The Relationships of Pathogenic Microbes, Chemical Parameters, and Biogas Production During Anaerobic Digestion of Manure-Based Biosolids

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The Relationships of Pathogenic Microbes, Chemical Parameters, and Biogas Production During Anaerobic Digestion of Manure-based Biosolids

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

James Scott Rosenblum, MPH

Graduate Program in Public Health

The Ohio State University

2013

Dissertation Committee:

Michael S. Bisesi, PhD, Advisor

Jiyoung Lee, PhD

Jay Martin, PhD

J.R.Wilkins III, BCE, DrPH

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Copyright by

James Scott Rosenblum

2013

Abstract

Biosolids are biological wastes that primarily originate from animal manures, food wastes, crop residues, and wastewater treatment sludge. They are often treated through anaerobic digestion in order to produce a more stable product. This process simultaneously degrades the waste, reduces pathogens, generates a nutrient rich fertilizer, and produces alternative energy source in the form of biogas. Due to these benefits, anaerobic digestion is utilized globally, on both large and small scales. The majority of the anaerobic digestion literature focuses on large-scale digestion at mesophilic and thermophilic temperatures, with minimal attention paid towards the performance of the more than 40 million small-scale digesters which frequently operate at psychrophilic temperatures. Therefore, this research aimed to assess anaerobic digestion and its efficiency in reducing pathogens and producing biogas at psychrophilic temperatures.

This research also aimed to identify a predictive chemical indicator that is related with microbial levels. The later was pursued with the goal of identifying a particular chemical indicator that could act as a surrogate for microbial levels, thus identifying a rapid method by which to determine biosolids classification without microbial quantification.

Results from chapter 2 indicated that anaerobic digestion at 10°C and 20°C, using various inoculum-to-substrate ratios, could reduce levels of indicator organisms; E. coli was

ii reduced in all treatments, while Enterococci was decreased in only a few treatments.

These reductions appear to be the result of changing environmental conditions, such as substrate limitation for E. coli, and increased volatile fatty acid (VFA) levels for

Enterococci. Lower inoculum-to-substrate ratios resulted in higher average levels of indicator organisms, as well as less stable conditions, based on various chemical parameters. These included more acidic pH, higher VFA levels, and reduced biogas production. The study results suggests that inoculum-to-substrate ratios influence chemical parameters and the levels of indicator organisms during the anaerobic digestion of cattle manure in batch reactors at psychrophilic temperatures.

Results from chapter 3 showed that class B biosolids were generated from the eight lab- based digesters, according to the USEPA guidelines (<2x106/g Fecal Coliforms), regardless of loading rate or temperature. Reduced loading rates led to a more stable environment (decreasing VFAs, and increasing total inorganic carbonate) as well as lower levels of indicator organisms, but generated slightly less biogas. Overall, these results provide important data by which to improve the performance of small-scale psychrophilic digesters, specifically by reducing loading rates to prevent souring during winter months.

Results from chapter 4 suggest an inverse relationship exists between levels of humic acid and both pathogenic and indicator organisms. The suggested inverse relationship between humic acid and microbes is further supported by comparison of the two

iii temperatures (treatments), with the treatment conducted at 37°C generating reduced levels of microbes and higher levels of humic acids when compared to the treatment conducted at 25°C. This study also validated the use of extracellular polymeric substances (EPS) extraction methods to separate humic acid from the EPS of biosolids, and further demonstrated the effectiveness of chemical luminescence with an acidic

Ce(IV)-rhodamine 6G complex in quantifying extracted humic acids.

Dedication

This document is dedicated to my lovely wife Rebecca Goldberg for all her love, support,

and patience during these years. I want to additionally dedicate this document to my

parents for their ongoing support during my life and academic career.

Acknowledgments

I wish to thank my lead advisor Professor Michael Bisesi for his support, encouragement, patience, and intellectual insight, which made this dissertation possible. I cannot thank him enough for all of his mentorship through this process, which helped me grow from an ambitious student to a scientist.

I would also like to thank the esteemed members of my dissertation committee, Professor

Jiyoung Lee, Professor Jay Martin, and Professor Jay Wilkins for their support during this dissertation. In particular, I would like to thank Professor Lee and Martin, for their guidance in the lab and during my studies at OSU.

I also want to thank my student colleagues, both at CPH and FABE, who contributed their time and hard work in making this project possible. Lastly, I would like to thank

Juan Peng for assisting in my statistical analyses, and Michael Post for helping edit this dissertation.

Vita

June 27, 1983 ...... Born- Southfield, Michigan, U.S.A

2005 ...... B.S. Biochemistry, Eastern Michigan

University

2008 ...... M.P.H, Environmental Health Sciences, The

Ohio State University

2009 to present ...... Graduate Research Associate,

Environmental Health Sciences, The Ohio State University

Publications

Rosenblum J.S. & Chongtao G., Bohrerova Z., Yousef A., Lee J. “Ozonation as a Clean

Technology for Fresh Produce Industry and Environment: Its Disinfection Efficiency and

Wastewater Quality” Journal of Applied Microbiology, Oct. 2012, 113(4): 837-845

Fields of Study

Major Field: Public Health, Environmental Health Sciences

vii

Table of Contents Abstract ...... ii

Dedication ...... v

Acknowledgments ...... vi

Vita ...... vii

Fields of Study ...... vii

List of Tables ...... xii

List of Figures ...... xiii

Chapter 1 ...... 1

Introduction ...... 1

1.1 Sources and Characteristics of Biosolids ...... 1

1.2 Potential Impact of Pathogens and Chemicals on Humans ...... 6

1.3 Statement of the Problem ...... 12

1.3.2 Indicators of Stabilization ...... 15

1.4. Rationale for Research ...... 17

1.5 Specific Aims ...... 19

Chapter 2 ...... 21

The Impact of Inoculum-to-Substrate Ratios on Microbial Concentrations during Psychrophilic

Anaerobic Digestion of Dairy Manure ...... 21

Abstract ...... 21

2.1 Introduction ...... 22

2.2. Methods ...... 26 viii

2.2.1 Anaerobic inoculum and manure ...... 26

2.2.2 Experimental set-up ...... 27

2.2.3. Measurement of biogas ...... 28

2.2.4. Measurement of chemical parameters ...... 28

2.2.5. Measurement of fecal indicators ...... 28

2.2.6. Statistical analysis ...... 29

2.3. Results ...... 30

2.3.1. Fecal indicators ...... 30

2.3.2. Mixed Model ...... 33

2.3.3. Chemical parameters ...... 37

2.4. Discussion ...... 40

2.4.1. Fecal indicators ...... 40

2.4.2. Chemical indicators ...... 46

2.5. Conclusion ...... 47

Chapter 3 ...... 48

Influence of Seasonal Fluctuation and Loading Rates on the Level of Fecal Indicator Bacteria

During Semi-Continuous Anaerobic Digestion ...... 48

Abstract ...... 48

3.1 Introduction ...... 49

3.2 Methods ...... 54

3.2.1 Experimental set-up ...... 54

3.2.2 Substrate and inoculum loading rates ...... 55

3.2.3 Temperature factors ...... 56

3.2.4 Microbial analyses ...... 57

3.2.5 Chemical analyses ...... 57

3.2.6 Statistical analyses ...... 58

3.3 Results ...... 58

3.3.1 Fecal indicators ...... 59

3.3.2 Chemicals ...... 62

3.3.2.1 pH ...... 64

3.3.2.2 Biogas ...... 66

3.3.3 Mixed model ...... 67

3.4 Discussion ...... 68

3.4.1 Chemical indicators ...... 68

3.4.2 Fecal indicators ...... 70

3.5 Conclusion ...... 73

Chapter 4 ...... 75

Rapid Assessment of Biosolid Stabilization with Chemical Luminescence ...... 75

of Humic Acid ...... 75

Abstract ...... 75

4.1 Introduction ...... 77

4.2 Methods ...... 83

4.2.1 Feedstock ...... 83

4.2.2 Experimental Set-up and Procedure ...... 84

4.2.3 Chemical Analysis ...... 84

4.2.3.1 Reagents ...... 84

4.2.3.2 Extraction of Humic Acids ...... 85

4.2.3.3 Humic Acid Determination ...... 87

4.2.3 Microbial Analysis ...... 87

4.2.4. Statistical Analysis ...... 89

4.3 Results ...... 89

4.3.1 Chemical Luminescence of Commercial Humic Acid ...... 89

4.3.2 EPS extraction methods and resulting Humic Acid levels ...... 91

4.3.3 Fecal Indicators and Pathogenic Organisms ...... 93

4.4 Discussion ...... 96

4.4.1 Extraction and Determination of Humic Acids ...... 96

4.4.2 Relationship between Humic Acids and both Indicator and Pathogenic

Microorganisms ...... 99

4.5 Conclusion ...... 100

Chapter 5 ...... 102

Conclusion and Recommendations ...... 102

5.1 Conclusion ...... 102

5.2 Recommendations and Future Research ...... 105

References ...... 108

List of Tables

Table 1: Average microbial concentration in batch reactors over the 25 day study period

(log CFU/ml) ...... 32!

Table 2: Comparison of each treatment at their respective temperature, versus one another, with regards to average levels of fecal indicators and their trends over the course of the study. Table values represent the estimates between the treatments (log CFU/ml) and their standard errors, generated by the mixed model. Shading indicates the treatments were significantly different (P < 0.05)...... 35!

Table 3: Comparison of each treatment at the two temperatures (versus themselves) with regards to average levels of both indicator bacteria and their trends over the course of the study. Table values represent the estimates between the treatments (log CFU/ml) and their standard errors, generated by the mixed model. Shading indicates the treatments were significantly different (P < 0.05)...... 36!

Table 4: Chemical factors measured at three time points over the course of the study .... 39!

Table 5: Comparison of average log CFU/dry g over the course of the study. These average and their standard errors, were generated by the mixed model...... 62!

Table 6: Chemical factors measured at five time points over the course of the study ..... 64!

Table 7: Average Humic Acid Levels over the Course of the Study (mg/L) ...... 93!

Table 8: Salmonella spp. Concentration over the course of the study! (average MPN/4g dry solids)……………………………………………………………...... 95!

xii

List of Figures

Figure 1: Sources of Biosolids ...... 3!

Figure 2: Effect of temperature and inoculum-to-substrate ratios on E. coli and

Enterococci over the 25 day study period. A) The greatest reductions occurred for E. coli at 20°C, while1I:1S exhibited the lowest final concentration. B) Enterococci at 20°C had almost no change for the five treatments, with manure achieving the greatest log reduction. C) E. coli reductions were observed at 10°C, with the 1I:1S treatment exhibiting the lowest final concentration among the I:S ratios. D) There was almost no change at 10°C for Enterococci, illustrated by the nearly flat lines over the 25 day study period...... 31!

Figure 3: Experimental design of the four-liter digesters housed within a temperature controlled water bath with gas meter and water chiller schematics detailed ...... 55!

Figure 4: Concentrations of indicator organisms in digester effluent over time for the substrate (average specific fecal indicator level for the study), and the treatments, low

(0.3), medium (0.8), and high (1.3 kg VS day-1) ...... 61!

Figure 5: pH values over the course of the study, which illustrates the low loading rate never dropped below 7 and actually rose as the temperature increased following our simulated winter while constant decline of pH for the medium and high treatments occurred...... 65!

xiii

Figure 6: Daily biogas production over the course of the study, which depicted that following the simulated winter, only the low loading rate was able to recover and produce biogas during the warm period at the end of the experiment...... 66!

Figure 7: Detailed procedure for each method utilized in the extraction of Humic acids 86!

Figure 8: Standard curve for Humic Acid [1-100mg mg L-1] + Acidic Ce(IV) 0.05 mol L-

1 + rhodamine 6G 0.0001 mol L-1, which details an R2 value of 0.997. This standard curve depicts the relationship exhibited by an increase in humic acids concentration and the subsequent increase in RLU...... 90!

Figure 9: Figure 8: Chemical Luminescence signals from the reagents and their various combinations. This details the lack of signal from the reagents by themselves, and the combination of rhodamine 6G and humic acid alone. The inclusion of Ce(IV) with either humic acid or rhodamine 6G demonstrates that it is the oxidizing agent within the reaction (Ce(IV)). As seen in figure 8, the increase in humic acid concentration is followed by an increase in RLU...... 91!

Figure 10: Extracted humic acids over the course of the study for each extraction method, including elevated temperature. A) EDTA exhibited the highest levels of extracted humic acids, for both 25 and 37°C; B) Formaldehyde + NaOH yielded the second highest levels of humic acid; C) PBS + Heat demonstrated similar concentrations between the two temperatures, but exhibited the lowest levels among the extraction methods; D) Control yielded the lowest levels of humic acids, while the 25°C illustrated almost no change over time...... 92!

xiv

Figure 11: Changing levels of E. coli (A) and fecal coliforms (B) and Enterococci (C). E. coli and fecal coliforms had nearly identical trends, with large reductions at both temperatures while the 37°C achieved faster reductions and a lower final concentration compared to the 25°C. Enterococci levels had smaller reductions, but the 37°C digesters did achieve faster reductions compared to the 25°C, however both temperatures had similar final concentrations...... 94!

Chapter 1

Introduction

1.1 Sources and Characteristics of Biosolids

Biological residuals (untreated organic wastes) originate from various waste sources, such as animal manures, food wastes, and pre-treated municipal sludges. Biological residuals represent untreated organic waste in their raw form; following processing or treatment (extended times to advanced biological treatment), these raw biological residuals are more appropriately referred to as biosolids. According to regulatory guidelines, “Biosolids are the solid organic matter produced from private or community wastewater treatment processes that can be beneficially used, especially as a soil amendment,” (USEPA, 1999). However, this definition does not consider the multitude of other sources, both raw (biological residuals) and treated that are not processed by water resource recovery facilities (WRRF). Biosolids are simply defined by this general

EPA definition, while biological residuals are often used within the literature to describe organic waste sources. The lack of an overarching definition to describe “biological waste” either processed or unprocessed leaves something to be desired, which is why the use of “biosolids” will be used throughout the dissertation to describe both treated and raw sources of biological waste.

Biosolids consist of primarily water (90-98%) with 2-10% of dissolved, colloidal and settleable solids. They typically come in the form of a slurry (liquid), but raw sources

(biological residuals) such as manures or raw vegetables can generate a semi-solid or solid with up to 10% and even 30% solids for vegetables (Russ and Meyer-Pittroff,

2004). The solid fraction is composed primarily of organic matter, with a smaller portion consisting of inorganic matter. The organic fraction comprises living and once-living matter. Microbial biomass accounts for a relatively high percentage of the solids, in addition to organic bio-macromolecules such as carbohydrates, lipids, amino acids, proteins, humic and fulvic acids, and a very small fraction of synthetic organic matter

(i.e. pharmaceutical and commercial products). The inorganic fraction consists primarily of soil, salts, and metals. The ratio of organic to inorganics depends on the sources contributing to the waste stream.

