxic waste sites like the Love Canal and Times Beach, public awareness, along with the creation of environmental bills such as the
Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA or
Superfund) brought to light appropriate chemical and waste disposal practices to the United
States. This statute gave the EPA the authority to clean up uncontrolled hazardous waste sites and spills to protect human health and the environment.
Due to the increasing number of contaminants found in the environment today (Zogorski et al., 2006; Yu et al., 2015), there is a large demand for practical and efficient remediation technologies that are cost-effective and sustainable, such as bioremediation (Boopathy, 2000;
Mazzeo et al., 2010; Naidu, 2013). Traditional treatment technologies, such as pump-and-treat, air sparging, and soil vapor extractions are the dominant choices for remediation. The use of chemical and physical treatment of groundwater can be both applied ex situ and in situ. These traditional treatment methods have proved limitations and high costs of continuous operations.
In situ bioremediation (also known as natural attenuation) may use the natural bacteria in the soil and groundwater to transform the organic compounds to acceptable limits or become less mobile
1 compounds in the environment, which include petroleum hydrocarbons and chlorinated solvents
(USEPA, 1990). Bioremediation is a cost-effective remediation technology and is primarily related to the costs of site evaluation and monitoring (Khan et al., 2004). The research presented in this dissertation is based on the use of bioremediation as a viable technology to destroy a variety of contaminants in diverse anaerobic bioreactor systems.
1.2 Purpose of Study
The research outlined in this dissertation is geared towards the understanding of alternate electron acceptors in anaerobic bioremediation (ABRs) systems while understanding the mechanisms and the functional roles of bacteria and geochemistry through diversifying TEAs.
Specifically, a variety of experiments are conducted using two different ABR systems, one anaerobically treating BTEX, while the other is sequentially anaerobically and aerobically treating 1,1-dichloroethane (1,1-DCA) and chloroethane (CA). These represent investigations of two significant challenges for bioreactor systems: degrading VOC contaminants that are difficult to degrade like benzene under anaerobic conditions and providing oxygen to degrade contaminants where further transformation ceases and is not typically observed in the absence of oxygen such as chloroethane. These experiments will be supplemented by the field measurements of microbial populations in the first field scale application of these bioreactors at the ReSolve, Inc. Superfund Site (Site) in southern Massachusetts. The general challenge that these studies address is how to alter the geochemical conditions present in these systems to accomplish efficient biodegradation in the smallest volumes possible. The experimental plan consists of testing the biodegradation of varying TEA, from methanogenic conditions, to sulfate reducing conditions, to amending with oxygen to treat CA. Both of these bioreactor systems will identify the characterizations of varying microbial communities throughout the experiments
2 using DNA extractions, followed by sequencing for the 16S rRNA gene using next generation sequencing (NGS) techniques.
1.3 Research Objectives
The principal objectives and tasks of this dissertation research is to better understand how alternate electron acceptors can manipulate the conditions in anaerobic bioreactors to create biogeochemical conditions for a range of biodegradation reactions. The specific objectives and tasks of the research are as follows:
(1) Assessment of enhanced microbial treatment of benzene, toluene, ethylbenzene, and total xylenes in ABRs systems and the evaluation of varying terminal electron acceptor processes.
These activities will include evaluation and identification of microbial genomes and their transformations between TEA processes for bioremediation of BTEX;
A) Task 1: Assessment of enhanced microbial treatment of BTEX and the evaluation of
TEA processes
B) Task 2: Genomic DNA analysis of laboratory scale ABRs for bioremediation of
BTEX
(2) The examination of pure oxygen input into ABRs for chloroethane aerobic biodegradation; including the evaluation of chloroethane biodegradation in bioreactor treatment beds and the identification of microorganisms associated with biodegradation, where oxygen is the only viable electron acceptor;
C) Task 3: Examination of oxygen input into bioremediation beds for chloroethane
aerobic biodegradation
D) Task 4: Evaluation of chloroethane biodegradation and genomic DNA analysis
associated with biodegradation
3 (3) Evaluation of system performance and microbial populations within a full-scale ABR system at a chlorinated solvent CERCLA site in southern Massachusetts including assessment of performance and the existing microbial populations using next generation sequencing.
E) Task 5: Evaluate the degradation of chlorinated solvents and microbial populations on
system performance in a full-scale ABR system
1.4 Organization of Dissertation
A journal format was adopted for this dissertation with each chapter representing an independent project. Chapter 1 provides a general introduction into the research, followed by the purpose of study, and a list of the research objectives obtained for this dissertation.
Chapter 2 comprises a review of recent literature on a brief introduction into the VOCs of interest, along with the varying elements of BTEX and chlorinated solvent biodegradation, as well as the development of ABRs and the enhancement of ABR treatment approaches.
Chapter 3 presents the three-column ABRs system enhancement studies using alternate electron acceptors for the bioremediation of benzene, toluene, ethylbenzene, and total xylenes.
A comparative study will conclude the degradation performances of BTEX between the two
TEA processes. Further analysis of the ABRs media will detail the microbial populations during the varying redox conditions through the use of Next Generation Sequencing and analysis through the single software platform, Mothur.
In Chapter 4, serum bottle experiments were conducted to observe the biodegradation of chloroethane without the complication of transport processes. Specifically detailing the use of oxygen as a viable electron acceptor to biodegrade chloroethane. This chapter also includes a subsequent large-scale reactor study determining biodegradation rates of chloroethane within an
4 aerobic zone of an ABR. The genomic DNA evaluation of the media during the reactor study is used to determine the microbial populations associated with biodegradation of chloroethane.
Chapter 5 includes the evaluation of the system performance in the full-scale ABR system, while comprising the analysis of genomic DNA within the water and media to link the system performance to the findings at the site. This chapter contains data collection over 2014 and 2015, which includes two sampling trips during the summer months.
Chapter 6 comprises a brief summary of the results and observations from all the experimental studies in this dissertation. Suggested recommendations for future work are also highlighted for optimum treatment of ABRs system design and operations.
5 CHAPTER 2. LITERATURE REVIEW
2.1 A Brief History Into the Volatile Organic Compounds of Interest
Volatile Organic Compounds are prevalent in our groundwater due to the widespread and long-term use of these compounds within agriculture, manufacturing, and public practice.
Between 1945 and 1985, the production of many VOCs has increased by more than an order of magnitude, which has led to an increase of VOCs found in the environment today (Zogorski et al., 2006; Squillance et al., 1999). Petroleum by-products and chlorinated solvents have entered the environment through different means, such as leakage of underground storage tanks (USTs), waste incinerators and composting facilities, various chemical plants and refineries, and the misuse and mishandling of these organic compounds (Yassaa et al., 2006; Chen et al., 2005;
Buswell, 2001; Stringer & Johnston, 2001). VOCs in groundwater are a concern for many of those who use that same water as a drinking source.
Due to the frequency of VOCs found in groundwater, along with the compounds persistence in the environment and the availability to transport within aqueous phases, which can pose a threat to the safety and health of humans and the environment, an interest in environmental research and toxicology is prevalent today (Han et al., 2016). The dissertation here is examining VOCs that include the petroleum products (BTEX, along with chlorinated ethanes, which contain, TCA, 1,1-DCA and CA. Many of these petroleum compounds and chlorinated solvents are placed on the Substance Priority List (SPL), which is based on the frequency of these compounds found at contaminated sites and their known or suspected toxicity threat to the environment (ATSDR, 2013).
Benzene, toluene, ethylbenzene, and xylenes encompassed together are significant constituents of fossil fuels and other oil derivatives that are the most intensively and widely used
6 VOCs in industrial processes. Between 1980 and 2000, the median production of all gasoline hydrocarbons was 20 trillion gallons annually, with approximately 16 percent of a typical gas blend containing BTEX (Firmino et al., 2015; Fu et al., 2015; Weelink et al., 2010; Zogorski et al., 2006; Lawrence, 2006). Contamination of groundwater plumes occurs from discharges, spills or leakages of the chemicals. Due to the slow dissolution of BTEX, contamination can cause widespread concentratio
BTEX components can lead to neurological damage, noting that these compounds comprise of the largest VOC group associated with human-health effects (Lawrence, 2006). Benzene is a known carcinogen among the group of compounds, while ethylbenzene is a known possible carcinogen. This leads to concerns of maintaining the maximum contaminant levels (MCLs) allowed in groundwater due to the adverse health effects. The EPA’s MCLs in drinking water sources for BTEX is 0.005 mg/L of benzene, 1 mg/L of toluene, 0.7 mg/L of ethylbenzene, and
10 mg/L of total xylenes (USEPA, 2016). Sites contaminated with BTEX in the groundwater, may use benzene as a major design parameter to determine effectiveness of the remedial technologies being used due to the heightened known health risk of the benzene compound (Fu et al., 2015; Xin et al., 2013).
Throughout history, the production of synthetic chemicals has been used over a century to offer solutions to problems in commerce, industry, households, and military sites (Pankow &
Cherry, 1996). Due to the frequency of use, spills and subsequent exposure of chlorinated solvents has brought an interest in research of environmental health and toxicological effects.
Seven of the fifteen most frequently detected VOCs in aquifers around the United States are chlorinated solvents (Zogorski et al., 2006). Concerns of chlorinated solvents arise due to their presence as a Dense Non-Aqueous Phase Liquids (DNAPLs), along with a relative low
7 solubility. As a DNAPL, the contaminants penetrate the deep subsurface of the aquifers and bedrock, making remedial approaches more challenging to complete. Chlorinated aliphatic hydrocarbons (CAHs) were first discovered in a Michigan superfund site as groundwater contamination (An et al., 2004). Two chlorinated solvents studied in the environment are 1,1,2- trichloroethene (TCE) and 1,1,1-trichloroethane (TCA). TCA production peaked in the 1980’s shortly after the discovery of adverse human health effects of TCE exposure in the 1970’s. As of
January 1, 2002, all production and utilization of TCA was to cease as part of an amendment proposed to the Clean Air Act (CAA) (USEPA, 2013). TCA has been detected at approximately
23% of all industrial facilities and waste disposal sites that are listed as an active National
Priorities List (NPL) in the EPA.
Common daughter products of TCA include 1,1-dichloroethane, 1,1-dichloroethene (1,1-
DCE), vinyl chloride (VC), and chloroethane (Scheutz et al., 2011; Zogorski et al., 2006).
Toxicological reports have determined that both TCE and TCA can impact the developmental stages of organs, cardiovascular and neurological systems, while TCE and VC is listed as known carcinogens (ATSDR, 2013). TCA has an MCL of 0.2 mg/L, while one of its daughter products vinyl chloride has an MCL of 0.0002 mg/L (USEPA, 2016). Other daughter products of TCA are not listed on the national primary drinking water regulations, but 1,1-DCA is found in the
California Department of Public Health (CDPH) as listed to have an MCL of 0.005 mg/L and
Maryland Department of the Environment (MDE) with an MCL of 0.09 mg/L (USEPA, Region
IX, 2014; MDE, 2008). On the other hand, the daughter product CA is enforced in the state of
Louisiana at 0.01 mg/L and New York at 0.0036 mg/L (MDE, 2008; LDEQ, 2003). For the purpose of this dissertation research, the daughter products CA will be evaluated.
8 2.2 Biodegradation of BTEX
For biodegradation of BTEX to occur, organisms must overcome the structure of the aromatic carbon ring system that is found in each of the compounds. The compound structure of the BTEX substrates becomes inert towards simple oxidation or reduction reactions, requiring intricate biodegradation strategies to occur for biodegradation of BTEX (Fuchs et al., 2011).
BTEX biodegradation is known to readily occur under aerobic conditions (El-Nass et al., 2012;
Pruden et al. 2003), and is able to biodegrade under anaerobic conditions (Stasik et al., 2015;
Weeklink et al. 2010). Benzene has been known to be more recalcitrant under anaerobic conditions (Schreiber & Bahr, 2002). The reaction pathways are unique to the present organisms, specific compound and terminal electron acceptors as seen in Figure 2.1 (Johnson et al., 2003).
Figure 2.1. Generalized BTEX biodegradation pathways (Johnson et al., 2003).
9 Transformation of BTEX occurs through peripheral (upper) pathways, which leads to central intermediates, such as catechol, protocatechuate (aerobic pathways) or benzoyl-CoA
(anaerobic pathways). The central intermediates become activated for oxidative ring cleavage
(aerobic pathways) or through reduction of the aromatic ring (anaerobic pathways). Cleavage of the aromatic ring occurs through the central (lower) pathways of the transformation process, which then convert the central intermediates into intermediary metabolites such as acetyl-CoA, succinyl-CoA, and pyruvate. The intermediary metabolites can be used for microbial growth
(Fuchs et al. 2011). Figure 2.2 depicts the transformation pathways for aromatic ring biodegradation taken from Fuchs et al., (2011). During aerobic peripheral pathways, oxygenases is used to activate oxygen and produce the central intermediates for ring cleavage, while anaerobic biodegradation pathways use hydrolysis to facilitate the transfer of electrons to the ring for ring reduction (Fuchs et al., 2011).
Figure 2.2. Microorganisms collect aromatic substrates, such as BTEX, to form central intermediates to cleave the ring structure and form intermediary metabolites (Fuchs et al., 2006).
10 There are many viable treatment technologies for the biodegradation of BTEX constituents. Some of the traditional remediation tools range from pump-and-treat, to permeable reactive barriers (PRBs), advance oxidation processes, air stripping and adsorption. Another treatment approach is the use of biological processes, which is known as bioremediation
(Firmino et al., 2015). Many of the traditional remedial techniques can be very intrusive and often times relatively expensive (Johnson et al., 2003). Anaerobic biodegradation of aromatic pollutants has been the subject of research over the last two decades and counting (Cao et al.,
2009). More attention is being placed on the use of anaerobic biodegradation at the source of contamination because of its known economical, efficient, and environmentally friendly approach (Firmino et al., 2015; Juretic et al., 2014; Nourmoradi et al., 2012; Vignola et al.,
2011). There have been studies that have reported continuous-flow reactors for the biodegradation of BTEX under anaerobic conditions (Firmino et al., 2015; Farhadian et al.,
2008; de Nardi et al., 2005). Therefore there is an absence of knowledge on the operational parameters of anaerobic BTEX biodegradation systems. Some operational parameters that need extensive research on is the hydraulic retention time, pollutant concentration, the loading rate and others, which can influence the biodegradation of these compounds (Firmino et al., 2015).
Anaerobic biodegradation of BTEX compounds has been demonstrated under alternate electron acceptors, also known as TEAs. Electron availability of TEAs are used in decreasing order of oxidation-reduction potential. The most prevalent electron acceptor is oxygen, used as the sole electron acceptor in aerobic conditions. Within anaerobic conditions, electron preferences are as follows: nitrate > Mn(IV) > Fe(III) > sulfate > carbon dioxide; concluding that the most favorable electron acceptors will be depleted first (Bin et al., 2002; Lovley, 1997). The overall reactions for benzene, toluene, ethylbenzene and xylenes isomers biodegradation
11 stoechiometies in both aerobic and anaerobic conditions are given in Figure 2.3 (Farhadian et al.,
2008).
Figure 2.3. BTEX compound properties and degradation reactions (Farhadian et al. 2008).
Studies have shown that the addition of TEAs can enhance BTEX biodegradation (Ramos et al., 2013; Da Silva et al., 2005; Coates et al., 2002; Bin et al., 2002). Enhancement of anaerobic BTEX biodegradation is known for its simplicity in applications in the field, as well as economical practice (Farhadian et al., 2008). The enhancement of TEAs, specifically sulfate, to reduce BTEX contamination in groundwater was studied in this dissertation. The use as sulfate as an electron acceptor has some advantages and disadvantages. Some advantages of using sulfate as a TEA is that its’ highly soluble, it has a high assimilative capacity, and the production of hydrogen sulfide could complex and precipitate with inhibitory heavy metals. Some cause for concerns of sulfate as a TEA is that the production of H2S can prevent the biodegrading bacteria from multiplying at concentrations higher than 200 mg/L, also the aesthetics of the water can be unpleasing to the community, from smell and taste (Da Silva et al., 2005). It has been
12 demonstrated that the addition of sulfate to BTEX contaminated groundwater can stimulate anaerobic BTEX biodegradation (Staski et al., 2015; Dou et al., 2008; Roychoudhury et al.,
2006; Sublette et al., 2006; Da Silve et al., 2005; Nakagawa et al., 2002; Cunningham et al.,
2001; Anderson & Lovley, 2000; Wilkes et al., 2000; Weiner et al., 1998).
BTEX can undergo biotic processes, which involves microorganisms for degradation to occur. Anaerobic biodegradation of BTEX has a variety of microorganisms that are effective in degrading BTEX. Researchers were given the ability to study microorganisms and genes involved in the anaerobic biodegradation of BTEX through the development and research of redox processes coupled with molecular microbial methods. Specific groups of microorganisms have been linked to anaerobic biodegradation of BTEX through alternate electron acceptors (Bin et al. 2002). Under denitrifying conditions, anaerobic BTEX biodegradation has been linked through the Betaproteobacteria belonging to the cluster Azoarcus/Thauera (Rabus et al., 1999;
Hess et al., 1997). This cluster has since grown to over 30 pure cultures known to degrade BTEX anaerobically in nitrifying conditions (Bin et al., 2002). Coates et al., (2002) has linked anaerobic benzene degradation to nitrate reduction through the two strains in the genus
Dechloromonas (RCB and JJ) within the beta-subclass of Proteobacteria. Studies have revealed that BTEX degradation coupled to Fe(III) reduction is linked to Geobacters, belonging to the class Deltaproteobacteria. These microorganisms can also be found in petroleum-polluted aquifers (Snoeyenbos-West et al., 2000; Rooney-Varga et al., 1999; Anderson et al., 1998;
Coates et al., 1996). Studies have isolated pure cultures of sulfate-reducing bacteria (SRB) from known BTEX contaminated sites enriched with sulfate (Wilkes et al., 2000; Rabus & Heider,
1998; Beller et al., 1996). The members of the family Anaerolineaceae are known obligate anaerobes, and have been linked to oil degradation under sulfate reducing conditions
13 (Rosenkranz et al., 2013; Sherry et al., 2013). Some known SRBs are within the class
Deltaproteobacteria and members of the family Syntrophobacteraceae (Loy et al., 2004 & Gittel et al.,2009). It was only within the past two decades that pure cultures were known to degrade benzene anaerobically. These pure cultures were isolated from Dechloromonas strains (RCB and JJ) and Azoarcus strains (DN11 and AN9) (Kasai et al. 2016; Coates et al., 2001). Some other major known anaerobes are listed below in Figure 2.4 (Cao et al., 2009).
Figure 2.4. Major groups of bacteria in BTEX anaerobic biodegradation (Cao et al., 2009).
Even though there is a number of studies out there on the biochemistry of anaerobic
BTEX biodegradation and the identification of the microorganisms involved in the process (Key et al., 2014; Weeklink et al., 2010; Carmona et al., 2009), there is an absence of understanding on the way ecosystems function through biodiversity of the environment and its components like evenness, richness, and dynamics (Firmino et al., 2015). The link between environmental fluctuations through varying TEAs and microbial community structures is one of the most challenging issues in natural and engineered environments (Briones & Raskin, 2003). This dissertation is looking to link the varying TEAs with the biodegradation of BTEX.
14 2.3 Biodegradation of Chlorinated Solvents
In the 1980’s, biodegradation of chlorinated solvents in anoxic conditions was discovered. Shortly after, McCarty & Vogel (1985) performed a column study and confirmed mineralization of tetrachloroethylene (PCE) to carbon dioxide through anaerobic biodegradation via reductive dechlorination. Biodegradation via reductive dechlorination occurs because of the electronegative character of the chlorine atoms attached to the compounds, which behave as electron acceptors, or oxidants (Aulenta et al., 2006). During reductive dechlorination, as one chlorine atom is removed, a hydrogen atom replaces it, resulting in the typical dechlorination sequence. The chlorinated organic compound acts as the TEA, while the H2 atom acts as the electron donor (Li et al., 2010).
Prior to McCarty & Vogel’s (1985) conclusions, it was assumed that chlorinated solvents could only degrade through aerobic processes. Degradation can occur abiotically through hydrolysis and/or dehydrohalogenation, or through biotic processes via sequential reductive dechlorination. Figure 2.5 illustrates the individual degradation pathways for biotic and abiotic anoxic transformations in groundwater of 1,1,1-TCA, while Figure 2.6 depicts the broad range of degradation mechanisms of 1,1,1-TCA and its daughter products (Scheutz et al., 2011).
Figure 2.5. Primary transformation pathways for 1,1,1-TCA in groundwater (Scheutz et al., 2011).
15 Biodegradation can be completed to harmless products like ethene and ethane. Studies have observed sequential anaerobic reductive dechlorination through the use of pure cultures in batch reactors (Sun et al., 2002; Long et al., 1993; Holliger et al., 1990; Egli et al., 1987), mixed cultures in batch reactors (Grostern & Edwards, 2006; Gander et al., 2002; Adamson & Parkin,
2000), and microcosm studies (Scheutz et al., 2008; Aulenta et al., 2005; Kromann &
Christensen, 1998; Doong & Chen, 1996). Dechlorination of chlorinated ethenes and ethanes can also be achieved through cometabolic processes, where the chlorinated compound is degraded as a result of some other intended process (Maymo-Gatell et al., 1997). Cometabolism requires oxygen for enzymatic degradation (Pant & Pant, 2010). Chlorinated compounds completely mineralize to carbon dioxide and chloride through aerobic degradation making cometabolism an attractive alternative where it can be sustained (Conrad et al., 2010).
Figure 2.6. Broad aerobic and anaerobic biodegradation pathways of 1,1,1-TCA (Scheutz et al., 2011).
16 Traditional treatment processes for chlorinated solvents were based on high-energy input systems, such as pump-and-treat (Beeman & Bleckmann, 2002), soil excavation and venting, groundwater extractions (Bankston et al., 2002), and permeable reactive barriers (Nooten et al.
2008). These treatment methods can diminish in effectiveness over time, become costly, and be environmentally disruptive (Bankston et al., 2002). The use of these traditional treatment practices can result in not meeting the regulatory requirements in a timely manner, especially at sites with complex hydrogeology, large plumes, presence in DNAPL or sorbed onto aquifer solids. Bioremediation of chlorinated solvents has developed into an active remedial technology through the use of reductive dechlorination along side the correlated biodegradation pathways and redox conditions.
The use of biological tools for remediation purposes can have its limitations if the site has unfavorable conditions, such as lack of suitable microorganisms or redox conditions are uncomplimentary for complete dechlorination. Bioaugmentation of enrichment cultures can optimize in situ dechlorination at contaminated sites where biological factors are missing (Major et al., 2002), while the use of biostiumulation with electron donors can promote complete dechlorination where site chemistry is inadequate (Macbeth et al., 2004). A number of studies have determined that the combined use of bioaugmentation and biostimulation to enhance bioremediation of chlorinated ethenes and ethanes are effective (Scheutz et al., 2010; Conrad et al., 2010; Majone et al., 2007; Edwards et al., 2006; Adriaens et al., 2003). The use of manipulating site conditions to promote or hinder growth to affect chlorinated solvent degradation performance of ABRs is a delicate process.
Bacteria capable of biodegradation of chlorinated ethenes and ethanes are very diverse and can be classified into four groups based on their metabolism: anaerobic reductive
17 dechlorination, anaerobic oxidation, aerobic cometabolism, and aerobic assimilation (Mattes et al., 2010). Known bacteria capable of anaerobic reductive dechlorination of tetrachloroethene
(PCE) and trichloroethene (TCE) to cis-dichloroethene (cDCE) are Dehalobacter restrictus
(Holliger et al., 1998), Dehalospirillum multivorans (Neaumann & Wackett, 1997; Scholz-
Muramatsu et al., 1995), and Geobacter lovleyi (Sung et al., 2006). Only members of the genus
Dehalococcoides are known to reduce PCE or TCE beyond DCE. Strain 195 (Dehalococcoides ethenogenes), is the only known strain to completely reduce PCE to ethane (Mattes et al., 2010).
Cometibolic oxidation by aerobic microorganisms can occur in the presence of oxygenases
(Lorah et al., 2001). The aerobic bacteria known as Mycobacterium sp. can degrade 1,1,1-TCA and TCE using ethane as a substrate (Yagi et al., 1999). This genus of bacteria has been known to cometabolically degrade other related compounds such as 1,1-DCA, 1,-2DCA, 1,1,2,2-TeCA, and cis-1,2-DCE (Yagi et al., 2000). However, some organisms can use haloorganic compounds by using them as an electron acceptor for growth and energy benefits, which are referred as dehalorespiring (Maymo-Gatell et al., 1997).
Although 1,1,1-TCA may biodegrade to ethene or ethane under anaerobic conditions, chloroethane has been found to accumulate or even resist under biological dechlorination.
Multiple studies have demonstrated that 1,1-DCA (Long et al., 1993; Klecka et al., 1990; Egli et al., 1987) or CA (Grostern & Edwards, 2006; Sun et al., 2002; Adamson & Parkin, 2000; Chen et al., 1999) was the terminal dechlorination product of 1,1,1-trichloroethane biodegradation.
The common theme between these papers show that biodegradation of 1,1,1-TCA via reductive dechlorination is possible in anaerobic systems; 1,1-DCA dechlorination has a slower degradation rate than 1,1,1-TCA; and dependent on the microorganisms present and/or the redox conditions of the system, 1,1-DCA or CA may accumulate or resist dechlorination (Scheutz et
18 al., 2011). As reductive dechlorination occurs, the tendency of the chlorine atom to be removed decreases (Tiehm & Schmidt, 2011). There is evidence of persistence CA at field sites where
TCA dechlorination is present, which implies that CA is the apparent daughter product in the environment (Borden, 2007; Hoekstra et al., 2005). This study proved that the use of an aerobic layer within the ABR could completely eliminate the accumulated CA.
2.4 Development of ABRs for Treatment of VOCs
According to the EPA, the use of in situ bioremediation can degrade or transform a sizeable amount of volatile organic compounds to environmentally acceptable concentrations or less mobile compounds (Khan et al., 2004; USEPA, 1990). The ability of natural wetlands to improve water quality has been accepted for more than 40 years (Knight et al., 1999), while natural wetlands have documented large decreases in VOC concentrations from the source of contamination (Lorah & Olsen, 1999a,b). Studies have shown that natural wetlands can act as an in situ bioremediation technology, which can successfully support the biodegradation of BTEX
(Wemple & Hendricks, 2000) and chlorinated ethenes and ethanes (Lorah & Voytek, 2004;
Lorah & Olsen, 1999a ; McCarty & Semperini, 1994). Based on the results of natural wetlands as effective treatment, constructed wetlands (CW) are possible for treatment of a variety of
VOCs. The design of a CW mimics the natural wetland ecosystems that are associated with contaminant load, media, microbial populations, and plants (Tang et al., 2009; Imfeld et al.,
2009). Designing a CW is dependent on the interactions between these environmental conditions, like the physicochemical properties to effectively treat VOCs. Table 2.1 shows the physicochemical properties of BTEX and chlorinated solvents within treatment wetlands.
19 Table 2.1. Range of values for selected physicochemical properties of VOCs of interest regarding treatment in CWs (Imfeld et al., 2009).
Physico-chemical Properties Organic Hd 25oC Expected processes in S a 25oC P b 25oC logK c Compound w v ow (Pa*m3/ Constructed Wetlands (mg/L) (hPa) (25oC) mol) BTEX1 131 - 2167 8.8 - 131 1.6 - 3.4 272 - 959 Microbial degradation, volatilization(+), sorption2-6 Plant uptake and Chlorinated metabolism(+), Solvents 150 - 4.7 - 25 - volatilization(+), 0.6 - 3.4 (1-2 C- 20,000 5,746 3,080 phytovolatilization, atoms)1 microbial degradation(-), sorption6-13 Subscript numbers are associated with literature references with the provided physicochemical properties. (+) or (-) signs associated with specific removal processes. 1Mackay et al. (2006); 2 Keefe et al. (2004); 3 Bedessem et al. (2007); 4 Wallace (2002); 5 Wallace & Kadlec (2005); 6 Williams (2002); 7 Wang et al. (2004); 8 Bankston et al. (2002); 9 Pardue (2002); 10 Ma & Burken (2003); 11 Amon et al. (2007); 12 Kassenga et al. 2003; 13 Kassenga et al. 2004. a Water solubility b Vapor pressure c Octanol-water partition coefficient d Henry’s law constant
There are two main types of constructed wetlands, the first is a surface flow (SF) wetland and the second is a subsurface flow (SSF) wetland. A SF wetland is frequently designed to maximize ecological habitats and reuse opportunities, while improving water quality, while on the other hand, a SSF wetland has a higher surface area and can achieve more biological treatment in a given area (USEPA, 1988). A SSF can be designed to be horizontal-flow or vertical-flow. Figure
2.7 depicts the two main SF and SSF constructed wetland designs.
The treatment of petroleum hydrocarbons and chlorinated VOCs through constructed wetlands has been studied (Seeger et al., 2011; Tang et al., 2009; Eke & Scholz, 2008; Bedessem et al., 2007; Wallace & Kadlec, 2005; Imfeld et al., 2008; Keefe et al., 2004; Kassenga, et al.,
2003, 2004; Bankston et al., 2002; Pardue et al., 1999). Through constructed wetlands treatment
20 of BTEX involves microbial contaminant oxidation, which is dependent on the present electron acceptors (Seeger et al., 2011). Degradation pathways within involves BTEX being microbially degraded under aerobic and anaerobic conditions (Phelps & Young, 1999; Wilson & Bouwer,
1997), where as other studies conducted under SF system assigned a significant part of BTEX degradation to volatilization (Machate et al., 1999).
Figure 2.7. Two main types of constructed wetland treatment systems (Knight et al., 1999).
Bedessem et al. (2007) determined that CW treating concentrations of less than 2 mg/L of BTEX would see removal efficiencies between 88-100%. Since the early 1970’s, the use of a surface flow CW has been used to treat petroleum wastewaters from Amoco’s Mandan, North
Dakota facility (Litchfield, 1993). More popular CW is the SSF wetland, due to the ability to achieve higher biological treatment efficiencies (USEPA, 1988). In a study completed by Tang et al., (2009), the performance of multiple vertical SSF wetlands was used to evaluate the effective treatment of benzene and other water quality variables. The results of the study indicate that
21 higher benzene removal efficiencies are correlated to warmer temperatures and the in the presence of aggregates, but the presence of vegetation had no significant impact on benzene removal (Tang et al., 2009). Seeger et al., (2011) recently did a study to determine the effectiveness of horizontal flow CWs between a gravel-filter-based system and a plant root mat system with an unplanted system as a reference contaminated with benzene, MTBE and ammonia. This study assessed the various filter materials, environmental factors, along with the soil filter material additives on pollutant removal. Benzene and ammonia removal was higher than MTBE removal, while the use of the plant root mat system had a higher pollutant concentration decline. It was noted that the various filter materials (no additive vs. charcoal vs. ferrous oxide) did not have a significant effect in the long-term pilot-scale study (Seeger et al.,
2011). Both Tang et al., (2009) and Seeger et al., (2011) determined in their long-term studies that the concentrations of VOCs biodegrade more rapidly in the summer months, exhibiting that removal efficiency is seasonally dependent.
Most likely degradation pathways in a CW for treatment of chlorinated ethenes and ethanes are microbial, specifically through reductive dechlorination and aerobic oxidation
(Imfeld et al., 2009). Investigation of biodegradation of cis- and trans-1,2-DCE contamination within a constructed wetland was characterized through hydrogeochemical and compound- specific isotope analyses (CSIA) (Imfeld et al., 2008). Imfeld et al., (2008) concluded that both aerobic and anaerobic biodegradation pathways could occur within a constructed wetland. A study conducted by Kassenga et al., (2003) investigated the processes of chlorinated VOCs removal in bench-scale constructed wetland. This study highlighted the importance of understanding the geochemical properties of the substrate, along with understanding the hydrodynamics of the VOC-contaminated water through the CW to assess the ability of complete
22 biodegradation of the compound. It was determined that after a 12-week operating time of the
CW, approximately 90% of cis-1,2-DCE had degraded to VC between the inlet and the first sampling point. Though breakthrough for chlorinated ethenes occurred within the first 8 cm of the bed, chlorinated ethanes did not breakthrough till later in the CW, which indicated that a deeper wetland would be necessary to fully treat these compounds to harmless end-products otherwise accumulation could occur within the constructed wetland (Kassenga et al., 2003).
The use of aquatic vegetation within a constructed wetland could be useful through plant uptake, by removing any remaining parent or daughter products in the water (Burken & Schnoor,
1998). Processes such as phytovolatilization, phytoaccumulation, and plant metabolic transformations can assist in the removal processes for organic contaminants (Imfeld et al.,
2009). In a study conducted by Chen et al., (2012), the effects of vegetation were determined in a pilot-scale constructed wetland contaminated with PCE. The study compared a planted system with Phragmites australis and unplanted system. There was no significant difference in PCE removal between the planted and unplanted CW. However, the planted CW indicated that removal of PCE occurred faster within the system. This indicates that aquatic vegetation may enhance the microbial reductive dechlorination, but it can be assumed to only play a minor role in the biodegradation pathway of chlorinated compounds (Chen et al., 2012). When a constructed wetland lacks vegetation, the bioremediation technology is more accurately described as an anaerobic bioreactor or ABR.
Potential advantages of ABRs built from highly organic soils to treat a variety of VOCs are the use of naturally occurring bacteria to convert complex organic into harmless products.
High organic matter content is correlated with a wide variety of microbial populations and large microbial quantifications. The organic media serves as a storehouse of carbon and energy, which
23 can be used by various microorganisms for growth (Boopathy, 2000). In systems treating chlorinated compounds, the high organic matter can maintain fermentation, where fermentive organisms can convert complex organics into simple organic compounds. These simple organic compounds can be converted into acetate and hydrogen. Dehalorespiring bacteria can use the hydrogen as an electron donor during reductive dechlorination of chlorinated VOCs (Holliger et al., 1998).
Imfeld et al., (2009), discusses the importance of interactions between microbial populations, contaminant loads, nutrient requirements and/or existing TEAs, to determine treatment effectiveness of a variety of organic chemicals. It is important to identify the treatment goals within these TEA manipulations to evaluate their efficiency on removal processes. During the design stages for an ABR, a thorough understanding of relevant removal processes/pathways, along with treatment goals are required (Imfeld et al., 2008; Pardue et al., 1999). The design requirements can change based on the targeted contaminant, desired retention time for pollutant removal, media composition and microbial populations present within the system.
2.5 Enhancement of ABR Treatment Design and Approach
ABRs can be successfully implemented by a thorough understanding of the interactions of the microbes and the nutrient requirements of existing TEAs with the contaminants. Through enhancement of the ABRs, biodegradation can accelerate the natural processes by providing nutrients, electron acceptors, and competent degrading microorganisms (Scow & Hicks, 2005).
For example, a study completed by Sublette et al., (2006) wanted to stimulate biodegradation of
BTEX by amending treatment with injection of electron acceptors, such as sulfate. The sulfate injection was shown to increase the biodegradation rates of BTEX. Along with higher biodegradation rates, the subsurface microbial community became more characteristically
24 anaerobic as sulfate utilization was evident by its depletion within the aquifer (Sublette et al.,
2006). This type of technology is called biostimulation, which involves the addition of nutrients, electron acceptors or donors depending on the targeted compound (Scow & Hicks, 2005).
The use of bioaugmentation within an ABR could also enhance treatment efficiency.
Bioaugmentation introduces specific microorganisms aimed at enhancing the biodegradation of the target compound (Scow & Hicks, 2005). Prior to the use of bioaugmentation, a better understanding of microorganisms and the targeted contaminant is needed. During a study conducted by Kassenga et al., (2004), dechlorination of cis-1,2-DCE and 1,2-DCA was examined using microcosms with ABR media, where cis-1,2-DCE degraded before 1,2-DCA during this study. Hydrogen concentrations were very high during degradation of 1,2-DCA, which lead to methanogenesis. Unlike during the dechlorination of cis-1,2-DCE when hydrogen was below threshold limits for methanogenesis. Kassenga et al., (2004) suggests that halorespirers were responsible for dechlorination of cis-1,2-DCE, while 1,2-DCA dechlorination was cometabolic through the process of methanogenesis.
The hydrodynamic properties of an ABR must prove to be efficient for treatment of targeted compounds. An issue that can occur is the formation of methane during normal anaerobic metabolism within the ABR bed. Bubbles can form and obstruct the flow in an ABR system when methane concentrations within the media exceed the threshold, such that the partial pressure of dissolved gases within the reduced media exceeds the sediment hydrostatic pressures
(Altor & Mitsch, 2008; Walter & Heinman, 2000). Due to this problem, operating characteristics, like soil organic matter content, pH, and temperature, must be selected carefully to minimize the effect of methane formation to insure that the groundwater can flow through the
ABRs without interference.
25 CHAPTER 3. A SUSTAINABLE BIOREMEDIATION APPROACH FOR BTEX-CONTAMINATED GROUNDWATER UNDER METHANOGENIC AND SULFATE-REDUCING CONDITIONS
3.1 Introduction
Due to the swelling number of hazardous contaminants, such as petroleum by-products found in the environment today, there is a push for cost-effective and sustainable treatment methods for in situ remediation, like bioremediation, that can provide practical and efficient remediation technologies (Boopathy, 2000; Mazzeo et al., 2010; Naidu, 2013; Zogorski et al.,
2006; Basu et al. 2015; Yu et al., 2015). Bioremediation is a viable treatment method because it can accelerate the natural metabolic processes found in the environment resulting in transformation or degradation of organic molecules into harmless end products (Antizar-
Ladislao, 2010; Biswas et al., 2015). The use of anaerobic bioreactors is a form of bioremediation treatment technology often associated with natural attenuation, used to encourage reductive microbial processes. The design and use of ABRs for treatment of volatile organic compounds have many advantages over conventional remediation methods, by eliminating chemical dependency and energy necessary to drive physical processes.
Petroleum contamination in aquifers has been linked to both aerobic and anaerobic environments within the treatment area (Weigner & Loveley, 1998). BTEX is readily degraded under aerobic conditions, however current research interest in bioremediation is linked to anaerobic conditions due to the prevalence of anaerobic zones located at the source of contamination (Loveley, 1997). Recent studies have shown that the capability of anaerobic biodegradation is higher than previously assumed, while VOCs formerly considered recalcitrant are now known to be biodegradable in the absence of oxygen (Coates, 2002). Within anaerobic treatment zones, understanding of the biogeochemical controls is pertinent to determining how
26 petroleum hydrocarbons are degrading. Determining the present electron acceptors, along with the types microorganisms responsible for anaerobic biodegradation, is information needed to succeed in designing a full-scale ABR treatment system to remediate BTEX in groundwater.
Anaerobic processes infer a lower biomass production and conduct good electron acceptor availability. Natural electron acceptors found in anaerobic conditions are nitrate, manganese, ferric, sulfate and carbon dioxide, in preferential order from most favorable to least
(Farhadian et al. 2008 & Bin et al. 2002). The study examines redox manipulations between methanogenic and sulfate-reducing conditions as viable treatment options for BTEX in anaerobic conditions. Methanogenesis requires a correlation between proton-reducing bacteria and hydrogen consuming methanogenic microorganisms in order to obtain energy. Proton-reducing bacteria within methanogenic conditions provide hydrogen and acetate for growth of the present methanogens for degradation of BTEX (Bin et al. 2002). Sulfate-reducing conditions within the environment can be created from rainwater runoff of fertilized lands, leaching from landfills and industrial wastes, as well as naturally occurring within marine environments (Bin et al. 2002).
Another way is to stimulate anaerobic environments is through injection or addition of sulfate directly into the petroleum contaminated sites. Sulfate-reducing conditions have been known to increase the degradation rates of BTEX (Sublette et al. 2006, Da Silva et al. 2005, Cunningham et al. 2001). By changing redox conditions within ABRs, microorganisms become more active and degradation rates of BTEX can increase.
The present study investigates redox manipulation in ABRs while understanding the mechanisms and the functional roles of bacteria and geochemistry through diversifying TEAs.
The use of laboratory scale ABRs to anaerobically treat 10 mg/L of BTEX represent a significant challenge for ABR systems: degrading VOC contaminants that are difficult to degrade like
27 benzene under anaerobic conditions. The general challenge that this study addresses is how to manipulate the geochemical conditions present in the systems to accomplish efficient degradation in the smallest volumes possible. The objective of the study is to examine the degradation of varying TEAs, from methanogenic conditions, to sulfate reducing conditions, along with identifying the characterizations of varying microbial communities throughout the depths of the reactors.
3.2 Materials and Methods
3.2.1 ABR Experimental Design
Triplicate laboratory-scale ABRs to test treatment of BTEX was prepared from glass columns (10 cm in diameter, 70 cm in height) (Figure 3.1). The columns were equipped with four sampling ports located at 15, 30, 46, and 61 cm from the inlet to obtain concentration profiles. Each bioreactor was packed with a 50/50 percent by weight of media compost-manure mixture (Phillips Bark Processing Co., Inc.; Brookhaven, MS) and sand (Quickrete Companies,
Inc.; Atlanta, GA). Port 4, at 61 cm was just below the media to allow for effluent samples to be taken while minimizing volatilization that would occur with a free water head. The compost was commercial pine bark compost with 80% organic matter (Phillips Bark Processing Co., Inc.;
Brookhaven, MS). The bottom five centimeters of each ABR was packed with glass beads
(VWR; Radnor, PA) to allow for uniform flow distribution in an up-flow direction. A synthetic
BTEX waste stream was prepared from tap water deaerated by passing through a 49-liter dissolved oxygen scrubber (DOS) filled with compost media to remove dissolved oxygen from the water though biological action. Following deaeration, water was spiked with benzene, toluene, ethylbenzene and xylenes equivalent to 10 mg/L each. Prior to the addition of BTEX to the ABRs, water was placed into a 76-liter plexiglass aquarium (influent tank) fitted with a
28 floating cover to minimize headspace and reduce volatilization of BTEX. The bioreactors were placed in a Conviron Adaptis A350 Chamber (Adaptis) (Winnipeg, Mantioba; Canada) to control temperature, humidity and light intensity. The environmental program for the Adaptis was 20oC, with a relative humidity level of 40% from 18:00 to 11:00 hours, 30oC with a relative humidity of 55% from 11:00 to 18:00 hours, and a light program consisting of dark conditions from 20:00 to 08:00 hours and light conditions otherwise.
Figure 3.1. ABR system design.
The reactors each received 1.6 liters per day of synthetic BTEX wastewater at a flow-rate of 1.1 mL/minute with a residence time of 1.3 days. The initial phase of the experiment was used to investigate BTEX degradation without the addition of exogenous electron acceptors. Since methane accumulated along the flowpath, this phase was termed the “methanogenic” phase
(Phase 1). All ABRs were continuously fed BTEX, at 10 mg/L dissolved in synthetic groundwater and distributed to the ABRs in a vertical up-flow mode at a loading rate of 1.93 g
BTEX/m2/day. This phase was conducted for 93 days, with an acclimation period of 21 days
29 prior to sampling (day 0). Following the methanogenic phase, the influent synthetic BTEX wastewater of the ABRs was amended with MgSO4·7H2O at a concentration to produce 500
2- mg/L of sulfate (SO4 ) to determine operational success under sulfate reducing conditions
(Phase 2). This second phase was operated for 90 days until sulfate profiles were at steady state.
Samples were collected periodically to measure the concentrations of BTEX, methane, and sulfate. At the end of each phase, media samples were collected and extracted for genomic analysis for each respective redox condition.
3.2.2 Chemical Analysis
At weekly intervals, samples of water (25 mL) were collected at the influent, and each of the four sampling ports during Phase 1 and Phase 2. The fourth port, 61 cm from the inlet was in a thin overlying media layer and was assumed to be equivalent to the effluent concentration before any volatilization could occur. Each sample was placed in a 30 mL glass serum bottle, leaving 5 mL of headspace and immediately crimp-sealed with a Teflon coated septum (Sigma-
Aldrich; St. Louis, MO). Samples were inverted to establish equilibrium in the headspace.
Samples were inverted for at least three hours (but never more than 24 hours) before analysis.
Analysis of BTEX and methane were performed using a gas chromatograph (Agilent
Technologies; Model No. 6850) equipped with a packed column (Supelco 2.1mm x 2.4m, packed 1% SP) fitted with a flame ionization detector (FID). The initial oven temperature on the gas chromatograph was programmed at 100oC, following an increase of 10oC/min until reaching a temperature of 200oC, and holding for 25 minutes. The carrier gas utilized was helium at a flow rate of 40 mL/min while hydrogen and oxygen served as the fuel gas with flow rates of 35 and 100 mL/min for the FID. Samples were removed from the serum bottles (1.5 mL) using a gas tight syringe (Hamilton Company; Reno, NV) and directly injected onto the column.
30 External standards (4 points) prepared from neat (methanolic) stock solutions (Sigma Aldrich, St.
Louis, MO) were used to calibrate the GC for benzene, toluene, ethylbenzene and total xylenes.
Methane calibration- was completed using a 1% gas mixture (American Air Liquide, Houston,
TX). The GC was calibrated with the prepared stocks using distilled water in the same gas:liquid volumetric ratio as the weekly collected samples.
Analysis of sulfate was performed using a SmartChem 170 Discrete Analyzer (Unity
Scientific, Inc.; Milford, MA), using EPA Method 375.4 on 2 mL of aqueous samples from the influent and each sampling port within the ABRs. Sulfate concentrations were measured during both Phase 1 and Phase 2 of the experimentation to assess the baseline concentrations and changes in the concentrations after sulfate amendment.
3.2.3 Microbial Analysis
Compost:sand media samples were collected throughout each of the ABRs located at Port
1-4 after Phase 1 and Phase 2 of the study. Media was removed from the sampling ports described above by coring horizontally in from the side of the ABR sampling ports and aseptically removing a compost:sand media sample of approximately 1 g. DNA was extracted from the media samples using a Powersoil DNA Isolation Kit (Mo Bio Laboratories, Inc.;
Carlsbad, CA) to isolate microbial genomic DNA per the manufacturer’s user protocol manual for total nucleic acid extraction from samples without modifications (Mo Bio User Protocol; https://www.qiagen.com/us/resources/resourcedetail?id=5c00f8e4-c9f5-4544-94fa-653a
5b2a6373&lang=en, accessed July, 2014). The final extracted DNA samples were quantified using a Nanodrop-1000 spectrophotometer (Thermo Fisher Scientific, Inc.; Wilmington, DE) and then stored at -20oC prior to amplification and sequencing. PCR amplification was carried out using the primer set 515F (5’-GTG CCA GCM GCC GCG GTA A - 3’) and 806R (5’- GGA
31 CTA CHV GGG TWT CTA AT- 3’) for the V4 region of 16S rRNA gene (Caporaso et al.,
2011; Zhang et al., 2015; Yang et al., 2016).
In total, 19 samples were successfully amplified (16s rRNA) and sequenced using the
Illumina Miseq platform. Sequenced data was processed and analyzed using a single software platform, mothur v.1.29.2, (University of Michigan; Ann Arbor, MI) (Schloss et al. 2009).
Analysis of the 16S rRNA gene sequences followed the MiSeq standard operating procedure
(Schloss et al. 2013; https://www.mothur.org/wiki/MiSeq_SOP, accessed February, 2015). For all sequences, paired-end reads were combined to produce single sequences, and then screened for quality. Sequences containing any mismatched pairs, length outside the range between 248 and 253 bp, ambiguous bases, or homopolymers longer than 8 bp were excluded (Veach et al.,
2016; Gomez-Smith et al., 2015). Remaining sequences were aligned against the SILVA
(SILVA 111, released 2012) bacterial 16S rRNA database, and chimeric sequences were removed using the UCHIME algorithm (Edgar et al., 2011). Sequences were clustered into operational taxonomic units (OTUs) at a cutoff of 97% similarity threshold. Sequences were then assigned to the taxonomic affinities using the naïve Bayesian classifier (Wang et al., 2007) with a 50% threshold against the Ribosomal Data Project (RDP) reference files (version 16).
Sequences not assigned to the domain bacteria or archaea (including chloroplasts, mitochondria, and unclassified) were not included in further analysis.
The relative abundance of an OTU was computed by the number of sequences classified in the OTU, divided by the total number of sequences for each sample for bacteria and archaea
(Table A.2 and Table A.5). The relative abundance computation ignores 16S rRNA gene copy diversity, which may alter the distribution (Vetrovsky and Baldrian, 2013). For each sample, taxonomy information is provided for OTUs greater than 1% of total abundance, while any
32 OTUs below 1% of total abundance is placed in the ‘other’ category without taxonomic identification.
Genera identified in the RDP classification greater than 50% were examined for potential anaerobic hydrocarbon degraders. Bacteria that were identified in mothur as potential anaerobic hydrocarbon degraders were classified again using the naïve Bayesian classifier (Wang et al.,
2007) with a 50% threshold against the Ribosomal Data Project (RDP) reference files (version
16) (http://rdp.cme.msu.edu/classifier/classifier.jsp; accessed, August 2017) to identify the percent level of classification (Table 3.4; Table 3.11; Table A.3; Table A.6). These OTUs underwent additional comparisons of sequences using the National Center for Biotechnology
Information (NCBI) Basic Local Alignment Search Tool (BLAST) GenBank (Table 3.4 and
Table 3.11). OTU sequences were classified against the 16S ribosomal RNA sequences
(Bacteria and Archaea) database using the BLASTN algorithm within the NCBI BLAST tool
(https://blast.ncbi.nlm.nih. gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=Blast
Search&LINK_LOC=blasthome; accessed August, 2017). The top three GenBank hits based on the BLASTN algorithm are reported below (Table 3.4 and Table 3.11).
After classification of bacteria and archaea was completed, OTU-based analysis of α diversity measurements was calculated using mothur. First, the calculation of species richness and diversity of the samples were performed for OTUs by computing the species richness indicator Chao1, and the Shannon diversity index (H’) (Schloss et al., 2009; Gomez-Smith et al.,
2015). To prevent bias, OTU data from each sample was normalized by randomly subsampling to the smallest number of sequences observed in any sample (Table A.2 and Table A.5) prior to computation of Chao1 and H’. Chao1 is a non-parametric estimator of species richness, which
33 uses a “mark-release-recapture” statistic that is derived from the number of OTUs appearing either one or two times in a given library and is calculated as:
2 퐹1 퐹1 퐹2 퐶ℎ푎표1 = 푆표푏푠 + − 2 2(퐹2 + 1) 2(퐹2 + 1) where Sobs is the number of phylotypes observed in the library, F1 and F2 are the number of
OTUs occurring either one to two times (Kemp & Aller, 2004; Bailey et al., 2013). The
Shannon index (H’) is a non-parametric estimator of diversity, which reflects the number of different species in a community and assumes that individuals are truly randomly sampled from within an infinitely large community and is calculated as:
′ 퐻 = − ∑ 푝푖 ln 푝푖
th where pi is the proportion of clones in the i OTU (estimated using ni/N), where ni is the number of sequences in the ith OTU, and N is the total number of sequences (Hill et al., 2003; Bailey et al., 2013). Statistical analysis using the two-tailed Student T-test with a 95% confidence interval was conducted on the number of sequences after quality screening (Nseqs), OTUs, Chao1, and H’ parameters to distinguish the statistical significance of differences between treatment zones within the ABRs, and between the methanogenic and sulfate-reducing conditions of bacteria and archaea populations.
3.3 Results and Discussion
3.3.1 Methanogenic Results
During the study, loss of BTEX under methanogenic conditions occurred consistently over the 90-day incubation period after a three-week equilibration period (Figure 3.2). Aqueous samples were collected along the vertical profile of the three ABRs weekly over a time period of approximately 90 days. The influent concentrations of each BTEX compound ranged from 7 to
34 13 mg/L, averaging 10.1 ± 0.3 mg/L, while the effluent (Port 4) concentrations of BTEX ranged from 0.002 mg/L to 2 mg/L, averaging 0.26 ± 0.08 mg/L. During this period, approximately
93.7 ± 1.0 % loss in BTEX was observed. Losses were inferred from changes in aqueous concentrations in the ABR while acknowledging that BTEX mass is also sorbed on the solid particles in the bed which could not be regularly sampled. These are discussed below.
Significant losses of ethylbenzene and toluene occur in between the influent and the first 15 cm of distance into the ABR. From the influent concentration to Port 1, approximately 88 ± 0.4% of ethylbenzene concentration was reduced and 80 ± 0.3% of toluene concentration was reduced. In contrast, total xylenes and benzene was reduced by only 64 ± 0.5% and 44 ± 0.4% respectively.
This data can be converted to a volumetric rate of BTEX degradation by multiplying the removal by the flow-rate by 1.6 L/day (Figure 3.3). Expressing the data, quantifies the enhanced removal of BTEX compounds within Zone 1 (influent to 15 cm) and Zone 2 (15 to 30 cm from the inlet) of the ABRs. Ethylbenzene and toluene had the highest average rates of removal in
Zone 1 of 11.61 ± 0.41 g/m3·day and 10.83 ± 0.32 g/m3·day, respectively while the benzene and total xylenes had lower rates at 5.92 ± 0.36 g/m3·day and 7.63 ± 0.52 g/m3·day, respectively. As a result, Zone 2 degradation is primarily benzene with some total xylene degradation. This preference for toluene and ethylbenzene under anaerobic conditions is consistent with other controlled serum bottle studies examining microbial degradation of BTEX compounds (Zhi-feng et al. 2007; Dou et al. 2008; Yeh et al. 2010). Further up in the ABR away from the inlet, the average rate of BTEX degradation decreases significantly, varying between 0 and 0.61 g/m3·day, while averaging a combined rate of degradation of 0.48 ± 0.05 g/m3·day. This data indicates that the majority of the treatment of BTEX removal is occurring within the first 30 cm of the ABR.
The upper portion of the ABRs provides polishing at a much lower loading rate. The overall
35 removal efficiency throughout the ABR was ethylbenzene (96 ± 0.1%), followed by toluene (94
± 0.1%), benzene (94 ± 0.1%), and total xylenes (91 ± 0.10%). Given that lower benzene concentrations will be required to meet discharge limits, its removal efficiency likely controls design. For a given flow rate, enhanced removal can be obtained by decreasing the volumetric loading rate (by increasing the ABR surface area) or increasing the depth or both.
Redox indicators measured during this portion of the study are summarized in Table 3.1.
Methanogenesis was confirmed in the study with a steady increase in methane production from
0.005 mg/L of CH4 to 0.02 /L of CH4 over the 93-day time period as shown in Table 3.1.
The anaerobic conditions were confirmed with the dissolved oxygen (DO) concentrations remaining below 1.0 mg/L throughout the ABR cores. Baseline concentrations were taken of sulfate, which determined that the concentrations during methanogenesis were between 4 and 6
2- mg/L SO4 . These concentrations were stable throughout the experiment.
36
Figure 3.2. Average concentrations in ABRs of BTEX in column ports vs time during methanogenic study.
37
Figure 3.3. The rate of degradation of BTEX (g/m3*day) versus the distance from the influent during methanogenic conditions.
Table 3.1. Average concentration profile for sulfate (mg/L) and methane (mg/L) during methanogenic study in ABRs.
2- Sample SO4 Methane ID (mg/L) (mg/L) Influent 5.34 ± 0.72 - Port 1 5.64 ± 0.22 0.005 ± 0.001 Port 2 4.79 ± 0.34 0.011 ± 0.002 Port 3 5.09 ± 0.45 0.017 ± 0.004 Port 4 4.82 ± 0.27 0.02 ± 0.006
38 The bacterial and archaeal microbial community structure throughout the depth of the
ABRs was determined through Illumina Miseq profiles of PCR-amplified 16S rRNA gene fragments. Representative data is shown from ABR 2. Samples were collected in each ABR by coring horizontally in from the side of the ABR sampling ports and aseptically removing a compost:sand media sample. Replicate samples were successfully sequenced for Zone 1 (N=3) and Zone 4 (N=2). After combining the forward and reverse reads, there were a total of
1,048,701 sequences combined in the seven samples between all three ABRs prior to processing through mothur (Table A.1). After processing aligned sequences through initial quality filtering and removal of chimeras, a total of 80,281 sequences from the ABRs remained (Table A.2) in the seven samples at an average of 11,469 ± 1,658 sequences per sample (Table 3.2). These sequences were clustered into OTUs at the 97%-similarly and were subsequently classified using the RDP classifier tool. The number of OTUs ranged from 236 to 360 for the seven samples
(Table 3.2). The Shannon diversity index (H’) for the methanogenic phase ranged from 4.97 to
6.13 (Table 3.2). These are consistent with forest soil diversity determined from 16S OTUs
(Cong et al., 2015) but higher than geographically distributed soil bacterial diversity indices computed from T-RFLP (Fierer and Jackson, 2006). The Chao1 estimator of species richness for the methanogenic phase ranged from 4,943 to 7,217. Statistically, there was no significant difference (p > 0.05) between treatment zones 1 and 4 during the methanogenic phase (Table
3.3) for each of the respective measurements of diversity and richness of the microbial samples collected described in Table 3.2.
OTU data were mined for bacteria associated with anaerobic hydrocarbon degradation including those associated with toluene and benzene degradation. Potential anaerobic hydrocarbon degradation bacteria were initially classified from the Proteobacteria phylum,
39 including the genera Caulobacter, Azoarcus, Geobacter, and the family Comamonadaceae
(Rosenberg et al. 2014a; Cupples, 2011; Sun & Cupples, 2011; Da Silva & Corseuil, 2012; and
Rosenberg et al. 2014b). These genera of bacteria accounted up to 4 % of the relative abundance of total OTUs found within the ABRs (Table 3.4). The relative abundance of these organisms detected during Phase 1 is described in Table 3.5. The table describes the total relative abundance of each bacteria/cluster further classified in Table 3.4 for each port along the length of the ABR.
Table 3.2. Diversity and richness measures computed for ABRs during the methanogenic phase including number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%. 2- BTEX prior to SO4 Addition Sample Zone 1 Zone 2 Zone 3 Zone 4 a Nseqs 13,825 ± 3,299 10,563 10,469 8,887 ± 3,029 OTUs 360 ± 21 359 333 236 ± 61 Chao1ab 7,217 ± 1,287 5,323 5,194 4,943 ± 1,134 H'ab 5.79 ± 0.13 6.13 5.96 4.97 ± 0.37 a Mean and standard error b Computed after normalization to 5,858 sequences to prevent bias
Table 3.3. Statistical analysis of between Zone 1 and Zone 4 during methanogenic phases of the number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%. Methanogenic Phase 1 Zone 1 Zone 4 p-value Statistical Analysis
Nseqs 13,825 ± 3,299 8,887 ± 3,029 0.381
OTUs 360 ± 21 236 ± 61 0.098
Chao1 7,217 ± 1,287 4,943 ± 1,134 0.309 H' 5.79 ± 0.13 4.97 ± 0.37 0.086 *Statistical significance (p<0.05).
40
Table 3.4. Further classification of OTU sequences within Phase 1 for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) Azoarcus olearius strain DQS-4 16S ribosomal RNA NR_108183.1 99% gene, partial sequence Azoarcus Methyloversatilis discipulorum strain FAM1 16S OTU_012 NR_136516.1 98% (68%) ribosomal RNA, complete sequence Methyloversatilis universalis strain FAM5 16S ribosomal NR_04813.1 98% RNA gene, partial sequence Caulobacter fusiformis strain ATCC 15257 16S NR_025320.1 99% ribosomal RNA gene, partial sequence Caulobacter Caulobacter fusiformis strain ATCC 15257 16S OTU_021 NR_112033.1 99% (100%) ribosomal RNA gene, partial sequence Caulobacter daechungensis strain H-E3-2 16S ribosomal NR_118485.1 98% RNA gene, partial sequence Diaphorobacter polyhydroxybutyrativorans strain SL-205 16S ribosomal RNA, partial sequence NR_137222.1 99% Unclassified Ottowia shaoguanensis strain J5-66 16S ribosomal RNA OTU_026 Comamonadaceae gene, partial sequence NR_125656.1 99% (66%) Ottowia beijingensis strain GCS-AN-3 16S ribosomal RNA, partial sequence NR_133803.1 99%
41 Table 3.4 (continued). Further classification of OTU sequences within Phase 1 for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%)
Geobacter metallireducens strain GS-15 16S ribosomal NR_075011.1 100% RNA gene, complete sequence Geobacter OTU_32 Geobacter metallireducens strain GS-15 16S ribosomal NR_025895.1 100% (100%) RNA gene, complete sequence Geobacter grbiciae strain TACP-2 16S ribosomal RNA NR_10456.1 100% gene, complete sequence Azoarcus olearius strain DQS-4 16S ribosomal RNA NR_108183.1 99% gene, partial sequence Azoarcus Azoarcus indigens strain VB32 16S ribosomal RNA OTU_038 NR_024851.1 99% (94%) gene, partial sequence Thauera linaloolentis strain 47Lol 16S ribosomal RNA NR_114137.1 98% gene, partial sequence
42 Table 3.5. Relative abundance of described in Table 3.4 detected during Phase 1.
Relative Zone OTU ID Abundance (%) OTU_012 1.68 OTU_021 3.89 Port 1 OTU_026 0.42 OTU_032 0.42 OTU_038 0.11 OTU_012 1.54 OTU_021 0.39 Port 2 OTU_026 0.60 OTU_032 2.08 OTU_038 2.47 OTU_012 2.72 OTU_021 1.07 Port 3 OTU_026 0.49 OTU_032 0.27 OTU_038 0.42 OTU_012 1.10 OTU_021 0.50 Port 4 OTU_026 0.17 OTU_032 0.10 OTU_038 2.04
Potential anaerobic hydrocarbon degraders were classified further using NCBI BLAST to obtain a higher-level confidence in the identification. OTU_032 was identified as a sequence consistent with Geobacter with >97% identity percentage. For Azoarcus, high identity percentages were identified for OTU_012 and OTU_038. OTU_026 was identified as a sequence consistent with Caulobacter. OTU_026 was identified as a sequence consistent with
Diaphorobacter and/or Ottowia, within the Comamonadaceae family, with 99% identity. These organisms show a diverse population of Proteobacteria present in the ABRs.