The origin of biosolids can be divided into four main sources (Figure 1): municipal wastewater, food production and processing industries, agriculture residues, and animal manures (Sahlstrom, 2003). The most commonly recognized form is derived from municipalities consisting of residences and businesses. Their waste, which is often mixed with storm water, is discharged into a network of sewers, which flow into designated municipal WRRF. WRRF utilize primary (settling) and secondary (biological processes) treatment methods to separate the solid fraction (biosolids) from the water, and ultimately discharge the treated water back into the environment. Waste from on-site treatment

2 systems and industries may also be transported to a WRRF. The U.S. alone generates an estimated 7.4-8.2 million tons of biosolids annually from WRRF, which meet stabilization requirements for land application and are referred to as Class B biosolids

(Tanner et al., 2008; Sahlstrom, 2003; Bevacqua et al., 2009).

Figure 1: Sources of Biosolids

Additionally, waste from food production and processing industries constitutes another primary source of biosolids. These biosolids consist of waste generated during the

3 preparation, processing and production of meat, fish, grain, fruit, vegetables, coffee, beverages, sugar, milk, and baked goods (Russ and Meyer-Pittroff, 2004). Collection is easier for this form of waste, since it is typically generated in a controlled environment in which the waste is separated from the desired product. Food production and processing waste contain a wide range in terms of water content, with beverage waste having greater than 95% and raw fruit or vegetables containing around 70% (Russ and Meyer-Pittroff,

2004). As opposed to biosolids from fecal-generated waste, pathogens are less of a concern in processed food wastes, with more of the focus on high strength wastewater

(chemical oxygen demand [COD] and biological oxygen demand [BOD]), disinfectants, and odors from food waste. The food processing industry is estimated to produce 80 billion gallons of wastewater and 9 million tons of solid residuals annually within the

U.S. (Hang, 2004; Rose, 1971, Hudson, 1971).

The largest fraction of biological waste generated globally is from agricultural residues, with an estimated 1.2 billion dry tons generated annually within the U.S. alone (Carneiro et al., 2010). These crop residues typically have low moisture but high volatile solids content, and have little in the way of available fermentable substrates (not easily degraded) (Carneiro et al., 2010). This is due to the nature of lignocellulosic wastes and the time needed for hydrolysis, which can be offset with pre-treatments, though this can be costly. Agricultural residues are carbon rich, and similar to food waste it is often co- digested with nitrogen-containing wastes (such as animal manure) to maximize biogas yield and carbon-to-nitrogen ratios.

The final primary source comes from animal manures, which are generated in vast quantities globally, from small farms to concentrated animal feeding operations

(CAFOs). In the U.S. alone, there are an estimated 106 million dry tons of animal manures produced each year (Perlack et al., 2005). It originates from poultry, swine, equine, cattle, and other farm-based animals. The immense quantity of organic residual generated from animal manure poses a major challenge to our global environment due to manure’s high levels of pathogens and nutrients (potential contaminants) (Vanotti, 2009).

To avert environmental degradation, animal manures must be frequently collected (1-3 times per day). Collection typically occurs by washing pads that animals reside on, housing animals over a grate to allow feces to drop into a pit, or manually scraping and shoveling manure into containers for storage and disposal. The 100 million cattle in the

U.S. alone generate 40 million tons of wet manure each year, making them the largest producer of manure waste in the nation. These manures pose similar concerns to that of municipal waste since they may contain an array of pathogens.

The water and organic content of manures can vary a great deal from animal to animal.

This is often due to their feed (carbohydrate vs. protein diet), as well as differences in the animals’ digestive tracts. However, all manures typically form a semi-solid, with a high water-content (<15% solids) and few inorganics. Cattle manure, specifically, is a mixture of urea, uric acid, living and dead microorganisms, partially decomposed residues from organic feed, and metabolic products. This mixture has high levels of nitrogen and water

5 content, as well as small amounts of readily fermentable carbohydrates (Yu et al., 2010).

Although biosolids are a composite of biological residuals and can vary a great deal depending on their source, they are largely the same elemental components but at differing ratios. These general components include water and solids, which are suspended, colloidal, and settleable. The solids consist of labile carbon, hydrogen, oxygen, nitrogen, sulfur, phosphorus, and co-factors, which under anaerobic conditions can yield biogas (as a source of alternative energy). In addition, this labile matter will generate a nutrient-rich fertilizer (soil amendment) that can be used to improve the soil’s water capacity, aeration, structure, and available nutrients, ultimately improving crop yields and soil fertility (USEPA, 1999). The re-introduction of biosolids is important for the recycling of nutrients into the environment, making the collection, treatment, and utilization of biosolids of great importance for the environment as a whole.

1.2 Potential Impact of Pathogens and Chemicals on Humans

Biosolids contain pathogens and chemicals, which have the potential to contaminate water, soil, food, and air (USEPA 1999; Sinha et al., 2010; Rana et al., 2010; Bicudo and

Goyal, 2003). Biosolids typically enter the environment through land application practices, which entail spreading or spraying them upon agricultural fields to increase soil nutrients and crop yields. Land application, although beneficial relative to potentially improving the structure and nutrient characteristics of soil, can potentially create a source of contamination (if not properly managed), which can lead to exposure pathways of 6 chemicals and pathogens that could subsequently cause illness or pollute the environment.

Although the introduction of toxic chemicals and pathogenic microbes into the environment may have associated adverse impacts, there has not yet been a study that causally links biosolids to an increased risk of illness. Multiple studies on the issue have found non-significant results, while others have implied that there is potential for increased risk of human illness. Khuder et al. (2007) surveyed residents near land application sites, and suggested that an increased risk exists for certain respiratory, gastrointestinal, and other diseases among the residents of these areas. However, the authors cite limitations within the study and state that further research is needed to validate their findings (Khuder et al., 2007). That said, there have been multiple studies that have quantified pathogenic organisms in and around land application sites of biosolids. Tanner et al. (2005) investigated residential land applied biosolids. They investigated bioaerosols’ plumes and transport models, upon residential impact from land applied biosolids. They concluded that exposure to aerosolized pathogenic microorganisms is lower then previously estimated, since the duration of bioaerosol exposure immediately downwind of land-applied biosolids is brief and the plume generated is small (Tanner et al., 2005). Brooks et al. (2005) studied the residential impact of biological aerosols by obtaining and analyzing 350 aerosol samples (SKC bio- samplers) from 10 sites throughout the U.S. They concluded that the greatest risk exists during the loading operations of biosolids, but ultimately suggest that bioaerosol

7 exposure from biosolids operations poses little community risk (Brooks et al., 2005).

These studies, which suggest the risk is modest from the land application of biosolids, still demonstrate the potential of land-applied biosolids to create potential exposure pathways even if the risk is small for properly treated biosolids. Nonetheless, there is concern in regards to the use of untreated or unclassified biosolids, as demonstrated by several outbreaks of gastroenteritis related to livestock operations (Pell, 1997; Guan and

Holley, 2003; Spencer et al., 2004). In summary, the proper management and stabilization of biosolids is of great importance in minimizing public health risks and preventing future exposures.

Contamination from inappropriately applied biosolids can occur through a multitude of pathways, from both point and non-point sources. Of these contamination sources, the most commonly reported are those from soil overloading, biological aerosols, ground and surface water pollution, and food contamination (USEPA, 1999; Vanotti, 2009; Masse et al., 2011). Soil contamination can occur through overloading of soil, which may accumulate metals or pathogens. Biological aerosols are biological particles, which may contain pathogenic microbes that have been aerosolized by the land application of biosolids (Brooks et al., 2005). Water contamination can occur from runoff into surface water or leaching into ground water, resulting in nutrification of the water, accumulation of chemicals, and, most importantly, an avenue for pathogens to reach source waters

(Masse et al., 2011). The last primary contamination source of concern to the public is food contamination. Food-borne illness is estimated to cause 8.9 million illnesses

8 annually from known pathogenic organisms, and there is potential that some of these illnesses have resulted from inappropriately applied biosolids (Mead et al., 1999). This could be due to pre-harvest pollution that is thought to originate from fecal contamination, which is often the result of land-applied biosolids that were not adequately stabilized (Richards, 2001).

Biosolids contain a mixture of chemicals that can vary a great deal depending on the source of the waste. The chemicals that could be introduced into the environment from biosolids include steroid hormones, metals, pharmaceuticals, and personal care products

(PCPs), to list a few (Wang et al., 2007; Bevacqua et al., 2009; Sabourin et al., 2009).

Concerns arise from the ability for these potentially toxic substances to reach source waters via runoff, and from the accumulation of them within amended soils. Steroid hormones, for example, can have a negative effect upon aquatic life, disrupting their endocrine systems. Metals have the potential to accumulate within land-applied soils, leading to the uptake of metals by plants grown within these soils, and causing subsequent bioaccumulation within consumers of the plants. Cadmium is the metal of greatest concern, due to its potential concentration within municipal biosolids, soil chemistry, and ability to accumulate in plants (Logan and Chaney, 1983; USEPA, 1985).

Environmental effects from PCPs and pharmaceuticals are not well known, but biosolids are a sink for them, which generate another possible exposure pathway. Overall, chemicals are a concern, but reduction of pathogens through proper stabilization is the primary focus of biosolids treatment and management.

Pathogens are the emphasis in biosolids stabilization, as they are commonly found in the gut or excrement of infected humans or livestock, and can easily be spread throughout the environment if collection and treatment are not properly conducted. In other words, biosolids management (i.e. handling, use, and disposal) is vital given that these pathogens are sources of disease. Types of pathogens or indicator microorganisms (105-106 CFU/g of dry solids) that can be found in biosolids (Carneiro et al., 2010; Masse et al., 2011) include protozoans (e.g., Cryptosporidium spp.; Giardia spp.), bacteria (Escherichia Coli spp.; Salmonella spp.), enteric viruses (Adenovirus; Enterovirus), and helminthes

(Ascaris spp.) (Thomaz-Soccol et al., 1997; Sinha et al., 2010).

Bacteria, specifically Salmonella, are one of the most likely pathogens to be spread in the environment by slurry and sludge (Jones, 1980). All serovars of Salmonella are potentially pathogenic to both animals and humans (Sahlstrom, 2003). There are an estimated 2 to 4 million cases of salmonellosis annually in the U.S. (Bicudo and Goyal,

2003). Salmonellosis results in diarrhea, fever, and abdominal cramps, which can last for

4 to 7 days (Bicudo and Goyal, 2003). Another common bacterial pathogen spread by biosolids is Escherichia coli O157:H7. Its symptoms of infection are similar to

Salmonella, but 10-20% of patients might also develop a more serious disease such as hemorrhagic colitis or hemolytic uremic syndrome (Bicudo and Goyal, 2003). Protozoa such as Cryptosporidium and Giardia are robust organisms, which require a low infective dose to cause disease. These protozoans also require the host for reproduction and are

10 difficult to inactivate due to their oocysts, which utilize a thick spore coat that protects them against many disinfectants (Bicudo and Goyal, 2003). Outbreaks from protozoans typically occur through the contamination of water. Enteric viruses such as Enterovirus and Adenovirus are also commonly found in biosolids (Wong et al., 2010). Human and animal adenoviruses are present in aquatic environments, and have been found in rivers, coastal water, drinking water, and sewage worldwide (Wei et al., 2009). This contamination in aquatic environments is most likely linked to untreated biosolids (Wei et al., 2009).

Overall, the improper management of biosolids can produce an exposure pathway capable of causing disease in humans. This is demonstrated by the World Health

Organization (WHO) estimate that water-related diseases are the most common cause of illness and death among individuals in developing countries, specifically those under the age of five (WHO, 2008). It is further demonstrated by the WHO’s estimate that 1.5 million deaths per year can be attributed to unsafe water, poor sanitation, and lack of hygiene. While there is significant growth towards investing in sanitation, almost 41 percent worldwide (~2.6 billion people) live without proper sanitation facilities (WHO,

2008). In conclusion, the management and treatment of waste is a global issue, one that requires future work to ensure the protection of our overall environment and of those living within it.

1.3 Statement of the Problem

1.3.1 Small-Scale Biosolids Treatment Systems (SSBTS)

The proper management of biosolids can lead to a variety of benefits, such as generation of a nutrient rich fertilizer, production of alternative energy in the form of biogas, improved soil fertility, and increased crop yields, to list a few. In order to achieve the full potential of biosolids while minimizing environmental degradation, proper treatment is a necessity. The stabilization of biosolids currently requires specific treatments that include digestion at specific times and temperatures, chemical additives, or a tertiary treatment method (USEPA, 1999; Puchajda and Oleszkiewicz, 2006 a and b; USEPA, 1994). If these USEPA standards are not achieved, microbial quantification is required in order to determine the level of stabilization. Anaerobic digestion is often used to treat biosolids due to its multitude of benefits, and is very prevalent globally, with 40 million small- scale biosolids treatment systems (SSBTS) and numerous WRRF utilizing anaerobic digestion (Burns, 2009). These SSBTS are primarily operated at psychrophilic temperatures (< 25°C), and thus are not able to achieve the strict time and temperature requirements of regulatory stabilization. This dilemma, coupled with the lack of literature related to anaerobic digestion under psychrophilic temperatures and alternative assessment tools to determine stabilization illustrates the need for future research in this area.

The lack of research in this area is due primarily to current stabilization practices, which dictate treatment based on time-temperature requirements, the addition of a chemical

(lime), or a tertiary treatment method (i.e. composting) (USEPA, 1999; Puchajda et al.,

2006). Time-temperature requirements are met through anaerobic or aerobic digestion at mesophilic (~37°C) or thermophilic (~55°C) temperatures for hours to days. Chemical or alkaline stabilization is also used, utilizing quicklime (CaO) or hydrated lime (Ca[OH]2).

This is normally done using a mechanical mixer, which must generate a pH of at least 12 for 2 hours in order to achieve stabilization (USEPA, 1999). Tertiary treatment most often consists of composting, which requires large areas and the constant flow of oxygen through compost piles (biosolids mixed with lignocellulosic biomass) to utilize microbes for further degradation of residual organic matter; it can reach temperatures of 60°C, which destroys most pathogens (USEPA, 1999). These methods are validated in full- scale operations, which have high throughput and access to reliable energy sources.