43 Spatial patterns of species previously associated with anaerobic hydrocarbon degradation correlate with patterns of BTEX removal in the ABR reactors. Geobacter (OTU_32) and
Azoarcus (OTU_12 and OTU_38) had their highest relative abundances (2.08% and 2.47%, respectively) in the zone sampled by port 2 where benzene and ethylbenzene removal was occurring. Members of the Azoarcus/Thauera cluster degrade BTEX under denitrifying conditions ((Hess et al., 1997; Rabus et al., 1999). Geobacter degrade BTEX under iron-reducing conditions (Coates et al., 1996; Lovley et al., 1989). Caulobacter had the highest relative abundance (3.89%, respectively) in the initial zone (port 1) where toluene, and ethylbenzenes were primarily degraded. While Caulobacter does not degrade BTEX directly, it degrades aromatic intermediates aerobically (Chatterjee and Bourquin, 1987). Caulobacter strains can be facultatively anaerobic with the ability to degrade complex molecules like cellulose (Song et al.,
2013). Comamonadaceae were the key components in BTEX degrading cultures with nitrate as an electron acceptor (Sun and Cupples, 2012). These organisms are candidates for further study for potential BTEX degradation and may be using trace nitrate or iron associated with reactor media. BTEX methanogenic cultures have been described
For the overall microbial community structure, the dominant bacteria phyla observed during Phase 1 were Proteobacteria (33.1 – 41.5%), Bacteroidetes (5.3 – 23.9%), and
Verrucomicrobia (2.8 – 18.6%) (Figure 3.4). These figures depict the relative abundance within
ABR-2. Within the Proteobacteria phylum, the Alphaproteobacteria class was the most abundant (18.4 ± 3.9%), followed by Betaproteobacteria (6.7 ± 0.7%), Deltaproteobacteria (5.1
± 2.1%) and Gammaproteobacteria (4.0 ± 1.0%).
44 Out of the total OTUs detected within the samples, between 1 and 8 OTUs were classified as Archaea, which represents 2.0% of the total sequences found within the ABRs, with the remaining 98% representing bacteria. The dominant archaea phyla were Euryarchaeaota (80
– 100%) and Crenarchaeota (0 – 20%) (Figure 3.5). Figure 3.5 depicts the relative abundance within ABR-2. Within the Euryarchaeaota phylum, the Methanomicrobia classes were the most abundant averaging 10.9 ± 6.6%, followed by Methanobacteria (2.3 ± 1.0%). The genus
Methanosarcina within the class Methanomicrobia was the most dominant archaeal genera found within the ABRs. The largest percent of abundance of Methanosarcina is found within
Zone 1 and Zone 2 averaging 8.6 ± 1.4% of the total archaea. Methanosarcina species are capable of methanogenesis through all three known pathways and are capable of using no less than nine methanogenic substrates, including acetate (Galagan et al., 2002). The presence of these organisms provides further evidence that methanogenic conditions were operative in the
ABR during this phase.
45
Figure 3.4. Methanogenic Conditions – Attached Bacteria; All samples from ABR 2 displaying the distribution of bacterial communities in each treatment zone.
46
Figure 3.5. Methanogenic Conditions – Attached Archaea; All samples from ABR 2 displaying the distribution of bacterial communities in each treatment zone.
47 3.3.2 Sulfate-Reducing Results
Sulfate reducing conditions were obtained during the second phase of the study through
2- the addition of 500 mg/L SO4 . Loss of BTEX under sulfate-reducing conditions occurred consistently over the 93-day incubation period after a three-week equilibration period (Figure
3.6). Aqueous samples were collected along the vertical profile of the three ABRs weekly over a time period of approximately 90 days. The influent concentrations of each BTEX compound ranged from 7 to 13 mg/L, averaging 10.3 ± 0.3 mg/L, while the effluent (Port 4) concentrations of BTEX ranged from 0.01 mg/L to 1 mg/L, averaging 0.18 ± 0.07 mg/L. During this period, approximately 95.5 ± 1.3 % loss in BTEX was observed. Using a Two-tailed Student T-test with a 95% confidence interval, there is no significant difference (p = 0.343) between treatment conditions for BTEX biodegradation. Losses were inferred from changes in aqueous concentrations in the ABR while acknowledging that BTEX mass is also sorbed on the solid particles in the bed which could not be regularly sampled. These are discussed below.
Significant losses of toluene, ethylbenzene, and total xylenes occur in between the influent and the first 15 cm of distance into the ABR. From the influent concentration to Port 1, approximately 92 ± 0.2% of toluene concentration was reduced, 88 ± 0.3% of ethylbenzene concentration was reduced, and 82 ± 0.5% of total xylene concentration was reduced. In contrast, benzene was reduced by only 65 ± 0.6% respectively. The analysis of variance (ANOVA) revealed that the change in degradation in Zone 1 for benzene was significant, p = 0.0071. This data can be converted to a volumetric rate of BTEX degradation by multiplying the removal by the flow-rate by 1.6 L/day (Figure 3.7). Expressing the data, quantifies the enhanced removal of
BTEX compounds within Zone 1 (influent to 15 cm) and Zone 2 (15 to 30 cm from the inlet) of the ABRs. Ethylbenzene and toluene had the highest average rates of removal in Zone 1 of
48 12.45 ± 0.33 g/m3·day and 12.06 ± 0.19 g/m3·day, respectively while the total xylenes and benzene had lower rates at 10.93 ± 0.48 g/m3·day and 8.10 ± 0.55 g/m3·day, respectively. As a result, Zone 2 degradation is primarily benzene. This preference for toluene and ethylbenzene under anaerobic conditions is consistent with other controlled serum bottle studies examining microbial degradation of BTEX compound (Zhi-feng et al. 2007; Dou et al. 2008; Yeh et al.
2010 ). Further up in the ABR away from the inlet, the average rate of BTEX degradation decreases considerably, varying between 0.11 and 2 g/m3·day, while averaging a combined rate of degradation of 0.71 ± 0.25 g/m3·day. This data indicates that the majority of the treatment of
BTEX removal is occurring within the first 30 cm of the ABR, while the upper portion of the
ABRs provides polishing at a much lower loading rate. These results are similar treatment trends found during Phase 1 of the study. The overall removal efficiency throughout the ABRs were toluene (98±0.1%), followed by ethylbenzene (97±0.2%), total xylenes (93.3±0.3%), and benzene (92.9±0.3%). Given that lower benzene concentrations will be required to meet discharge limits, its removal efficiency likely controls design. For a given flow-rate, enhanced removal can be obtained by decreasing the volumetric loading rate (by increasing the ABR surface area) or increasing the depth or both.
Redox indicators measured during this portion of the study are summarized in Table 3.6.
The total concentration of methane in the ABRs averaged at 4.2E04 ± 0.0001 /L CH4 over the
93-day time period. Anoxic conditions were confirmed with the dissolved oxygen (DO) concentrations remaining below 1.0 mg/L throughout the ABRs. Sulfate reducing conditions were confirmed during Phase 2 with concentrations of sulfate averaging from 455 ± 19.6 mg/L
2- 2- SO4 in the influent to 72 ± 16.3 mg/L SO4 in the effluent (Port 4).
49
Figure 3.6. Average concentrations in ABRs of BTEX in column ports vs. time during sulfate-reducing study.
50
Figure 3.7. The rate of degradation of BTEX (g/m3*day) versus the distance from the influent during sulfate reducing conditions.
Table 3.6. Average concentration profile for sulfate (mg/L) and methane (mg/L) during sulfate reducing study in ABRs. Methane Sample ID Sulfate (mg/L) (mg/L) Influent 454.55 ± 19.56 - Port 1 261.38 ± 16.22 4.7E-4 ± 0.001 Port 2 146.47 ± 47.05 3.8E-4 ± 0.001 Port 3 95.83 ± 27.63 4.3E-4 ± 0.001 Port 4 71.77 ± 16.33 4.0E-4 ± 0.002
The bacterial and archaeal microbial community structure throughout the depth of the
ABRs was determined through Illumina Miseq profiles of PCR-amplified 16S rRNA gene fragments. Representative data is shown from ABR 2. Samples were collected in ABR-1, ABR-
2, and ABR-3, by coring horizontally in from the side of the ABR sampling ports and aseptically
51 removing a compost:sand media sample. Replicate samples were successfully sequenced for
Zone 1, 2, 3, and 4 (N=3). After combining the forward and reverse reads, there were a total of
1,511,097 sequences combined in the twelve samples from the ABRs prior to processing through mothur (Table A.4). After processing aligned sequences through initial quality filtering and removal of chimeras, a total of 984,019 sequences remained from the ABRs (Table A.5) in the twelve samples at an average of 82,002 ± 6,383 sequences per sample (Table 3.7). These sequences were clustered into OTUs at the 97%-similarly and were subsequently classified using the RDP classifier tool. The number of OTUs ranged from 414 to 520 for the twelve samples
(Table 3.7), which is an increase in classified bacteria and archaea from the methanogenic study.
The Shannon diversity index (H’) for the methanogenic phase ranged from 5.86 to 5.99
(Table 3.7). These are consistent with forest soil diversity determined from 16S OTUs (Cong et al., 2015) but higher than geographically distributed soil bacterial diversity indices computed from T-RFLP (Fierer and Jackson, 2006). The Chao1 estimator of species richness for the methanogenic phase ranged from 14,850 to 19,717. There were no statistically significant difference (p > 0.05) between treatment Zones 1 and 4 during the sulfate-reducing phase (Table
3.8) for each of the respective measurements of diversity and richness of the microbial samples collected described in Table 3.7.
Although there was no significant difference between degradation of BTEX during each
Phase (1 & 2), with the exception of the benzene compound in treatment Zone 1, there is however a significant difference in the microbial samples between methanogenic and sulfate- reducing conditions. Statistical analysis was conducted on the methanogenic and sulfate- reducing phases between treatment Zone 1 and Zone 4 (Tables 3.9; Table 3.10). Within Zone 1, there is a significant difference between the number of sequences (p = 0.04) and the number of
52 OTUs (p = 0.02) between treatment phases (Table 3.9). Zone 4 shows a significant difference between numbers of sequences (p = 0.006), number of OTUs (p = 0.004), and species richness,
Chao1 (p = 0.016) (Table 3.10). The Shannon Index, determining the diversity of bacteria and archaea, did not have any significant difference in treatment zone 1 and 4 between Phase 1 and
Phase 2 of the study, indicating that the bacteria and archaea found within both studies had similar diversity measurements.
Table 3.7. Diversity and richness measures computed for ABRs during the sulfate-reducing phase including number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%.
2- BTEX after to SO4 Addition Sample Zone 1 Zone 2 Zone 3 Zone 4 OTUs 485 ± 35 491 ± 24 497 ± 5 491 ± 21 a Nseqs 68,479 ± 21,698 79,266 ± 13,780 85,594 ± 8,217 94,308 ± 14,437 Chao1a 14,850 ± 3,930 16,529 ± 2,115 18,166 ± 341 19,717 ± 3,233 H'a 5.86 ± 0.10 5.94 ± 0.14 5.99 ± 0.13 5.92 ± 0.19 a Mean and standard error b Computed after normalization to 25,094 sequences to prevent bias
Table 3.8. Statistical analysis of between Zone 1 and Zone 4 during the sulfate-reducing phase of the number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%.
Sulfate-Reducing Zone 1 Zone 4 p-value Phase
Nseqs 68,479 ± 21,698 94,308 ± 14,437 0.318
OTUs 485 ± 35 491 ± 21 0.869
Chao1 14,850 ± 3,930 19,717 ± 3,233 0.334 H' 5.86 ± 0.10 5.92 ± 0.19 0.738 *Statistical significance (p<0.05).
53 Table 3.9. Statistical analysis of treatment Zone 1 in methanogenic and sulfate-reducing phases. Methanogenic Sulfate-Reducing Zone 1 Comparison p-value Conditions Conditions
Nseqs 13,825 ± 3,299 68,479 ± 21,698 0.038*
OTUs 360 ± 21 485 ± 35 0.018*
Chao1 7,217 ± 1,287 14,850 ± 3,930 0.0940 H' 5.79 ± 0.13 5.86 ± 0.10 0.6842 *Statistical significance (p<0.05).
Table 3.10. Statistical analysis of treatment Zone 4 in methanogenic and sulfate-reducing phases. Methanogenic Sulfate-Reducing Zone 4 Comparison p-value Conditions Conditions
Nseqs 8,887 ± 3,029 94,308 ± 14,437 0.006*
OTUs 236 ± 61 491 ± 21 0.004* Chao1 4,943 ± 1,134 19,717 ± 3,233 0.016* 4.97 ± 0.37 5.92 ± 0.19 H' 0.059 *Statistical significance (p<0.05).
OTU data were mined for bacteria associated with sulfate reducing conditions linked to anaerobic hydrocarbon degradation including those associated with toluene and benzene degradation. These genera of bacteria accounted up to 15% of the relative abundance of total
OTUs found within the ABRs (Table 3.11 and 3.12). Potential sulfate reducers were initially classified from the Proteobacteria and Chloroflexi phylums, including the genera Geobacter,
Sulfurisoma, Smithella, Syntrophobacter, and the family Anaerolineaceae (Coates et al., 1996;
Lovley et al., 1989; Kojima & Fukui, 2014; Harmsem et al., 1993; Liu et al., 1999; Rosenkranz et al., 2013; Sherry et al., 2013). These genera of bacteria accounted up to 15 % of the relative abundance of total OTUs found with the ABRs (Table 3.11). Table 3.12 describes the total relative abundance of each bacteria/cluster further classified in Table 3.11 for each port along the length of the ABRs.
54
Table 3.11. Bacteria potentially associated with sulfate-reducing conditions linked to anaerobic hydrocarbon degradation detected in Phase 2 ABRs describing further classification of the sequences using RDP at 50% classification and NCBI Blast gene bank. Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) Ornatilinea apprima strain P3M-1 16S ribosomal RNA NR_109544.1 89% Unclassified gene, partial sequence Anaerolineaceae OTU_002 Thermanaerothrix daxensis strain GNS-1 16S ribosomal NR_117865.1 89% (73%) RNA gene, partial sequence
Thermomarinilinea lacunifontana strain SW7 16S ribosomal NR_132293.1 88% RNA, partial sequence Geobacter sulfurreducens strain PCA 16S ribosomal RNA NR_075009.1 100% gene, complete sequence Geobacter Geobacter sulfurreducens strain PCA 16S ribosomal RNA OTU_008 NR_029179.1 100% (100%) gene, partial sequence Geobacter anodireducens strain SD-1 16S ribosomal RNA NR_126282.1 99% gene, partial sequence Georgfuchsia toluolica strain G5G6 16S ribosomal RNA NR_115995.1 98% gene, partial sequence Unclassified OTU_010 Betaproteobacteria Sulfurisoma sediminicola strain BSN1 16S ribosomal RNA NR_125471.1 97% (100%) gene, partial sequence Azovibrio restrictus strain S5b2 16S ribosomal RNA gene, NR_028678.1 96% partial sequence
55 Table 3.11 (continued). Bacteria potentially associated with sulfate-reducing conditions linked to anaerobic hydrocarbon degradation detected in Phase 2 ABRs describing further classification of the sequences using RDP at 50% classification and NCBI Blast gene bank. Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) Smithella propionica strain LYP 16S ribosomal RNA gene, NR_024989.1 93% partial sequence Smithella OTU_019 (87%) Syntrophus gentianae strain HQgoe1 16S ribosomal RNA NR_029295.1 92% gene, partial sequence Syntrophus buswellii strain DM-2 16S ribosomal RNA NR_029294.1 92% gene, partial sequence Syntrophobacter wolinii strain DB 16S ribosomal RNA NR_028020.1 97% gene, partial sequence Syntrophobacter Syntrophobacter fumaroxidans strain MPOB 16S ribosomal OTU_023 NR_075002.1 96% (100%) RNA gene, complete sequence Syntrophobacter fumaroxidans strain MPOB 16S ribosomal NR_118363.1 96% RNA, partial sequence Sulfurisoma sediminicola strain BSN1 16S ribosomal RNA NR_125471.1 99% gene, partial sequence Sulfurisoma OTU_024 Sulfuritalea hydrogenivorans strain sk43H 16S ribosomal NR_113147.1 98% (85%) RNA gene, partial sequence Georgfuchsia toluolica strain G5G6 16S ribosomal RNA NR_115995.1 95% gene, partial sequence
56 Table 3.12. Relative abundance of bacteria potentially associated with sulfate-reducing conditions linked to anaerobic hydrocarbon degradation detected during Phase 2.
Relative Zone OTU ID Abundance (%) OTU_002 14.74 OTU_008 1.32 OTU_010 1.52 Port 1 OTU_019 1.67 OTU_023 1.27 OTU_024 1.25 OTU_002 9.99 OTU_008 4.93 OTU_010 2.84 Port 2 OTU_019 0.75 OTU_023 0.59 OTU_024 0.87 OTU_002 7.95 OTU_008 1.79 OTU_010 3.94 Port 3 OTU_019 0.77 OTU_023 0.62 OTU_024 0.01 OTU_002 4.12 OTU_008 1.09 OTU_010 6.06 Port 4 OTU_019 0.41 OTU_023 0.39 OTU_024 3.02
Potential sulfate reducers were classified further using NCBI BLAST to obtain a higher- level confidence in the identification. OTU_008 was identified as a sequence consistent with
Geobacter with >97% identity percentage. For Syntrophobacter, high identity percentages were identified for OTU_023. OTU_024 was identified as a sequence consistent with Sulfurisoma.
The remaining organisms cannot be assigned classification with the same level of confidence.
57 Clearly, however, they show a diverse population present in the system. OTU_002, which could not be clearly classified to the genus level, had abundances higher than those described above, suggesting that a potential role in the sulfate-reduction process exists for other diverse
Anaerolineaceae organisms that have not been fully described.
Spatial patterns of species previously associated with sulfate reducing anaerobic hydrocarbon degradation correlate with patterns of BTEX removal in the ABR reactors. The unclassified Anaerolineaceae (OTU_002) had the highest relative abundances within port 1, 2, and 3 (14.74%, 9.99%, and 7.85%, respectively), where toluene, ethylbenzenes, and total xylenes were primarily degraded. Members of the family Anaerolineaceae are known obligate anaerobes, and have been linked to oil degradation under sulfate reducing conditions (Rosenkranz et al.,
2013 & Sherry et al., 2013). The decrease in relative abundance from port 1 to port 4 could indicate a correlation of population of the unclassified Anaerolineaceae family linked to BTEX degradation between the concentration of BTEX compounds and the amount of sulfate within the
ABRs. Geobacter (OTU_008) had the second highest relative abundance (4.93%) in port 2 after
Anaerolineaceae (9.99%), where BTEX removal was occurring. The Geobacter (OTU_008) cluster had it’s highest relative abundance (4.93%) in the zone sampled by port 2 where benzene removal was occurring. Geobacter degrade BTEX under iron-reducing conditions (Coates et al.,
1996; Lovley et al., 1989). Both Syntrophobacter (OTU_023) and Smithella (OTU_019) had their largest relative abundance (1.27% and 1.67%, respectively) occur within port 1, where toluene, ethylbenzenes, and total xylenes were primarily degraded. These organisms are known sulfate-reducers (Harmsem et al., 1993; Liu et al., 1999). Both Sulfurisoma (OTU_024) and
Unclassified Betaproteobacteria (OTU_010) had their highest relative abundance (3.02% and
6.06%, respectively) in the effluent (port 4). Various bacteria associated with sulfate reducing
58 anaerobic hydrocarbon degradation are within the Betaproteobacteria class (Rosenburg et al.,
2014b). Sulfurisoma can grow chemolithoautorophically by the oxidation of thiosulfate, elemental sulfur and hydrogen (Kojima & Fukui, 2014). These organisms are candidates for further study for potential BTEX degradation via sulfate reduction and may be using trace iron associated with reactor media.
For the overall microbial community structure, the dominant phyla observed during
Phase 2 were were Proteobacteria (31.7 – 50.5%), followed by Chloroflexi (9.8 – 22.9%)
(Figure 3.8). These figures depict the relative abundance within ABR-2. Within the
Proteobacteria phylum, Betaproteobacteria was the most abundant (12.4 ± 2.7%), followed by
Deltaproteobacteria (7.0 ± 1.2%), Gammaproteobacteria (3.1 ± 0.6%), and then
Alphaproteobacteria (2.3 ± 0.6%).
Out of the total OTUs detected within the samples, between 10 and 21 OTUs were classified as Archaea, which represents 2.0% of the total sequences found within the ABRs, with the remaining 98% representing bacteria. The dominant archaea phyla were Euryarchaeaota (73
– 93%) and Crenarchaeota (5 – 9%) (Figure 3.9). Figure 3.9 depicts the relative abundance with
ABR-2. Within the Euryarchaeaota phylum, the Methanomicrobia classes were the most abundant averaging 50.3 ± 8.2%, followed by Methanobacteria (33.2 ± 9.4%). The genus
Methanoregula within the class Methanomicrobia was the most dominant archaeal genera found within the ABRs, followed by the genus Methanosarcina. The largest percent of abundance of
Methanoregula is found within Zone 1, Zone 2, and Zone 3 averaging 26.6 ± 5.0% of the total archaea. Methanoregula species are capable of the production of methane from H2 or CO2, and are strict anaerobes (Brauer et al., 2011; Yashiro et al., 2011). The largest percent of abundance of Methanosarcina is found within Zone 1 and Zone 2 averaging 10.5 ± 3.0% of the total
59 archaea. Methanosarcina species are capable of methanogenesis through all three known pathways and are capable of using no less than nine methanogenic substrates, including acetate
(Galagan et al., 2002).
The genus Methanobacterium within the class Methanobacteria had a large relative abundance within the ABRs. The largest percent of abundance of Methanobacterium is found within Zone 3 and Zone 4 averaging 46.2 ± 13.6% of the total archaea, while in Zone 1 and Zone
2 averaging 20.3 ± 3.0%. Methanobacterium species are known hydrogen-consuming archaea capable of the production of methane (Kotelnikova et al., 1998; Ma et al., 2005). The presence of these organisms provides further evidence that sulfate-reducing conditions were operative in the ABR during this phase.
60
Figure 3.8. Sulfate Reducing Conditions – Attached Bacteria; Samples from ABR 2 showing the distribution of the bacterial communities from each treatment Zone.
61
Figure 3.9. Sulfate Reducing Conditions – Attached Archaea; Samples from ABR 2 showing the distribution of the archeal communities from each treatment Zone.
62 3.4 Design Implications
The present study indicates treatment of BTEX in groundwater is possible in a laboratory scale ABR system using three column bioreactors. The study suggests that the use of anaerobic
2- degradation of BTEX has significant potential to use sulfate (SO4 ), carbon dioxide, or acetate as terminal electron acceptors to degrade hydrocarbons in aqueous form. Currently treatment of high concentrations of BTEX can be reduced to approximately 93-95% of the original compounds using either of these electron acceptors. On a larger scale, the volume of an ABR bed to treat a loading rate of 35 gallons per minute (gpm), would take a volume of 248 m3 based on the same retention time as the ABRs used in the present study of 1.3 days. For a bed 2 meters in depth, this would require a surface area of 124 m2 for treatment. For an up-flow ABR bed, the treatment zone would be targeted in the bottom half of the bed, while the upper portion of the bed would be used for polishing treatment of the BTEX compounds.
The data suggests that by targeting specific redox conditions, different BTEX-degrading microorganisms shifted as the primary source of degradation within the treatment bed. The media chosen for the design of a full-scale ABR bed for treatment of BTEX would be a compost- manure media and sand mixture, 50/50 percent by weight. The significance of the study showed that with a high concentration loading of the BTEX compounds, the biological components of the media adapted for treatment. Thus determining that a highly organic media is best suited for adaptability of the present microorganisms to degrade the BTEX contaminants.
63 CHAPTER 4. COMPLETE DEGRADATION OF CHLORINATED ETHANES IN SEQUENTIAL BIOREACTORS OPERATED UNDER VARYING REDOX CONDITIONS
4.1 Introduction
Passive treatment of chlorinated solvents using permeable reactive barriers (PRBs) is widely practiced in shallow groundwater plumes. These barriers are designed to intercept groundwater and provide in situ treatment in the aquifer using an integrated set of sorption, biological and chemical reaction processes (Baric et al., 2012; Verginelli et al., 2017; Chen et al., 2016; Guo & Blowes, 2009; Rabideau et al., 2005). An analogous treatment process can be used to perform ex situ treatment for these compounds using similar principles. Porous media systems, termed ABRs, are engineered reactors that operate as treatment beds for contaminated groundwater. Biogeochemical conditions and microbial populations are managed to maximize contaminant retention and destruction Kassenga et al., 2004; Kassenga & Pardue, 2006;
Kassenga et al., 2003; Pardue et al., 1999). For solvent contaminants, these conditions include highly reducing conditions with a consistent flux of H2 to feed organohalide respiring reactions
(Kassenga et al., 2004). Highly organic peats and composts serve as key components for the media for these reactors insuring sufficient residence time while driving organohalide respiring processes.
Chlorinated ethanes, such as 1,1,1-trichloroethane (1.1.1-TCA), comprise a group of groundwater contaminants that could potentially be treated using an ABR approach. 1,1,1-TCA was widely used in legacy industrial applications and is often present in mixtures at complex sites. 1,1,1-TCA dechlorinates to 1,1-dichloroethane (1,1-DCA) and monochloroethane (CA)
(Grostern et al., 2009). These reactions are driven by organohalide-respiring populations such as
Dehalobacter (Grostern et al., 2009; Grostern & Edwards, 2006a; Grostern & Edwrads, 2006b).
64 Dechlorination ceases at CA and further transformation is not typically observed in the absence of oxygen. By contrast, aerobic transformation of CA has been documented cometabolically by enzymes including ammonia monooxygenase (Hommes et al., 1998; Rasche, 1990 #988;
Alvarez-Cohen & McCarty, 1991). Direct oxidation of CA has also been reported (Rittmann &
McCarty, 2001).
An ABR treatment system designed to dechlorinate parent 1,1,1-TCA or 1,1-DCA compounds would produce CA as a product of treatment. This would require an additional treatment step that would likely require some additional unit operation, adding to the complexity and cost of the passive ABR. Initiating aerobic conditions in a portion of the ABR bed is one strategy that may allow for complete treatment of 1,1,1-TCA and metabolites through the use of direct aerobic metabolism or cometabolism of CA. Another advantage to using direct oxidation is that it does not require an external growth substrate or produce toxic by-products (Mattes et al., 2010). Nevertheless, oxygen demand of water flowing through these peat-based systems can be substantial and may be difficult to meet in an economical manner.
The research described in this chapter investigated the potential for complete treatment of chlorinated ethanes via redox manipulation in ABRs while developing knowledge on the microbial populations present and the associated terminal electron acceptors. The use of sequential anaerobic/aerobic pilot-scale ABRs is proposed to reductively dechlorinate 1,1,1-TCA to CA, and then to aerobically treat approximately 1 mg/L of CA. This approach represents a challenge for ABR systems including determining when reductive dechlorination is completed and where to add direct oxidation or aerobic cometabolism to degrade the final CA compound.
The general challenge that this study addresses is how to manipulate the geochemical conditions present in these systems to accomplish efficient biodegradation in the smallest volumes possible.
65 The addition of an aerobic layer within the ABRs was attempted by the addition of pressurized porous tubing. The objective of the study is to determine whether the manipulation of redox conditions in the ABR system can result in a microbial response that completely degrades 1,1,1-
TCA.
4.2 Materials and Methods
4.2.1 Reactor Experimental Design
Two duplicate reactors (A and B) operated in parallel in an up-flow mode were prepared for anaerobic/aerobic treatment studies of chlorinated ethanes (Figure 4.1). The dimensions of reactors A and B are 45 cm x 45 cm x 66 cm, with a volume equal to 0.13 m3 each. Reactors were constructed from polyethlylene tanks fitted with a floating cover to minimize headspace and reduce volatilization of chlorinated ethanes. Each reactor was equipped with 3 sampling ports located at 24, 42, and 60 cm from the inlet. The influent port was also sampled to obtain influent concentrations. Reactors A and B were operated in an up-flow direction, with the bottom
6 cm of each reactor packed with a sand layer (Quikrete Companies Inc.; Atlanta, GA) to allow for uniform distribution of flow. Above this layer was a 25 cm deep layer of 60/40% w/w, compost (Hope Agri Products Inc.; Hope AZ)/sand (respectively) (Figure 4.1). This compost/sand layer was designed to optimize reductive dechlorination of parent chlorinated ethanes such as 1,1,1-TCA. Following this layer, an aerobic layer was created by first adding a layer of sand (5 cm) termed the aerobic layer below. Within the 5-cm deep aerobic layer, 50 feet of continuous Silastic Laboratory Tubing (Dow Corning Cat. No. 508-010) was placed and capped with a 1/8” barbed plug (Cole Parmer Cat. No. 31220-13; Vernon Hills, IL) on the end of the silastic tubing. The tubing bled pure oxygen into the sand layer. The silastic tubing was connected to a pure oxygen gas cylinder using a section of non-bleeding tubing. Above the
66 aerobic layer, a final layer of 60/40% compost/sand mixture 28-cm deep was established to provide any further treatment. The remaining 2 cm of the reactor was composed of topsoil
(Scott’s Miracle-Gro Company; Marysville, OH). Geofabric separated each layer, preventing media movement between the reactor layers. The configuration of Reactor A and Reactor B is displayed in Figure 4.1.
Figure 4.1. Internal and external configuration of air-supply to Reactors A and B.
A greenhouse scale ABR system described by Boudreau (2013) was used to generate the influent chloroethane concentrations through the reductive dechlorination of 1,1,1-TCA. Water was first passed through a 132-liter dissolved oxygen scrubber filled with highly organic compost media to remove DO from tap water (Figure 4.2). Prior to the addition of 1,1,1-TCA to the system, water was placed into a 76-liter plexiglass aquarium (influent tank 1 and 2) fitted with a floating cover to minimize headspace and reduce volatilization of 1,1,1-TCA. The greenhouse scale system consisted of two additional ABR reactors (ABR-1 and ABR-2) in series
67 designed as described above. ABR-1 was initially dosed with 300 mL of saturated 1,1,1-TCA solution with the intention of reductively dechlorinating 1,1,1-TCA to CA prior to application to
Reactors A and B. The pilot-scale system is located in a greenhouse with temperatures that do not exceed 30oC. The process flow diagram (Figure 4.2) below shows the complete ABR system design.
ABR-1 received 9.5 L/day of synthetic 1,1,1-TCA wastewater in a down-flow mode with a loading rate of 0.36 g /m2/day of 1,1,1-TCA. ABR-1 and ABR-2 were operated in series for several years and were completely acclimated to 1,1,1-TCA dechlorination to 1,1-DCA and CA.
The greenhouse system was used to study aerobic biodegradation of CA for a time period of approximately 130 days, with the first 21 days of the experiment serving as an acclimation period. Both reactors (A and B) received 9.5 L per day of continuously fed CA wastewater in a vertical upflow mode with concentrations of approximately 1 mg/L of CA. This is equivalent to a loading rate of 0.022 g/m2/day of CA with a flow rate of 6.6 mL/minute and a residence time of
2 days.
Samples were collected weekly to measure the concentrations of chlorinated ethanes, specifically observing the loss of CA. At weekly intervals, samples of water (100 mL) were collected at the influent, and each of the three sampling ports from the ABR reactor study. The third port, 60 cm from the inlet was in the overlying media layer and was assumed to be equivalent to the effluent concentration before any volatilization could occur. Each sample was placed in a 160 mL glass serum bottle, leaving 60 mL of headspace, and immediately crimp- sealed with a Teflon-coated septum (Sigma-Aldrich; St. Louis, MO). Samples were inverted to establish equilibrium in the headspace. Samples were inverted for at least three hours (but never more than 24 hours) before analysis. At the end of the pilot study, the reactors were disassembled
68 and media samples were collected at varying depths. DNA was extracted for 16S rRNA for analysis of microbial populations within the media samples.
st 1 Stage: ABR Reactors; 1,1,1-TCA dechlorination 2nd Stage: ABR Reactors in parallel; 1,1DCA to 1,1-DCA and CA (Boudreau, 2013). and CA anaerobic/aerobic
Figure 4.2. Complete Pilot-Scale ABR System Design.
4.2.2 Serum Bottle Study
A supplemental 34-day serum bottle study on CA biodegradation was implemented to observe biodegradation under a more controlled environment without the complication of transport processes. A total of nine serum bottles were prepared for the CA study, with three treatments in triplicate. The treatments consisted of an anaerobic treatment with no oxygen added, an oxygen amended treatment and a killed control. The glass serum bottles (160 mL) were used with a silicon septum and aluminum crimp cap (Sigma-Aldrich; St. Louis, MO). The
CA-containing anaerobic water that was added to the bottles was generated from the ABR greenhouse system described above.
69 First, each bottle was marked with a 100 mL liquid volume line, and then the bottles were placed in an autoclave (Eray Medical Supplies Inc., Holbrook, NY). After autoclaving, a total of
5 grams of microbial source media was placed in each bottle. The media was a homogenized core sample from the top portion of Reactor B taken after acclimation. After placing the media slurry in the serum bottles, the experiment preparation took place in an anaerobic glove box under a N2 atmosphere. The killed control was prepared by adding a total of 3 mL of formaldehyde/methanol solution, followed by the addition of 1 mL of resazurin stock solution, leaving a final concentration of formaldehyde and resazurin at 1% by volume and 1 mg/L, respectively (Figure 4.3). Finally, the bottles were filled with the CA-containing water to the
100 mL volume mark, sealed and crimped in the anaerobic glove box. The anaerobic treatment was prepared by filling the serum bottles with the CA-containing water to 100 mL, and 1 mL of resazurin. The oxygen amended serum bottles were set up in the same way as the anaerobic treatment, initially. The bottles were sampled for time zero concentrations of CA. The bottles were then amended with 4 mL of pure oxygen gas via a gas tight syringe for a final concentration of approximately 2.65 mg O2/L in the liquid phase after mixing. Subsequent additions of 2 mL of oxygen gas were performed immediately following weekly sampling of the serum bottles. As the CA concentrations depleted quickly, the bottles were amended with CA at intervals to maintain approximately 1 mg/L. All bottles were mixed continuously using a tumbler throughout the entirety of the study and analyzed over a period of approximately 30 days.
70
Figure 4.3. Supplemental serum bottle study for setup.
4.2.3 Chemical Analysis
Analysis of 1,1-DCA and CA were performed using a gas chromatograph (Agilent
Technologies; Model No. 6850) equipped with a packed column (Supelco 2.1 mm x 2.4 m, packed 1% SP) fitted with a flame ionization detector (FID). The initial oven temperature on the gas chromatograph was kept at 60oC, following an increase of 10oC/min until reaching a temperature of 130oC, and holding for 3 minutes. The carrier gas utilized was helium at a flow rate of 40 mL/min, while oxygen and hydrogen served as fuel gas for the FID at 100 and 35 mL/min respectively. Samples were removed from the serum bottles (1.5 mL) using a gastight syringe (Hamilton Co.; Reno, NV) and directly injected into the column. External standards (6 points) prepared from methanolic stock solutions (Sigma Aldrich, St. Louis, MO) were used to calibrate the GC for 1,1,1-TCA, cis-1,2-DCE, 1,1DCA, chloroethane, and vinyl chloride.
Methane calibration was completed using a 1% gas mixture (American Air Liquide, Houston,
71 TX). The GC was calibrated with the prepared stocks using DI water in the same gas:liquid volumetric ratio as the weekly collected samples.
Analysis of dissolved oxygen (DO) during the serum bottle study was performed using a
Unisense O2 Microsensor (Unisense; Aarhus, Denmark) connected to a multichannel Microsense multimeter outputting to a PC equipped with Sensor-Trace Pro software (Unisense). Prior to each use, the Unisense was calibrated with a zero oxygen solution (0.1 M NaOH and sodium ascorbate) and a fully saturated solution generated by purging water with an air pump for approximately 10 minutes. Measurements were conducted weekly during the 34-day study.
During the reactor study, DO was measured using a calibrated Accumet XL-40 meter equipped with a DO probe (Thermo Fisher Scientific; Waltham, MA). DO measurements of the reactors were taken bi-weekly for the entirety of the study. Approximately 45 mL were removed from the influent and all three ports and placed in an Erlenmeyer flask with a sealed stopper prior to analysis.
4.2.4 Microbial Analysis
Compost:sand media samples were collected throughout each of the reactors at varying depths after the completion of the study. Media was removed from the reactors by sequentially removing 12 cm layers of media at a time for sampling and then aseptically removing a compost:sand sample of approximately 1 g. DNA was extracted from the media samples using a
Powersoil® DNA Isolation Kit (Mo Bio Laboratories, Inc. Carlsbad, CA) to isolate microbial genomic DNA per the manufacturer’s user protocol manual for total nucleic acid extraction from samples without modifications (Mo Bio User Protocol; https://www.qiagen.com/us/resources/ resourcedetail?id=5c00f8e4c9f5454494fa653a5b2a6373&lang=en, accessed June, 2016). The final extracted DNA samples were quantified using a Nanodrop-1000 spectrophotometer
72 (Thermo Fisher Scientific, Inc., Wilmington, DE) and then stored at -20oC prior to amplification and sequencing. PCR amplification was carried out using the primer set 341F (5’ - CCT ACG
GGN GGC WGC AG - 3’) and 785R (5’ - GAC TAC HVG GGT ATC TAA TCC - 3’) for the
V4 region of 16S rRNA gene (Klindworth et al., 2013; Beckers et al., 2016; Thijs et al., 2017).
In total, 10 samples were successfully amplified (16S rRNA) and sequenced using the
Illumina Miseq platform. Sequenced data was processed and analyzed using a single software platform, mothur v.1.29.2, (University of Michigan; Ann Arbor, MI) (Schloss et al. 2009).
Analysis of the 16S rRNA gene sequences followed the MiSeq standard operating procedure
(Schloss et al. 2013; https://www.mothur.org/wiki/MiSeq_SOP, accessed May, 2017). For all sequences, paired-end reads were combined to produce single sequences, and then screened for quality. Sequences containing any mismatched pairs, length outside the range of 400 and 450 bp, ambiguous bases, or homopolymers longer than 8 bp were excluded (Veach et al., 2016;
GomezSmith et al., 2015). Remaining sequences were aligned against the SILVA (SILVA 111, released 2012) bacterial 16S rRNA database, and chimeric sequences were removed using the
UCHIME algorithm (Edgar et al., 2011). Sequences were clustered into operational taxonomic units (OTUs) at a cutoff of 97% similarity threshold. Sequences were then assigned to the taxonomic affinities using the naïve Bayesian classifier (Wang et al., 2007) with a 50% threshold against the Ribosomal Data Project (RDP) reference files (version 16). Sequences not assigned to the domain bacteria or archaea (including chloroplasts, mitochondria, and unclassified) were not included in further analysis.
The relative abundance of an OTU was computed by the number of sequences classified in the OTU, divided by the total number of sequences for each sample for bacteria and archaea
(Table A.9 and Table A.11). The relative abundance computation ignores 16S rRNA gene copy
73 diversity, which may alter the distribution (Vetrovsky and Baldrian, 2013). For each sample, taxonomy information is provided for OTUs greater than 1% of total abundance, while any
OTUs below 1% of total abundance is placed in the ‘other’ category without taxonomic identification.
Genera identified in the RDP classification greater than 50% were examined for potential organohalide respiring bacteria. Bacteria that were identified in mothur as potential organohalide respirers were classified again using the naïve Bayesian classifier (Wang et al., 2007) with a
50% threshold against the Ribosomal Data Project (RDP) reference files (version 16)
(http://rdp.cme.msu.edu/classifier/classifier.jsp; accessed, August 2017) to identify the percent level of classification (Table 4.6; Table A.12). These OTUs underwent additional comparisons of sequences using the National Center for Biotechnology Information (NCBI) Basic Local
Alignment Search Tool (BLAST) GenBank (Table 4.6). OTU sequences were classified against the 16S ribosomal RNA sequences (Bacteria and Archaea) database using the BLASTN algorithm within the NCBI BLAST tool
(https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=Blast
Search&LINK_LOC=blasthome; accessed August, 2017). The top three GenBank hits based on the BLASTN algorithm are reported below (Table 4.6).
After classification of bacteria and archaea was completed, OTU-based analysis of α diversity measurements was calculated using mothur. First, the calculation of species richness and diversity of the samples were performed for OTUs by computing the species richness indicator Chao1, and the Shannon diversity index (H’) (Schloss et al., 2009; GomezSmith et al.,
2015). To prevent bias, OTU data from each sample was normalized by randomly subsampling to the smallest number of sequences observed in any sample ((Table A.9 and Table A.11) prior
74 to computation of Chao1 and H’. Chao1 is a nonparametric estimator of species richness, which uses a “mark-release-recapture” statistic that is derived from the number of OTUs appearing either one or two times in a given library and is calculated as:
2 퐹1 퐹1 퐹2 퐶ℎ푎표1 = 푆표푏푠 + − 2 2(퐹2 + 1) 2(퐹2 + 1) where Sobs is the number of phylotypes observed in the library, F1 and F2 are the number of
OTUs occurring either one to two times (Kemp & Aller, 2004; Bailey et al., 2013). The
Shannon index (H’) is a nonparametric estimator of diversity, which reflects the number of different species in a community and assumes that individuals are truly randomly sampled from within an infinitely large community and is calculated as:
′ 퐻 = − ∑ 푝푖 ln 푝푖
th where pi is the proportion of clones in the i OTU (estimated using ni/N), where ni is the number of sequences in the ith OTU, and N is the total number of sequences (Hill et al., 2003; Bailey et al., 2013). Statistical analysis using the two-tailed Student T-test with a 95% confidence interval was conducted on the number of sequences after quality screening (Nseqs), OTUs, Chao1, and H’ parameters to distinguish the statistical significance of differences between treatment zones within the Reactor A and Reactor B of bacteria and archaea populations.
4.3 Results and Discussion
4.3.1 Serum Bottle Study Results
During the serum-bottle study, loss of CA under aerobic conditions occurred consistently over the 34-day study (Figure 4.4). Figure 4.4 displays the concentration of both 1,1-DCA and
CA during the serum bottle study in each treatment. In the oxygen amended bottles, CA concentrations significantly decreased (p = 0.022) while oxygen was steadily added to the bottles
75 between day 0 and day 34. Due to the significant decrease of CA within the first 4 days, the supplemental addition of CA on days 4, 25, and 28 was performed to maintain the concentration of CA high enough to observe degradation during the study. Between day 0 and day 8, concentrations of 1,1-DCA degraded to below detection, from a low initial 1,1-DCA concentration averaging 0.04 ± 0.003 mg/L. The concentrations of 1,1-DCA remained below detection through the entirety of the study after day 8. The mass of 1,1-DCA loss within the first
3 days was 1.07 ± 0.32 g while the mass of CA loss was 21.9 ± 6.8 g of CA. After the first addition of CA on day 4, the concentration of CA increased to 2.37 ± 0.16 mg/L of CA. From day 4 to day 25, loss in mass of 1,1-DCA was 1.54 ± 0.22 g while 286.3 ± 35.4 g of CA mass loss occurred. The total loss of CA from day 25 to 34 was 142.9 ± 33.9 g. There was no detection of 1,1-DCA in the oxygen Amended bottles between day 8 and day 34. During this study, an average loss of 91.5 ± 4.2% of CA was observed.
The anaerobic treatment showed a slight increase of the total mass of CA over the course of the study (Figure 4.4) averaging from 50.4 ± 2.1 g of CA to 66.1 ± 1.2 g of CA, which was not statistically significant (p = 0.198). On the other hand, 1,1-DCA showed a significant decrease (p < 0.001) between day 0 and 34. During the study, 1,1-DCA decreased by a total mass of 40.8 ± 7.4 g (0.42 M), while chloroethane increased total mass by 15.7 ± 3.0 g (0.25
M). This data implies that approximately 60% of 1,1-DCA lost can be accounted for as CA gain from reductive dechlorination. The killed control treatment did not show a significant difference in CA (p = 0.944) or 1,1-DCA (p = 0.33) between days 0 and 34 during the study.
Microorganisms in the oxygen amended bottles quickly acclimated to CA and degraded it effectively when compared to the killed control and the anaerobic treatments. The findings from this serum bottle study support the strategy that the presence of oxygen can alter microbial
76 processes to remove aqueous CA concentrations. Repetitive spiking of CA increased the efficiency of the process suggesting that direct oxidation of CA was occurring. The source of inoculum was the acclimated compost:sand mixture used in the experiments described below.
O2 only added
CA only added
CA and O2 added
Figure 4.4. Killed Control, Anaerobic, and Oxygen Amended serum bottle studies of chloroethane and 1,1-dichloroethane vs. time.
77 4.3.2 Reactor Study Results
During the reactor study, loss of 1,1-DCA and CA occurred over the study period (Figure
4.5) in Reactors A and B. Aqueous samples were collected along the vertical profile of both
Reactor A and Reactor B weekly over a time period of approximately 130 days. The influent concentrations of 1,1-DCA ranged from 0.13 mg/L to 1.0 mg/L, averaging 0.65 ± 0.05 mg/L, while the effluent concentrations of 1,1-DCA ranged from below detection (<0.005 mg/L) to
0.13 mg/L, averaging 0.01 ± 0.01 mg/L. During this period, approximately 99.3 ± 0.4 % loss of
1,1-DCA was observed in the reactors. The influent concentrations of CA ranged from 0.83 mg/L to 0.02 mg/L, averaging 0.38 ± 0.04 mg/L. During many of the sampling events, CA had higher concentrations within Port A1 and Port B1 than that of the Influent. Since Reactors A and
B are up-flow, Port 1 is located in an anaerobic zone where 1,1-DCA is being dechlorinated to produce CA. The concentrations of CA increased between the Influent and Port 1, averaging
0.54 ± 0.05 mg/L. The effluent (Port 3) concentrations of chloroethane ranged from below detection (<0.005 mg/L) to 0.04 mg/L, averaging at 0.01 ± 0.0 mg/L. During this study, approximately 98.7 ± 1.2 % loss in CA was observed. Losses were inferred from changes in aqueous concentrations in the reactors while acknowledging that 1,1-DCA and CA mass may also be sorbed on the solid particles in the bed, which could not be regularly sampled. These are discussed below.
Significant losses of 1,1-dichloroethane (p<0.01) occurred between the influent and the first 24 cm (Zone 1) of distance into the reactors. Between the influent and first sampling port approximately 69.3 ± 12.7 % of 1,1-dichloroethane was reduced. In contrast, CA significantly increased (p = 0.0015), by approximately 40.6 ± 15.6 % (represented in Table 4.1 as a negative value, indicating an increase or formation of chloroethane) between the influent and Port 1.
78
Figure 4.5. Concentrations in Reactor A and Reactor B of 1,1-Dichloroethane (top) & Chloroethane (bottom) of ABR ports vs. time.
79 Significant loss of CA (p = 7.37E-07) was observed between 24 and 42 cm (Port 1 to Port 2,
Zone 2), with approximately 80.3 ± 7 % reduction of the compound. This is the treatment zone where oxygen is being added using porous tubing. The average percent degradation losses of
CA and 1,1DCA in both reactors and the significant difference (p <0.05) of loss in respective treatment zones are displayed in Table 4.1. The data can be converted to a volumetric rate of biodegradation by multiplying the removal by the flow-rate by 9.5 L/day (Table 4.2). 1,1-DCA had an average rate of removal in Zone 1 of 126.5 ± 17.6 g/m3·day, while CA had an increase of of 51.74 ± 10.0 g/m3·day respectively. On a molar basis this represents a rate of loss of 1.29 moles/m3-day for 1,1-DCA and a production of 0.81 moles/m3-day, a recovery of 62%, very similar to the recovery of 60% in the serum bottle study. Additional transformation by-products may be responsible for the additional mass loss.
Table 4.1. Average percent degradation loss of chloroethane and 1,1DCA in reactors and the significant difference (p <0.05) of loss in respective treatment zones.
Treatment Chloroethane 1,1Dichloroethane Zone % Degradation p-value % Degradation p-value Zone 1 40.57 ± 15.6 0.0015* 69.29 ± 12.7 2.14E-07* Zone 2 80.26 ± 6.99 7.37E-07* 42.74 ± 2.09 0.015* Zone 3 95.13 ± 4.52 0.0013* 93.89 ± 4.69 0.0045* * Statistical significance (p<0.05).
Table 4.2. Average biodegradation rate of chloroethane and 1,1dichloroethane.
Average CA Average 1,1DCA Distance from Treatment Zone Degradation Rate Degradation Rate Influent (cm) (mg/m3*day) (mg/m3*day) Zone 1 24 51.74 ± 10.0 126.5 ± 17.6 Zone 2 42 123.8 ± 15.3 26.3 ± 10.3 Zone 3 60 29.3 ± 7.4 32.4 ± 9.1 Note: A negative degradation rate is a rate of formation.
80 The preference for dechlorination of 1,1-DCA under anaerobic conditions is consistent with published studies (Holliger et al., 1990; Sun et al., 2002; Grostern & Edwards, 2006;
Scheutz et al., 2011). As a result of the aerobic layer between the 1st and second port, degradation is primarily oxidation of CA with some minor 1,1-DCA degradation. CA has an average rate of removal of 123.8 ± 15.3 g/m3·day respectively within this zone. Further up in the reactors away from the inlet, the average rate of CA and 1,1-DCA degradation decreases, while averaging a combined degradation rate of 30.8 ± 1.56 g/m3·day respectively. This data indicates that the majority of the treatment of 1,1-DCA and CA occurs within the first 42 cm of the reactors. The upper portion of the reactors provides polishing at a much lower loading rate. The overall removal efficiency throughout the reactors was 1,1-DCA (99.3 ± 0.4 %) and CA (98.7 ±
1.2 %) respectively. For a given flow rate, enhanced removal can be obtained by decreasing the volumetric loading rate (by increasing the reactor surface area) or increasing the depth or both.
Figure 4.5 displays the concentration of 1,1-dichloroethane and chloroethane in water vs. time within Reactor A and Reactor B during the reactor study. Both reactors had a continuous supply of pure oxygen during the entirety of the study from day 0 to day 133, with the exception of a malfunction of oxygen supply around day 84. The impacts of the pure oxygen malfunction can be seen more clearly in the chloroethane Reactor A graph, where Port 2 concentrations vary from week to week between 0 mg/L of CA and 0.56 mg/L of CA. With the exception of two sampling events in Reactor A, Figure 4.5 displays all CA concentrations decreased between Port
1 and Port 2 in both reactors, where pure oxygen was being applied in the aeration zone.
Contaminant dosing remained constant throughout the study, which attributed to the improved performance of 1,1-DCA degradation in ABR reactors feeding both Reactor A and Reactor B shown by the upward trend in the CA influent concentrations in both reactors after day 21.
81 Aeration of the system began at time 0, as shown in the figure, as to show acclimation time of the aerobic layer within both reactors; this is why complete degradation of chloroethane doesn’t occur within the first 21 days.
Dissolved oxygen was measured during the study and is summarized in Figure 4.6.
Oxygen increased after the 21-day acclimation period in Port 2 and Port 3, which is after the aerobic layer in the reactors. Oxygen increased on average from 0.4 ± 0.04 mg/L of O2 in the effluent to 4.4 ± 0.6 mg/L of O2 in Port 3 over the entirety of the study. The anoxic conditions within the first 24 cm of the reactors were observed with DO concentrations remaining below 1.0 mg/L of O2. DO concentrations decreased in Port 2 and Port 3 around day 84 due to the malfunction of the pure oxygen gas cylinder, which can be seen to increase after the malfunction, and was fixed around day 100.
Figure 4.6. Average concentration (mg/L) of dissolved oxygen during the CA Reactor Study.
82 4.3.3 Microbial Results
The bacterial and archaeal microbial community structure throughout Reactor A and
Reactor B of the study was determined by Illumina Miseq profiles of PCR-amplified 16S rRNA gene fragments. Multiple samples were collected from Reactor A and Reactor B through various depths of the ABRs. Two anaerobic zones (0 -12 cm and 12-24 cm from the influent) were sampled as well as a zone (24-31cm) just below the aerobic layer where the silastic tubing delivered oxygen (at 31-36 cm). Samples were also taken above the oxygen delivery zone (36-
48 cm and 48-60 cm) where aerobic conditions were present. After combining the forward and reverse reads, there was a total of 974,453 sequences combined in the five samples from Reactor
A prior to processing through mothur (Table A.8). After processing aligned sequences through initial quality filtering and removal of chimeras, a total of 350,266 sequences remained from
Reactor A (Table A.9) in the five samples at an average of 70,053 ± 3,275 sequences per sample
(Table 4.3). These sequences were clustered into OTUs at 97% similarly and were subsequently classified using the RDP classifier tool. The number of OTUs ranged from 263 to 434 for the five Reactor A samples (Table 4.3). The Shannon diversity index (H’) for Reactor A ranged from 4.11 to 5.55 (Table 4.3). These are consistent with forest soil diversity determined from
16S OTUs (Cong et al., 2015) but higher than geographically distributed soil bacterial diversity indices computed from TRFLP (Fierer and Jackson, 2006). The Chao1 estimator of species richness in Reactor A ranged from 8,497 to 12,216.
In Reactor B, after combining the forward and reverse reads, there was a total of 820,900 sequences combined in the five samples (Table A.10). After processing aligned sequences through initial quality filtering and removal of chimeras, a total of 286,262 sequences remained
(Table A.11) for the five samples for an average of 57,252 ± 4,503 sequences per sample (Table
83 4.4). These sequences were clustered into OTUs at 97% similarly and were subsequently classified using the RDP classifier tool. The number of OTUs ranged from 281 to 439 for the five samples (Table 4.4).
Table 4.3. Diversity and richness measures computed for Reactor A including number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%.
Reactor A Sample 0-12 cm 12-24 cm 24-31 cm 36-48 cm 48-60 cm Nseqs 72,639 71,165 58,569 78,658 69,235 OTUs 329 263 365 401 434 Chao1a 9,594 8,902 8,497 12,063 12,216 H'a 4.11 5.36 4.85 4.47 5.55 a Computed after normalization to 58,569 sequences to prevent bias
Table 4.4. Diversity and richness measures computed for Reactor B including number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%.
Reactor B Sample 0-12 cm 12-24 cm 24-31 cm 36-48 cm 48-60 cm a Nseqs 72,298 49,828 54,617 61,996 47,523 OTUs 301 281 439 427 434 Chao1a 10,850 8,313 13,171 12,142 12,811 H'a 5.33 5.07 6.22 5.35 5.81 a Computed after normalization to 47,523 sequences to prevent bias
Statistically, there was a significant difference between zones in the rector for species richness and the number of OTUs for the samples in Table 4.3 and Table 4.4 when grouped by aerobic (36-48 cm and 48-60 cm) versus anaerobic (0-12 cm, 12-24 cm, and 24-31 cm) conditions (Table 4.5).
84 Table 4.5. Statistical analysis of between anaerobic and aerobic zones in Reactor A and Reactor B of the number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%.
Anaerobica Aerobica p-value
Nseqs 66,483 ± 5,560 64,353 ± 6,566 0.568 OTUs 294 ± 14 424 ± 7.8 0.009* Chao1 9,415 ± 545 12,308 ± 171 0.023* H' 4.97 ± 0.29 5.29 ± 0.29 0.127 a Mean and standard error *Statistical significance (p<0.05).
OTU data were mined for bacteria associated with anaerobic reductive dechlorination including those associated with 1,1,1-trichloroethane and 1,1-dichloroethane reductive dechlorination. Potential organohalide-respiring bacteria were initially classified from the
Chloroflexi phylum, including the genera Dehalogenimonas, and the Firmicutes phylum, including the genera Dehalobacter (Key et al., 2016; Bowman et al., 2013; Maness et al., 2012;
Nijenhuis et al., 2007; Sun et al., 2002; Kengen et al., 1999; El Fantroussi et al., 1998). These genera of bacteria accounted up to 2 % of the relative abundance of the total OTUs found within the reactors (Table 4.6). Relative abundance of these organisms was largely found in the anaerobic portion of the reactors (0-12 cm, 12-24 cm, and 24-31cm), but also found some of these organisms in the aerobic layers of the reactors (36-48cm and 48-60cm) (Table 4.7a and
Table 4.7b).
85 Table 4.6. Further classification of OTU sequences within Reactor A and Reactor B for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) Ornatilinea apprima strain P3M-1 16S ribosomal RNA NR_109544.1 86% gene, partial sequence Unclassified OTU_028 Anaerolineaceae Bellilinea caldifistulae strain GOMI-1 16S ribosomal RNA NR_041354.1 86% (57%) gene, partial sequence Thermomarinilinea lacunifontana strain SW7 16S NR_132293.1 85% ribosomal RNA, partial sequence Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_074337.1 90% 16S ribosomal RNA gene, complete sequence Unclassified Chloroflexi Dehalogenimonas lykanthroporepellens strain BL-DC-9 OTU_031 NR_044550.1 90% (93%) 16S ribosomal RNA gene, partial sequence
Dehalogenimonas alkenigignens strain IP3-3 16S NR_109657.1 89% ribosomal RNA gene, partial sequence Dehalogenimonas alkenigignens strain IP3-3 16S NR_109657.1 89% ribosomal RNA gene, partial sequence Unclassified Bacteria Dehalogenimonas lykanthroporepellens strain BL-DC-9 OTU_033 NR_044550.1 88% (100%) 16S ribosomal RNA gene, partial sequence
Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_074337.1 88% 16S ribosomal RNA gene, complete sequence
86 Table 4.6 (continued). Further classification of OTU sequences within Reactor A and Reactor B for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%)
Thermomarinilinea lacunifontana strain SW7 16S NR_132293.1 88% ribosomal RNA, partial sequence Longilinea Longilinea arvoryzae strain KOME-1 16S ribosomal RNA OTU_094 NR_041355.1 87% (85%) gene, partial sequence Bellilinea caldifistulae strain GOMI-1 16S ribosomal NR_041354.1 86% RNA gene, partial sequence Dehalogenimonas alkenigignens strain IP3-3 16S ribosomal RNA gene, partial sequence NR_109657.1 89% Dehalogenimonas Dehalogenimonas lykanthroporepellens strain BL-DC-9 OTU_138 NR_044550.1 89% (53%) 16S ribosomal RNA gene, partial sequence Dehalogenimonas lykanthroporepellens strain BL-DC-9 16S ribosomal RNA gene, complete sequence NR_074337.1 89%
Litorilinea aerophila strain PRI-4131 16S ribosomal RNA, NR_132330.1 88% complete sequence Litorilinea OTU_140 Caldilinea tarbellica strain D1-25-10-4 16S ribosomal NR_117797.1 87% (55%) RNA gene, partial sequence
Caldilinea aerophila strain DSM 14535 16S ribosomal NR_074397.1 87% RNA gene, complete sequence
87 Table 4.6 (continued). Further classification of OTU sequences within Reactor A and Reactor B for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%)
Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_074337.1 77% 16S ribosomal RNA gene, complete sequence Unclassified Bacteria OTU_160 Streptomyces ederensis strain NBRC 15410 16S NR_112457.1 77% (96%) ribosomal RNA gene, partial sequence Streptomyces ederensis strain NRRL B-8146 16S NR_116384.1 77% ribosomal RNA gene, partial sequence
Thermomarinilinea lacunifontana strain SW7 16S NR_132293.1 87% ribosomal RNA, partial sequence Longilinea Ornatilinea apprima strain P3M-1 16S ribosomal RNA OTU_169 NR_109544.1 87% (56%) gene, partial sequence Bellilinea caldifistulae strain GOMI-1 16S ribosomal NR_041354.1 87% RNA gene, partial sequence Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_044550.1 91% 16S ribosomal RNA gene, partial sequence Unclassified Chloroflexi Dehalogenimonas lykanthroporepellens strain BL-DC-9 OTU_180 NR_074337.1 91% (85%) 16S ribosomal RNA gene, complete sequence
Dehalogenimonas alkenigignens strain IP3-3 16S NR_109657.1 89% ribosomal RNA gene, partial sequence
88 Table 4.6 (continued). Further classification of OTU sequences within Reactor A and Reactor B for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_074337.1 84% 16S ribosomal RNA gene, complete sequence Unclassified Bacteria Dehalogenimonas alkenigignens strain IP3-3 16S OTU_224 NR_109657.1 84% (99%) ribosomal RNA gene, partial sequence Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_044550.1 84% 16S ribosomal RNA gene, partial sequence Longilinea arvoryzae strain KOME-1 16S ribosomal RNA NR_041355.1 86% gene, partial sequence Unclassified Bacteria Thermoflexus hugenholtzii strain JAD2 16S ribosomal OTU_267 NR_125668.1 86% (100%) RNA gene, partial sequence Caldilinea aerophila strain DSM 14535 16S ribosomal NR_074397.1 86% RNA gene, complete sequence Anaerolinea thermolimosa strain IMO-1 16S ribosomal NR_040970.1 87% RNA gene, partial sequence Ornatilinea Anaerolinea thermophila strain UNI-1 16S ribosomal OTU_311 NR_074383.1 87% (65%) RNA gene, complete sequence Anaerolinea thermophila strain UNI-1 16S ribosomal NR_036818.1 87% RNA gene, partial sequence
89 Table 4.6 (continued). Further classification of OTU sequences within Reactor A and Reactor B for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) Dehalogenimonas alkenigignens strain IP3-3 16S NR_109657.1 89% ribosomal RNA gene, partial sequence Dehalogenimonas Dehalogenimonas lykanthroporepellens strain BL-DC-9 OTU_351 NR_044550.1 88% (59%) 16S ribosomal RNA gene, partial sequence Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_074337.1 88% 16S ribosomal RNA gene, complete sequence Longilinea arvoryzae strain KOME-1 16S ribosomal RNA NR_041355.1 84% gene, partial sequence Unclassified Bacteria Caldicoprobacter guelmensis strain D2C22 16S ribosomal OTU_352 NR_109614.1 84% (100%) RNA gene, partial sequence Thermoflexus hugenholtzii strain JAD2 16S ribosomal NR_125668.1 84% RNA gene, partial sequence Litorilinea aerophila strain PRI-4131 16S ribosomal RNA, NR_132330.1 86% complete sequence Unclassified Thermomarinilinea lacunifontana strain SW7 16S NR_132293.1 86% OTU_366 Anaerolineaceae ribosomal RNA, partial sequence (50%) Ornatilinea apprima strain P3M-1 16S ribosomal RNA NR_109544.1 85% gene, partial sequence
90 Table 4.6 (continued). Further classification of OTU sequences within Reactor A and Reactor B for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) Dehalobacter restrictus strain PER-K23 16S ribosomal NR_121722.1 99% RNA gene, complete sequence Dehalobacter Dehalobacter restrictus strain PER-K23 16S ribosomal OTU_523 NR_026053.1 99% (99%) RNA gene, partial sequence Syntrophobotulus glycolicus strain DSM 8271 16S NR_074993.1 96% ribosomal RNA gene, complete sequence Dehalogenimonas alkenigignens strain IP3-3 16S NR_109657.1 98% ribosomal RNA gene, partial sequence Dehalogenimonas Dehalogenimonas lykanthroporepellens strain BL-DC-9 OTU_805 NR_074337.1 97% (100%) 16S ribosomal RNA gene, complete sequence Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_044550.1 97% 16S ribosomal RNA gene, partial sequence
91 Table 4.7a. Relative abundance of bacteria described in Table 4.6 detected in Reactor A.
Relative Zone OTU ID Abundance (%)
OTU_028 0.37 OTU_031 0.29 OTU_033 0.66 OTU_094 0.35 OTU_138 0.15 OTU_140 0.1 OTU_169 0.1 0-12 cm OTU_180 0.15 OTU_224 0.16 OTU_267 0.14 OTU_311 0.14 OTU_352 0.11 OTU_366 0.13 OTU_523 0.002 OTU_805 0.03 OTU_028 0.32 OTU_031 1.84 OTU_033 0.49 OTU_094 0.28 OTU_138 0.14 OTU_160 0.31 12-24 cm OTU_180 0.11 OTU_267 0.11 OTU_311 0.1 OTU_351 0.12 OTU_352 0.11 OTU_805 0.03 OTU_031 0.24 24-31 cm OTU_033 0.17 OTU_028 0.18 36-48 cm OTU_031 0.18 OTU_033 0.13 OTU_028 0.33 48-60 cm OTU_031 0.24
92 Table 4.7b. Relative abundance of bacteria described in Table 4.6 detected in Reactor B.
Relative Zone OTU ID Abundance (%) OTU_028 0.36 OTU_031 1.49 OTU_033 0.38 OTU_094 0.26 0-12 cm OTU_138 0.17 OTU_352 0.1 OTU_523 0.003 OTU_805 0.03 OTU_028 0.54 OTU_031 1.42 OTU_033 0.54 12-24 cm OTU_094 0.26 OTU_138 0.28 OTU_140 0.12 OTU_805 0.02 OTU_031 0.38 24-31cm OTU_094 0.13 OTU_805 0.02 36-48 cm OTU_031 0.27 48-60 cm OTU_031 0.17
Potential organohalide respirers were classified further using NCBI Blast to obtain a higher-level confidence in the identification. OTU_805 was identified as a sequence consistent with Dehalogenimonas with >97% identity percentage. For Dehalobacter high identity percentages were identified for OTU_523. Taken together the higher confidence identification of the Dehalogenimonas OTU represents an organism with a relative abundance less than 0.1% in both Reactor A and Reactor B. For Dehalobacter, this represents a sequence with a relative abundance of less than 0.01%. The remaining organisms cannot be assigned classification with the same level of confidence. Clearly, however, they show a diverse population of Chloroflexi
93 present in the system. The low abundance of Dehalobacter was not expected because it remains the organism with the known ability to dechlorinate 1,1-DCA to CA (Sun et al., 2002). OTU_31, which could not be clearly classified to the genus level, had abundances 1-2 orders of magnitude higher than those described above, suggesting that a potential role in the dechlorination process exists for other diverse Chloroflexi organisms that have not been fully described.