In contrast, SSBTS often do not have access to energy sources or have the technology to meet these stringent requirements. Due to this lack of resources, a wide range of treatment methods are employed. The most common is anaerobic digestion, which is utilized by the 40 million SSBTS found worldwide (Burns, 2009). Anaerobic digestion is an effective waste treatment technology that harnesses naturally occurring microbes that are most active in the absence of oxygen. Anaerobic digestion involves a synergistic process consisting of four sequential phases: hydrolysis, acidogenesis, syntrophic acetogenesis, and methanogenesis (Yu and Schanbacher, 2010). Each phase is facilitated by a distinct functional group, or guild, of microbes. These guilds work in unison to bring about anaerobic decomposition, which reduces the waste volume, generates biogas, and

13 decreases levels of microbial pathogens (Tanner et al., 2008). Biogas as an alternative energy source also has an impact upon climate change due to methane’s potential as a green house gas; methane is 21 times more potent than CO2 in this respect (USEPA,

2008). Another benefit of biogas utilization is the reduction of biomass combustion, which can cause serious environmental degradation problems, such as deforestation, while also reducing indoor air pollution from individuals burning biomass indoors which can lead to respiratory illness (Smith, 1994; Yu et al., 2008).

Time and temperature requirements are principally used to ensure the reduction of specific pathogens or indicator organisms, the levels of which are used to determine the degree of stabilization (USEPA, 1999). There is a plethora of literature within this realm, specifically on anaerobic digestion and its ability to reduce both pathogenic and indicator organisms under thermophilic and mesophilic conditions (USEPA, 1999; Puchajda and

Oleszkiewicz, 2006; Puchajda et al., 2006; USEPA, 1994). However, there is little research that exists for anaerobic digestion under psychrophilic conditions (Masse et al.,

2011; Cote et al., 2006). This is significant because the majority of SSBTS are buried to minimize temperature fluctuations and resist freezing during winter months, placing them in the psychrophilic temperature range. This does not necessarily mean that the biosolids treated at this temperature range are not stable and cannot be land applied; there is simply an absence of research and guidelines by which to assess their level of stability and whether or not further treatment is needed without microbial quantification (Masse et al.,

2011, Cote et al., 2006). As such, the assessment of SSBTS-treated biosolids is needed in

14 order to maximize the potential of biosolids, to generate renewable energy in the form of biogas and act as a source of nutrients for agriculture, while minimizing potential exposures and maintaining the health of the overall environment (Nielsen-Holm et al.,

2009).

The potential for SSBTS utilizing anaerobic digestion is growing, with China expecting

60 million SSBTS by 2020 (Helmut Kaiser Consultancy, 2008). There is also a growing trend for SSBTS in the U.S., but research is needed to overcome the theory that only large-scale farming operations can utilize an anaerobic digester. This is reinforced by the

USEPA AgStar Handbook, which lists a minimum of 500 cows as a necessity for operating an anaerobic digester in a cost-effective manner (Swindal et al., 2010; USEPA,

2007). In the U.S., there are 57,640 dairies with fewer than 100 cows and 14,357 dairies with between 100 - 499 cows; these account for 95.8% of total dairy farms within the

U.S. (USEPA, 2007). The growing trend toward SSBTS technology could benefit farms, families, industries, and the already existing 40 million SSBTS globally (Burns, 2009).

The escalating trend toward the utilization of SSBTS demonstrates the need for further research to establish standards that ensure protection of the overall environment, while maximizing the potential of biosolids.

1.3.2 Indicators of Stabilization

As previously mentioned, the degree of stabilization is determined through the use of various treatments at specified times and temperatures or through microbial 15 quantification. However, there is potential for the use of other methods, such as chemical indicators, to determine stabilization. During the anaerobic digestion of biosolids, there are various chemical indicators that are used to take the pulse of a digester and its overall environment. These include pH, volatile fatty acids (VFA), total inorganic carbonate alkalinity (TIC), VFA/TIC ratio, total solids, and volatile solids. These chemical measures assess the digesters state and advise the operator on what modifications could be made to optimize the system—for example, increasing or decreasing the loading rate.

Although these indicators are frequently measured, there have been few studies that have investigated the potential association between chemical indicators and microbial levels.

Therefore, utilizing statistical methods to compare chemical and microbial relationships during the digestion of biosolids could uncover new procedures for assessing their level of stabilization.

In addition to the commonly used indicators (mentioned above), there is the potential to find new, innovative indicators to assess the level of biosolids stabilization. For example, the use of humic substances, which are actually generated through polymerization reactions during the degradation of organic substrates, demonstrates potential in this area.

Humic substances have been studied for centuries, yet there currently exists no clear definition or chemical structure for humus or humic substances. Humus has been described by some as a certain fraction of organic matter in soils and compost, while others recognize humus as the organic materials of natural origin in advanced stages of decomposition, whether in soils, compost, digesters or peat bogs, and whether plant or

16 animal in nature (Waksman, 1952). In further detail, their formation is better described by condensation polymerization reactions, amino acid sugar interactions, and animal/plant decay, which form humic or fulvic acids (Qu et al., 2012). These two fractions of humic substances are thought to play an important role in global carbon and nitrogen cycles, as well as the regulation, mobility, and fate of plant nutrients, environmental contaminants, and heavy metals (Qu et al., 2012).

The formation of humic substances is thought to be the direct result of organic substrate decomposition. Therefore, as biosolids are being degraded, humic substances are being formed, and thus higher levels of humic substances could indicate stabilization. The study of humic substances and their role in biosolids has received minimal attention, as has their role as an indicator for stabilization. The extraction and quantification of humic substances from biosolids has the potential to demonstrate a new and innovative indicator for biosolids stabilization.

1.4. Rationale for Research

Small-scale biosolids treatment systems utilizing anaerobic digestion are a powerful public health tool due to their variety of benefits. These include their ability to simultaneously generate alternative energy in the form of biogas, stabilize the waste stream, and create a valuable fertilizer. An important element of stabilization is the reduction of harmful microorganisms. The factors that aid or inhibit these organisms are an area in need of research. Furthermore, this could be specifically stated as the 17 controlling factors of pathogen or indicator organism levels within an anaerobic digester.

Ideals differ on what parameters mitigate their survival during digestion. Among these possible parameters are pH, slurry composition, mixing, species and serotype, specific and total volatile fatty acids, style of digestion (batch or continuous), substrate limitation, degree of stabilization, and temperature (Levett et al., 1993; Masse et al., 2011; Kumar et al., 1999; Puchajda and Oleszkiewicz, 2006a; Smith et al., 2005; Bicudo and Goyal,

2003). Numerous articles point to temperature being the most important factor

(Sahlstrom, 2003; Levett et al., 1993; Kumar et al., 1999), while Smith et al. believe that mixing and stabilization, not temperature, most directly controls the rate of inactivation

(Smith et al., 2005). Understanding how these factors control microorganism levels within an anaerobic digester is essential to determining optimal treatment practices for

SSBTS and establishing new and effective assessment tools (such as humic substances) to ensure proper stabilization of biosolids.

In an effort to maximize SSBTS usage and ensure optimal biosolids stabilization, additional research is required to address the paucity of information related to biogas production and pathogenic microbe reduction under psychrophilic temperatures, and on finding new indicators of stabilization. Conducting relevant research on SSBTS operating at these temperatures could have significant implications for the potential of SSBTS worldwide, from both a public health and an engineering perspective. Also, comparing several current chemical indicators (VFAs, pH, mV, biogas, and TIC) and newly developed markers (humic acid) could result in novel methods to classify the stabilization level of biosolids. Ideally, this research will contribute baseline data in regards to

18 indicator and pathogenic microbe levels under psychrophilic temperatures, while also identifying key chemical indictors that share a relationship with pathogens. Overall, the proper treatment and assessment of biosolids is vital to capitalize on the potential of biosolids, while also insuring the protection for both the environment and the public health.

1.5 Specific Aims

Specific Aim 1: Evaluate the effectiveness of psychrophilic anaerobic digestion in reducing viable populations of fecal indicator bacteria under various inoculum-to- substrate (I:S) ratios over a 25 day study period

Hypothesis 1: These low temperatures can reduce indicator organism levels

Hypothesis 2: Increased I:S ratios improve both biogas production and indicator

organism levels

Hypothesis 3: Demonstrate a digester performance indicator is predictive of fecal

indicator levels in the treated biosolids

Specific Aim 2: Investigate semi-continuous flow anaerobic digestion of dairy manure at temperatures simulating seasonal change (27C-10C-27C), utilizing various feeding rates, to investigate their effects upon both chemical and microbial levels over time

Hypothesis 1: Class B biosolids are produced by SSBTS

Hypothesis 2: Loading rates (increased) will have a direct relationship upon levels

of indicator organisms 19

Hypothesis 3: An optimally performing digester with regards to biogas production

will result in lower levels of fecal indicator organisms

Hypothesis 4: Seasonal fluctuation (temperature) does effect levels of indicator

organisms

Specific Aim 3: Digest biosolids anaerobically at varying times to demonstrate the relationship between humic acid and the levels of pathogenic and indicator organism, using chemical luminescence and microbial plate counts, respectively.

Hypothesis 1: As microbial levels decrease, humic acid levels will increase

Hypothesis 2: Utilizing modified EPS extraction methods will allow us to

quantify extracted humic acids with an acidic Ce(IV)-rhodamine 6G complex

Chapter 2

The Impact of Inoculum-to-Substrate Ratios on Microbial Concentrations during

Psychrophilic Anaerobic Digestion of Dairy Manure

Abstract

! The goal of this study was to evaluate the effectiveness of psychrophilic anaerobic digestion in reducing viable populations of fecal indicator bacteria (Escherichia coli and

Enterococci spp.) under various inoculum-to-substrate (I:S) ratios. The study was conducted using laboratory-scale batch reactors at 10°C and 20°C for 25 days, with dairy manure as the substrate. The inoculum consisted of effluent from a mesophilic anaerobic digester that processes a mixture of food wastes and waste resource recovery facility sludge. The five treatments were inoculum (control inoculum or inoculum only [CI]), manure (control manure or substrate only [CM]), and I:S ratios of 3I:1S, 1I:1S, and 1I:3S.

The results demonstrated that E. coli was reduced in all treatments, while Enterococci was decreased in only a few treatments. The treatment with the 1:1 ratio achieved the greatest reduction of E. coli at 20°C (3.11 log), whereas the CM showed the greatest E. coli reduction at 10°C (2.33 log). The treatment with the 1I:3S ratio resulted in the greatest reduction of Enterococci at 20°C (1.82 log), whereas CM produced the greatest reduction at 10°C (0.49 log). These reductions appear to be the result of environmental

21 conditions, namely substrate limitation for E. coli, and increased VFA levels for

Enterococci. Lower inoculum-to-substrate ratios resulted in higher average levels of indicator organisms; they also produced less stable conditions with more acidic pH, higher volatile fatty acid levels, and reduced biogas production. The results suggest that a treatment with the 1I:1S ratio is ideal for starting a digester at 20°C, whereas 3I:1S appears optimal for 10°C. Overall, I:S ratios do influence chemical parameters and the levels of fecal indicators during the anaerobic digestion of cattle manure in batch operations at psychrophilic conditions. Continued use of I:S ratios (1I:1S) might be beneficial following start-up to improve stability of a digester at reduced temperatures.

2.1 Introduction

The United States Environmental Protection Agency (USEPA) AgStar

Handbook recommends a minimum of 500-cow operations to be able to afford and efficiently operate a large-scale digester to anaerobically decompose and stabilize manure

(USEPA, 2007). This can cost upward of one million dollars. This restriction has limited the US to operating approximately 150 large-scale agricultural digesters, mostly on large dairy farms (Swindal et al., 2010). The EPA’s recommendations exclude approximately

96% of U.S. dairy operations since they have fewer than 500 cows (USEPA, 2007).

However, large-scale digesters are not the only way to capitalize on the natural, anaerobic decomposition of wastes, as has been demonstrated by the expanded use of small-scale systems globally (Rajendran et al., 2012). Psychrophilic anaerobic digestion is the most common form of engineered digestion worldwide, with approximately 35 to 40 million 22 small-scale in operation (Rajendran et al., 2012). This number is expected to increase dramatically in the near future, with China alone planning to construct 60 million small- scale digesters by the year 2020 (Burns R., 2009; Helmut Kaiser Consultancy [HKC],

2008).

Anaerobic digestion involves a synergistic process consisting of four sequential phases: hydrolysis, acidogenesis, syntrophic acetogenesis, and methanogenesis (Yu and

Schanbacher, 2010). Each phase is facilitated by a distinct functional group, or guild, of microbes. These guilds collectively work to decompose labile organic matter, and simultaneously reduce waste volume, generate biogas as an alternative energy source, and decrease the level of pathogenic microbes (Tanner et al., 2008). Both large and small-scale anaerobic digesters are used to treat many types of biological wastes and residues worldwide. These residues include sewage sludges, crop residues, animal manures, and food production and processing wastes. The proper treatment of these wastes is important due to their frequent application as soil amendments and the related need to ensure that levels of pathogenic microbes and toxic chemicals are controlled

(USEPA, 1999a). These organisms and chemicals have the potential to contaminate water, air, soil, and food if not appropriately managed (USEPA, 1999a; Bicudo and

Goyal, 2003; Sinha et al., 2010; Rana et al., 2010).

From a public health perspective, levels of pathogenic microbes and toxic chemicals must be reduced during the decomposition, and stabilization of biological wastes and residues.

Concomitantly, from the engineering perspective, the volume of waste and residues must be reduced and the generation of methane increased during the decomposition and stabilization processes. To properly achieve both the public health and the engineering goals through anaerobic digestion, there are several important factors to consider. These include temperature, type of substrate, pH, mixing, volatile fatty acid (VFA) distribution and concentration, carbon-to-nitrogen ratio, and inoculum-to-substrate ratio (Puchajda et al., 2006; Levett et al., 1993; Smith et al., 2005; Kumar et al., 1999; Bicudo et al., 2003).

Of these factors, temperature is considered the key component in achieving optimal biogas production and pathogen destruction (Sahlstrom L., 2003; Kumar et al., 1999).

However, most small-scale systems operate under mostly passive conditions

(uncontrolled internal temperature levels), as a result they generally operate within the psychrophilic range. A better understanding of the other operational factors within these digesters is essential for optimizing and further developing psychrophilic anaerobic digestion. Inoculum-to-substrate ratios were the operational variable focused upon in this study.