OTU data were also mined for bacteria associated with aerobic oxidation of chlorinated compounds and were detected within the Proteobacteria and Actinobacteria phylums. Various bacteria associated with degradation of chlorinated solvents via aerobic pathways were detected, such as Pseudomonas, Xanthobacter, Methylobacterium, Methylophilus, Hyphomicrobium,
Methylosinus, and Arthrobacter (Heath et al., 2006; Loffler et al., 2004; Verce et al., 2000;
Gilbert & Crowley, 1997; Fetzner & Lingens, 1994; Castro et al., 1992; Ensign et al., 1992;
Leisinger et al., 1993; Fox et al., 1990; Marks et al., 1984). These genera of bacteria accounted between 1 – 10% of the relative abundance of the total OTUs found within the reactors.
For the overall microbial community structure, the dominant bacteria phyla were
Proteobacteria (12.9 – 51.5%), Chloroflexi (5.6 – 36.3 %), Firmicutes (4.3 – 12.7%), and
Actinobacteria (4.9 – 8.2%) (Figure 4.7a; Figure 4.7b; Figure 4.9a; Figure 4.9b). These figures depict the relative abundance within Reactor A and B. Within the Proteobacteria phylum, the
Betaproteobacteria class was the most abundant (13.8 ± 6.3%), followed by
Alphaproteobacteria (11.8 ± 4.5%) and Deltaproteobacteria (4.0 ± 2.2%). Within the
Chloroflexi phylum, the Anaerolineae class was the most abundant (16.7 ± 6.2%). Within the
Actinobacteria phylum, the genus Arthrobacter was the most abundant (2.3 ± 1.3%).
Out of the total OTUs detected within the samples, between 9 and 22 OTUs were classified as Archaea, which represents on average 15.2 ± 6.9% of the total sequences found
94 within the reactors, with the remaining 85% sequences representing bacteria. The dominant archaea phyla were Euryarchaeaota (71 – 82%) and Crenarchaeota (15 – 27%) (Figure 4.8a;
Figure 4.8b; Figure 4.10a; Figure 4.10b). These figures depict the relative abundance within
Reactor A and B. Within the Euryarchaeaota phylum, the Methanomicrobia classes were the most abundant averaging 68.5 ± 2.1%, followed by Methanobacteria (2.7 ± 0.3%). The genus
Methanosaeta within the class Methanomicrobia was the most dominant archaeal genera found within the reactors. The largest percent of abundance of Methanosaeta is found in the aerobic layers averaging 67.2 ± 4.6% of the total archaea. Methanosaeta species are acetateusing methanogens. Acetic acid is used as the sole energy source, while the metabolism results in the production of methane and carbon dioxide. Low concentrations of acetate create more favorable environments to strains of Methanosaeta.
Statistically, there was a significant difference between zones in both Reactor A and
Reactor B (p = 0.04) for total percent abundance of archaea for the samples described in Figure
4.8a; Figure 4.8b, Figure 10.a, and Figure 10.b when grouped by aerobic (3648 cm and 4860 cm) versus anaerobic (012 cm, 1224 cm, and 2431 cm) conditions (Table 4.8). Through the addition of oxygen within the two reactors, the dominant archaea organisms shift between anaerobic conditions to aerobic conditions. Within the anaerobic conditions, the percent abundance of archaea averages 24.5 ± 5.9%, while in the aerobic conditions the percent abundance of archaea decreases by approximately 82%, averaging 4.3 ± 0.9% of the total archaea found within the aerobic conditions.
95 Table 4.8. Statistical analysis of between anaerobic and aerobic zones in Reactor A and Reactor B of the total percent relative abundance of archaea.
Anaerobica Aerobica p-value Relative Abundance of 24.5 ± 5.9 4.3 ± 0.9 0.04* Archaea (%) a Mean and standard error *Statistical significance (p<0.05).
96
Figure 4.7a. Attached bacteria located in Reactor A displaying the distribution of bacterial communities in treatment Zone 1 (0-12cm), Zone 2 (12-24cm), and Zone 3 (24-31cm); Zone 1, 2, and 3 are located below the aerobic layer.
97
Figure 4.7b. Attached bacteria located in Reactor A displaying the distribution of bacterial communities in treatment Zone 4 (36- 48cm), and Zone 5 (48-60cm); Zone 4 and 5 are located above the aerobic layer.
98
Figure 4.8a. Attached archaea located in Reactor A displaying the distribution of archaeal communities in treatment Zone 1 (0-12cm), Zone 2 (12-24cm), and Zone 3 (24-31cm); Zone 1, 2, and 3 are located in the anaerobic treatment zone.
99
Figure 4.8b. Attached archaea located in Reactor A displaying the distribution of archaeal communities in treatment Zone 4 (36- 48cm), and Zone 5(48-60cm); Zone 4 and 5 are located in the aerobic treatment zone.
100
Figure 4.9a. Attached bacteria located in Reactor B displaying the distribution of bacterial communities in treatment Zone 1 (0-12cm), Zone 2 (12-24cm), and Zone 3 (24-31cm); Zone 1, 2, and 3 are located below the aerobic layer.
101
Figure 4.9b. Attached bacteria located in Reactor B displaying the distribution of bacterial communities in treatment Zone 4 (36- 48cm), and Zone 5 (48-60cm); Zone 4 and 5 are located above the aerobic layer.
102
Figure 4.10a. Attached archaea located in Reactor B displaying the distribution of archaeal communities in treatment Zone 1 (0- 12cm), Zone 2 (12-24cm), and Zone 3 (24-31cm); Zone 1, 2, and 3 are located in the anaerobic treatment zone.
103
Figure 4.10b. Attached archaea located in Reactor B displaying the distribution of archaeal communities in treatment Zone 4 (36- 48cm), and Zone 5(48-60cm); Zone 4 and 5 are located in the aerobic treatment zone.
104 4.4 Design Implications
The present study indicates treatment of 1,1-dichloroethane and chloroethane is possible through a sequential anaerobic/aerobic treatment bed. The study suggest the use of anaerobic reductive dechlorination to further transform 1,1-DCA to CA, followed by the use of aerobic biodegradation of CA to CO2 is possible in a relatively small volume. Treatment efficiency was approximately 99% of the original compound concentrations using the sequential anaerobic/aerobic treatment beds. On a larger scale, the volume of a ABR bed to treat a loading rate of 35 gpm, would take a volume of 382 m3 based on the same retention time as the reactors of two days. For a bed 2 meters in depth, this would require a surface area of 191 m2 for treatment. For an up-flow ABR bed, the anaerobic treatment zone for dechlorination would be targeted in the bottom half of the bed, while the upper portion of the bed would receive the oxygen input from diffusion through the tubing. This expands potential designs for other compounds classes that may benefit from combined anaerobic and aerobic treatment strategies such as chlorinated pesticides.
105 CHAPTER 5. ANALYSIS OF SYSTEM PERFORMANCE OF FULL- SCALE ANAEROBIC BIOREACTOR TREATMENT BEDS
5.1 INTRODUCTION
5.1.1 Site History & Background
From 1956 to 1980, the Re-Solve, Inc. Superfund Site (Site) in North Dartmouth, MA was a former chemical waste reclamation facility (Figure 5.1). The Site facility handled various types of industrial and commercial hazardous materials, including solvents, polychlorinated biphenyls (PCBs), acids, alkalis, waste oils, organic and inorganic liquids and solids.
Figure 5.1. The Site location relative to bodies of water (Re-Solve, Inc. Superfund Site, 2009)
106 Residues, liquid sludge waste, and burned tires were disposed in four on-site unlined lagoons (Figure 5.2). One area of the site was designated for land farming of oil waste sludge, which was also used to control dust by spreading the sludge across the Site. Waste disposal practices resulted in contamination of soil, sediments, and groundwater at the Site. In 1981, the
Site was proposed for the National Priority List (NPL) and became finalized as an NPL site in
1983 to conduct remedial efforts on the approximately 6-acre site. PCBs, along with chlorinated ethenes and ethanes, including cis-1,2-dichloroethene (cDCE), vinyl chloride (VC), 1,1,2- trichloroethene (TCE), 1,1-dichloroethene (DCE), and 1,1,1-trichloroehtane (1,1,1-TCA), along with TCA’s daughter products 1,1-dichloroethane (1.1-DCA), and chloroethane (CA) are currently found in the groundwater, surface water, soils and sediments (USEPA, 2015).
Figure 5.2. Aerial view of Re-Solve Site showing original contamination sites on property (Re- Solve, Inc. Superfund Site, 2001).
107 The Site is located over a glacial till aquifer and is upstream from the Copicut River by approximately 500 feet. The aquifer serves as a drinking water source for private well owners.
Water quality from contaminant discharge to the Copicut River is also of concern. During the time as an EPA NPL site, treatment process for the Site have been continuously improved, implementing new and eliminating old steps that make the treatment process more cost efficient, sustainable, and environmentally friendly.
5.1.2 Physical-Chemical Management of Migrations
After the Site remediation began, all debris, drums, and buildings were removed from the
Site, with the exception of the contents in the lagoons. Initial remediation work consisted of excavation of soil and sediments, along with on-site thermal desorption of PCBs and chlorinated volatile organic compounds (cVOCs). Extensive contamination of PCBs, BTEX, and cVOCs remained at the Site and further remedial activities were needed to continue clean up action. In
1998, a traditional pump and treat system was implemented at the Site. The system comprised of a 40 gallon per minute (gpm) flow rate, phase separation, metals precipitation, flocculation, sludge settling, multi-media filtration, air stripping, and liquid-phase carbon adsorption (Figure
5.3). The original treatment system was effective in meeting discharge limits, but it was based on a mechanical design that left a significant environmental “footprint” in the area. The original pump and treat system was in operation until 2013, when sustainability treatment enhancements were implemented.
108
Figure 5.3. Original pump and treat system design at the Re-Solve, Inc. Superfund Site (USEPA, 2015).
5.1.3 Anaerobic Bioreactors Implementation
Various sustainability enhancement studies were conducted on-site from 2002 through
2011. These studies aimed to use natural treatment processes to reduce the energy and chemical use, residual generation, and disposals associated with the Site (USEPA, 2015). Two full-scale
ABR pilot treatment systems were constructed in 2011 and fully operational in July of 2012.
The ABRs were designed within the subsurface and composed of a peat-sand media mixture, designed to anaerobically degrade cVOCs via reductive dechlorination processes. Within the beds are bacterial communities suspected to contain populations including Dehalococcoides and
Dehalobacter, which have been proven to dehalogenate chlorinated ethenes and ethanes through reductive dechlorination (Holliger et al. 1998; Kassenga et al. 2004; Mattes et al. 2010). The primary contaminants of concern are chlorinated ethenes (cDCE and VC), chlorinated ethanes
(1,1,1-TCA, 1,1-DCA, and CA), and small quantities of BTEX compounds. The two ABRs constructed can operate in series or parallel to treat the primary cVOCs in groundwater (Figure
5.4).
Groundwater is extracted from eight extraction wells at a flow rate of 35 gallons per minute followed by processing through a phase separator and equalization tank. Solids collected
109 in the equalization tank and phase separator are drained to the sludge holding tank and dewatered. The processed water is then pumped into the two pretreatment carbon vessels to remove PCBs. The water flows into a degassing membrane system to reduce total dissolved gasses before entering the two ABR beds in parallel. The process water then travels through polishing carbon vessels to provide further treatment to comply with discharge limits. The final step is aeration and pH adjustment to achieve surface water DO and pH levels for discharge
(USEPA, 2015). The process flow diagram of the system is shown in Figure 5.5.
Figure 5.4. View between both ABRs at the Site.
Figure 5.5. Re-Solve, Inc. Superfund Site process flow diagram incorporating ABR treatment systems (USEPA, 2015).
110
This study was conducted to understand the microbial community structure of the ABR treatment system after operations had reached an approximate steady state. Piezometer sampling and direct coring were utilized to collect media for characterization of microbial populations in the aqueous and solid phases in the beds. Understanding the composition of the microbial community and the connection with system performance will improve the reliability of ABR systems for sustainable treatment of chlorinated ethenes and ethanes.
5.2 MATERIALS AND METHODS
5.2.1 ABR Experimental Design
Two full-scale anaerobic bioreactors (ABR-1 and ABR-2) were implemented in 2012 at the Re-Solve, Inc. Superfund Site in North Dartmouth, MA. The ABRs were constructed as partially raised beds and can operate in series or parallel. ABR-1 and ABR-2 are 84 feet long, 44 feet wide, and 8 feet deep with a 558 ft3/gpm flow of process water based on the 35-gpm influent flow rate from 8 extraction wells. Process water is distributed uniformly at a depth of 1 foot below the top of the ABR beds through 10 influent pipelines, with approximately 3 feet of fill material over the treatment media. The influent process water flows by gravity down through the
ABR media. The treatment zone within the ABRs is 6 feet deep, with the treatment media comprised of a mixture of 50% sand and 50% peat by volume. A filter fabric is installed above and under the treatment media, followed by a 1 foot thick layer of crushed stone, which contains
6 well screens that are connected to a subsurface pipeline that runs from the ABR beds to the treatment plant. The ABR beds operate in the temperature range between 55-62oF (Weston
Solutions, Inc., 2014). Figure 5.6 displays the cross-sectional view of the designed ABRs at the
Site.
111
Figure 5.6. Cross-Sectional View of Anaerobic Bioreactors Design (Re-Solve, Inc. Superfund Site; 2009).
Two sampling trips were completed in the summer of 2014 and 2015 to collect groundwater samples from wells in both ABRs, as well as collection of suspended and attached media samples for microbial analysis. Each ABR has a total of 18 wells (piezometers) positioned at 6 locations along the length of the ABR with three different well depths at each location (Figure 5.7). The shallow piezometers are a total depth of 5 feet below the top fill layer, screened at 1-2 feet below the influent line in the treatment media. The middle piezometers are a total depth of 7 feet below the top fill layer, screened at 3-4 feet below the influent line in the treatment media. The deep piezometers are a total depth of 10 feet below the top fill layer, screened at 6-7 feet below the influent line within the crushed stone drainage layer. Figure 5.7 shows the cross sectional view of the three piezometers, along with the vent designed at each of the 6 piezometer locations.
112
Figure 5.7. Cross-Sectional View of Piezometers and Vent in ABR-1 and ABR-2 (Re-Solve, Inc. Superfund Site; 2009).
In June 2014, groundwater samples were collected from the influent (prior to entering the treatment plant), ABR-1 at all six piezometer locations and depths (shallow, middle, and deep), and the effluent (prior to being discharged from the treatment plant). A total of 20 groundwater samples were collected throughout the length and depth of ABR-1. Groundwater samples for suspended bacteria were collected from piezometers NW, MW, and SW at various piezometer depths in ABR-1 for microbial analysis. A second sampling trip was completed in July 2015.
Groundwater samples were collected from the influent, ABR-2 at 5 of the 6 piezometer locations
(Piezometer-MW was purged dry at all depths) and depths (shallow, middle, and deep), and the effluent. A total of 19 groundwater samples were collected along the length and width of ABR-2.
Attached bacteria were collected in two separate cores located at piezometer-MW (21-28” deep)
113 and piezometer-NE (33-80” deep) in ABR-2. The Figure 5.8 displays the aerial view of the ABR beds and location of samples collected. All groundwater samples were analyzed for cVOCs and used to assess the system performance of the Site.
Figure 5.8. Aerial view of ABR-1 and ABR-2; Well locations and core extraction labeled.
5.2.2 Chemical Analysis
During the two sampling trips in the field, 21 samples of groundwater (160 mL) in 2014 and 19 samples of groundwater (160 mL) in 2015 were collected at the influent (prior to entering the treatment plant), each of the three piezometer depths and six locations at ABR-1 (2014) and
ABR-2 (2015), and at the effluent, prior to leaving the treatment plant. Groundwater sampling followed low-flow techniques using a peristaltic pump to purge the groundwater from the selected wells (Yeskis & Zavala, 2002). During groundwater purging, a YSI meter was used to
114 collect water quality measurements (temperature, conductivity, DO, DO%, and pH). The depth to water was also measured using a water level meter (Solinst Canada, Ltd.; Georgetown, ON) during groundwater sampling. The water quality measurements taken during both sampling trips can be found in Table A.14 in the Appendix. Four samples were collected at each well and filled to capacity with no headspace in 40 mL volatile organic analyte (VOA) vials with screw-top septa and shipped to the laboratory for analysis. Figure 5.9 is a picture from in the field collecting groundwater samples.
Figure 5.9. Groundwater sampling table at the Site.
115 In the laboratory, a total of 100 mL of each sample was extracted from the VOA vials using a gas-tight syringe (Hamilton Co.; Reno, NV) and placed into a 160 mL glass serum bottle, leaving 60 mL of headspace and immediately crimp-sealed with a Teflon coated septum (Sigma-
Aldrich; St. Louis, MO). Samples were inverted to establish equilibrium in the headspace for at least three hours (but never more than 24 hours) before analysis. Analysis of 1,1,1-TCA, cDCE,
VC, 1,1-DCA and CA were performed using a gas chromatograph (Agilent Technologies; Model
No. 6850) equipped with a packed column (Supelco 2.1mm x 2.4m, packed 1% SP) fitted with a flame ionization detector (FID). The initial oven temperature on the gas chromatograph was programmed at 60oC, following an increase of 10oC/min until reaching a temperature of 130oC, and holding for 3 minutes. The carrier gas utilized was helium at a flow rate of 40 mL/min, while oxygen and hydrogen served as fuel gas for the FID at 100 and 35 mL/min respectively to the flame. Samples were removed from the serum bottles (1.5 mL) using a gas tight syringe
(Hamilton Co.; Reno, NV) and directly injected into the column. External standards (6 points) prepared from methanolic stock solutions (Sigma Aldrich, St. Louis, MO) were used to calibrate the GC-FID for 1,1,1-TCA, cDCE, 1,1-DCA, CA, and VC. The GC was calibrated with the prepared stocks using distilled water in the same gas:liquid volumetric ratio as the samples analyzed. Methane calibration was completed using a 1% gas mixture (American Air Liquide,
Houston, TX).
5.2.3 Microbial Analysis
During the sampling trip in the summer of 2014, groundwater samples were collected into 4-liter amber glass bottles with screw caps (VWR; Radnor, PA) to assess the suspended bacteria in ABR-1. Samples were obtained from wells NW-shallow, NW-middle, NW-deep,
MW-shallow, MW-middle, MW-deep, SW-shallow and SW-middle (reference Figure 5.8 for
116 piezometer locations) and shipped to the laboratory for DNA extractions. Suspended media was removed from the groundwater through filtration using a vacuum pump, an EMD millipore glass vacuum filter holder, and filter paper (Thermo Fisher Scientific, Inc., Waltham, MA) in triplicates (if possible) from each well. After filtration, approximately 1 g was removed aseptically from the filter paper. DNA was extracted from the suspended media samples using a
Powersoil® DNA Isolation Kit (Mo Bio Laboratories, Inc. Carlsbad, CA) to isolate microbial genomic DNA per the manufacturer’s user protocol manual for total nucleic acid extraction from samples without modification (Mo Bio User Protocol; https://www.qiagen.com/us/resources/ resourcedetail?id=5c00f8e4-c9f5-4544-94fa-653a5b2a6373&lang=en, accessed August, 2014).
The final extracted pure DNA samples were quantified using a Nanodrop-1000 spectrophotometer (Thermo Fisher Scientific, Inc., Waltham, MA) and then stored at -20oC prior to amplification and sequencing.
In 2015, media samples were collected from ABR-2 for attached bacteria analysis at various depths by taking two cores located at well MW and NE. Media was removed from the cores by digging up the top fill layer of the bed, followed by cutting the filter fabric out, and begin coring (Figure 5.10). Core MW was a total depth of 20 cm, starting approximately 53 cm deep in the treatment media. Core NE was a total depth of 120 cm, starting approximately 84 cm deep in the treatment media. After the cores were removed, 5 cm layers of media at a time were collected for sampling and aseptically removing the media into a 250 mL clear glass straight- sided jars with lined caps (Thermo Fisher Scientific, Inc., Waltham, MA) and shipped to the laboratory for DNA extractions. Approximately 1 g of media was removed aseptically from the sample jars. DNA was extracted from the media samples using a Powersoil® DNA Isolation Kit
(Mo Bio Laboratories, Inc. Carlsbad, CA) to isolate microbial genomic DNA per the
117 manufacturer’s user protocol manual for total nucleic acid extraction from samples without modification (Mo Bio User Protocol; https://www.qiagen.com/us/resources/ resourcedetail?id=
5c00f8e4-c9f5-4544-94fa-653a5b2a6373&lang=en, accessed August, 2015). The final extracted pure DNA samples were quantified using a Nanodrop-1000 spectrophotometer (Thermo Fisher
Scientific, Inc., Waltham, MA) and then stored at -20oC prior to amplification and sequencing.
Figure 5.10. Core extraction process in ABR-2 in 2015 at the Site.
PCR amplification was carried out for both 2014 and 2015 samples using the primer set
515F (5’- GTG CCA GCM GCC GCG GTA A-3’) and 806R (5’-GGA CTA CHV GGG TWT
CTA AT- 3’) for the V4 region of 16S rRNA gene (Caporaso et al., 2011; Zhang et al., 2015;
Yang et al., 2016). In total, 20 samples were successfully amplified (16S rRNA) and sequenced using the Illumina Miseq platform. Sequenced data was processed and analyzed using a single software platform, mothur v.1.29.2, (University of Michigan, Ann Arbor, MI) (Schloss et al.
2009). Analysis of the 16S rRNA gene sequences followed the MiSeq standard operating procedure (Schloss et al. 2013; https://www.mothur.org/wiki/MiSeq_SOP, accessed January,
2016). For all sequences, paired-end reads were combined to produce single sequences, and then
118 screened for quality. Sequences containing any mismatched pairs, length outside the range between 248 and 253 bp, ambiguous bases, or homopolymers longer than 8 bp were excluded
(Veach et al., 2016; Gomez-Smith et al., 2015). Remaining sequences were aligned against the
SILVA (SILVA 111, released 2012) bacterial 16S rRNA database, and chimeric sequences were removed using the UCHIME algorithm (Edgar et al., 2011). Sequences were clustered into operational taxonomic units (OTUs) at a cutoff of 97% similarity threshold. Sequences were then assigned to the taxonomic affinities using the naïve Bayesian classifier (Wang et al., 2007) with a 50% threshold against the Ribosomal Data Project (RDP) reference files (version 16).
Sequences not assigned to the domain bacteria or archaea (including chloroplasts, mitochondria, and unclassified) were not included in further analysis.
. The relative abundance of an OTU was computed by the number of sequences classified in the OTU, divided by the total number of sequences for each sample for bacteria and archaea
(Table A.16 and Table A.19). The relative abundance computation ignores differences in the number of 16S rRNA gene copy diversity, which may alter the distribution (Vetrovsky and
Baldrian, 2013). For each sample, taxonomy information is provided for OTUs greater than 0.5% of total abundance, while any OTUs below 0.5% of total abundance is placed in the ‘other’ category without taxonomic identification.
Genera identified in the RDP classification greater than 50% were examined for potential organohalide respiring bacteria. Bacteria that were identified in mothur as potential organohalide respirers were classified again using the naïve Bayesian classifier (Wang et al., 2007) with a
50% threshold against the Ribosomal Data Project (RDP) reference files (version 16)
(http://rdp.cme.msu.edu/classifier/classifier.jsp; accessed, August 2017) to identify the percent level of classification (Table 5.4; Table 5.7; Table A.17; Table A.20). These OTUs underwent
119 additional comparisons of sequences using the National Center for Biotechnology Information
(NCBI) Basic Local Alignment Search Tool (BLAST) GenBank (Table 5.4). OTU sequences were classified against the 16S ribosomal RNA sequences (Bacteria and Archaea) database using the BLASTN algorithm within the NCBI BLAST tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi
?PROGRAM =blastn&PAGE_TYPE=Blast Search&LINK_LOC=blasthome; accessed August,
2017). The top three GenBank hits based on the BLASTN algorithm are reported below (Table
4.6).
After classification of bacteria and archaea was completed, OTU-based analysis of α diversity measurements was calculated using mothur. First, the calculation of species richness and diversity of the samples were performed for OTUs by computing the species richness indicator Chao1, and the Shannon diversity index (H’) (Schloss et al., 2009; Gomez-Smith et al.,
2015). To prevent bias, OTU data from each sample was normalized by randomly subsampling to the smallest number of sequences observed in any sample (Table A.16 and Table A.19) prior to computation of Chao1 and H’. Chao1 is a non-parametric estimator of species richness, which uses a “mark-release-recapture” statistic that is derived from the number of OTUs appearing either one or two times in a given library and is calculated as:
2 퐹1 퐹1 퐹2 퐶ℎ푎표1 = 푆표푏푠 + − 2 2(퐹2 + 1) 2(퐹2 + 1) where Sobs is the number of phylotypes observed in the library, F1 and F2 are the number of
OTUs occurring either one to two times (Kemp & Aller, 2004; Bailey et al., 2013). The
Shannon index (H’) is a non-parametric estimator of diversity, which reflects the number of different species in a community and assumes that individuals are truly randomly sampled from within an infinitely large community and is calculated as:
120 ′ 퐻 = − ∑ 푝푖 ln 푝푖
th where pi is the proportion of clones in the i OTU (estimated using ni/N), where ni is the number of sequences in the ith OTU, and N is the total number of sequences (Hill et al., 2003; Bailey et al., 2013). Statistical analysis using the Two-tailed Student T-test with a 95% confidence interval was conducted on the number of sequences after quality screening (Nseqs,), OTUs, Chao1, and H’ parameters to distinguish the statistical significance of differences between treatment zones within both ABR-1 and ABR-2 of the bacteria and archaea populations.
5.3 RESULTS AND DISCUSSION
5.3.1 ABR 2014 Results
5.3.1.1 cVOC ABR-1 Concentration Results
Groundwater samples were collected along the length of ABR-1 through various depths of the sampling piezometers including the influent (at 0 ft) and effluent concentrations (at 8 ft)
(Figure 5.11a; Figure 5.11b). Shallow piezometers were located at 2 ft below the fill layer, followed by middle piezometers at 4 ft, and deep piezometers at 7 ft in the treatment media.
Measurements conducted during the field study showed loss of chlorinated ethenes and ethanes occurring consistently in 2014 within ABR-1, with the exception of CA formation (Figure 5.12a;
Figure 5.12b). During the sampling trip, the influent concentrations of cDCE and VC were 236
g/L and 111 g/L, respectively, while the effluent concentrations were 3.2 g/L and 3.3 g/L, respectively. An average of 91.0 ± 7.6% cDCE and 98.5 ± 1.5% VC were reduced through reductive dechlorination. The influent concentrations of 1,1,1-TCA, 1,1-DCA, and CA were to
22.3 g/L, 7.0 g/L, and 1.45 g/L respectively, while the effluent concentrations were below detection, 9.8 ± g/L, and 4.82 ± g/L, respectively. Both 1,1,1-TCA and 1,1-DCA decreased in concentration, while CA was formed. Approximately 100.0 ± 0.0% of 1,1,1-TCA and 50.6 ±
121 7.6% 1,1-DCA were reduced through reductive dechlorination. CA concentrations were almost
tripled and formed approximately 320 ± 18.4% from the influent to the effluent of ABR-1.
Middle Deep Piezometer Piezometer Shallow Piezometer
Fill Layer I nfluent 0’ in ABR 2’ Treatment Media 4’
7’ Crushed Stone Layer 8’ Effluent Side View
Figure 5.11a. Side view of the ABRs at the Site showing piezometer depths and locations.
Figure 5.11b. Top view of the ABRs at the Site showing piezeometer locations along the length of the beds.