Inoculum provides buffering and a source of indigenous microbes, especially anaerobes, to seed the anaerobic digestion process. These microbes thrive within a digester, forming a working community, and can be re-used in a manner similar to the recycled activated sludge process (RAS). Lopes et al. (2004) concluded that the use of inoculum substantially improved the anaerobic treatment of solid waste, due to the increased levels of indigenous anaerobic microbes that contribute to the degradation of organic matter

(Lopes et al., 2004). Inoculum and inoculum-to-substrate ratios are frequently utilized and investigated due to their relevance to the start-up of digesters; following start-up, substrate is typically the only product added. However, there are potential advantages by adding inoculum in a manner similar to RAS (always loading as an I:S ratio) because it could increase retention times and possibly improve both buffering capacity and the stabilization of the biological residuals. At this time, the literature on inoculum has focused mostly on its impact in start-up operations and performance of digesters, with less attention having been paid to its effect upon reduction of pathogens and fecal indicator levels.

Few studies have focused upon the ability of low-temperature anaerobic digestion to effectively reduce the levels of both pathogen and indicator organisms. Kumar et al.

(1999) utilized lab-based reactors at 35°C and at room temperature (18-25°C); their results showed E. coli reduction was greater at 35°C (2 log at day five and below detectible limits at day 15) compared to 25°C (0.4 log reduction at day five and below detectible limits on day 25). These results illustrate the potential of psychrophilic anaerobic digestion to achieve significant reductions of E. coli. Cote et al. (2006) investigated the efficiency of psychrophilic anaerobic digestion in sequencing batch reactors to reduce indicator and pathogen levels in swine slurries. Experimental investigation was conducted in sequencing batch reactors at 20°C and two 20 day cycles

(total of 4 weeks of digestion). These conditions showed reductions of 1.6 – 4.2 log

(CFU/g) of total coliforms, and 2.5 - 4.2 log (CFU/g) of E. coli, whereas Salmonella,

Cryptosporidium, and Giardia decreased below detectable limits (<100 CFU/g) (Cote et al. 2006). These studies provide baseline data on the ability of batch-style anaerobic digestion in reducing the levels of fecal indicators and pathogens at psychrophilic temperatures. However, there exist few other studies published on the efficiency of anaerobic digestion at psychrophilic temeperatures to remove pathogens or indicators in biological residuals (Massé et al., 2010).

The objectives of this study were: 1) to evaluate the effectiveness of psychrophilic anaerobic digestion (10°C and 20°C) in batch-style digesters over a 25 day study period in reducing fecal indicator levels (E. coli and Enterococci spp.); 2) to investigate the impact of variable inoculum-to-substrate (I:S) ratios on indicators of digester performance (pH, VFAs, TIC, VFA/TIC ratio, and biogas) over time; and 3) to determine if any of the performance indicators are predictive of fecal indicator levels in biosolids.

2.2. Methods

2.2.1 Anaerobic inoculum and manure

Fresh dairy cow manure was used as substrate in this study, and was obtained from the

Waterman Dairy Farm at The Ohio State University in Columbus, Ohio. The manure was collected fresh from the feeding lot pads and diluted at a ratio of 1:2 (water:manure) by volume. The inoculum, as seed organisms, was obtained from an active industrial scale anaerobic digester designed and operated by Quasar Energy Group in Columbus, OH.

This digester processes a mixture of food wastes with sludge provided by the local water resource recovery facility facil. Analyses of solids were conducted immediately after collection of both inoculum and manure, in order to determine inoculum-to-substrate ratios. During analyses, the inoculum and manure were stored at 4°C for approximately

24 hours. Calculations based on VS ratios were subsequently conducted to determine the five treatment groups: control-inoculum (CI, inoculum only), control-manure (CM, manure only), and three inoculum-to-substrate ratios based upon VS values of 3I:1S,

1I:1S, and 1I:3S. Upon determination of the I:S ratios for each treatment, the inoculum and substrate were added, accordingly, to individual digesters (40 digesters).

2.2.2 Experimental set-up

The experiment was conducted using 160ml glass vials operating as batch reactors, filled with 100ml of the I:S ratios described above. Following the addition of each mixture, the reactors were flushed with 99.9% N2 gas for one minute prior to being sealed. Each reactor contained a rubber stopper with a ⅛” stop-cock valve for releasing biogas generated from the reactors. The reactors were placed in temperature-controlled water baths (Techne ESRB-7, Princeton, NJ) for the 20°C ± 0.1°C system, and into a refrigerator with a water bath for the 10°C ± 1.0°C system. Mechanical mixing was done daily following biogas measurements using water displacement. The schedule consisted of sampling individual batch digesters for each treatment (CI, CM, 3I:1S, 1I:1S, and

1I:3S) at nine different time points (0, 1, 3, 5, 8, 12, 16, 20, and 25 days) for both 10°C

27 and 20°C. Each treatment had eight individual digesters, which were sampled at random for a total of 40 digesters per temperature (10°C and 20°C).

2.2.3. Measurement of biogas

Daily biogas generation was measured using a volumetric method with a barrier solution of 0.5% sulfuric acid (Müller et al., 2004). The digesters were connected by ⅛” tubing to a lure lock stop-cock valve and 50ml pipette for biogas measurement. Every 24 hours, biogas was vented, and displaced water was used to calculate the volume (ml) of biogas produced (Müller et al., 2004).

2.2.4. Measurement of chemical parameters

Total solids (TS), volatile solids (VS), total inorganic carbonate alkalinity (TIC), volatile fatty acids (VFA), and pH were analyzed immediately after the sample aliquots were removed from their respective digesters. The pH was measured with a pH/Ion 510-bench pH meter (Fisher Scientific, Pittsburgh, PA). VFA and alkalinity concentrations were measured using a titration method (Lossie and Putz, 2008). Standard methods (APHA,

2005) were used to determine TS and VS.

2.2.5. Measurement of fecal indicators

The indicator organisms studied were E. coli (USEPA, 2002a) and Enterococci spp.

(USEPA, 2002b). Their levels were assessed using EPA standard methods (USEPA,

2002a and 2002b). Following mechanical mixing, the digesters were opened, and fresh samples were dispensed into sterile 50ml-tubes and placed on ice. These samples were

28 transported and processed within 24 hours. Ten grams from each of the five digesters was diluted with 90ml of sterile phosphate buffered saline (PBS) for E. coli measurements, while another 10 grams was diluted with 90ml of sterile buffered peptone water (BPW) for measurement of Enterococci. Dilution was done using sterile 500ml Whirl-Pak bags

(Whirl-pak, NASCO, Fort Atkinson, WI). The bags were subsequently homogenized for

2 min in a Stomacher 80 (Seward Co., West Sussex, UK), followed by serial dilution for membrane filtration. Filtration was performed using a ¼ HP GAST vacuum pump

(GAST Inc., Benton Harbor, MI) with a vacuum flask, filter cassettes (Nalgene), and

47mm/0.45µm nitrocellulose membrane filters (Millipore, Billerica, MA). Following filtration, the membrane filters were placed upon their respective media, modified m-

TEC (E. coli) and m-EI (Enterococci), and incubated. After incubating at specific times and temperatures according to the respective standard methods, the colonies were counted, and then colony numbers were calculated after considering dilution rate and inoculum volumes (USEPA 2002a and 2002b). The results are expressed in CFU/ml. All measurements were done in triplicate.

2.2.6. Statistical analysis

The statistical comparison of the treatments was conducted using SAS 9.2 software (SAS

Institute Inc., NC, USA). The analysis utilized a mixed model approach (proc mixed) to determine the relationship present between each covariate so as to determine if they were predictive of outcome variables (levels of microbes). In addition to a mixed model examining each covariate, a comparison was conducted on the microbial levels over time, between the individual treatments (CI, CM, 1I:1S, 1I:3S, 3I:1S), and their trends between 29 the time periods (trend effect over time). This mixed model also encompassed a comparison of the two treatments between their respective temperatures (10°C and

20°C).

2.3. Results

2.3.1. Fecal indicators

E. coli levels were highest from the CM treatment and lowest from the CI treatment at

20°C over the course of the study (Figure 2 and Table 1). CM achieved a 1.35 log reduction over the 25 day study period (5.43 to 4.08), while CI attained a 1.4 log reduction (3.52 to 2.12). The three I:S treatments all attained a reduction greater than 2 logs at 20°C (3I:1M, 2.44 and 1I:3M, 2.81). The 1I:1S treatment achieved a reduction of

3.11 log, the highest of any treatment at 20°C. E. coli levels across the five treatments at

10°C were mostly similar to those at 20°C, with those of CM being the highest and CI the lowest (Table 1). However, there was an exception in the final three sampling points, at which 1I:3M had slightly greater levels of E. coli than CM (~0.2 log difference). The log reduction for the treatments at 10°C were 2.33 log for CM, no reduction for CI, 1.26 for

3I:1S, 2.03 for 1I:1S, and 1.27 for 1I:3S. At 10°C, only CI achieved an E. coli concentration of less than 3 log CFU/ml; in contrast, all of the 20°C treatments besides the CM (4 log CFU/ml) achieved levels below 3 log CFU/ml. The 20°C treatment trends for E. coli illustrated the greatest reductions (Figure 2) and lowest final concentrations, while the 10°C treatment did achieve marked reductions in E. coli, just not to the level of

20°C.

Figure 2: Effect of temperature and inoculum-to-substrate ratios on E. coli and Enterococci over the 25 day study period. A) The greatest reductions occurred for E. coli at 20°C, while1I:1S exhibited the lowest final concentration. B) Enterococci at 20°C had almost no change for the five treatments, with manure achieving the greatest log reduction. C) E. coli reductions were observed at 10°C, with the 1I:1S treatment exhibiting the lowest final concentration among the I:S ratios. D) There was almost no change at 10°C for Enterococci, illustrated by the nearly flat lines over the 25 day study period.

In contrast to the E. coli trends, Enterococci showed no significant differences between treatments, with lower reductions at both temperatures. The 20°C treatments showed 31 fluctuations in Enterococci concentrations throughout the study, but ultimately ended with log reductions of 1.81 log for CM, 0.84 for CI, no reduction for 3I:1S, no reduction for 1I:1S, and 1.82 for 1I:3S. Enterococci’s final concentrations varied slightly, with

3I:1S and 1I:1S both having concentrations of log 5 CFU/ml, whereas 1I:3S and CM had concentrations of log 4.61 and log 4.35 CFU/ml, respectively. The 10°C treatments, as depicted in Figure 2, showed almost no changes in microbe levels throughout the study, with Enterococci concentrations (CFU/ml) being directly correlated to the quantity of substrate; less substrate resulted in lower levels of indicator organisms (Table 1). Log reductions at 10°C were 0.49 for CM, no reduction for CI, 0.17 for 3I:1S, 0.43 for 1I:1S, and 0.03 for 1I:3S.

Table 1: Average microbial concentration in batch reactors over the 25 day study period (log CFU/ml)

Temperature Indicator Control Control 3I:1S 1I:1S 1I:3S Organism (CM) (CI) 20°C E. coli 5.01 ±0.46 2.77 ±0.57 3.43 ±0.98 3.46 ±1.30 4.09 ±1.08 Enterococci 5.37 ±0.92 3.69 ±0.74 5.19 ±0.63 5.41 ±0.66 5.67 ±0.71 10°C E. coli 4.89 ±0.87 1.76 ±0.19 3.70 ±0.38 4.25 ±0.81 4.54 ±0.55 Enterococci 5.99 ±0.18 3.36 ±0.30 5.07 ±0.12 5.48 ±0.23 5.60 ±0.24

2.3.2. Mixed Model

! The utilization of log reductions of fecal indicator levels is useful for determining the changes in bacterial levels at two time points (Cote et al., 2006; Kumar et al., 1999).

However, log reductions themselves are not a useful measure in order to understand patterns of changing levels of indicator organisms over time, since they compare only the initial and final concentrations. The use of statistical models (mixed models), better allows us to investigate patterns and relationships over time, by comparing indicator organism trends and average levels over the entire study period. A mixed model (proc mixed) takes into account changes over time to fecal indicators, and considers samples in close proximity to have greater relationships compared to those farther apart. This allows for enhanced comparisons of fecal indicators over time, and the changes that might occur between the initial and final sampling points thus allowing a better understanding of relationships between and within treatments.

The initial model for E. coli (10 and 20°C), involving time (days), treatment, and an interaction term for treatment and time, was statistically significant (P < 0.05; [Tables 2 and 3]). Conversely, Enterococci had a non-significant relationship between treatment and time (P > 0.05). These initial mixed models were also run with the addition of chemical indicators (listed in Table 4) in order to determine if these chemical indicators shared a significant relationship with the outcome variables, E. coli and Enterococci. It

33 was determined that none of the chemical indicators shared a significant relationship with fecal indicators (P > 0.05), since the overall model was not improved.!

The comparison of average E. coli levels between the five treatments at 20°C demonstrates CM, CI, and 1I:3M were statistically different from one another (P > 0.05,

Table 2), where as 3I:1S and 1I:1S were not (P < 0.05). Comparison of the five treatment trends over time at 20°C, demonstrated that CM and CI have statistically similar trends over time, and that all three mixtures (3I:1S, 1I:1S, 1I:3S) likewise, had statistically similar trends when compared with each other (P > 0.05, Table 2). These trends represent similar changes (reductions) over the course of the study, allowing us to further compare each treatment over time (Figure 1). Assessment of the five treatment trends over time at

10°C, demonstrated statistically similar trends over time between CI and 3I:1S (P >

0.05), and between CM, 1I:1S, and 1I:3S when compared with each other (P > 0.05,

Table 2). Evaluation of the average E. coli levels, when comparing the five treatments against themselves at the two temperatures (Table 3), resulted in CM and 3I:1S being statistically similar (P > 0.05), while CI, 1I:1S, and 1I:3S were statistically different (P <

0.05). Investigating the E. coli trends for the five treatments at the two temperatures

(Table 3), resulted in significantly different results (P < 0.05), illustrating different trends over the course of the study.

Within Treatment

Temperature Indicator Manure Manure Manure Manure Inoculum Inoculum Inoculum 3I:1S 3I:1S 1I:1S (°C) Organism Vs. Vs. Vs. Vs. Vs. Vs. Vs. vs. vs. vs. Inoculum 3I:1S 1I:1S 1I:3S 3I:1S 1I:1S 1I:3S 1I:1S 1I:3S 1I:3S

Comparing Average CFU/ml

20 E. coli 2.23±0.13 1.48±0.13 1.43±0.13 0.817±0.13 -0.754±0.13 -0.807±0.13 -1.42±0.13 -0.053±0.14 - -0.610±0.14 0.663±0.14 Entero 1.59±0.29 0.148±0.30 -0.07±0.30 -0.39±0.30 -1.45±0.30 -1.67±0.30 -1.99±0.30 -0.218±0.30 - -0.323±0.30 0.541±0.30 35 10 E. coli 3.04±0.13 1.09±0.13 0.535±0.13 0.259±.013 -1.94±0.14 -2.50±0.14 -2.78±0.14 -0.564±0.14 - -0.275±0.14

0.839±0.14 Entero 2.57±0.30 0.893±0.30 0.471±0.30 0.364±0.30 -1.67±0.30 -2.09±0.31 -2.20±0.31 -0.421±0.31 - -0.107±0.31 0.529±0.31 Comparing Trends

20 E. coli 0.011±0.015 0.065±0.016 0.100±0.016 0.078±0.016 0.054±0.016 0.089±0.016 0.067±0.016 0.035±0.017 0.013± - 0.017 0.022±0.017 Entero - - - - 0.007±0.036 0.023±0.036 0.072±0.036 0.016±0.037 0.065± 0.049±0.037 0.085±0.034 0.079±0.036 0.063±0.036 0.013±0.016 0.017 10 E. coli - - - - 0.058±0.016 0.105±0.016 0.079±0.016 0.047±0.017 0.021± - 0.011±0.015 0.048±0.016 0.001±0.016 0.027±0.016 0.017 0.026±0.017 Entero - - 0.001±0.036 - 0.011±0.036 0.022±0.036 0.015±0.036 0.010±0.037 0.004± - 0.020±0.034 0.009±0.036 0.005±0.036 0.037 0.007±0.037

Between Treatments Temperature Indicator Manure Inoculum 3I:1S 1I:1S 1I:3S (°C) Organism vs. vs. vs. vs. vs. Manure Inoculum 3I:1S 1I:1S 1I:3S 10 vs. 20 E. coli 0.136 ±0.13 0.938 ±0.13 -0.246 ±0.14 -0.756 ±0.14 -0.421 ±0.14 36 ±0.30 ±0.30 ±0.31 ±0.31 ±0.31

Entero -0.662 0.304 0.083 -0.121 0.095 Comparing Trends 10 vs. 20 E. coli 0.042 ±0.015 -0.075 ±0.015 -0.071 ±0.017 -0.059 ±0.017 -0.063 ±0.017 Entero -0.062 ±0.034 0.003 ±0.034 0.008 ±0.037 0.002 ±0.037 -0.052 ±0.039

Comparison of the average concentration of Enterococci across the five treatments at

20°C found the levels within the CI treatment to be statistically different from those of the other four treatments (P < 0.05, Table 2). Enterococci average concentrations for the other four treatments were all statistically similar (P > 0.05). The trends of Enterococci concentrations throughout the study were all similar to each other (P > 0.05), with the exception for CM versus CI and CM versus 3I:1S. Comparable results were seen at 10°C for average levels of Enterococci, with CI being significantly different from the other four treatments, and 3I:1S from CM. Further, none of the treatment groups at 10°C generated significantly different trends, with almost no change occurring throughout the study (Figure 1). Evaluation of the average Enterococci levels and trends, when comparing the five treatments against themselves at the two temperatures (Table 3), resulted in all of the treatments not being statistically different from one another (P >

0.05).

2.3.3. Chemical parameters

The results of the chemical parameters are summarized in Table 4. At 20°C, the treatments containing higher levels of substrate (CM and 1I:3S) produced higher VFA concentrations (>10,000 mg/L), decreased levels of TIC, and reduced levels of cumulative biogas production. The 1I:1S treatment generated the most biogas, and produced increasing TIC and decreasing VFA numbers (i.e. a more stable environment for biogas generation). VFAs play an integral role in anaerobic digestion, since their accumulation can result in a drop in pH and ultimate reactor failure (Chen Y. et al.,

2008). The 3I:1S treatment generated the second-most biogas, and displayed similar TIC and VFA trends to those of the 1I:1S treatment. The treatments at 10°C, meanwhile, exhibited reduced metabolic activity, and resulted in treatment 1I:1S (which produced the best results at 20°C) producing a less-than-favorable environment, with increasing VFAs and slightly increasing alkalinity levels. 3I:1S and 1I:1S had similar levels of alkalinity

(slightly increasing alkalinity) and biogas generation (18ml of biogas difference), but

3I:1S did however exhibit decreasing VFA levels.

Table 4: Chemical factors measured at three time points over the course of the study

Treatment Cumulative VS VFAs Alkalinity pH (Temperature Biogas (g/L) (mg Hac/L) (mg CaCO3) ) (ml)

Day 3 15 25 1 12 25 1 12 25 1 12 25 1 12 25 CM 35 183 230 84.30 68.08 66.24 8996 13296 14292 9000 7300 6900 7.78 6.95 6.77 (20°C) CI 25 97 145 35.14 34.83 31.12 1510 1676 1178 8600 8350 8950 7.61 7.68 7.67 (20°C) 3I:1S (20°C) 60 360 587 37.63 30.21 41.90 3004 1676 1344 9100 9900 10650 7.59 7.53 7.60

1I:1S 80 473 763 43.29 41.14 37.58 4166 2506 2838 9550 10300 12350 7.56 7.52 7.77

39 (20°C)

1I:3S 82 275 406 54.08 53.57 49.57 5826 7984 9810 9550 8850 8650 7.62 7.21 7.20 (20°C) CM (10°C) 34 139 199 61.75 40.45 66.61 7486 8980 10640 7400 6350 8700 7.77 6.63 7.50

CI 42 125 196 40.79 40.76 37.17 2174 1427 2174 8050 9450 10350 6.95 7.49 7.82 (10°C) 3I:1S (10°C) 53 190 329 47.05 43.05 46.47 3170 3502 2672 10150 9700 11300 7.77 7.42 7.76

1I:1S 54 198 347 50.04 48.36 53.15 5162 5328 6822 8900 9600 10300 7.77 7.23 7.46 (10°C) 1I:3S 57 236 406 56.22 56.18 55.91 5328 6988 6490 9300 9500 9850 7.73 7.04 7.58 (10°C)

2.4. Discussion

2.4.1. Fecal indicators

Because the substrate contained higher levels of fecal indicators compared to inoculum, it was expected that samples with greater amounts of substrate, relative to inoculum, would have greater initial and potentially final concentrations of fecal indicators. Higher levels of fecal indicators can also be explained by the presence of higher levels of substrate, which are able to support increased levels of fecal indicators. This trend was observed for the majority of the treatments, but there were several notable exceptions.

The results demonstrated that E. coli inactivation occurs at psychrophilic conditions, and that temperature plays a substantial role in their reduction even at 10°C and 20°C. The effect of I:S ratios upon E. coli levels was also demonstrated, in that lower substrate ratios resulted in lower levels of E. coli; this was expected, due to inoculum containing fewer indicator bacteria and lower available substrate with which to support a larger community. Nevertheless, the 1I:1S treatment exhibited the greatest reduction of E. coli

(3.11 logs) and also the lowest final concentration (log 1.72 CFU/ml) compared to 3I:1S

(log 2.03 CFU/ml) and CI (log 2.12 CFU/ml). This was unexpected due to its higher initial concentration of fecal indicators and substrate. This could be the result of increased alkalinity levels (1I:1S, 12,350mg CaCO3 ; 3I:1S, 10,650mg CaCO3; and CI, 8,950mg

CaCO3), since this was the only chemical variable of noteworthy difference other then

biogas (Table 4). However, this is unlikely, since there has been no literature to support this supposition, especially since alkalinity is a measure of a system’s buffering capacity, which presumably would not hinder levels of E. coli. The 10°C treatments exhibited a direct correlation to I:S ratios, with higher substrate ratios resulting in higher average

(Table 1) and final concentrations of E. coli (log 3.66 for CM, log 2.09 for CI, log 3.20 for 3I:1S, log 3.21 for 1I:1S, and log 3.76 CFU/ml for 1I:3S).

The initial mixed model for E. coli (treatment [I:S ratio], temperature, and time), demonstrated that there was a significant relationship between time and specific treatments for E. coli levels at both temperatures (P < 0.05). In other words a change in levels of E. coli occurred as the result of either the treatment (I:S ratios), time, or the combination of the two. This statistical relationship is supported by data displayed in

Figure 1, in which there is a change in the levels of E. coli over time for all the treatments at both temperatures.

E. coli trends and their differences between treatments provide further information with which to compare the levels of E. coli and their changes over time. The results demonstrated that at 20°C, the CM and CI exhibited statistically similar trends over time, as did the three I:S ratios (3I:1S, 1I:1S, and 1I:3S). The CM and CI trend results were unexpected; minimal change was anticipated for CI, while CM was expected to be associated with large reductions in E. coli levels over time. CM reductions were anticipated because VFA accumulation is thought to reduce the number of fecal coliforms

(Puchajda & Oleszkiewicz, 2006). These results suggest that substrate utilization/limitation might be an important variable for E. coli survivability/resistance, since reductions were seen at both temperatures. The 10°C treatment had statistically similar trends (P > 0.05) for CI and 3I:1M, and for CM, 1I:1S, and 1I:3S. Both of these were expected to due the initial levels of E. coli within each, and the absence of change expected at this low of temperature. Comparing the E. coli trends for the five treatments against one another (CM vs. CM) at the two temperatures (Table 3) resulted in significantly different results (P < 0.05) for all of the treatments. This further illustrates the effect that temperature had upon the levels of E. coli over time, since all of the treatments at 20°C had greater reductions over the course of the study (Table 3).

The Enterococci results demonstrated that anaerobic digestion at 20°C is capable of small reductions of Enterococci levels for treatments CM and 1I:3S, whereas 10°C did not achieve a marked reduction for any of the treatments. In contrast to the results for E. coli, there did not appear to be an association between log reduction and I:S ratios for

Enterococci. CM and 1I:3S, despite having higher initial concentrations and substrate levels, achieved lower final concentrations (CM 4.35, 1I:3M 4.61 CFU/ml) compared to the other I:S ratios (1I:1S 5.01, 3I:1S 4.95CFU/ml). This lack of correlation between log reductions and I:S ratios can possibly be explained by the presence of increased levels of

VFAs. This theory is supported by the results of treatments CM and 1I:3S, which had

VFA levels near or above 10,000 mg/L at 20°C and exhibited larger reductions (~1.8 log) than did the other three treatments (which ranged from -0.45 to 0.84 log), all of which

maintained lower VFA levels (< 3,000 mg/L). Furthermore, CM exhibited the greatest

Enterococci reductions among the treatments at 10°C while also having the highest VFA levels (10,000mg/L). This relationship between increased levels of VFAs and E. coli reductions were not seen.

The initial mixed model for Enterococci (treatment [I:S ratio], temperature, time), which produced a non-significant model (P > 0.05), demonstrated that there is not a significant relationship between time and specific treatments for Enterococci levels at either temperature. These non-significant results are described by the abscence of change in the levels of Enterococci (Figure 1). This can be further stated in that there was minimal change in the levels of Enterococci over time for all the treatments at both temperatures.

The trends of Enterococci concentrations throughout the study were mostly similar to one another (P > 0.05), exceptions being CM versus both CI and 3I:1S. These exceptions were most likely due to the lack of change in Enterococci levels within both CI and

3I:1S, while CM had the largest reduction over time of any of the treatments. As noted previously, this is thought to be the result of Enterococci’s sensitivity to VFA accumulation. Average Enterococci levels and trends, for the five treatments compared across (CM vs. CM) the two temperatures (Table 3), resulted in all treatments not being statistically different from one another (P > 0.05). These results further demonstrate the lack of change that occurred within the treatments and between the temperatures for levels of Enterococci (Table 3).

Comparison of the indicators studied is challenging, since they presented different concentrations, trends, and inactivation efficiencies throughout the study. These differing results suggest that E. coli and Enterococci vary in their ability to survive within a digester’s environment. Specifically, it appears that E. coli levels are most influenced by substrate levels, while Enterococci levels are most impacted by VFA levels. This is evident from E. coli levels decreasing at both 10°C and 20°C treatments, with 20°C treatment achieving greater reductions. This is most likely the result of higher substrate utilization and subsequent limitation (batch digestion), which would naturally be greater at 20°C. In contrast, Enterococci maintained high concentrations at both 10°C and 20°C, along with the high concentrations within the inoculum, suggesting that substrate limitation is of less a concern for Enterococci. However, as mentioned previously,

Enterococci appears to have sensitivity to increased VFA levels (>10,000mg/L).

There is a paucity of literature supportive of these specific controlling factors (substrate limitation and VFAs). Smith et al. (2005) was the only article uncovered which supports the importance of substrate limitation and its effect upon fecal indicators; none were uncovered which support Enterococci’s VFA sensitivity. Smith et al. (2005) concluded that “microbial competition and substrate limitation, linked to the efficient stabilization of bio-waste, are likely to be the primary factors responsible for reducing viability of enteric bacteria during mesophilic digestion.” However, research exists which details the difference in survivability between Enterococci and E. coli, (Cekmecelioglu D. et al.,

2004 ; Bonjoch X. and Blanch A.R. 2009). Niwagaba et al. (2009), who investigated E.

coli and Enterococci inactivation over time in stored feces, concluded that E. coli was inactivated faster than Enterococci. E. coli was below detectable levels after 2 months, whereas Enterococci did not decrease below detectable limits over the course of the study

(Niwagaba et al., 2009). Our findings likewise indicated that E. coli and Enterococci have differing abilities to resist change within their environment.

Our results suggest that the effect of increased temperatures in anaerobic digestion is the result of enhanced metabolic and enzymatic activity, which in turn, cause increased microbial growth, more extensive stabilization of organic matter, and higher biogas production. Therefore, increased temperatures within a batch system should result in increased utilization of substrate; this, in turn, should produce a nutrient-scarce environment, thus bringing about microbial inactivation with regards to E. coli. This theory suggests that substrate levels determine a theoretical carrying capacity (in terms of microbial levels) of a system. Accordingly, it would be expected that oxidation of labile organic matter (substrate) in a system would bring about a reduction in carrying capacity for pathogens and for most fecal indicators. Unfortunately, the application of this theory is difficult within an anaerobic digester, as too much substrate can acidify a system, aiding the growth of some microorganisms while hindering that of others. Nonetheless, in general terms, the presence of a greater amount of labile organic matter present should result in a larger carrying capacity of microorganisms. The conclusion of Smith et al.

(2005) mentioned earlier, supports the hypothesis that the reduction of E. coli within the batch reactors studied is likely the result of substrate limitation. Our results indicate that

the same is not necessarily true for Enterococci, perhaps due to an ability to better resist changes, specifically substrate-limited conditions, within a digester; this was highlighted by high Enterococci levels within the inoculum.

2.4.2. Chemical indicators

The pH, VFA, TIC, VFA/TIC ratio, and biogas volume (Table 4) were influenced by the different I:S ratios and the two temperatures. Significant variations in these chemical indicators were seen between the I:S ratios, which can partially be explained (similarly to fecal indicators) by their inherent levels within the substrate and inoculum. VS and VFA levels demonstrated a direct relationship with I:S ratios at both temperatures (10°C and

20°C) at the start of study; this is illustrated by the fact that increased substrate levels resulted in increased VFA and VS levels. The exception, as with the fecal indicator results, was the 1I:1S treatment which produced the most biogas, had decreasing VFA levels, and had an alkalinity level higher than that of any other treatment (Table 2).