122
Figure 5.12a. Concentration profiles of cDCE and VC in each well location (g/L) versus depth (ft) in ABR-1 during 2014 sampling trip.
123
Figure 5.12b. Concentration profiles of 1,1,1-TCA, 1,1-DCA, and CA in each piezometer location (g/L) versus depth (ft) in ABR-1 during 2014 sampling trip.
124 Significant losses of cDCE, VC, and 1,1,1-TCA occur in between the influent and the first 2 feet of distance into ABR-1 (p = 0.001; p = 0.01; p = 0.0001). From the influent to 2 feet below into the treatment media, approximately 75.1 ± 28.8 % of cDCE concentrations were reduced, 61.0 ± 13.0 % of VC concentrations were reduced, and 77.6 ± 2.7 % of 1,1,1-TCA was reduced. In contrast, CA was reduced by approximately 34.8 ± 0.2% and the concentration of
1,1-DCA was almost doubled (-181 ± 4.2%). This data can be converted to a volumetric rate of degradation by multiplying the removal by the flow rate, 35 gpm (Figure 5.13). Expressing the data in this way quantifies the enhanced removal of cDCE, VC, and 1,1,1-TCA compounds within Zone 1 (influent to 2 ft) and Zone 2 (2 to 4 ft from the inlet) of ABR-1. cDCE, VC, and
1,1,1-TCA had the highest average rates of removal in Zone 1 of 4.54 ± 0.60 mg/ft3·day, 1.79 ±
0.44 mg/ft3·day, and 0.46 ± 0.04 mg/ft3·day, respectively. 1,1,1-TCA rate of removal is smaller in comparison, but still significant, because the average influent concentration was 5 to 10 times less than that of VC and cDCE. As a result, Zone 1 and Zone 2 degradation is primarily cDCE,
VC, and 1,1,1-TCA. Zone 3 (4 to 7 ft from the inlet) and Zone 4 (7 to 8 ft from the inlet) provides polishing of cDCE, VC, and TCA at a much lower loading rate, while on the other hand, 1,1-DCA had the highest degradation rate approximately 0.72 ± 0.31 mg/ft3·day within
Zone 4 of ABR-1. This indicates reductive dechlorination occurring within the ABR due to the rapid decrease of 1,1,1-TCA, while the formation of 1,1-DCA occurs between Zone 1 and Zone
3. Once the 1,1,1-TCA is removed from the bed, 1,1-DCA can dechlorinate. This leaves CA to form during reductive dechlorination of 1,1-DCA within Zone 4.
125
Figure 5.13. The average rate of degradation of chlorinated ethenes and ethanes (mg/ft3*day) versus the distances from the influent within ABR-1.
Zone 1 and Zone 2 has significant loss on average of cDCE (p = 0.001; p = 0.046), VC
(p = 0.010; p = 0.0497), and 1,1,1-TCA (p = 0.0001; p = 0.014). cDCE had an average loss of concentration of 177 ± 88.6 g/L in Zone 1 and 45.7 ± 22.9 g/L in Zone 2, while VC had an average loss of concentration of 67.9 ± 33.9 g/L in Zone 1 and 35.6 ± 17.8 g/L in Zone 2.
1,1,1-TCA had an average loss of concentration of 17.3 ± 8.7 g/L in Zone 1 and 4.49 ± 2.2
g/L in Zone 2. The average loss of 1,1,1-TCA is smaller than that of cDCE and VC due to the lower influent concentrations. There is no significance of concentration changes of 1,1-DCA and
CA within treatment Zone 1 and 2. CA is the only compound that has a significant change in concentration within Zone 4 (p = 0.02012), and that is due to the formation of CA, represented
126 by a negative value, to indicate a gain instead of a loss of concentration. CA gained an average of
2.68 ± 1.34 g/L within Zone 4. The average loss of chlorinated ethenes and ethanes over each treatment zone within the media of ABR-1 is shown in Table 5.1.
Table 5.1. Average Loss of compounds (g/L) between treatment zones and the significant difference where p<0.05.
Influent to Shallow Middle Significant Difference Deep Piezos Shallow Piezos to Piezos to Between Treatment to Effluent Piezos Middle Piezos Deep Piezos Zones: ABR-1 Zone 1 (0-2') Zone 2 (2-4') Zone 3 (4-7') Zone 4 (7-8')
Average 0.398 ± 0.20 -1.40 ± 0.70 0.003 ± 0.001 - 2.68 ± 1.34 CA Lossa (g/L)
p-value 0.36199 0.31446 0.99859 0.02012*
Average 1,1- -14.28 ± 7.14 -2.04 ± 1.02 -0.43 ± 0.22 13.68 ± 6.84 Lossa (g/L) DCA p-value 0.12064 0.75067 0.97410 0.06456
Average 1,1,1- 17.3 ± 8.65 4.49 ± 2.25 -0.23 ± 011 0.72 ± 0.36 Lossa (g/L) TCA p-value 0.00005* 0.01462* 0.71063 0.20613
Average 67.9 ± 33.9 35.6 ± 17.8 4.16 ± 2.1 0.31 ± 0.15 VC Lossa (g/L)
p-value 0.01000* 0.04974* 0.43001 0.89811
Average cis-1,2- 177.24 ± 88.62 45.72 ± 22.86 3.44 ± 1.72 6.27 ± 3.13 Lossa (g/L) DCE p-value 0.00054* 0.04679* 0.74993 0.36125 a Mean and standard error *Statistical significance (p<0.05).
127 5.3.1.2 Suspended Microbial Results
The bacterial and archaeal microbial community structure throughout the depths and lengths of ABR-1 was determined through Illumina Miseq profiles of PCR-amplified 16S rRNA gene fragments. Representative data is shown from each sample location and depth. Multiple samples were collected from varying piezometer depths at piezometer located at SW, MW, and
NW. Replicate samples were successfully sequenced for all samples (N=3) with the exception of
SW-shallow, MW-middle, and NW-middle (N=2), SW-middle (N=1) and SW-deep where no samples were collected due to the piezometer being purged dry. After combining the forward and reverse reads, there are a total of 1,179,483 sequences between eight samples (Table A.15). After processing aligned sequences through initial quality filtering and removal of chimeras, a total of
109,535 sequences remained from ABR-1 (Table A.16) in the eight samples at an average of
14,817 ± 1,994 sequences per sample (Table 5.2). These sequences were clustered into OTUs at the 97% similarly and were subsequently classified using the RDP classifier tool. The number of
OTUs ranged from 142 to 244 for the nineteen samples (Table 5.2).
The Shannon diversity index (H’) ranged from 4.24 to 5.12 (Table 5.2). These are consistent with forest soil diversity determined from 16S OTUs and consistent with geographically distributed soil bacterial diversity indices computed from T-RFLP (Cong et al.,
2015; Fierer & Jackson, 2006). The Chao1 estimator of species richness for the ABR-1 ranged from 2,216 to 7,900.
Statistically speaking, there is significant difference (p<0.05) between some of the various piezometer depths (Table 5.3) for the measurements of number of sequences, the number of operational taxonomic units, species richness, and the species diversity of the suspended DNA samples collected described in Table 5.2. There was no statistical significant difference between
128 measurements of richness and diversity between piezometer locations along the length of the
ABR-1.
Table 5.2. Diversity and richness measures computed for ABR-1 during the 2014 Sampling Trip including number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%. 2014 Sampling Trip: ABR-1
Sample a a ab ab Nseqs OTUs Chao1 H' Location SW-shallow 12,893 ± 3,737 216 ± 23 5,719 ± 1,611 4.24 ± 0.06 SW-middle 5,813 153 2,216 5.19 SW-deep - - - - MW-shallow 15,538 ± 4,837 197 ± 27 7,546 ± 1,970 4.90 ± 0.07 MW-middle 7,398 ± 1,291 156 ± 14 3,547 ± 1,590 4.71 ± 0.34 MW-deep 12,736 ± 2,025 218 ± 14 7,900 ± 1,287 4.94 ± 0.09 NW-shallow 12,526 ± 1,121 154 ± 9 5,514 ± 792 4.41 ± 0.07 NW-middle 9,624 ± 1,396 174 ± 25 4,548 ± 2,294 5.12 ± 0.12 NW-deep 17,792 ± 526 238 ± 4 7,615 ± 626 5.07 ± 0.01 a Mean and standard error b Computed after normalization to 5,813 sequences to prevent bias Note: No samples were obtained from SW-deep piezometer during the 2014 sampling trip.
Table 5.3. Statistical analysis of between shallow, middle, and deep piezometers in ABR-1 of the number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%. Shallow Middle Shallow Shallow Middle vs vs vs Deep Piezosa Piezosa Piezosa Middle Deep Deep
p-value p-value p-value
Nseqs 13,747 ± 1,849 7,917 ± 949 15,264 ± 1,468 0.045* 0.008* 0.830
OTUs 186 ± 14 162 ± 10 228 ± 8 0.488 0.002* 0.126
Chao1 6,035 ± 968 3,681 ± 982 7,757 ± 643 0.194 0.011* 0.184
H' 4.55 ± 0.11 4.97 ± 0.16 5.01 ± 0.05 0.019* 0.899 0.003* a Mean and standard error *Statistical significance (p<0.05).
129 OTU data were mined for bacteria associated with anaerobic reductive dechlorination including those associated with chlorinated ethene and ethane reductive dechlorination. .
Potential organohalide-respiring bacteria were initially classified from the Chloroflexi phylum, including the genera Dehalogenimonas, and unclassified Anaerolineaceae, and the genera
Geobacter within the Proteobacteria phylum (Key et al., 2016; Bowman et al., 2013; Moe et al.,
2009; Imfeld et al., 2010; Duhamel & Edwards, 2006). These genera of bacteria accounted up to
12 % of the relative abundance of the total OTUs found within the reactors (Table 5.4 and Table
5.5).
Potential organohalide respirers were classified further using NCBI BLAST to obtain a higher-level confidence in the identification. OTU_003 was identified as Geobacter with >97% identity percentage. The genus Geobacter within the Proteobacteria phylum was found averaging 0.3 ± 0.04 % through the cores in ABR-2. The presence of the genus Geobacter is an indicator of potential dechlorinating activity within the ABR bed (Imfeld et al., 2010; Duhamel
& Edwards, 2006). Some species of Geobacter contain metabolically versatile iron-reducers, which include members that are capable of reductive dechlorination, coupled with iron reduction
(Imfeld et al., 2010; Nevin et al., 2007; Sung et al., 2006; Wever et al., 2000). The remaining organisms cannot be assigned classification with the same level of confidence. Clearly, however, they show a diverse population of Chloroflexi present in the system. OTU_022 and OTU_055, which could not be clearly classified to the genus level, had relatively large abundances described above, suggesting that a potential role in the dechlorination process exists for other diverse Chloroflexi organisms that have not been fully described.
130 Table 5.4. Further classification of OTU sequences within ABR-1 for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Accession Number (%) Geobacter lovleyi strain SZ 16S ribosomal RNA gene, NR_074979.1 98% complete sequence
Geobacter Pelobacter propionicus strain DSM 2379 16S ribosomal OTU_003 NR_074975.1 98% (96%) RNA gene, complete sequence
Geobacter thiogenes strain K1 16S ribosomal RNA gene, NR_028775.1 98% partial sequence Leptolinea tardivitalis strain YMTK-2 16S ribosomal NR_040971.1 96% RNA gene, partial sequence Unclassified Pelolinea submarina strain MO-CFX1 16S ribosomal OTU_022 Anaerolineaceae NR_133813.1 95% (100%) RNA, partial sequence Longilinea arvoryzae strain KOME-1 16S ribosomal NR_041355.1 95% RNA gene, partial sequence Dehalogenimonas alkenigignens strain IP3-3 16S ribosomal RNA gene, partial sequence NR_109657.1 87% Unclassified Dehalogenimonas lykanthroporepellens strain BL-DC-9 OTU_055 Chloroflexi 16S ribosomal RNA gene, partial sequence NR_044550.1 87% (65%) Dehalogenimonas lykanthroporepellens strain BL-DC-9 NR_074337.1 87% 16S ribosomal RNA gene, complete sequence
131 Table 5.5. Relative abundance of organohalide-respiring suspended bacteria detected in ABR-1.
Relative Zone OTU ID Abundance (%) OTU_003 4.27 SW-Shallow OTU_022 0.18 OTU_055 0.04 OTU_003 5.72 SW-Middle OTU_022 1.71 OTU_055 0.14 OTU_003 12.72 MW-Shallow OTU_022 0.31 OTU_055 0.54 OTU_003 9.43 MW-Middle OTU_022 0.44 OTU_055 0.02 OTU_003 9.63 MW-Deep OTU_022 1.75 OTU_055 0.20 OTU_003 0.61 NW - Shallow OTU_022 0.04 OTU_055 0.99 OTU_003 8.02 NW - Middle OTU_022 0.24 OTU_055 0.41 OTU_003 11.98 NW - Deep OTU_022 1.60 OTU_055 0.18
The dominant bacteria phyla consisted of Proteobacteria (28.8 – 82.5 %) and
Bacteroidetes (4.5 – 23.8 %) (Figure 5.14a; Figure 5.14b; Figure 5.14c). The number of OTUs ranged from 142 to 244 for the eight samples (Table 5.2). Other various bacteria associated with chlorine reduction through aerobic or anaerobic degradation pathways were detected in the
Proteobacteria phylum, such as the genomes Rhodopseudomonas, Pseudomonas,
Sphingomonas, Acidovorax, Xanthobacter, Methylobacterium, and Methylophilus (Field &
132 Sierra-Alvarez, 2008; Gu et al., 2004; Vogt et al., 2004a; Holliger et al., 1999; Miyauchi et al.,
1998; Castro et al., 1990).
Out of the total OTUs detected within the samples, between 4 and 15 OTUs were classified as Archaea, which represents 4.0% of the total sequences found within the ABRs, with the remaining 96% representing bacteria. The relative abundance within ABR-1 found that the dominant archaea phyla were Euryarchaeaota (83 – 99%) and Crenarchaeota (0 – 1%) (Figure
5.15a; Figure 5.15b; Figure 5.15c). Within the Euryarchaeaota phylum, the Methanomicrobia classes were the most abundant averaging 49.2 ± 9.3% was the most dominant archaeal genera found within ABR-1. The genus Methanoregula within the class Methanomicrobia was the most dominant archaeal genera found within the ABR-1 averaging 23.2 ± 4.8%. The largest percent of abundance of Methanoregula is found within the middle and deep wells averaging 30.6 ± 4.5% of the total archaea. Methanoregula species are capable of the production of methane from H2 or
CO2, and are strict anaerobes (Brauer et al., 2011; Yashiro et al., 2011). The presence of these organisms provides further evidence that methanogenic conditions were operative in ABR-1.
133
Figure 5.14a. Suspended bacteria located in the SW piezometers from ABR-1 displaying the distribution of bacterial communities in each piezometer depth (no bacterial data was obtained in the deep piezometer in this location).
134
Figure 5.14b. Suspended bacteria located in the MW piezometers from ABR-1 displaying the distribution of bacterial communities in each piezometer depth.
135
Figure 5.14c. Suspended bacteria located in the NW piezometer from ABR-1 displaying the distribution of bacterial communities in each piezometer depth.
136
Figure 5.15a. Suspended archaea located in the SW piezometers from ABR-1 displaying the distribution of archaeal communities in each piezometer depth (no archaeal data was obtained in the deep piezometer in this location).
137
Figure 5.15b. Suspended archaea located in the MW piezometer from ABR-1 displaying the distribution of archaeal communities in each piezometer depth.
138
Figure 5.15c. Suspended archaea located in the MW piezometer from ABR-1 displaying the distribution of archaeal communities in each piezometer depth.
139 5.3.2 ABR 2015 Results
5.3.2.1 cVOC ABR-2 Concentration Results
During the field study in 2015, loss of chlorinated ethenes and ethanes occurred consistently within ABR-2, with the exception of CA (Figure 5.16a; Figure 16b). Groundwater samples were collected along the length of ABR-2 through various depths of sampling pizeometers including the influent (at 0 ft) and effluent concentrations (at 8 ft) in the same manner as in 2014 (Reference Figure 5.11a and Figure 5.11b above). The influent concentrations of cDCE and VC were 196 g/L and 84 g/L, while the effluent concentrations were 2.65 g/L and below detection, respectively. An average of 98.9 ± 0.3% cDCE and 88.4 ± 11.6% VC were reduced through reductive dechlorination. The influent concentrations of 1,1,1-TCA, 1,1-DCA, and CA were to 10.9 g/L, 7.3 g/L, and 2.4 g/L respectively, while the effluent concentrations were 0.3 g/L, 6.0 ± g/L, and 7.8 ± g/L, respectively. Both 1,1,1-TCA and 1,1-DCA decreased in concentration, while CA was formed. Approximately 97.3 ± 1.8 % of 1,1,1-TCA and 64.8 ± 1.7 % 1,1-DCA were reduced through reductive dechlorination. Chloroethane formed approximately 225 ± 27.1% from the influent to the effluent of ABR-2.
Significant losses of cDCE, VC, and 1,1,1-TCA (p = 1.04E-7; p = 1.01E-5; p = 0.0005) occur in between the influent and the first 2 feet deep into ABR-2. From the influent to 2 feet into the treatment media, approximately 94.8 ± 29.2% of the cDCE concentration was reduced,
87.4 ± 11.7% of VC concentration was reduced, and 63.7 ± 1.1% of 1,1,1-TCA was reduced. In contrast, CA was reduced by approximately 33.2 ± 0.2% and the concentration of 1,1-DCA increased by approximately 102 ± 1.4%. This data can be converted to a volumetric rate of degradation by multiplying the removal by the flow rate, 35 gpm (Figure 5.17). Expressing the data in this way shows the removal of cDCE, VC, and 1,1,1-TCA compounds within Zone 1
140 (influent to 2 ft) and Zone 2 (2 to 4 ft from the inlet) of ABR-2. cDCE, VC, and 1,1,1-TCA had the highest average rates of removal in Zone 1 of 4.89 ± 0.05 mg/ft3·day, 1.95 ± 0.06 mg/ft3·day, and 0.18 ± 0.02 mg/ft3·day, respectively. The rate of removal of 1,1,1-TCA is smaller in comparison, but still significant, due to the average influent concentration 5 to 10 times less than that of VC and cDCE. As a result, Zone 1 and Zone 2 degradation is primarily cDCE, VC, and
1,1,1-TCA, while Zone 3 and Zone 4 provides polishing of cDCE, VC, and 1,1,1-TCA at a much lower loading rate, which is similar to the data previously seen collected in 2014 from ABR-1.
On the other hand, 1,1-DCA and CA had the highest degradation rates in Zone 4 (7 to 8 ft from the inlet) of ABR-2, at approximately 0.069 ± 0.12 mg/ft3·day and 0.064 ± 0.05 mg/ft3·day, respectively. This indicates the ethane reductive dechlorination is occurring within the ABR due to the rapid decrease of 1,1,1-TCA, accompanied by formation of 1,1-DCA occurs within Zone 1 and the formation of CA occurs in Zone 2 and Zone 3. Once the 1,1,1-TCA is removed from the bed within Zone 1, 1,1-DCA can dechlorinate in the deeper treatment media (2 to 8 ft).
141
Figure 5.16a. Concentration profiles of cDCE and VC in each piezometer location (g/L) versus depth (ft) in ABR-2 during 2015 sampling trip. (Piezo MW was purged dry during this sampling trip; samples were not taken in these piezometers).
142
Figure 5.16b. Concentration profiles of 1,1,1-TCA, 1,1-DCA, and CA in each piezometer location (g/L) versus depth (ft) in ABR-2 during 2015 sampling trip. (Piezometer MW was purged dry during this sampling trip; samples were not taken in these wells)
143
Figure 5.17. The rate of degradation of chlorinated ethenes and ethanes (mg/ft3*day) versus the distances from the influent within ABR-2.
Zone 1 and Zone 2 has significant losses of cDCE (p = 1.04E-7; p = 0.0229), VC (p =
1.01E-5; p = 0.016), 1,1,1-TCA (p = 0.0005; p = 0.0071), and 1,1-DCA (p = 0.0135; p = 0.0169). cDCE had an average loss of 186 ± 92.8 g/L in Zone 1 and 7.46 ± 3.7 g/L in Zone 2, while
VC has an average loss of concentration of 73.7 ± 36.8 g/L in Zone 1 and 10.6 ± 5.3 g/L in
Zone 2. 1,1,1-TCA has an average loss of concentration of 6.93 ± 3.5 g/L in Zone 1 and 3.45 ±
1.7 g/L in Zone 2, while 1,1-DCA has an average loss of 7.06 ± 3.5 g/L in Zone 2. The average loss of 1,1,1-TCA and 1,1-DCA is smaller than that of cDCE and VC due to the lower influent concentrations. 1,1-DCA has a significant change in concentration within Zone 1 (p =
0.0135), while CA has a significant change in Zone 2 (p = 0.0087), due to the formation of each
144 respective compound, represented by a negative value, to indicate a gain instead of a loss of concentration. 1,1-DCA gained an average of 7.46 ± 3.7 g/L within Zone 1, while chloroethane gained an average of 7.30 ± 3.7 g/L within Zone 2. The average loss of chlorinated ethenes and ethanes over each treatment zone within the media of ABR-2 is shown in Table 5.4.
Table 5.4. Average Loss of compounds (g/L) between treatment zones and the significant difference.
Shallow Significant Difference Influent to Piezos to Middle Piezos Deep Piezos Between Treatment Shallow Piezo Middle to Deep Piezos to Effluent Zones: ABR-2 Piezos Zone 1 (0-2') Zone 2 (2-4') Zone 3 (4-7') Zone 4 (7-8') Average 0.80 ± 0.40 -7.30 ± 3.65 -0.013 ± 0.067 1.21 ± 0.60 CA Loss a (g/L) p-value 0.14446 0.00872* 0.91765 0.27969 Average -7.46 ± 3.73 7.06 ± 3.53 0.37 ± 0.18 1.31 ± 0.66 1,1- Loss a (g/L) DCA p-value 0.01354* 0.01686* 0.80688 0.616 Average 6.93 ±3.47 3.45 ± 1.72 -0.03 ± 0.02 0.25 ± 0.13 1,1,1- Loss a (g/L) TCA p-value 0.00051* 0.00706* 0.44062 0.00157* Average 73.67 ± 36.8 10.66 ± 5.33 0.00 ± 0.00 0.00 ± 0.00 VC Loss a (g/L) p-value 0.00001* 0.01604* - - Average 185.51 ± 92.76 7.46 ± 3.73 0.53 ± 0.27 -0.49 ± 0.25 cis-1,2- Loss a (g/L) DCE p-value 1.04E-07* 0.02294* 0.48069 0.29972 a Mean and standard error *Statistical significance (p<0.05).
145 5.3.2.2 Attached Microbial Results
The bacterial and archaeal microbial community structure throughout ABR-2 was determined by Illumina Miseq profiles of PCR-amplified 16S rRNA gene fragments. Media samples were collected in ABR-2 from two separate cores, Core MW was located near the MW piezometers and Core NE was located near the NE piezometers within ABR-2 (Refer back to
Figure 5.8). After combining the forward and reverse reads, there were a total of 2,348,202 sequences between twelve samples (Table A.18). After processing aligned sequences through initial quality filtering and removal of chimeras, a total of 1,923,406 sequences remained from
ABR-2 (Table A.19) in the twelve samples at an average of 160,284 ± 21,919 sequences per sample (Table 5.5). These sequences were clustered into OTUs at the 97% similarly and were subsequently classified using the RDP classifier tool. The number of OTUs ranged from 266 to
372 for the twelve samples (Table 5.5).
The Shannon diversity index (H’) ranged from 5.93 to 6.15 (Table 5.5). These are consistent with forest soil diversity determined from 16S OTUs (Cong et al., 2015) but higher than geographically distributed soil bacterial diversity indices computed from T-RFLP (Fierer and Jackson, 2006). The Chao1 estimator of species richness ranged from 14,034 to 54,770.
There were no statistically significant differences (p > 0.05) between shallow cores (21-56”) and deep cores (56-80”) within ABR-2 (Table 5.6) for each of the respective measurements of diversity and richness of the microbial samples collected described in Table 5.5.
146
Table 5.5. Diversity and richness measures computed for ABR-2 during the 2015 Sampling Trip including number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%.
2015 Sampling Trip: ABR-2
Sample a a Nseqs OTUs Chao1 H' Location MW (21-23") 115,654 311 24,112 6.03 MW (26-28") 99,519 310 22,267 6.07 NE (33-35") 136,811 338 27,635 6.02 NE (37-39") 107,026 294 22,410 6.07 NE (48-52") 99,073 285 18,788 5.98 NE (52-56") 173,762 345 34,868 5.98 NE (56-60") 268,311 368 45,371 6.01 NE (60-64") 294,963 372 54,770 5.98 NE (64-68") 63,813 266 14,034 5.93 NE (68-72") 119,010 316 29,946 6.00 NE (72-76") 192,715 324 36,364 6.05 NE (76-80") 252,749 359 44,916 6.15 a Computed after normalization to 63,813 sequences to prevent bias
Table 5.6. Statistical analysis of between shallow cores (21-56”) and deep cores (56-80”) within ABR-2 of the number of sequences (Nseqs), number of operational taxonomic units (OTUs), species richness (Chao1 estimator) and species diversity (Shannon diversity index H’) at a similarity cutoff of 97%.
Shallow Shallow Cores Deep Cores vs Deep (21-56") (56-80") p-value Nseqs 121,974 ± 11,824 198,594 ± 37,244 0.109 OTUs 314 ± 7 334 ± 17 0.356 Chao1 25,013 ± 2,294 37,567 ± 5,843 0.105 H' 6.03 ± 0.02 6.02 ± 0.03 0.921 a Mean and standard error *Statistical significance (p<0.05).
147 OTU data were mined for bacteria associated with anaerobic reductive dechlorination including those associated with chlorinated ethene and ethane reductive dechlorination. Potential organohalide-respiring bacteria were initially classified from the Chloroflexi phylum, including the genera Dehalogenimonas, and unclassified Anaerolineaceae, and the genera Geobacter within the Proteobacteria phylum (Key et al., 2016; Bowman et al., 2013; Moe et al., 2009;
Imfeld et al., 2010; Duhamel & Edwards, 2006). These genera of bacteria accounted up to 3 % of the relative abundance of the total OTUs found within the reactors (Table 5.7 and Table 5.8).
Potential organohalide respirers were classified further using NCBI BLAST to obtain a higher-level confidence in the identification. OTU_044 was identified as Geobacter with >97% identity percentage. The genus Geobacter within the Proteobacteria phylum was found averaging 0.3 ± 0.04 % through the cores in ABR-2. The presence of the genus Geobacter is an indicator of potential dechlorinating activity within the ABR bed (Imfeld et al., 2010; Duhamel
& Edwards, 2006). Some species of Geobacter contain metabolically versatile iron-reducers, which include members that are capable of reductive dechlorination, coupled with iron reduction
(Imfeld et al., 2010; Nevin et al., 2007; Sung et al., 2006; Wever et al., 2000). The remaining organisms cannot be assigned classification with the same level of confidence. Clearly, however, they show a diverse population of Chloroflexi present in the system. OTU_010 and OTU_028, which could not be clearly classified to the genus level, had abundances higher than that described above, suggesting that a potential role in the dechlorination process exists for other diverse Chloroflexi organisms that have not been fully described.
The dominant phyla found within ABR-2 is Proteobacteria (14.9 – 24.4 %),
Bacteroidetes (12.7 – 20.7 %), Verrucomicrobia (12.8 – 18.0 %), followed by Chloroflexi (6.4 –
9.2 %) (Figure 5.18a; Figure 5.18b; Figure 5.18c).
148 Table 5.7. Further classification of OTU sequences within ABR-2 for additional comparisons using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. Accession Identity OTU ID RDP ID > 50% (%) NCBI BLAST Description Number (%) NR_040971.1 Leptolinea tardivitalis strain YMTK-2 16S 92% ribosomal RNA gene, partial sequence Unclassified NR_041355.1 OTU_010 Anaerolineaceae Longilinea arvoryzae strain KOME-1 16S ribosomal 91% (100%) RNA gene, partial sequence NR_041354.1 Bellilinea caldifistulae strain GOMI-1 16S 91% ribosomal RNA gene, partial sequence NR_109657.1 Dehalogenimonas alkenigignens strain IP3-3 16S 87% ribosomal RNA gene, partial sequence Unclassified NR_044550.1 OTU_028 Chloroflexi Dehalogenimonas lykanthroporepellens strain BL- 87% (68%) DC-9 16S ribosomal RNA gene, partial sequence NR_074337.1 Dehalogenimonas lykanthroporepellens strain BL- 87% DC-9 16S ribosomal RNA gene, complete sequence NR_074979.1 Geobacter lovleyi strain SZ 16S ribosomal RNA 97% gene, complete sequence Geobacter NR_074975.1 Pelobacter propionicus strain DSM 2379 16S 97% OTU_044 ribosomal RNA gene, complete sequence (100%) NR_028775.1 Geobacter thiogenes strain K1 16S ribosomal RNA 97% gene, partial sequence
149 Table 5.8. Relative abundance of bacteria described in 5.7 detected in ABR-2.
Unique Relative Zone Sequence Abundance (%) Number OTU_010 2.74 MW (21-23") OTU_028 1.39 OTU_044 0.36 OTU_010 2.96 MW (26-28") OTU_028 1.12 OTU_044 0.21 OTU_010 2.65 NE (33-35") OTU_028 0.75 OTU_044 0.68 OTU_010 1.64 NE (37 39") OTU_028 0.58 OTU_044 0.33 OTU_010 3.4 NE (48-52") OTU_028 0.22 OTU_044 0.4 OTU_010 1.85 NE (52-56") OTU_028 0.54 OTU_044 0.37 OTU_010 2.24 NE (56-60") OTU_028 0.64 OTU_044 0.21 OTU_010 2.32 NE (60-64") OTU_028 0.53 OTU_044 0.23 OTU_010 1.32 NE (64-68") OTU_028 0.36 OTU_044 0.47 OTU_010 1.59 NE (68-72") OTU_028 0.45 OTU_044 0.34 OTU_010 2.45 NE (72-76") OTU_028 0.49 OTU_044 0.13 OTU_010 1.95 NE (76-80") OTU_028 0.32 OTU_044 0.23
150 Out of the total OTUs detected within the samples, between 1 and 29 OTUs were classified as Archaea, which represents 3.5% of the total sequences found within the ABRs, with the remaining 96.5% representing bacteria. The relative abundance within ABR-1 found that the dominant archaea phyla were Euryarchaeaota (93 – 97%) and Crenarchaeota (1.2 – 3.2%)
(Figure 5.19a; Figure 5.19b; Figure 5.19c).
Within the Euryarchaeaota phylum, the Methanomicrobia classes were the most abundant averaging 39.0 ± 2.1% was the most dominant archaeal genera found within ABR-2.
The genus Methanoregula within the class Methanomicrobia was the most dominant archaeal genera found within the ABR-2 averaging 32.2 ± 2.2%. Methanoregula species are capable of the production of methane from H2 or CO2, and are strict anaerobes (Brauer et al., 2011; Yashiro et al., 2011). The presence of these organisms provides further evidence that methanogenic conditions were operative in ABR-2.