These results indicate that the 1I:1S ratio is the optimal ratio when attempting digester start-up at 20°C since it yielded the greatest amount of biogas, while generating a stable environment (decreasing VFAs and increasing TIC). Though the results at 10°C were not as clear compared to 20°C, it appears as if the use of 3I:1S would be the ideal ratio for start-up, since it exhibited decreasing VFAs and increasing TIC. At these reduced temperatures, the continued use of I:S ratios might serve a purpose beyond start-up, since increasing retention times and reducing loading rates through the re-use of inoculum

might provide an avenue to maintaining a healthy environment for biogas production at these lower temperatures.

2.5. Conclusion

The results suggest that anaerobic digestion in batch reactors can reduce levels of E. coli and, to a lesser extent, Enterococci spp. at psychrophilic temperatures. Due to the complexity of variables involved with anaerobic digestion, this could be attributed to a multitude of factors. The factors that are generally thought to have the greatest impact upon inactivation of microbes are temperature, levels of specific VFAs, pH, microbial competition, non-ionized organic acids, and substrate limitation (Puchajda and

Oleszkiewicz, 2006; Smith et al., 2005). Among these, temperature is widely considered the most important factor in the inactivation of microbes. Our results agree with this theory, but elaborate on what temperature is truly controlling, which is the microbial machinery to utilize substrate for growth and reproduction. Therefore, the relationship between levels of substrate and levels of fecal bacteria is an important factor in determining the concentration of certain microbes and their overall carrying capacity within a system. Also, environmental conditions, specifically VFA levels, have illustrated the potential to be an important variable in specific indicator organism levels. Therefore, designing treatment systems to address both substrate level (reduced loading rates or continued use of inoculum) and environmental conditions (separating hydrolysis and methanogenesis) would be ideal for reducing levels of indicator organisms in psychrophilic anaerobic digestion.

Chapter 3

Influence of Seasonal Fluctuation and Loading Rates on the Level of Fecal Indicator

Bacteria During Semi-Continuous Anaerobic Digestion

Abstract

Biological residuals originate from various organic waste sources, and primarily consist of animal manures, food wastes, and municipal wastewater treatment sludge. These materials are often treated through anaerobic digestion in order to produce a more stable product. This process simultaneously reduces the volume of waste, reduces levels of pathogenic microbes, and generates biogas that can be used as an alternative energy source. Due to these benefits, anaerobic digestion is utilized globally, on both large and small-scales. The majority of anaerobic digestion literature focuses on large-scale digestion at mesophilic and thermophilic temperatures. In contrast, minimal attention has been paid toward the performance of the more than 40 million small-scale digesters, which frequently operate at psychrophilic temperatures. The quantitative information on the changing levels of microbial and chemical indicators at various loading rates and temperatures is useful for improving treatment efficiency and management strategies for small-scale digesters. In this study, eight lab-scale, semi-continuous anaerobic digesters were operated at four different loading rates (control [inoculum only], 0.3, 0.8, and 1.3 kg volatile solids/ m3/day). These digesters were housed in an environment that simulated

seasonal change in a temperate climate (27.5°C to 10°C to 27.5°C). The results from this study showed that the biosolids generated from all eight digesters met Class B specifications in accordance with United States Environmental Protection Agency guidelines (< 2x106 CFU fecal coliforms/g dry biosolids), regardless of loading rate or temperature. Reduced loading rates led to a more stable environment (in terms of pH, levels of volatile fatty acids and total inorganic carbonate alkalinity) as well as lower levels of indicator bacteria, but generated less biogas. Temperature fluctuations did not appear to influence Escherichia coli (E. coli) and fecal coliform concentrations; however, temperature did appear to have an effect upon levels of Enterococci, illustrating an inverse relationship with the temperature fluctuations. Overall, the results demonstrate the capacity of small-scale digesters to reduce levels of fecal indicator bacteria, and provide important data to improve the performance of small-scale psychrophilic digesters, specifically by reducing loading rates to prevent souring during winter months.

3.1 Introduction

The treatment and management of biological residuals from various sources, primarily animal manures, food wastes, and municipal wastewater treatment sludge, is an environmental health priority since they may contain pathogenic microorganisms and toxic chemicals, both of which have the potential to contaminate water, soil, food, and air

(USEPA 1999a; Bicudo and Goyal, 2003; Sinha et al., 2010; Rana et al., 2010). Although biological residuals and their conversion to biosolids pose a potential hazard, they also

have positive environmental value. For example, the use of biological treatment technologies—most commonly anaerobic digestion—yields a source of alternative energy (biogas) and a nutrient-rich soil fertilizer. The latter is widely used in farming, with between seven and eight million tons of anaerobically-digested, Class B biosolids applied to agricultural lands annually in the United States (US) (Beecher et al., 2007).

However, uncertainty remains regarding potential adverse health impacts to individuals residing near application sites, as well as to workers who may be exposed to land applied biosolids (Khuder et al., 2007). Therefore, stabilization of biological residuals is of significant importance in the prevention of environmental health hazards (Ziemba and

Peccia, 2011; Evanylo et al., 2008).

In an effort to establish standards for the stabilization of biological residuals, and thus reduce the potential hazards, the US Environmental Protection Agency (EPA) developed regulations known as the Part 503 Biosolids Rules (USEPA, 1999b). These regulations provide requirements for the stabilization process, stipulating the use of specific temperatures for specific intervals during treatment (i.e. aerobic; anaerobic), as well as maximum allowable levels of indicator organisms or pathogens permissible in the stabilized product. These regulations focus on classifying treated biological residuals, known as biosolids, into Class A or Class B. Class A biosolids must have fecal coliform concentrations less than 1000 colony-forming units per gram of solids (CFU/dry g), or less than three most probable number (MPN) of Salmonella spp. per four grams of dried solids (MPN/4g). The criteria for Class B biosolids are less strict, requiring fecal coliform

levels to be less than 2 x 106 CFU/dry g (USEPA, 1999a). Class B biosolids are not stabilized to the level of Class A, and thus have higher levels of microbes and potentially a greater presence of pathogens. Accordingly, there are more restrictions for land application and/or other uses of Class B biosolids than Class A biosolids.

Unfortunately, small-scale biosolids treatment systems (SSBTS) are not able to meet these stringent time and temperature guidelines; this has led to a paucity of published data regarding the efficacy of SSBTS and the class of biosolids they are capable of producing.

This lack of available data exists because SSBTS frequently operate as passive systems

(no electrical energy input) with variable psychrophilic temperatures, whereas large-scale digesters are complex systems that typically incorporate heating, mixing, and other controls to maintain an ideal environment. Specifically, SSBTS exist as simple digesters

(fixed-dome, floating drum, and plug flow) with minimal engineered controls other than feeding rates, substrate-to-inoculum ratios, and intuitive designs to generate natural mixing (Rajendran et al., 2012). Consequently, these systems are more susceptible to souring, and producing a sub-optimal environment for the degradation of labile organic matter (pH < 6.8). Due to their sensitivity coupled with their large global presence (~40 million worldwide), an increased understanding of these SSBTS controls is of considerable importance (Rajendran et al., 2012).

The use of small-scale digesters could benefit from further research of factors that either aid or inhibit anaerobic digestion such as temperature, pH, mixing, volatile fatty acid

distribution (specific VFAs) and concentration, loading rate, and slurry composition

(Kearney et al., 1993; Bodik et al., 2000; Salsali et al., 2006; Chen et al., 2008; Smith et al., 2005; Kameswari et al., 2012). A better understanding of these could result in improved stabilization, and a greater capacity to optimize and develop variable- temperature anaerobic digestion within the psychrophilic range.

Little information is available regarding anaerobic digestion at the psychrophilic to low- mesophilic temperature range. No reports were identified regarding semi-continuous anaerobic digestion operating at various temperatures within the psychrophilic range that investigated both microbial and chemical outcomes. A survey of mesophilic, full-scale anaerobic digestion facilities that were considered to be well-operated found Escherichia coli (E. coli) log reductions between 1.35 to 3.36 with an average of 2.08 (UK Water

Industry Research, 1999). Another study, investigating several digestion sites in northwest England, found that primary digestion typically reduced levels of E. coli by 1.5 log (Le et al., 2000). Data from these studies reveal a range of reductions that are possible when performing continuous or semi-continuous digestion at mesophilic temperatures.

Additional studies, such as Lansing et al., (2011), employing semi-continuous feeding in tubular digesters (Taiwanese style) operating at ambient temperatures (26.8-23.5°C, average 25.5°C), also showed measureable reductions. The study quantified E. coli and total coliform levels over a nine-month period (40 day HRT), and found a 2.08 log reduction for total coliforms and a 1.54 log reduction for E. coli (Lansing et al., 2010).

These studies demonstrated the ability of anaerobic digestion to reduce microbial levels

during semi-continuous anaerobic digestion at mesophilic and low-mesophilic conditions.

However, as mentioned above, there are still no data that detail biosolids classification (A or B) for SSBTS operating at various psychrophilic temperatures.

The objective of this study was to evaluate the ability of lab-based anaerobic digesters operating at various psychrophilic temperatures to reduce levels of indicator bacteria (E. coli, fecal coliforms, and Enterococci spp.) while maintaining a stable environment (in terms of pH, VFA levels, and biogas production) across different loading rates. Three different fecal indicators were utilized for comparision. Fecal coliforms as a general group of fecal bacteria and two specific organisms, one (Enterococci) which has been demonstrated to be more resiliant than the others ([E. coli]; Cekmecelioglu D. et al.,

2004; Bonjoch X. and Blanch A.R. 2009). The output from this study was used to determine 1) if there is a relationship between seasonal fluctuation (in temperature) and levels of indicator organisms; 2) if there is a relationship between levels of indicator organisms and the loading rates utilized; and 3) if optimal performance on the part of a digester (in terms of biogas production) results in lower levels of fecal indicator organisms. Lastly, this study intended to provide baseline measurements of biosolids classification across various temperature conditions, while also considering additional or alternative performance indicators (such as loading rate) that may be more appropriate for small-scale systems operating at low-temperatures.

3.2 Methods

3.2.1 Experimental set-up

The experiment was conducted using eight lab-scale digesters configured for semi- continuous anaerobic digestion. Figure 3 shows a schematic of the lab-scale digesters constructed using bottles (Thermo Scientific Nalgene®, wide mouth square bottle HDPE,

4 liters) as single-phase digesters. The eight reactors utilized a cover with two ports: one sampling port for the removal of effluent and the addition of substrate, and another for the measurement of biogas generation using a wet-tip gas meter. Mixing was done mechanically before each sampling and after each loading (every other day). The digesters were housed within a water bath, which utilized a water chiller (Techne ESRB-

7, Princeton, NJ) to constantly re-circulate the water, maintaining the desired temperature with a precision of ± 0.1°C.

! Figure 3: Experimental design of the four-liter digesters housed within a temperature controlled water bath with gas meter and water chiller schematics detailed

3.2.2 Substrate and inoculum loading rates

Each digester was constructed identically, filled with 3.5 liters of inoculum (pH 7.90, VS

3.3%, wet basis), flushed with 99.9% N2 for one minute and sealed. The inoculum was obtained from a completely mixed anaerobic digester in Columbus, Ohio, that digested both agricultural and residential waste at mesophilic temperatures and was operated by

Quasar Energy Group (Columbus, OH, US). The digesters were left to equalize for 7 days prior to the introduction of substrate at three different loading rates and one control.

The substrate utilized for this study was cattle manure from Waterman Dairy Farm at The

Ohio State University in Columbus, Ohio (pH 6.72 ± 0.49, VS 5.76% ±0.83%, wet basis).

The loading rates consisted of a control (100% inoculum) and three substrate-loading rates: 0.3 (low), 0.8 (medium), and 1.3 (high) kgVS/m3/day. The hydraulic retention times for these three treatments (low, medium, high) were 188.26, 70.59, and 43.34 days,

respectively. The manure was collected and diluted at a ratio of 1:2 (water:manure) every seventh day, 24 hours prior to sampling (for microbial quantification) and loading in order to determine total solid (TS) and volatile solid (VS) levels for equal loading rates throughout the study (American Public Health Association [APHA], 2005). The manure was stored at 4°C during TS and VS determination. Once VS levels were measured and appropriate loading rates calculated for a batch of manure, the digesters were loaded immediately, and then every other day thereafter, for a total of four loadings per batch.

Each loading rate (control, low, medium, and high) was performed in duplicate, with the control using the initial inoculum stored at 4°C and being loaded with the same volume as the low loading rate (0.3 kgVS/m3/day) for the course of the study.

3.2.3 Temperature factors

The experimental procedures detailed occurred over a seven month time period, simulating typical temperature fluctuations over the course of a year in central Ohio, US.

The temperatures and rate of change for the study were chosen based on two years of logged data from a buried digester operating in Columbus, Ohio. The data collected from the field showed that the field digester reached a maximum temperature of 27.5°C in the summer and a minimum temperature of 10°C in the winter. These seasonally related temperature conditions were represented in the study, using a rate of change at 0.25 degrees per day with plateaus occurring in the simulated winter and summer months.

3.2.4 Microbial analyses

Microbial analyses were conducted every eighth day over the seven-month study period.

Following mechanical mixing of the eight digesters, 25-50ml of sample was removed from each digester and decanted in sterile 50ml conical tubes prior to loading. The tubes were subsequently stored on ice until sample analysis within 24 hours. Ten grams were sampled from each of the eight digesters and diluted with 90ml of sterile, phosphate- buffered saline (PBS) using sterile Whirl-Pak bags (Whirl-Pak NASCO, WI, U.S.), followed by mixing. This initial dilution of digester samples was then serially diluted with PBS to determine the levels of E. coli, Enterococci, and fecal coliforms. The enumerations were done using a membrane filtration method (USEPA 2002a and 2002b) with specific agar plates, modified m-TEC for E. coli, m-FC for fecal coliforms and m-EI for Enterococci (APHA, 2005; USEPA, 1999b), respectively. After incubating at specific times and temperatures, according to the respective standard methods, the colonies were counted and then colony numbers were calculated after considering dilution rate and inoculum volumes. The results are expressed as CFU/dry g.

3.2.5 Chemical analyses

TS, VS, total inorganic carbonate alkalinity (TIC), volatile fatty acids (VFA), biogas, and pH were analyzed over the seven-month study period from 8/2011 until 3/2012. The pH levels and biogas volume were measured every loading day, with a pH/Ion 510-bench pH meter (Fisher Scientific, PA, USA) and an electronic tip counter for the wet-tip gas meter

(Wet-Tip Gas Meter Company, TN, USA) respectively. VFA and TIC concentrations

were measured using a titration method (Lossie and Putz, 2008), and the Standard

Methods were used to determine TS and VS described in APHA (2005). These analyses were conducted every eighth day.

3.2.6 Statistical analyses

The comparison of the four treatments (control, low, medium, and high) was conducted using SAS 9.2 software (SAS Institute Inc., NC, USA). The analysis was done using a mixed-method approach (proc mixed). A mixed model is ideal for interpreting repeated measure data, since it takes into account the relationship between time points and their covariate structure. Initially, a basic model was constructed to compare time, temperature, and the outcome variables (indicator organisms). Each covariate was then introduced to see if it improved the model’s outcome variables (microbes). The model was then used to compare microbial levels over time for each of the individual treatments, as well as their trends between time periods (changing pattern over time).

3.3 Results

The results of this study present a comparison of fecal indicator densities from eight semi-continuous anaerobic digesters paired into four treatments, each with different loading rates and varying temperatures. In addition to levels of fecal indicators and their changes over time, concentrations of chemical indicators are also described. In order to compare the four replicate treatments, a T-Test and the Wilcoxon signed-rank test were

performed. The replicated treatments were not statistically different, indicating that an average of them could be used for our analyses and for presentation of the data. The data were log normally distributed, and the microbial results are presented as such.

3.3.1 Fecal indicators

The concentrations of E. coli, fecal coliforms, and Enterococci spp. over the course of the study are presented in Figure 4. Aside from some short-term fluctuations, densities of E. coli and fecal coliforms had nearly no overall change or trend during the study, demonstrated by all three treatments having an r-value below 0.1. The control (inoculum only) had an r-value of 0.08, depicting an almost flat line for both levels of E. coli and fecal coliforms over the course of the study. This result was expected, given the lack of substrate or nutrient addition to this treatment. A mixed model was constructed to determine if there was a statistical difference between the three treatments (low, medium, and high) and their changing patterns. The mixed model showed no statistical difference

(P > 0.05) in regards to changing patterns in the densities of E. coli and fecal coliforms over the course of the study across the three treatments (low, medium, and high).

Enterococci levels, too, did not exhibit a statistically significant difference (P > 0.05) in changing patterns across the three treatments. However, Enterococci levels did increase as temperature decreased, suggesting an inverse relationship with respect to the temperature profile.

Table 5 presents the average indicator organism concentration as CFU per gram of dry

solids for the substrate (manure), and the four treatments over the course of the study.

The substrate concentrations were similar for all three indicator organisms, 6.90 CFU/ dry g, 6.92 CFU/dry g, 6.88 CFU/dry g, respectively. A comparison of the concentrations

(or densities) of indicator organisms in the substrate showed statistically different concentrations from those found within the three treatments and the control (P < 0.05).

The densities of indicator organisms in the three treatments were also statistically different from those of the inoculum only (control) treatment (P < 0.05). In contrast to the substrate, average densities of Enterococci (4.03 CFU/dry g) were statistically different from E. coli (2.53 CFU/dry g) and fecal coliforms (2.44 CFU/dry g). The concentrations of the three categories of organisms from the low loading rate were statistically different

(lower ; [P < 0.05]) from those of the high and medium loading rates. The latter two were not statistically different from one another (P > 0.05).

Figure 4: Concentrations of indicator organisms in digester effluent over time for the substrate (average specific fecal indicator level for the study), and the treatments, low (0.3), medium (0.8), and high (1.3 kg VS day-1)

Table 5: Comparison of average log CFU/dry g over the course of the study. These average and their standard errors, were generated by the mixed model.

Treatment Substrate Control High Medium Low (Manure) (Inoculum) (1.3 kgVS/m3/ (0.8 (0.3 kgVS/m3/day) day) kgVS/m3/day) E. coli 6.84* ± 0.496 2.53 * ± 0.325 5.77 ± 0.318 5.63 ± 0.296 5.19 $ ± 0.345 fecal coliform 6.85* ± 0.524 2.46 * ± 0.325 5.78 ± 0.272 5.64 ± 0.306 5.15 $ ± 0.386 Enterococci 6.76* ± 0.542 4.03 * ± 0.325 6.08 ± 0.497 5.92 ± 0.473 5.57 $ ± 0.423 spp.

* Statistically different from all treatments (low, medium, high) $ Statistically different from treatments high and medium

3.3.2 Chemicals

The chemical measurements are summarized in Table 6. Expanded details have been presented for pH and biogas, since they are important performance indicators for anaerobic digesters. As typically experienced in field settings, the results showed that the characteristics of the substrate used as feed for the digesters varied over the seven month study, with pH ranging from 6.21 to 7.76 (average pH 6.83), total VFAs from 3,352 to

7,668 mg/L (average VFAs 4,881 mg/L), and TIC from 2,100 to 6,100 mg/L (average

TIC 4,018 mg/L).

The pH, VFA, TIC, VFA/TIC ratio, and biogas volume (Table 6) were influenced by the different loading rates and temperature changes. Significant variations were seen between the low, medium and high loading rate treatments in regards to pH, VFA, TIC, VFA/TIC ratio, and biogas volume. The low, medium, and high treatments all had similar pH

values at the start of the study (7.83 low, 7.87 medium, 7.85 high), all of which were well within 6.8-8.0, the ideal range for anaerobic digestion (Khalid et al., 2011). However, over time the pH in the digesters changed dramatically, reflecting clear chemical changes

(Figure 5). This was shown by the varying pH values at days 58 (7.61 low, 7.49 medium,

7.41 high), 100 (7.49 low, 6.90 medium, 6.48 high), and 212 (7.53 low, 5.86 medium,

5.54 high). As shown by these data, the pH for the high and medium treatments decreased throughout the study; in contrast, the low loading rate showed a decreasing pH until day

122 (7.18), the last day of the simulated winter, but was able to recover (to 7.53) as the temperature increased (Figure 5). The drop in pH and subsequent souring within the digesters utilizing the high and medium loading rates is also evident in the data on biogas production plotted in Figure 6. This figure shows initially high production of biogas

(Table 6) for the digesters utilizing the high and medium loading rates, followed by steep declines once the pH of 6.8 was reached, and no recovery in biogas production even after the temperature increased and stabilized. Another indicator of this change was the VFA concentrations, which were relatively similar (1,785 low, 2,688 medium, 2,190 high mg/L) on day two, varied considerably by day 100 (2,356 low, 3,601 medium, 7,087 high mg/L), and ended at 2,522, 11,320, and 10,158 mg/L for the low, medium and high treatments, respectively.

Table 6: Chemical factors measured at five time points over the course of the study

Chemical Temp Time High Medium Low Control Parameter (°C) (Days) (1.3 kgVS (0.8 kgVS (0.3 kgVS (Inoculum) /m3/day) /m3/day) /m3/day) pH 27.5 2 7.85 7.87 7.83 7.96 16.5 58 7.41 7.49 7.65 7.87 10 100 6.48 6.9 7.49 7.87 16.5 150 6.12 6.28 7.35 7.84 27.5 212 5.54 5.86 7.53 7.82 Volatile Fatty 27.5 2 2190 2688 1785 1360 Acids (mg Hac/L) 16.5 58 2688 2190 2356 3186 10 100 7087 3601 2356 3518 16.5 154 9328 9494 2522 2522 27.5 212 10158 11320 2522 3186 27.5 2 12700 13300 11170 10700 Carbonate 16.5 58 9500 12150 13350 15050 Alkalinity

(mg CaCO3/L) 10 100 5225 8475 12400 13850 16.5 154 3200 4200 9850 14650 27.5 212 1900 2500 9550 11700 27.5 2 .173 .203 .159 .129

VFAs/TIC 16.5 58 0.283 0.180 0.177 0.211 Ratio 10 100 1.37 0.429 0.190 0.254

16.5 154 2.55 1.98 0.388 0.211

27.5 212 5.65 4.54 0.264 0.272 Total Biogas 27.5 2 825.7 742.2 583.8 150 (mL Accumulated) 16.5 58 53,225.9 31,369.6 14,938.5 3,465 10 100 65,234.8 44,256.7 18,862.8 3,536 16.5 154 67,778.1 47,379.8 24,567.8 3,632 27.5 212 74,205.6 51,740.1 53,192.8 4,468

3.3.2.1 pH

Figure 5 presents pH values over the course of the study period. In order to provide a complete representation of pH trends, Table 6 presents five data points from the study as

a complement to Figure 5. All digesters started with the same inoculum (3.5L), resulting in almost identical starting pH values. The pH for the low loading rate decreased as the temperature declined, but ultimately recovered once the temperature started to rise, stabilizing at an approximate pH of 7.5 for the last 30 days of the study. The systems using high and medium loading rates were not able to recover from the pH drop that occurred during the simulated winter, each having values that continued to decline even after the temperature increased and stabilized. The control digester maintained a steady pH throughout the study.

7.5

7 pH 6.5 High Medium 6 Low Control 5.5 0 20 40 60 80 100 120 140 160 180 200 Time (days)

3.3.2.2 Biogas

Figure 6 presents daily biogas production over the course of the study, while Table 6 presents cumulative biogas production at five data points throughout the study. As shown in Figure 6, daily biogas production was minimal for the control throughout the course of the study, while the low loading rate had the lowest biogas production among the other three treatments before the simulated winter. Following the simulated winter, the low loading rate was able to recover and continue producing biogas as the temperature increased, whereas the treatments using the high and medium loading rates were not able to recover from the simulated winter.

2500 High

2000 Medium Low 1500 Control

1000 Biogas (ml) Biogas (ml)

500

0 0 20 40 60 80 100 120 140 160 180 200 Time (days)

3.3.3 Mixed model

A mixed model was used to identify individual parameters (Table 6) that might have had a significant relationship with the outcome variables (E. coli, fecal coliforms, and

Enterococci spp.). The initial model involving E. coli, time (days), and treatment, with an interaction term for treatment and time, produced a significant model (P = 0.001). This indicates a statistically significant relationship between time and the three treatments

(low, medium, and high) for E. coli over the course of the study. The same model was constructed for fecal coliforms (P = 0.001) and Enterococci (P = 0.032), both of which also had significant relationships between time and the three treatments. These results demonstrate that there is a significant relationship between time and each of the treatments, for the levels of the indicator organisms investigated. These results can be interpreted as follows--that a change in levels of fecal indicators occurred as the result of the treatment (control, low, medium, or high), time, or the combination of the two. This model’s significance allows us to test our specific hypothesis.

Individual chemical parameters were then introduced to the model, in order to determine if they improved the model; an affirmative result would suggest a relationship between the outcome variable (indicator organisms) and the covariate. None of the tested covariates improved the model (Table 6), indicating that none shared a significant relationship with the outcome variables. The final tests performed with this model compared both trends and average levels of indicator organisms between the treatments.

The results for all three organisms were not significant in regards to trend differences (P

> 0.05). Comparison of the average levels of indicator organisms between the treatments is summarized in Table 5.

3.4 Discussion

There are relatively few published studies regarding the treatment of biological residuals with small-scale, semi-continuous anaerobic digesters operating at varying temperatures.

Identifying effective management strategies, specifically in regards to loading rate, is essential to improving biogas production and reducing microbial levels within the effluent. The results discussed here reveal new insights into SSBTS-treated biosolids, such as their biosolids class, performance indicators, and indicator organism trends.

3.4.1 Chemical indicators

The outcomes for pH, biogas, and VFAs were anticipated for the digesters run at the high loading rate, because the reduced temperatures and system conditions (pH < 6.8) brought about by the simulated winter would hinder the methanogens within the digester. This hindrance would be the result of the methanogens’ inability to degrade increasing levels of VFAs, brought on by hydrolyzing and fermenting bacteria as well as the low temperatures (Chen et al., 2008). Hydrolyzing and fermenting bacteria are both extremely diverse and numerous within a digester when compared to methanogens, while also having differing nutritional requirements, physiology, pH optimums, growth and nutrient uptake kinetics, and ability to handle change (Anderson et al., 1994). Even though hydrolysis is often considered the rate-limiting step in anaerobic digestion, an

accumulation of VFAs will occur if the methanogens are unable to convert them to methane and carbon dioxide. This will contribute to a drop in pH within the digester, which will in turn, result in a soured environment (< 6.8). As was expected from field data gathered prior to this study, the digester utilizing the highest loading rate soured over the course of the study. The digester utilizing the medium-loading rate also soured, which indicates that even a loading rate of 0.8 kgVS/m3/day would also result in the same difficulties during the winter months. In contrast, the digester utilizing the low loading rate did not sour under these conditions, and was able to maintain an optimal range of pH

6.8-8.0 during the simulated winter and resumed producing biogas once the temperature rose above 15° C.

These results suggest the effects that loading rates have upon SSBTS in terms of maintaining an optimal range of pH between 6.8-8.0 for biogas production during variable-temperature digestion. The impact that loading rate has upon anaerobic digestion was similarly shown by Nges and Liu (2010), who investigated the anaerobic digestion of dewatered sludge at various solid retention times (SRT). Their study tested nine different

SRTs from three to 35 days, at both mesophilic and thermophilic temperatures. They found that shorter SRTs (higher OLRs) resulted in higher biogas production (5-35 day

SRTs), but also brought about a less stable environment, as was demonstrated by the

VFA/TIC ratios (0.02 at SRT 20-35 days, 0.27 at SRT 5 days, and 1.27 at SRT 3 days) and pH values (7.61-7.72 at SRT 20-35 days, 7.31 at SRT 5 days, and 6.96 at SRT 3 days). Although their SRTs were much higher than those used in this study (43.3, 70.6, and 188.3 days), similar relationships were observed with regards to increasing VFA/TIC

ratios and decreasing pH values at the highest loading rates. This suggests that overloading can create an unstable environment even at mesophilic and thermophilic temperatures.

3.4.2 Fecal indicators

The lab-scale digesters in this study produced Class B biosolids (EPA Class B, < 2x106 fecal coliforms/dry g) throughout the study period regardless of loading rate or temperature. The microbial reductions accomplished by the digesters are illustrated by the average log reductions (CFU/g) in E. coli and fecal coliforms of roughly 1.65 log for the low loading rate, 1.21 for the medium, and 1.07 for the high, while Enterococci achieved reductions of 1.19, 0.84, 0.68 log, respectively. These data on biosolids classifications, provide the first baseline measures available within the literature for varying-temperature, semi-continuous anaerobic digesters.