Both sampling trips in 2014 and 2015 did not reveal quantities of the commonly identifiable organohalide-respiring microorganisms (i.e., Dehalobacter and Dehalococcoides) that are responsible for reductive dechlorination through Illumina MiSeq. However in 2013, microbial analysis of the treatment media from the Site used in a microcosm study was analyzed using qPCR. This technique did confirm the presence of Dehalobacter and Dehalococcoides species within the treatment media (Akudo, 2013). Table 5.9 displays the quantities of Dhb and
Dhc found within the treatment media in the microcosm studies conducted by Akudo, 2013.
151 Table 5.9. qPCR results of ReSolve site media in microcosm studies (Akudo, 2013).
cis-1,2-DCE 1,1-DCA Dechlorinating bacteria microcosm microcosm (cells/mL) (cells/mL) Dehalobacter sp. (Dhb) 8.6 x 102 1.68 x 105 Dehalococcoides sp. (Dhc) 8.6 x 104 5.86 x 102 Dhc Functional genes bvcA reductase 3.97 x 104 N/A tceA reductase 7.29 x 104 N/A vcrA reductase 4.04 x 104 N/A
Though Dhc and Dhb were not detected in the suspended and attached bacterial populations using Illumina MiSeq, other bacteria capable of reductive dechlorination were detected in the samples like Geobacter and a variety of unclassified Chloroflexi. Chloroflexi represented up to 2% of sequences in the piezometer sampling and up to 3% of sequences in the cores. The genera included unclassified Anaerolineaceae. No other known organohalide respirers were detected.
152
Figure 5.18a. Attached bacteria located in ABR-2 displaying the distribution of bacterial communities at varying depths below the influent.
153
Figure 5.18b. Attached bacteria located in ABR-2 displaying the distribution of bacterial communities at varying depths below the influent.
154
Figure 5.18c. Attached bacteria located in ABR-2 displaying the distribution of bacterial communities at varying depths below the influent.
155
Figure 5.19a. Attached archaea located in ABR-2 displaying the distribution of archaeal communities at varying depths below the influent.
156
Figure 5.19b. Attached archaea located in ABR-2 displaying the distribution of archaeal communities at varying depths below the influent.
157
Figure 5.19c. Attached archaea located in ABR-2 displaying the distribution of archaeal communities at varying depths below the influent.
158 5.4 Design Implications
The Site has been successful in treating chlorinated ethenes and ethanes including cDCE,
VC, 1,1,1-TCA, 1,1-DCA and CA through ABR beds in a full-scale system using two reactors in parallel. The study demonstrates that the use of ABRs to reductively dechlorinate chlorinated ethenes and ethanes has significant potential to treat chlorinated solvents in aqueous form. Over the years of the study, treatment concentrations of cDCE, VC, and 1,1,1-TCA can be reduced to approximately 99 % of the original compounds using the ABR treatment beds at the Site. 1,1-
DCA can be reduced to approximately 65 % of the original compounds in the ABR beds, while
CA doubles in concentration. 1,1-DCA and CA compounds on average leaves the ABR beds with an effluent concentration averaging 8.2 ± 2.8 g/L and 6.3 ± 1.5 g/L, which is well below the average monthly discharge limit given by the EPA for the Site of 22 g/L and 110 g/L respectively. Figures A.16 and Figure A.17 found in the Appendix are given from Weston
Solutions (2014) of the average influent and effluent concentrations of 1,1-DCA and CA in the
ABRs over the life of the system.
The data suggests that the use of ABR treatment beds used to target chlorinated compounds will comprise of a variety of chlorine-degrading microorganisms and other bacteria as the primary source of biodegradation within the treatment beds. The significance of the study showed that even with low concentration loading of chlorinated compounds, the biological components of the media adapted for treatment of the compounds depending on location and depths in the ABRs. Thus determining that the treatment media is best suited for adaptability of the present microorganisms to biodegrade the chlorinated compounds.
159 CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS
Anaerobic bioreactor experiments were performed in column studies and greenhouse- scale systems to evaluate the effectiveness of treatment of VOCs in groundwater. Sampling and analysis was also conducted in a full-scale ABR treatment system at a CERCLA site in
Massachusetts. Degradation of BTEX compounds occurred consistently under methanogenic and sulfate-reducing conditions in laboratory scale ABRs. BTEX profile data measured during both terminal electron acceptor phases demonstrate that removal of BTEX compounds occurred within the first 30 cm of the ABR bed. Benzene degraded after removal of toluene and xylenes under methanogenic conditions and degraded simultaneously with other compounds under sulfate reducing conditions. The last 30 cm of the ABR beds provided polishing of BTEX at a much lower loading rate. The BTEX compounds on average were below the USEPA MCLs in the effluent port of the ABRs over the study period.
During methanogenic conditions, the microbial community consisted of Proteobacteria and Bacteroidetes as the dominant phyla. Approximately 0.1 - 4 % of the total bacteria found within the ABRs were Proteobacteria genera (Caulobacter, Azoarcus, Geobacter) and family
(Comamonadaceae) associated with anaerobic hydrocarbon degradation in the scientific literature. These diverse group of organisms are potentially involved in system performance and serve as targets for future study.
Changes in the terminal electron accepting process changed the characteristics of the microbial community. After the transition to sulfate-reducing conditions, the total OTUs of the
ABR bed increased by 31%, with the dominant phyla Proteobacteria and Chloroflexi. Bacteria associated with sulfate-reducing conditions consisted of the genera Syntrophobacter, Sulfurisoma and Geobacter, within the Proteobacteria phylum, which accounted for 1 – 5 % of the total
160 bacterial composition. The family Anaerolineaceae in the Chloroflexi phylum was found in the samples at a higher abundance within Zone 1 and Zone 2, which correlates to the higher degradation rates of BTEX found within the first 30 cm of the ABRs. Unclassified
Anaerolineaceae has been linked to oil degradation under sulfate-reducing conditions.
Near complete removal of BTEX occurred under methanogenic and sulfate reducing conditions in the ABR, Changes in microbial population occur as the terminal electron acceptor is changed but BTEX removal was nearly complete. Future work could extend the timing of the
ABR studies for each TEA and to expand the treatment of BTEX to include nitrate-reducing and iron-reducing conditions as other TEAs for degradation.
Altering the redox condition to encourage a range of degradation processes was extended to the chlorinated ethanes, common VOC groundwater pollutants that resist complete degradation. Under methanogenic conditions, chlorinated ethanes, such as 1,1,1-trichloroethane can be treated by organohalide-respiring populations, driving dechlorination to 1,1-DCA and
CA. Dechlorination ceases at CA and further transformation is not typically observed in the absence of oxygen. During a serum bottle study, complete degradation of CA occurred with the addition of pure oxygen. CA was stable in the absence of oxygen and in the killed control. This approach was then used in greenhouse-scale ABRs to determine if complete treatment of chlorinated ethanes is possible in these systems. In these greenhouse ABRs, complete dechlorination of 1,1-DCA to the daughter product CA occurred in the anaerobic treatment zone, while complete degradation of CA was observed in an aerobic treatment zone, created by the diffusive flux of O2 from porous tubing placed in the zone. Profile data indicate that the majority of the transformation of 1,1-DCA to CA occurs in an anoxic zone upstream of the aerobic zone.
161 CA is removed in the aerobic zone. The final portion of the ABRs provides polishing of 1,1-
DCA and CA.
The microbial community composition depended on the presence or absence of oxygen in the bed. Within the anaerobic treatment zones, Chloroflexi was the most dominant phyla, followed by Proteobacteria, Firmicutes, and Actinobacteria. Specific bacteria associated with reductive dechlorination of chlorinated ethanes and ethenes were identified including
Dehalobacter and Dehalogenimonas although most suspected organohalide degrading sequences were from a diverse unclassified Chloroflexi population were present that could not be conclusively assigned to the genus level. Proteobacteria was the most dominant phyla in the zone where oxygen was present. Various bacteria associated with degradation of chlorinated solvents via aerobic pathways were detected, such as such Pseudomonas, Xanthobacter,
Methylobacterium, Methylophilus, Hyphomicrobium, Methylosinus, and Arthrobacter (Loffler et al., 2004; Castro et al., 1992; Ensign et al., 1992; Leisinger et al., 1993; Fox et al., 1990).
Overall the chloroethane studies suggests that by providing both anoxic and aerobic zones within the ABR bacteria can adapt to completely remove these persistent compounds from groundwater percolating through these ABR systems.
Data collected from the ABR system at the Re-Solve, Inc. Superfund Site was used to determine the overall system performance at full scale. Samples taken in 2014 and 2015 indicate that the ABRs displayed consistent removal of chlorinated ethenes and ethanes, with the exception of minor CA accumulation in the groundwater passing through the ABR beds.
Groundwater profile data show significant losses due to reductive dechlorination of cDCE, VC, and 1,1,1-TCA in the first four feet of media. Between 4 – 8’ deep in the ABRs, polishing of
162 these compounds can be observed. Overall the system has met or exceeded all discharge limits for these compounds.
During 2014, suspended bacteria was collected from the groundwater piezometers, while attached bacteria was collected via ABR media cores in 2015. The bacterial composition consisted of Proteobacteria and Bacteroidetes as the dominant phyla. The organism Geobacter within the Proteobacteria phylum was found with a relatively high abundance within the suspended bacteria, and a smaller percentage in the attached bacteria. Some studies have indicated that Geobacter can imply the ongoing potential of dechlorinating activity within the
ABR bed (Imfeld et al., 2010; Duhamel & Edwards, 2006). While other species of Geobacter contain metabolically versatile iron-reducers, which include members that are capable of reductive dechlorination, coupled with iron reduction (Imfeld et al., 2010; Nevin et al., 2007;
Sung et al., 2006; Wever et al., 2000). Other various bacteria associated with chlorine reduction through aerobic or anaerobic degradation pathways were detected in the Proteobacteria phylum, such as the genomes Rhodopseudomonas, Pseudomonas, Sphingomonas, Stenotrophomas,
Acidovorax, Xanthobacter, Methylobacterium, Methylophilus, and Hyphomicrobium (Field &
Sierra-Alvarez, 2008; Gu et al., 2004; Vogt et al., 2004a; Holliger et al., 1999; Miyauchi et al.,
1998; Castro et al., 1990), which accounted for less than 1% of the total relative abundance of bacteria. The species richness changes significantly based on the depth of the ABRs.
Though organohalide-respiring microorganisms responsible for reductive dechlorination of chlorinated ethenes and ethanes (i.e., Dehalococcoides and Dehalobacter) were not detected through Illumina MiSeq, in 2013, microbial analysis of the treatment media from the Re-Solve site used in a microcosm study (Akudo, 2013) was analyzed using qPCR. This technique did confirm the presence of Dehalobacter and Dehalococcoides species within the treatment media.
163 Though these organisms were not detected in the suspended and attached bacterial populations using Illumina MiSeq, other bacteria capable of reductive dechlorination were detected in the samples like Geobacter. Chloroflexi represented 0.1 – 2 % of sequences in the piezometer sampling and 6 – 9 % of sequences in the cores. The genera included unclassified
Anaerolineaceae and unclassified Chloroflexi. No other known organohalide respirers were detected.
The data presented in this report are based on laboratory studies designed to understand changing the terminal electron process to enhance the performance of ABR systems for use as a sustainable treatment option for a variety of volatile organic compounds, like BTEX and chlorinated ethenes and ethanes. These laboratory and greenhouse studies were coupled with field measurements at the Re-Solve, Inc. Superfund Site as a case study to determine if the enhancement of terminal electron acceptors within ABRs is possible to treat targeted compounds. The results obtained from the various studies assist in understanding the importance of the relationship between manipulations of terminal electron acceptors, basic biodegradation processes of contaminants of concern, and treatment system optimization challenges involved in the design and operation of a full-scale ABR system. Implementing ABR systems to remediate hazardous contaminants in groundwater could replace energy intensive conventional physical/chemical treatment processes for some contaminant streams.
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189 APPENDIX
Benzene
40000 35000
30000 25000 y = 2574.1x 20000 R² = 0.95996 15000
Peak area (PA) 10000 5000
0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00
Liquid Concentration (mg/L)
Figure A.1. Benzene standard curve for FID analysis in mesocosms study.
Toluene 70000
60000
50000
40000
30000 y = 4334.2x
Peak Peak area (PA) 20000 R² = 0.98151
10000
0 0 2 4 6 8 10 12 14 16 Liquid Concentration (mg/L)
Figure A.2. Toluene standard curve for FID analysis in mesocosms study.
190 Ethyl benzene 18000
16000
14000
12000
10000
8000 y = 1180.6x
6000 R² = 0.97006 Peak Peak area (PA) 4000
2000
0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Liquid Concentration (mg/L)
Figure A.3. Ethyl benzene standard curve for FID analysis in mesocosms study.
Total Xylenes 10000 9000 8000 7000 6000 5000 y = 680.93x 4000 R² = 0.94579
Peak Peak area (PA) 3000 2000 1000 0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 Liquid Concentration (mg/L)
Figure A.4. Total xylenes standard curve for FID analysis in mesocosms study.
191
Table A.1. BTEX Phase 1 – Sample ID and number of sequences after combining forward and reverse reads for each sample.
Number of Sample ID Sequences C1P1 121,970 C1P4 172,040 C2P1 227,020 C2P2 124,358 C2P3 128,681 C2P4 117,928 C3P1 156,704 Total Number of 1,048,701 Sequences
Table A.2. BTEX Phase 1 – Sample ID and number of sequences after processing aligned sequences through initial quality filtering and removal of chimeras.
Number of Bacteria Number of Archaea Total Number Sample ID Sequences Sequences of Sequences C1P1 8,421 13 8,434 C1P4 11,905 10 11,915 C2P1 19,690 124 19,814 C2P2 10,543 20 10,563 C2P3 10,363 106 10,469 C2P4 5,857 1 5,858a C3P1 13,177 51 13,228 Total Number 79,956 325 80,281 of Sequences a The number of sequences used for subsampling to prevent bias.
192 Table A.3. Bacteria OTU sequences detected during Phase 1 in the ABRs that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
RDP ID > 50% OTU ID Sequence (%)
TACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGC AGGCGGTTTTGTAAGACAGGTGTGAAATCCCCGGGCTTAACCTGGGAAC Azoarcus TGCGCTTGTGACTGCAAGGCTAGAGTACGGCAGAGGGGGGTGGAATTCC OTU_12 (68%) ACGTGTAGCAGTGAAATGCGTAGAGATGTGGAGGAACACCGATGGCGA AGGCAGCCCCTTGGGCCAATACTGACGCTCATGCACGAAAGCGTGGGGA GCAAACAGG
TACGAAGGGGGCTAGCGTTGCTCGGAATTACTGGGCGTAAAGGGAGCGT AGGCGGATAGTTCAGTCAGAGGTGAAAGCCCAGGGCTCAACCTTGGAAC Caulobacter OTU_21 TGCCTTTGATACTGGCTATCTTGAGTTTGGGAGAGGTGTGTGGAACTCCG (100%) AGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAG GCGACACACTGGCCCAATACTGACGCTGAGGCTCGAAAGCGTGGGGAGC AAACAGG
TACGTAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGC AGGCGGTTTTGTAAGACAGAGGTGAAATCCCCGGGCTCAACCTGGGAAC Unclassified TGCATTTGTGACTGCACGGCTGGAGTGCGGCAGAGGGGGATGGAATTCC Comamonadaceae OTU_26 GCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGA (66%) AGGCAATCCCCTGGGCCTGCACTGACGCTCATGCACGAAAGCGTGGGGA GCAAACAGG
193
Table A.3 (continued). Bacteria OTU sequences detected during Phase 1 in the ABRs that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
RDP ID > 50% OTU ID Sequence (%)
TACGGAGGGTGCAAGCGTTGTTCGGAATTATTGGGCGTAAAGCGC GTGTAGGCGGTTTCTTAAGTCTGATGTGAAAGCCCCGGGCTCAAC CTGGGAAGTGCATTGGAAACTGGGAGACTTGAGTACGGGAGAGG Geobacter OTU_032 GTAGTGGAATTCCTAGTGTAGGAGTGAAATCCGTAGATATTAGGA (100%) GGAACACCGGTGGCGAAGGCGGCTACCTGGACCGATACTGACGCT GAGACGCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGT GCGCAGGCGGTTTGGTAAGACAGGTGTGAAATCCCCGGGCTTAAC CTGGGAACTGCGCTTGTGACTGCCAGGCTAGAGTACGGCAGAGGG Azoarcus OTU_038 GGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGTGGAG (94%) GAACACCGATGGCGAAGGCAGCCCCCTGGGCCTGTACTGACGCTC ATGCACGAAAGCGTGGGGAGCAAACAGG
194
Table A.4. BTEX Phase 2 – Sample ID and number of sequences after combining forward and reverse reads for each sample.
Number of Sample ID Sequences C1P1 38,964 C1P2 77,986 C1P3 114,402 C1P4 119,688 C2P1 123,537 C2P2 140,736 C2P3 139,560 C2P4 151,193 C3P1 130,527 C3P2 154,843 C3P3 124,471 C3P4 195,190 Total Number of 1,511,097 Sequences
Table A.5. BTEX Phase 2 – Sample ID and number of sequences after processing aligned sequences through initial quality filtering and removal of chimeras.
Number of Bacteria Number of Archaea Total Number of Sample ID Sequences Sequences Sequences C1P1 24,557 537 25,094a C1P2 51,211 866 52,077 C1P3 91,633 2,264 93,897 C1P4 74,927 457 75,384 C2P1 88,961 2,088 91,049 C2P2 94,543 2,224 96,767 C2P3 67,969 1,554 69,523 C2P4 84,237 646 84,883 C3P1 87,007 2,287 89,294 C3P2 86,982 1,971 88,953 C3P3 92,811 1,631 94,442 C3P4 121,401 1,255 122,656 Total Number 966,239 17,780 984,019 of Sequences a The number of sequences used for subsampling to prevent bias.
195 Table A.6. Bacteria OTU sequences detected during Phase 2 in the ABRs that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
RDP ID > 50% OTU ID Sequence (%)
AACGTAGGATCCGAGCGTTATCCGAATTCACTGGGCGTAAA Unclassified GCGCGTGTAGGCGGCTTTGTAAGTTGGATGTAAAATCTCCCG Anaerolineaceae OTU_002 GCTCAACTGGGAGGGGACGTTCAAGACTGCAAGGCTAGAGG (73%) GCAGTAGAGGGAGGCGGAATTCCCGGTGTAGTGGTGAAATG CGTAGATATCGGGAGGAACACCAGAGGCGAAGGCGGCCTCC TGGACTGCATCTGACGCTCAGACGCGAAAGCTAGGGGAGCA AACGGG
TACGGAGGGTGCAAGCGTTGTTCGGAATTATTGGGCGTAAA GCGCGTGTAGGCGGTCTTTTAAGTCTGATGTGAAAGCCCCGG Geobacter GCTCAACCTGGGAAGTGCATTGGAAACTGGGAGACTTGAGT OTU_008 (100%) ACGGGAGAGGAGAGTGGAATTCCTAGTGTAGGAGTGAAATC
CGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGGCTCTC TGGACCGATACTGACGCTGAGACGCGAAAGCGTGGGTAGCA AACAGG
TACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAA GCGTGCGCAGGCGGTTTTGTAAGACAGATGTGAAATCCCCG GGCTTAACCTGGGAACTGCGTTTGTGACTGCAAGACTTGAGT Unclassified OTU_010 GCGGCAGAGGGGGGTGGAATTCCACGTGTAGCAGTGAAATG Betaproteobacteria CGTAGAGATGTGGAGGAACACCGATGGCGAAGGCAGCCCCC (100%) TGGGTCGACACTGACGCTCATGCACGAAAGCGTGGGTAGCA AACAGG
196 Table A.6 (continued). Bacteria OTU sequences detected during Phase 2 in the ABRs that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
RDP ID > 50% OTU ID Sequence (%)
TACGGGGGGTGCAAGCGTTGTTCGGAATCATTGGGCGTAAAGCG CGCGCAGGCGGCTTGGTAAGTCAGATGTGAAAGCCCTGGGCTTA
ACCCAGGAAGTGCATTTGAAACTGCCTCGCTAGAGTACGGGAGA Smithella OTU_019 GGAAAGTGGAATTCCTGGTGTAGAGGTGAAATTCGTAGATATCA (87%) GGAGGAACACCGGTGGCGAAGGCGACTTTCTGGCCCGATACTGA CGCTCAGGCGCGAAAGCGTGGGGAGCAAACAGG
TACGGAGGGTGCAAGCGTTATTCGGAATTACTGGGCGTAAAGCGCGTGCA GGCGGGCCTGCAAGTCTGATGTGAAAGCCCCGGGCTTAACCTGGGAAGTGC ATTGGAAACTGCAGGTCTTGAGTGCTGGAGAGGAAGGGGGAATTCCCGGT Syntrophobacter OTU_023 GTAGAGGTGAAATTCGTAGAGATCGGGAGGAATACCAGTGGCGAAGGCGC (100%) CCTTCTGGACGGCAACTGACGCTGAGACGCGAAAGCGTGGGGAGCAAACA GG
TACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCG TGCGCAGGCGGCTCTATAAGACAGATGTGAAATCCCCGGGCTCA ACCTGGGAACTGCGTTTGTGACTGTAGGGCTCGAGTGTGGCAGA Sulfurisoma OTU_024 GGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGT (85%) GGAGGAACACCGATGGCGAAGGCAGCCCCCTGGGTCAACACTG ACGCTCATGCACGAAAGCGTGGGTAGCAAACAGG
197 Table A.7. Unclassified bacteria sequences detected in the ABRs during both Phase 1 and Phase 2 of the study.
RDP ID > 50% OTU ID Sequence (%)
TACGTAGGAGGCGAGCGTTGTCCGGATTTACTGGGCGT AAAGGGCGCTCAGGCGGTCTTATAAGTCAGAGGTGAAA Unclassified TCCCAGTGCTTAACTCTGGAACTGCCTTTGATACTGGAA Phase 1 Bacteria OTU_001A GGCTTGAGGATGGAAGAGGTGATGGAATTCCGGGTGGA (100%) GCAGTGAAATGCGTAGATATCCGGAAGAACACCAGTGG CGAAGGCGGGTAACTGGTCCATAACTGACGCTGAGGCG CGAAAGCGTGGGGAGCAAACAGG
TACAGAGGGTGCGAGCGTTGTCCGGAATCACTGGGCGT AAAGGGCGCGTAGGCGGCCTGGTAAGTCGGGGGTGAA Unclassified AGCCCGGGGCTCACCCCCGGAAGTGCATTGGATACTGG Phase 2 OTU_001B Deltaproteobacteria GAGGCTGGAGTACCGGAGAGGAGGGTGGAATTCCTGGT
(56%) GTAGCGGTGAAATGCGTAGATATCAGGAGGAACACCGG TGGCGAAGGCGGCCCTCTGGACGGATACTGACGCTGAG GTGCGAAAGCGTGGGGAGCAAACAGG
198
cis-1,2-DCE 1600
1400
1200
1000
800 y = 737.99x R² = 0.9953
600 Peak Peak Area (pA) 400
200
0 0.00 0.50 1.00 1.50 2.00 Liquid Concentration [mg/L]
Figure A.5. cis-1,2-dichlorethene standard curve for serum bottle and reactor study.
Vinyl Chloride 6000
5000
4000 y = 6360.4x 3000 R² = 0.9973
2000 Peak Peak Area (pA)
1000
0 0.00 0.20 0.40 0.60 0.80 1.00 Liquid Concentration (mg/L)
Figure A.6. Vinyl chloride standard curve for serum bottle and reactor study.
199
1,1,1-TCA 14000
12000
10000
8000 y = 2739.6x R² = 0.9851 6000
Peak Peak Area (pA) 4000
2000
0 0.00 1.00 2.00 3.00 4.00 5.00 Liquid Concentration (mg/L)
Figure A.7. 1,1,1-trichloroethane standard curve for serum bottle and reactor study.
1,1-DCA 2500
2000
1500 y = 1086.3x R² = 0.9981
1000 Peak Peak Area (pA)
500
0 0.00 0.50 1.00 1.50 2.00 Liquid Concentration (mg/L)
Figure A.8. 1,1-dichloroethane standard curve for serum bottle and reactor study.
200 Chloroethane 3500
3000
2500 y = 2800.2x 2000 R² = 0.9957
1500
Peak Peak Area (pA) 1000
500
0 0.00 0.20 0.40 0.60 0.80 1.00 1.20 Liquid Concentration (mg/L)
Figure A.9. Chloroethane standard curve for serum bottle and reactor study.
Figure A.10. 1,1-dichloroethane and chloroethane generation from Stage 1 of the reactor study.
201 Table A.8. CA Study Reactor A – Sample ID and number of sequences after combining forward and reverse reads for each sample.
Number of Sample ID Sequences RA 0-12cm 201,914 RA 12-24cm 163,518 RA 24-31cm 194,645 RA 36-48cm 217,052 RA 48-60cm 197,324 Total Number of 974,453 Sequences
Table A.9. CA Study Reactor A – Sample ID and number of sequences after processing aligned sequences through initial quality filtering and removal of chimeras.
Number of Bacteria Number of Archaea Total Number Sample ID Sequences Sequences of Sequences RA 0-12cm 67,180 5,459 72,639 RA 12-24cm 59,179 11,986 71,165 RA 24-31cm 56,321 2,248 58,569a RA 36-48cm 76,272 2,386 78,658 RA 48-60cm 66,184 3,051 69,235 Total Number 325,136 25,130 350,266 of Sequences a The number of sequences used for subsampling to prevent bias prior to computation of Chao1 and H’.
202
Table A.10. CA Study Reactor B – Sample ID and number of sequences after combining forward and reverse reads for each sample.
Number of Sample ID Sequences RB 0-12cm 195,167 RB 12-24cm 128,453 RB 24-31cm 139,067 RB 36-48cm 163,463 RB 48-60cm 194,750 Total Number of 820,900 Sequences
Table A.11. CA Study Reactor B – Sample ID and number of sequences after processing aligned sequences through initial quality filtering and removal of chimeras.
Number of Bacteria Number of Archaea Total Number Sample ID Sequences Sequences of Sequences RB 0-12cm 55,104 17,194 72,298 RB 12-24cm 42,074 7,754 49,828 RB 24-31cm 51,931 2,686 54,617 RB 36-48cm 60,048 1,948 61,996 RB 48-60cm 46,460 1,063 47,523a Total Number 255,617 30,645 286,262 of Sequences a The number of sequences used for subsampling to prevent bias prior to computation of Chao1 and H’.
203 Table A.12. Bacteria OTU sequences detected in Reactor A and Reactor B that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. RDP ID > 50% OTU ID Sequence (%)
TAAGGGATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAGTGATG AAGGCCTTCGGGTTGTAAAACTCTTTTCTGAGGGACGAGCAAGGACGGTACCTCAGG AATAAGTCCCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGGGGCGAGCG Unclassified TTATCCGGATTTACTGGGCGTAAAGCGCGCGCAGGCGGTTTGGTAGGTTGGACGTTA Anaerolineaceae OTU_028 (57%) AAGCTCTTGGCTTAACTAAGAGAGGTCGTTCAAAACCACCAGACTTGAGGTCGGTAG AGGGGAGTGGAATTCCCGGTGTAGTGGTGGAATGCGCAGATATCGGGAGGAACACC TGTGGCGAAAGCGGCTCCCTGGACCGCACCTGACGCTCATGCGCGAAAGCGTGGGG AGCGAACGGGATTAGATACCCCGGGTAGTC
GAGCGCGTAGGTGGTCCTTCAAGTCAGATGTGAAATCTCCCGGCTTAACTGGGAGCG GTCATCTGATACTGCTGGACTTGAGGACGGTAGAGGGAGGTGGAATTCCCGGTGTAG Unclassified TGGTGAAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAAGCGGCCTCCTAGGC CGTTCCTGACACTGAGGCGCGAAAGCGTGGGGAGCGAACAGGTGCCAGCAGCCGCG Chloroflexi OTU_031 (93%) GTAATACGTAGGTGGCAAGCGTTATCCGGATTCACTGGGCGTAAAGAGCGCGTAGGT GGTCCTTCAAGTCAGATGTTAAATCTCCCGGCTTAACTGGGAGTGGTCATCTGATACT GCTGGACTTGAGGGCGGTAGAGGGAGGTGGAATTCCCGGTGTAGTGGTGAAATGCG TAGATATCGGGAGGAACACCAGTGGCGAAAGCGG
CAAGGAATATTGGGCAATGGGCGAAAGCCTGACCCAGCGACACCGCGTGGAGGAAG AAGACCTTCGGGTTGTAAACTTCTTTTATTGGGGAAGAATAATGACGGTACCCGATG Unclassified AATAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCG TTATCCGGATTTACTGGGCGTAAAGCGGGTGTAGGCGGCCTGTCAAGTCAGATGTGA Bacteria OTU_033 (100%) AATCTCCCGGCTCAACTGGGAGGGTGCATTTGAAACTGATGGGCTAGAGTGCAGCAG AAGAAGATGGAATTCCCGGTGTAGTGGTGAAATGCGTAGATATCGGGAAGAACACT AGTGGCGAAGGCGGTCTTCCGGGCTGTAACTGACGCTGAGACCCGAAGGCGTGGGG AGCAAACAGGATTAGATACCCCGGTAGTC
204 Table A.12 (continue). Bacteria OTU sequences detected in Reactor A and Reactor B that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. RDP ID > 50% OTU ID Sequence (%) TGAGGAATATTGCGCAATGGACGGAAGTCTGACGCAGCAACGCCGCGTGGGCGATGA AGGCCTTCGGGTCGTAAAGCCCTTTTCTGGGGGACGAGGAAGGACGGTACCTCAGGA ATCAGTCTCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGAGGCAAGCGTT Longilinea ATCCGGATTTATTGGGCGTAAAGCGCATGCAGGTGGTTTTACAAGTCGGATGTGAAA OTU_094 (85%) GCTCCTGGCTTAACTGGGAGAGGTCGTTCGAAACTGTAAGACTGGAGGTCGGAAGAG GGAAGCGGAATTCCCGGTGTAGTGGTGGAATGCGTAGATATCGGGAGGAACACCAGT GGCGAAAGCGGCTTCCTGGTCCGATTCTGACACTCAGATGCGAAAGCGTGGGGAGCG AACAGGATTAGATACCCCGGGTAGTC CAAGGAATATTGGGCAATGGGCGAAAGCCTGACCCAGCGACACCGCGTGGGGGAAG AAGACCTTCGGGTTGTAAACCCCTTTTATTAGGGAAGAATAATGACGGTACCTGATGA ATAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTT Dehalogenimonas ATCCGGATTTATTGGGCGTAAAGCGGGCGTAGGCGGCTCGCCAAGTCAGATGTGAAA OTU_138 (53%) TCTCCCGGCTCAACTGGGAGGGTGCATTTGATACTGGCGGGCTAGAGTGCAGCAGAG GAAGATGGAATTCCCGGTGTAGTGGTGAAATGCGTAGATATCGGGAAGAACACTAGT GGCGAAGGCGGTCTTCTGGGCTGTAACTGACGCTGAGGTCCGAAGGCGTGGGGAGCA AACAGGATTAGATACCCCTGTAGTC TGAGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCAACGCCGCGTGAGGGACGA AGGCCCTCGGGTCGTAAACCTCTTTTCTGGGGGACGAGACCAGGACGGTACCTCAGG AATAAGTCTCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGAGGCGAGCGT Litorilinea TATCCGGATTTACTGGGCGTAAAGCGCACGCAGGCGGCTTCGTGAGTCGGATGTGAA OTU_140 (55%) AGCTCCTGGCTTAACTGGGAGAGGCCATTCGATACTGCGAGGCTTGAGGTTGGGAGA GGGGTGTGGAATTCCCGGTGTAGTGGTGGAATGCGTAGATATCGGGAGGAACACCAG TGGCGAAAGCGGCACCCTGGCCCACACCTGACGCTGATGTGCGAAAGCGTGGGGAGC AAACAGGATTAGATACCCCTGGTAGTC
205 Table A.12 (continue). Bacteria OTU sequences detected in Reactor A and Reactor B that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. RDP ID > 50% OTU ID Sequence (%) CCGAAAATATTGGACAATGGGCGAAAGCCTGATCCAGCGACATTGCGTGAGGGATGA AGACGTTCGCGCCGTAAACCTCTTTTCTGGGGGAAGAAGTAATTGACGGTACTCCAG GAATAAGCGGGGGCTAATTCTGTGCCAGCAGCAGCGGTAATACAGAACCCGCAAGCG Unclassified TTATCCGGATTTATTGGGCGTAAAGTGTTCGTAGGCGGTATGGAAAGTCTGATATTAA Bacteria OTU_160 ATACCCGGGCTCAACTTGGGACATGTATTGGAAACTTCCAAACTCGAAGATGCTAGG (96%) GGCTATCGGAACGCACGGTGTAGGGGTGAAATCCGTTGATATCGTGCGGAACACCAA AAGCGAAGGCAGATAGCTGGGGCAATCTTGACGCTGAGGAACGAAAGCGTGGGGAG CAAAAAGGATTAGATACCCCTGTAGTC TGAGGAATATTGCGCAATGGACGGAAGTCTGACGCAGCAACGCCGCGTGAGCGATGA AGGCCTTCGGGTCGTAAAGCTCTTTTCTGGGGGACGAGGAAGGACGGTACCTCAGGA ATAAGCCTCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGAGGCGAGCGTT Longilinea ATCCGGATTTATTGGGCGTAAAGCGCATGCAGGTGGTTCTGCGAGTCGGATGTAAAA OTU_169 (56%) GCTCCCGGCTTAACTGGGAGAGGTCGTTCGAAACTGCAGGACTGGAGGTCGGAAGAG GGAAGCGGAATTCCCGGTGTAGTGGTGGAATGCGTAGATATCGGGAGGAACACCAGT GGCGGAAGCGGCTTCCTGGTCCGCACCTGACACTCAGATGCGAAAGCGTGGGGAGCG AACAGGATTAGATACCCCTGGTAGTC
CAAGGAATTTTGCGCAATGGGCGAAAGCCTGACGCAGCAACGCCGCGTGAGGGAAG AAGGCCTTCGGGTTGTAAACCTCTTTTCCCAGGGAAGAATAATGACGGTACCTGGGG Unclassified AATAAGTCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGT Chloroflexi OTU_180 TATCCGGATTCACTGGGCGTAAAGAGCGCGTAGGTGGTCCTTCAAGTCAGATGTTAA (85%) ATCTCCCGGCTTAACTGGGAGCTGTCATCTGATACTGCTGGACTTGAGGATGGTAGAG GGAGGTGGAATTCCCGGTGTAGTGGTGAAATGCGTAGATATCGGGAGGAACACCAGT GGCGAAAGCGGCCTCCTAGGCCATTCCTGACACTGAGGCGCGAAAGCGTGGGGAGCG AACAGGATTAGATACCCCGGTAGTC
206 Table A.12 (continue). Bacteria OTU sequences detected in Reactor A and Reactor B that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. RDP ID > 50% OTU ID Sequence (%) CAAGGAATTTTGCGCAATGGGCGAAAGCCTGACGCAGCGACGCCGCGTGGGGGAC GAAGACCTTCGGGTTGTAAACCCCTTTTCCTGGGGAAGAATTATGACGGTACCCAG GGAATAAGTCTTGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGAGGCGAA Unclassified CGTTATTCGGATTTACTGGGCGTAAAGCGGGTGTAGACGGCTTCTTAAGTCGGTTGT Bacteria OTU_224 GAAATCTCTTGACTCAATTGAGAGGGGACATCCGATACTAGGAGGCTTGAAGGCGG (99%) CAGAGGAAAGCGGAACTCCCGGAGTAGCGGTGAAATGCGTAGATATCGGGAGGAA CACCAGTGGCGAAGGCGGCTTTCTAGGCCGATCTTGACGTTGAGGCCCAAAAGTGT GGGTAGCGAAACGGATTAGATACCCCTGTAGTC TGAGGAATATTGCGCAATGGGGGAAACCCTGACGCAGCGACGCCGCGTGGGCGAC GAAGGCCTTCGGGTCGTAAAGCCCTTTTGGCAGGGACGAGTGAGGACGGTACCTGT CGAATAAGTCTCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGAGGCGAG Unclassified CGTTATCCGGATTCACTGGGCGTAAAGCGCGCGTAGGCGGTTGGGTAAGTCGGACG Bacteria OTU_267 TGAAAGCCCCTGGCTCAACTGGGGGAGGCCGTTCGATACTGCTCGGCTTGAGGGTG (100%) AGAGAGGGAAGTGGAATTCCCGGTGTAGCGGTGGAATGCGTAGAGATCGGGAGGA ACACCAGTGGCGAAAGCGGCTTCCTGGCTCACCCCTGACGCTGATAGCGCGAAAGC GTGGGGAGCAAACGGGATTAGATACCCGGGTAGTC TAAGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAGTGACG AAGGCCTTCGGGTTGTAAAACTCTTTTCTGAGGGATGAGGAAGGACAGTACCTTAG GAATAAGTCTCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGAGGCGAGC Ornatilinea GTTATCCGGATTTACTGGGCGTAAAGGGCAAGTAGGCGGTGGTTCAAGTTGGGCGT OTU_311 (65%) GACAGATCCTGGCTTAACTGGGAGAGGTCGTTCAAAACTGGATCACTAGAGGGCAG GAGAGGGGAGCGGAATTCCTAGTGTAGCGGTGGAATGCTCAGATATTAGGAAGAAC ACCAGTGGCGAAAGCGGCTTTCTGGCCTGCACCTGACGCTGAAGGGCGAAAGCTAG GGGAGCGAACGGGATTAGATACCCGGGTAGTC
207 Table A.12 (continue). Bacteria OTU sequences detected in Reactor A and Reactor B that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. RDP ID > 50% OTU ID Sequence (%) CAAGGAATATTGGGCAATGGGCGAAAGCCTGACCCAGCGACACCGCGTGGGGGAA GAAGCCTTCGGGTTGTAAACTCCTTTTATTGGGGAAGAATAATGACGGTACCCAAT GAATAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAG Dehalogenimonas CGTTATCCGGATTTACTGGGCGTAAAGCGGACGTAGGTGGTTTGTCAAGTCGGATG OTU_351 (59%) TGAAATCTCCCGGCTCAACTGGGAGGGTGCATTCGAAACTGATGGACTAGAGTGCA GCAGAGGGAGGTGGAATTCCCGGTGTAGTGGTGAAATGCGTAGATATCGGGAGGA ACACTAGTGGCGAAGGCGGCCTCCTGGGCTGTAACTGACACTGAGGCCCGAAGGC GTGGGTAGCAAACAGGATTAGATACCCCGTGTAGTC TGAGGAATATTGCGCAATGGGGGAAACCCTGACGCAGCGACGCCGCGTGGGTGAC GAAGGCCCTCGGGTCGTAAAACCCTTTTTGCAGGGACGAGTGAGGACGGTACCTGC AGAATCAGTCTCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGAGGCGA Unclassified GCGTTATCCGGATTTACTGGGCGTAAAGCGCGCGTAGGCTGTTGGTCAAGTCGGGT Bacteria OTU_352 GTGAAAGCCCCTGGCTCAACTAGGGGAGGCCATTCGATACTGGCCGGCTTGAGGGT (100%) GAGAGAGGAAAGTGGAATTCCCGGTGTAGCGGTGGAATGCGTAGAGATCGGGAGG AACACCAGTGGCGAAAGCGGCTTTCTGGCTCACCCCTGACGCTGATTGCGCGACAG CGTGGGGAGCAAACGGGATTAGATACCCGGTAGTC TGAGGAATATTGCACAATGGGGGCAACCCTGATGCAGCAACGCCGCGTGGGTGAT GAAGGCCTTCGGGTCGTAAAACCCTTTTCTGGGGGACGAGAACGGACGGTACTCCA Unclassified GGAATAAGTCCCGGCTAACTACGTGCCAGCAGCCGCGGTAAAACGTAGGGGGCGA GCGTTATCCGGAATTACTGGGCGTAAAGCGCGTGCAGGCGGCTGGAAAGGTTGGA Anaerolineaceae OTU_366 CGTGAAAGCTCCTGGCTCAACTGGGAGAGGCCGTTCAAAACCATCCGGCTTGAGG (50%) GCAGAAGAGGGGAGTGGAATTCCCGGTGTAGTGGTGGAATGCGTAGATATCGGGA GGAACACCTGTGGCGCAAGCGGCTCCCTGGTCTGCACCTGACGCTCAGACGCGAA AGCGTGGGGAGCGAACGGGATTAGATACCCCTGGTAGTC
208 Table A.12 (continue). Bacteria OTU sequences detected in Reactor A and Reactor B that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank. RDP ID > 50% OTU ID Sequence (%) TGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGTATGAA GAAGGCCTTCGGGTTGTAAAATACTGTTGTTAGGGAAGAACGGCATCTGTGTAAA TAATGCAGGTGATTGACGGTACCTAACGAGGAAGCCCCGGCTAACTACGTGCCAG CAGCCGCGGTAATACGTAGGGGGCAAGCGTTGTCCGGAATCATTGGGCGTAAAGG Dehalobacter GCGCGTAGGCGGCTATATAAGTCTGATGTGAAAGTGCGGAGCTTAACTCCGTAAA OTU_523 (99%) GCATTGGAAACTGTATGGCTTGAGGACAGGAGAGGAAAGTGGAATTCCACGTGTA GCGGTGAAATGCGTAGAGATGTGGAGGAACACCAGTGGCGAAGGCGACTTTCTG GACTGTAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCGAACAGGATTAGATAC CCCTGTAGTC
CAAGGAATCTTGGGCAATGGGCGAAAGCCTGACCCAGCGACGCCGCGTGAGGGA TGAAGGCTTCGGGTTGTAAACCTCTTTTCTCAGGGAAGAATAATGACGGTACCTG AGGAATAAGTCTCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGAGGCG Dehalogenimonas AGCGTTATCCGGATTTATTGGGCGTAAAGTGGGCGTAGGTGGTCTTTCAAGTCGG OTU_805 (100%) ATGTGAAATCTCCTGGCTTAACTGGGAGGGGTCATCCGATACTGTTGGACTTGAGT ATGGCAGGGGAAAATGGAATTCCCGGTGTAGTGGTGAAATGCGTAGATATCGGGA GGAACACCAGAGGCGAAGGCGATTTTCCAGGCCAAAACTGACACTGAGGCCCGA AAGCGTGGGGAGCGAACAGGATTAGATACCCGGGTAGTC
209 Table A.13. Unclassified bacteria sequences detected in Reactor A and Reactor B. RDP ID > 50% OTU ID Sequence (%)
TGAAGAATCTTGGGCAATGGGCTAACGCCTGACCCAGTGAGAACACATGCATGAT GAATGCGCACAGTTTTCTGTAAAGTGCTATTCGCGGGATCAAGAAAACGACACAA GCCCGAGATGAAGCGCCGGCCAACTCCGTGCCAGCAGCCGCGGTAAGACGGAGG Unclassified GCGCGAGCGTTATTCGTTTTGATTGGGTGTAAAGGGTATGTAGGCGGCCcccAGTAT Bacteria OTU_004 TTTGGCCAAAAGAGCGGAGTGTTCCTATGCTATATGGCCTTTGAAGAAGGgggCTT (99%) GTGTTGATGATAGGTCGGGGCAAGTTCTTATGTAGGGGTAGAATCCCACCATATG AGAACGAACGTCAGCTGGCGAAGGCGCCCTTCCCGCTTCAACAGACGCTAAGGTA CGGAAGCTTAGGTAGCAAACGGGATTAGATACCCCGGTAGTCC
TCGAGAATATTCGACAATGGGCGAAAGCCTGATCGAGCGACGCCGCGTGCGGGA AGAAGGCCTTCGGGTCGTAAACCGCTTTTGCGTGGGAAGAATTTTGACGGTACCA CGAGAATAAGGGGTTGCTAACTCTGTGCCAGCAGCAGCGGTAATACAGAGACCCC Unclassified AAGCATTATCCGGAATTACTGGGCGTAAAGAGCGAGCAGCCGGTCTTtttAGTCCGA Bacteria OTU_038 TGTTAAATCATGGGGCCCAACCTCATGTCCGCATTGGAAACGGAgagaCTAGAGTA (99%) GATGAAAGGGAGGCGGAACGTATGGTGTAGGGGTGAAATCCACTGATATCATAC GGAACACCAAAAGCGAAGGCAGCCTCCTGGCGTCTTACTGACGGTCAGTCGCGAA AGCGTGGGGATCAAaaaGGATTAGATACCCCTGTAGTC
TCAAGAATCTTCCCCAATGGACGAAAGTCTGAGGGAGCGACGCCGCGTGCAGGAA GAAACCCCTCGGGGCGTAAACTGCTTTTCTCTGGGAATAACTCTGAAGGTACCAG AGGAATAAGAGGCTGCTAACTTCGTGCCAGCAGCAGCGGTAATACGAAGGCCTCA Unclassified AGCGTTATCCGGATTTATTGGGCGTAAAGAGTTCAACGGTGGTTTGGAACGTTCTC Bacteria OTU_086 GGTCAAATTGCATCGCTCAACGGTGCAACTGCTGGGAAAACGGCCAGACTCGAGC (99%) CTGGGAGAGGCAGACGGAATTGTCGGTGTAGTAGTAAAATGCGCTAATATCGACA AGAACACCAATAGCGAAAGCAGTCTGCTGGAACAGTGCTGACACTTCACGAACGA AAGCGTGGGGAGCGATACGGATTAGATACCCTTGTAGTC
210
cis-1,2-DCE 700
600
500
400 y = 2.4817x R² = 0.9872 300
Peak Peak Area (pA) 200
100
0 0.00 50.00 100.00 150.00 200.00 250.00 300.00 Liquid Concentration (ug/L)
Figure A.11. cis-1,2-dichlorethene standard curve for Re-Solve ABR performance study.
Vinyl Chloride 4000
3500
3000
2500 y = 19.43x 2000 R² = 0.983
1500 Peak Peak Area (pA) 1000
500
0 0.00 50.00 100.00 150.00 200.00 Liquid Concentration (ug/L)
Figure A.12. Vinyl chloride standard curve for Re-Solve ABR performance study.
211 1,1,1-TCA 300
250 y = 6.7967x 200 R² = 0.996
150
100 Peak Peak Area (pA)
50
0 0.00 10.00 20.00 30.00 40.00 50.00 Liquid Concentration (ug/L)
Figure A.13. 1,1,1-trichloroethane standard curve for Re-Solve ABR performance study.
1,1-DCA 250
200
150 y = 4.8623x R² = 0.9735
100 Peak Peak Area (pA)
50
0 0.00 10.00 20.00 30.00 40.00 50.00 Liquid Concentration (ug/L)
Figure A.14. 1,1-dichloroethane standard curve for Re-Solve ABR performance study,
212 Chloroethane 700
600
500 y = 13.881x 400 R² = 0.998
300
Peak Peak Area (pA) 200
100
0 0.00 10.00 20.00 30.00 40.00 50.00 Liquid Concentration (ug/L)
Figure A.15. Chloroethane standard curve for Re-Solve ABR performance study.
213 Table A.14. 2014 Piezometer Data at the Site from ABR-1. Sample Piezometer Temp. Conductivity Cond.-C DTW pH ORP Location Depth (C) (mS/cm) (mS/cm) (ft) Shallow - - - - - 6.41 Sampled Middle 14.97 6.78 -46.8 0.226 0.279 7.5 14.19 5.8 -29.2 0.209 0.263 - 14.36 5.72 -31.1 0.209 0.263 - 14.84 5.87 -38.9 0.2 0.25 - SE Sampled Deep 13.94 5.98 -36.5 0.191 0.246 10.51 14 5.79 -28.5 0.189 0.24 - 13.9 5.8 -32.8 0.188 0.235 - 14.02 5.85 -35.5 0.188 0.238 - Sampled Shallow 6 Sampled Middle 7.7 Sampled Deep 14.25 6.29 -59 0.171 0.216 10.32 ME 13.97 6.24 -57 0.171 0.216 14.03 6.18 -57 0.171 0.216 14.12 6.17 -56.12 0.171 0.215 14.27 6.11 -56.6 0.171 0.215 14.29 6.1 -56.3 0.171 0.215 Sampled Shallow 15.2 5.54 -8.5 0.227 0.184 5.7 14.82 5.38 -4.4 0.227 0.183 14.87 5.34 -5 0.226 0.181 14.49 5.34 -7.2 0.223 0.18 14.6 5.37 -9.9 0.224 0.18 Sampled Middle 13.92 5.55 26.2 0.216 0.169 7.9 NE 13.8 5.16 46.1 0.209 0.165 Sampled Deep 14.03 5.46 3.18 0.227 0.178 10.6 13.46 5.22 0.74 0.22 0.172 13.59 5.2 0.39 0.22 0.172 13.72 5.24 0.25 0.22 0.173 13.53 5.25 0.22 0.221 0.172 Sampled
214 Table A.14 (continued). 2014 Piezometer Data at the Site from ABR-1. Cond.- Sample Piezometer Temp. Conductivity DTW pH ORP C Location Depth (C) (mS/cm) (ft) (mS/cm) Shallow 14.97 6.62 -8.1 0.194 0.24 6.29 14.66 5.15 -8.9 0.188 0.234 15.16 5.04 Sampled Middle 14.31 5.09 41.4 0.277 0.22 7.78 Sampled SW Deep 13.65 5.57 -24.5 0.235 0.185 10.63 13.87 5.5 -19.1 0.234 0.185 13.8 5.52 -20.1 0.233 0.183 13.72 5.52 -20.8 0.232 0.182 13.84 5.56 -24.1 0.232 0.183 Sampled Shallow 14.89 5.11 -19.9 0.241 0.192 6.1 14.55 5.58 -23.7 0.228 0.183 14.5 5.49 -22.5 0.227 0.182 Sampled Middle 14.81 5.41 -14.3 0.238 0.191 8.9 14.63 5.26 -11.5 0.235 0.189 14.77 5.25 -12.1 0.236 0.189 MW 14.51 5.47 -6.9 0.232 0.185 14.36 5.46 -5.3 0.23 0.183 Sampled Deep 15.22 5.59 -31.4 0.231 0.188 9.87 15.17 5.53 -18.3 0.229 0.186 15.27 5.54 -16.6 0.229 0.186 15.19 5.5 -18 0.228 0.185 Sampled Shallow 14.71 5.19 -18.1 0.223 0.179 5.7 14.85 5.14 -19.2 0.221 0.178 14.25 5.18 -20.3 0.22 0.117 Sampled Middle 14.76 5.76 -14.5 0.224 0.179 8.1 NW 14.08 5.42 -8.4 0.223 0.176 Sampled Deep 13.83 5.59 -17 0.231 0.181 10.2 13.66 5.48 -14.6 0.229 0.179 13.59 5.46 -14.7 0.229 0.179 Sampled
215 Table A.14 (continued). 2015 Piezeometer Data at the Site from ABR-2. Tem Sample Piez. Conductivit DO DO DTW Final Gas p. pH Location Depth y (mS/cm) (mg/L) (%) (ft) Saturation (oC) Deep 13.72 205 0.68 6.5 5.84 10.92 - 13.66 204 0.57 5.4 5.87 10.91 - 13.55 203 0.43 4.1 5.87 10.92 - 13.54 203 0.39 3.7 5.87 10.93 - Sampled at 1050 Middle 15.57 218 0.8 7.7 6.08 7.69 - 15.76 208 0.39 4 6.05 8.14 - SE 15.88 209 0.34 3.4 6.06 8.18 - 15.65 209 0.28 2.8 6.07 8.14 - Sampled at 1140 Shallow *well purged dry* - let recharge - 17.74 228 3.17 33 5.8 7.19 - -55 (on *well purged dry* - let recharge ABR-1) Sampled at 1245 Deep 14.68 197 1.37 13.4 5.64 10.84 - 14.66 195 0.83 8.1 5.7 10.86 - 14.64 195 0.74 7.5 5.75 10.87 - 14.45 194 0.49 5.1 5.79 10.87 - Sampled at 1445 Middle 15.04 205 0.75 7.6 5.99 8.08 - 14.99 195 0.49 4.9 5.96 8.35 - NE 15.35 195 0.34 3.3 5.97 8.26 - 15.47 194 0.26 2.6 5.96 8.19 - Sampled at 1520 Shallow 16.91 217 2.13 21.9 6.13 6.29 - 17.21 217 1.99 20.6 6.14 6.29 - 17.61 209 1.86 19.3 6.14 6.28 - 17.85 207 1.69 17.8 6.13 6.33 -127 Sampled at 1600
216 Table A.14 (continued). 2015 Piezeometer Data at the Site from ABR-2. Sample Piez. Temp. Conductivity DO DO DTW Final Gas pH Location Depth (oC) (mS/cm) (mg/L) (%) (ft) Saturation Shallow 17.88 274 3.73 39.3 6.36 7.43 - 17.66 233 3.25 34.1 6.22 7.84 - *well purged dry* - let recharge -61 NW Sampled at 1635 Middle -28 Sampled Deep 13.9 191 1.3 12.6 6.12 11.15 - 13.64 190 0.83 7.9 6.15 11.13 - 13.51 188 0.61 5.8 6.14 11.16 - 13.63 189 0.55 5.2 6.16 11.12 -1 Sampled at 0830 Middle *well purged dry* - let recharge - ME 16.92 226 2.39 24.9 6.02 8.87 - 16.54 222 2.31 23.6 6.02 9.14 21 Sampled at 0915 Shallow *well purged dry* - let recharge - *well purged dry* - let recharge -32 Sampled at 1010 C-Deep ------1 C- MW Middle ------49 C- Shallow ------11
217 Table A.15. ReSolve (2014) ABR-1 – Sample ID and number of sequences after combining forward and reverse reads for each sample.
Number of Sample ID Sequences NW - shallow A 139,945 NW - middle A 81,921 NW - deep A 175,431 SW - shallow A 200,135 SW - middle A 58,046 MW - shallow A 268,576 MW - middle A 94,855 MW - deep A 160,574 Total Number of 1,179,483 Sequences
Table A.16. ReSolve (2014) ABR-1 – Sample ID and number of sequences after processing aligned sequences through initial quality filtering and removal of chimeras.
Number of Bacteria Number of Archaea Total Number of Sample ID Sequences Sequences Sequences NW - shallow A 13,000 623 13,623 NW - middle A 7,893 335 8,228 NW - deep A 15,979 1,359 17,338 SW - shallow A 16,585 45 16,630 SW - middle A 5,777 36 5,813a MW - shallow A 24,157 592 24,749 MW - middle A 8,589 99 8,688 MW - deep A 13,841 624 14,465 Total Number 105,821 3,713 109,534 of Sequences a The number of sequences used for subsampling to prevent bias.
218 Table A.17. Bacteria OTU sequences detected in ABR-1 that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
RDP ID > 50% OTU ID Sequence (%)
TACGGAGGGTGCAAGCGTTGTTCGGAATTATTGGGCGTAAAGCGCGTGTA GGCGGTTTGTTAAGTCTGATGTGAAAGCCCTGGGCTCAACCCGGGAAGTG Geobacter CATTGGATACTGTCAGACTTGAATACGGGAGAGGGTAGTGGAATTCCTAG OTU_003 (96%) TGTAGGAGTGAAATCCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCG GCTACCTGGACCGATATTGACGCTGAGACGCGAAAGCGTGGGTAGCAAAC AGG
AACGTAGGAGGCGAGCGTTATCCGGATTTACTGGGCGTAAAGCGCGTGCA GGCGGTTTGGTAAGTTGGATGTGAAAGCTCCTGGCTTAACTGGGAGAGGT Unclassified CGTTCAATACTGCCAGACTTGAGGATGGTAGAGGGAGGTGGAATTCCGGG Anaerolineaceae OTU_022 TGTAGTGGTGAAATGCGTAGATATCCGGAGGAACACCAGTGGCGAAAGCG (100%) GCCTCCTGGACCATTTCTGACGCTCAGACGCGAAAGCTAGGGTAGCAAAC GGG
GACGTAGGTGGCTAGCGTTATCCGGATTTACTGGGCGTAAAGCGTGCGCA GGCGGCCTTTCAAGTCAGATGTAAAATCTCCCGGCTCAACTGGGAGGGAC Unclassified CATCTGATACTGTTGGGCTAGAGTACAGCAGAGGGAAGTGGAATTCCCGG Chloroflexi OTU_055 TGTAGTGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAAGCG (65%) GCTTTCTGGGCTGTTACTGACGCTGTTGCACGAAAGCGTGGGGAGCAAAC AGG
219 Table A.18. ReSolve (2015) ABR-2 – Sample ID and number of sequences after combining forward and reverse reads for each sample.
Number of Sample ID Sequences MW (21-23") 137,907 MW (26-28") 124,431 NE (33-35") 170,238 NE (37-39") 129,460 NE (48-52") 118,398 NE (52-56") 209,381 NE (56-60") 321,855 NE (60-64") 362,785 NE (64-68") 75,929 NE (68-72") 149,247 NE (72-76") 237,978 NE (76-80") 310,593 Total Number of 2,348,202 Sequences
Table A.19. ReSolve (2015) ABR-2 – Sample ID and number of sequences after processing aligned sequences through initial quality filtering and removal of chimeras.
Number of Bacteria Number of Archaea Total Number of Sample ID Sequences Sequences Sequences MW (21-23") 112,928 2,726 115,654 MW (26-28") 96,948 2,571 99,519 NE (33-35") 133,390 3,421 136,811 NE (37-39") 103,959 3,067 107,026 NE (48-52") 95,463 3,610 99,073 NE (52-56") 169,402 4,360 173,762 NE (56-60") 261,124 7,187 268,311 NE (60-64") 287,672 7,291 294,963 NE (64-68") 61,823 1,990 63,813a NE (68-72") 115,327 3,683 119,010 NE (72-76") 187,434 5,281 192,715 NE (76-80") 244,516 8,233 252,749 Total Number 1,869,986 53,420 1,923,406 of Sequences a The number of sequences used for subsampling to prevent bias.
220 Table A.20. Bacteria OTU sequences detected in ABR-2 that were further classified using the naïve Bayesian classifier with a threshold of 50% against the RDP database and using the NCBI BLAST GenBank.
RDP ID > 50% OTU ID Sequence (%)
CACGTAGGAGGCAAGCGTTATCCGGATTTACTGGGCGTAAAGCGCGTGCAG Unclassified GTGGTTTGGTAAGTTGGATGTGAAAGCTCCTGGCTCAACTGGGAGAGGTCG Anaerolineaceae OTU_010 TTCAATACTGCCGAACTTGAGAATAGTAGAGGAAGATGGAATTCCCGGTGT (100%) AGTGGTGAAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAAGCGGTCT TCTGGACTATTTCTGACACTCAGACGCGAAAGCTAGGGTAGCAAACGGG
GACGTAGGTGGCGAGCGTTATCCGGATTTACTGGGCGTAAAGCGTGCGCAG Unclassified GCGGCCTTTCAAGTCAGATGTAAAATCTCCCGGCTCAACTGGGAGGGACCA Chloroflexi OTU_028 TCTGATACTGTTTGGCTAGAGTACAGCAGAGGGAAGTGGAATTCCCGGTGT (68%) AGTGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAAGCGGCTT TCTGGGCTGTTACTGACGCTGTTGCACGAAAGCGTGGGGAGCAAACAGG
TACGGAGGGTGCGAGCGTTGTTCGGAATTATTGGGCGTAAAGCGCGTGTAG GCGGTTTGGTAAGTCTGATGTGAAAGCCCTGGGCTCAACCTGGGAAGTGCA Geobacter TTGGAAACTGTCAGACTTGAATACGGGAGAGGGTAGTGGAATTCCTGGTGT OTU_044 (100%) AGGAGTGAAATCCGTAGATATCAGGAGGAACACCGGTGGCGAAGGCGGCT GCCTGGACCGATATTGACGCTGAGACGCGAAAGCGTGGGTAGCAAACAGG
221
Table A.21. Resolve Unclassified OTUs – Sample ID and sequences in both ABR-1 and ABR-2. RDP ID > 50% OTU ID Sequence (%)
TACAGAGGTGGCAAGCGTTGTTCGGATTTATTGGGCGTAAAGGGTCC GTAGGCGGCCTGGAAAGTTGGATGTGAAATCCCACAGCTTAACTGTG Unclassified GAACTGCATTCAAAACTTCTAGGCTCGAGTGTGATAGGGGTAAAGGG ABR-1 Bacteria OTU_001a AATTCCCGGTGTAGGGGTGAAATGCGCAGAGATCGGGAGGAACACC (98%) AGTCGCGAAGGCGCTTTACTGGATCACTACTGACGCTGAGGGACGAA AGCGTGGGGAGCAAACAGG
GACAGAGGTGGCGAGCGTTGTTCGGAATTACTGGGCTTAAAGGGCGA GTAGGCGGTGGGACAAGTCTGATGTGGAATCTTAGGGCTTAACCCTA AAACTGCATTAGAAACTGTTTTACTTGAGTCAGTGAGGGGAGGGCGG Unclassified AATTCCCGGTGTAGCGGTGAAATGCGTAGATATCGGGAGGAAGGCCG ABR-2 Bacteria OTU_001b GTGGCGAAGGCGGCCCTCTGGCACTGTACTGACGCTGAGGCGCGAAA (100%) GCGTGGGGAGCAAACAGG
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Figure A.16. 1,1-DCA Concentrations in ABRs at the Site over the years (Weston Solutions).
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Figure A.17. CA Concentrations in ABRs at the Site over the years (Weston Solutions).
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Leslie M. Pipkin was born and raised in Lakeland, FL. She graduated from Florida State
University in Tallahassee, FL with a Bachelor of Science dual degree in Civil & Environmental
Engineering in 2010. After completing her undergraduate degree, she continued her studies at
Florida State University and obtained her Master of Science degree in Civil Engineering in 2012.
While working on her Masters, she pursued an internship working with MACTEC f/k/a AMEC part time as a Remediation Engineer. After the completion of her Masters degree, she enrolled at
Louisiana State University in Baton Rouge, LA to pursue her doctoral degree in Civil
Engineering. She is looking forward to completing her program of study in the December of
2017, and will continue her career in the field of environmental engineering through research, consulting, and teaching.
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