Table 5 demonstrates a direct relationship whereby increased loading rates resulted in increased levels of indicator bacteria. The data suggest that the incorporation of loading rate into time and temperature guidelines could aid in determining the class of biosolids produced from SSBTS. The data also suggest that a relationship may exist between an ideal digester environment, in terms of pH or VFA concentrations, and levels of indicator organisms, since higher pH and lower VFAs resulted in fewer indicator organisms.

However, there does not appear to be a relationship between an optimally performing digester, with respect to biogas production, and levels of indicator bacteria. This is suggested in that the digesters using the high loading rate produced the most biogas, and

also had the highest levels of indicator bacteria (Table 5 and Table 6). Although this was the case, the digesters utilizing the high loading rate produced nearly the same total volume biogas in only 58 days (53.23 L) as those using the low loading rate (53.19 L).

The high loading rate not only contained higher indicator organism levels, but also generated a soured environment (pH < 6.8), reducing biogas production for the remainder of the study. In comparison, the digesters utilizing the low loading rate, were able to start producing biogas after the winter and maintained lower indicator organism levels throughout the study. It is also worth noting that had the study period continued a few days longer, the digesters utilizing the low loading rate would have outpaced those using the high loading rate in terms of total biogas production. As such, it seems that SSBTS utilizing semi-continuous loading under varying temperature conditions could benefit from utilizing variable loading rates based upon seasonal conditions; low loading rates would be preferable under colder temperatures, and high loading rates most appropriate during warmer periods.

These observations regarding loading rates and their relationship to biogas production and levels of indicator organisms are similar to those reported by Chen et al. (2012), who investigated sludge retention times on reactor performance and indicator/pathogen removal in a mesophilic anaerobic digester. Comparison of their three retention times (of

11, 16, and 25 days) resulted in average log reduction values for E. coli of 1.93, 2.98, and

3.01 MPN/g, respectively, over the 36-day study (Chen et al., 2012). These data support the correlation between lower loading rates and greater elimination of indicator organisms. Chen et al. (2012) concluded that the suitable retention time (or OLR) for a

digester depends on whether one wants to maximize biogas production or minimize levels of indicator organisms and pathogens within the effluent. This is especially true in variable temperature systems, which are especially prone to souring at higher loading rates.

Because temperature fluctuations have the potential to impact levels of indicator bacteria, the relationship between microbial indicators and seasonal fluctuation is important to understand. In this study, trends for Enterococci, E. coli and fecal coliform levels, showed no significant difference (P > 0.05) between the loading rates utilized. Seasonal temperature fluctuations had similarly non-significant findings (P > 0.05) for levels of E. coli and fecal coliforms. This was contrary to what was expected based on the literature; according to Puchajad et al. (2006), a drop in pH (to a value < 6) and the accompanying increase in VFAs should result in fecal coliform inactivation due to the presence of un- ionized VFAs. However, that was not the case in this experiment, which maintained relatively stable concentrations of both E. coli and fecal coliforms over the course of the study even as the pH decreased and the VFA concentrations increased (Table 6). Because there was no statistical difference between the three treatments in regards to trends and the covariates (pH, VFAs, TIC, biogas) inability to improve the model (illustrating no significant relationship with our outcome variables), there does not appear to be a controlling variable within this experiment, which predicts reductions in E. coli or fecal coliforms.

Enterococci levels, in contrast to E. coli and fecal coliforms levels, did appear to exhibit seasonal fluctuation; this was evident by an inverse relationship with temperature over

time (Figure 4). Though the environmental conditions (pH, VFAs, TIC, VFA/TIC ratio, and biogas volume) within the digesters were different across the three treatments,

Enterococci levels nevertheless followed the same trend in all three loading rates. As such, it appears that temperature, the only uniformly consistent environmental variable across the three treatments, is a key component in Enterococci levels. Unfortunately, this relationship cannot be confirmed, given the multitude of confounding factors (i.e. VFA concentrations, pH, etc.) that could have contributed to these results.

3.5 Conclusion

1. The stabilized products met USEPA Class B biosolids criteria (2x106 fecal

coliforms/dry g) over the course of the study, regardless of loading rate or

temperature.

2. None of the chemical factors (pH, VFAs, TIC, VFA/TIC ratio, and biogas volume)

showed a relationship with levels of indicator bacteria.

3. There was an observed relationship between loading rate and concentrations of

fecal indicator bacteria, with higher loading rates resulting in higher levels of

indicator bacteria.

4. The use of lower loading rates led to a more stable environment based on pH, VFA,

and TIC levels, and allowed the digesters to resume biogas production following

the simulated winter while also maintaining lower levels of indicator organisms.

However, the low and high loading rates produced almost the same amount of

biogas over the course of the study.

5. Incorporating loading rate into the time and temperature guidelines could aid in

determination of biosolids class that is produced from SSBTS.

6. There was no indication that temperature had any influence upon E. coli and fecal

coliform levels, but it did appear to have an effect upon levels of Enterococci spp.

Chapter 4

Rapid Assessment of Biosolid Stabilization with Chemical Luminescence

of Humic Acid

Abstract

Biosolids are labile organics such as animal manures and wastewater treatment sludges.

Extended time or engineered treatments are required to stabilize organic content, which results in the reduction of pathogens. Following treatment, biosolids are classified (A or

B) based on indicator or pathogen levels. This practice is designed to protect public health during the land application of biosolids. A field-based, rapid assessment method could assist in monitoring the biosolids to ensure that an acceptable level of stabilization and pathogenic microbial decrease has occurred. Stabilization of organic matter, whether by natural or engineered processes, simultaneously synthesizes and increases relatively stable polyphenolics in the form of fulvic and humic acids. The objective of this study was to optimize a rapid detection method measuring humic acids and, in turn, determine if there was a concomitant decrease in viable indicator or pathogenic microorganisms correlating with a measureable increase of humic acids. The specific aims of this study were: 1) to investigate whether humic acids can be used as a surrogate of pathogenic or indicator organism levels within biosolids; and, 2) to optimize an extraction protocol and

chemical luminescence-method (CL) to measure levels of humic acid extracted from biosolids. Lab-scale anaerobic digesters were utilized at two temperatures (25°C and

37°C) over a 35-day study period. Levels of pathogens (Salmonella) and indicator organisms (Eshcerichia coli, fecal coliforms, and Enterococcus) were examined over time using EPA standard methods. Various extraction protocols (EDTA, formaldehyde-

NaOH, PBS-heat, and control) of humic acid were compared. The biosolids extracts were quantified with the CL-based method using acidic Cerium (IV) Sulfate and Rhodamine

6G. The levels of all studied microorganisms decreased over time, with greater reductions occurring from the treatment at 37°C compared to the treatment at 25°C. The CL method demonstrated linearity (r = 0.997) in the range of 1mg-100mg commercial humic acids.

All extraction methods showed increasing levels of humic acids over time; extraction with EDTA exhibited the greatest humic acid levels among the four extraction methods.

Levels of humic acids were higher for the treatment at 37°C (EDTA: 215 mg L-1) than for the treatment at 25°C (EDTA: 189 mg L-1). Collectively, the results show an inverse relationship between humic acid and microbes, as well as refined methods for both the extraction and quantification of humic acids from biosolids. This suggests the potential use of humic acids as a rapid chemical indicator for the determining of microbial decrease, thereby, illustrating potential for their use in the classification of biosolids.

4.1 Introduction

Biological residuals (untreated organic wastes) originate from various waste sources, such as animal manures, food wastes, and pre-treated municipal sludges. Treated biological residuals are often referred to as biosolids, which hold substantial environmental potential (energy and fertilizer) through waste treatment technologies.

Anaerobic digestion (a common treatment technology) of biosolids simultaneously yields an alternative energy source (biogas), reduces pathogen levels, and leaves a nutrient-rich residual for potential use as soil fertilizer. The fertilizer is commonly used in agriculture, with seven to eight million tons of class B biosolids being land applied annually within the U.S. (Beecher et al., 2007). However, biosolids pose a potential environmental and human health hazard since they may contain pathogenic microbes and toxic chemicals, both of which have the potential to contaminate water, soil, food, and air (USEPA 1999a;

Bicudo and Goyal, 2003; Sinha et al., 2010; Rana et al., 2010). Thus, stabilization of biosolids is vital for preventing environmental hazards from the land application of these biosolids (Ziemba and Peccia, 2011; Evanylo et al. 2008).

In an effort to establish standards for the stabilization of biosolids, and thus reduce the potential hazards of their use as a soil amendment, the United States Environmental

Protection Agency (USEPA) developed regulations known as the Part 503 Biosolids

Rules (USEPA, 1999b). These regulations specify requirements for the stabilization process, with the use of specific temperatures within specific time intervals during treatment (aerobic, anaerobic, etc.). Based on the levels of indicator organisms and

pathogens, biosolids are classified into Class A and Class B, each of which have their own treatment requirements and regulations for use.

Biosolids can be further described by their composition. Most are primarily composed of water (>90%), appearing as slurries. Others are found as semi-solids (manure) or solids

(crop residues), composed of up to 10% and 30% solids, respectively (Russ W., 2004).

The solid fraction of these residuals typically consists of living and once living organic matter, with few inorganics, but can vary based on the waste source. The organic matter primarily consists of microbial biomass, polysaccharides, proteins, nucleic acids, and lipids.

The stabilization of this organic matter (biosolids) involves dissimilation of cellular and biomacrmolecules (proteins, lipids, carbohydrates and nucleic acids). This oxidation results in the formation of various byproducts, consisting primarily of new microbial biomass, CH4, CO2, H2O, N2, H2, volatile fatty acids (VFAs), microbial aggregates, and newly formed polymeric substances. This process often results in the formation of microbial aggregates, such as flocs, biofilms, and granules; these microbial aggregates consist of extracellular polymeric substances, inorganic particles, and multivalent cations

(Sutherland, 2011; Avella et al., 2010). The extracellular polymeric substances are of special interest.

The extracellular polymeric substances (EPSs) are formed from the degradation of

organic matter and primarily consist of carbohydrates, proteins, nucleic acids, lipids, humic substances, and other polymeric compounds (Sheng G., 2010 and Frølund et al.,

1995). EPS formation is generally considered to be the result of high-molecular-weight secretions from microbes, and the products of both cellular lysis and the hydrolysis of macromolecules. It is thought that the components of EPS form a protective layer for microbes against the harsh external environment, and may also serve as an energy source in nutrient-limited conditions (Liu et al. 2002). In sum, the EPS, which consists of various biomacromolecules, has a multitude of functions (many of which are still unknown), and form a complex matrix, which is thought to provide a protective organic framework for microbial cells. Humic substances are the only primary biomacromolecule of EPS that is formed directly from the stabilization process, and thus might be able to act as a surrogate in determining the level of stabilization within biosolids.

Humic substances have been described as the fraction of organic matter in soils and compost, or as the organic materials of natural origin in advanced stages of decomposition, whether in soils, compost, digesters or peat bogs, and whether plant or animal in nature (Waksman S., 1952). Their formation is thought to occur from condensation polymerization reactions, amino acid sugar interactions, and animal/plant decay, which form a new complex substrate (Qu et al., 2012). Humic substances are typically divided into two fractions, humic and fulvic acids, and are often identified by their color and solubility (acidic or basic). Because they may have the potential to act as an indicator for the degree of stabilization within biological residuals, a better

understanding of their relationship within EPS of biosolids could produce an innovative indicator for biosolids stabilization.

Determination of the levels of humic substances within the EPS of biosolids requires the separation of humic substances from the EPS and subsequent quantification. Separation is accomplished by various EPS extraction methods. Extraction methods are typically either physical or chemical techniques, but there is no standard procedure for extraction (Liu et al., 2002 ; Sheng et al., 2010). As such, various methods must be selected, optimized, and compared for each specific sample, with the most appropriate method being chosen for future analyses.

The methods used for quantifying EPS fractions vary in the published literature. EPS polysaccharides are generally quantified with the anthrone or phenol-sulfuric acid methods, while protein content is measured with the Lowry or Bradford methods.

However, humic substances are more complex and have an undefined composition and structure, making the measurement of their content more difficult (Shen et al., 2012).

There are various methods, utilizing spectroscopic techniques and fluorescence, which attempt to quantify, characterize, and determine the complexity of humic substances. The most common methods utilize UV-Vis, which measures humic substance absorbance— thought to be the result of the substances’ aromaticity—that occurs from 800 to 200 nm

(Abbt-Braun et al., 2004). Another spectroscopic method used is the determination of

E2/E3, E4/E6, and E2/E4 ratios (absorbances in the 200, 300, 400, and 600 nm range),

which have been used to characterize natural organic matter in aquatic environments

(Filella M., 2010). These spectroscopic methods provide an estimate of the aromaticity percentage and molecular mass, and are thought to be useful for tracking variations in organic matter origins and type within a system (Filella M., 2010). Fluorescence spectroscopy is also commonly used for the study of humic substances, and is thought to have greater sensitivity.

The development of a method to quantify humic substances in their entirety is challenging (Filella M., 2010). However, the ability to quantify the core and backbone of humic substances, which consists of phenolic and aromatic moieties, could provide information on relative concentrations of humic substances within a sample and provide ample information on their levels within various organic matrices. Chemical luminescence (CL) has recently seen greater use in the study of humic substances— specifically humic acids within aquatic environments—and these methods could be applied to quantify the extracted humic acids from biosolids.

There have been several studies within the literature that have used chemical luminescence as a means by which to study various analytes, specifically those containing phenols, carbonyl, nitrogen and sulfur containing compounds. Because of this background and humic acid’s richness in phenolics, hydroxyls, carbonyls, and both nitrogen and sulfur, CL has seen increasing utilization in the study of humic acid. Cheng et al. (2007) developed a flow-injection CL method utilizing acidic cerium for the

detection of humic acid in water, optimizing the concentration of Ce(IV) and acidic solution, and found a linear relationship with humic acid in the range of 0.03- 10.0 µg mL-1. Cheng et al. also applied these methods for the determination of humic acid within tap water, spring water, and river water, and found recoveries from 90.0 to 110.0%. This initial study utilized acidic cerium alone, which produced a much weaker signal compared to other studies that have added rhodamine 6G (fluorophore) to the system. Cui et al. (2006) utilized CL and an acidic Ce(IV)-rhodamine 6G complex to detect phenolic compounds. They noted previous work that found that the oxidation reaction between

Ce(IV) and rhodamine 6G in a sulfuric acid medium generated a weak CL signal, which was strongly enhanced by phenolic compounds. Accordingly, the authors investigated 53 organic compounds, of which 32, all of them phenolic compounds, enhanced the CL signal. They ultimately concluded that the addition of phenols could facilitate the acidic

Ce(IV)-rhodamine 6G reaction, greatly enhancing the light emission. The most recent study that investigated humic acid was performed by Qu et al. (2012), who utilized a solid phase extraction device, combined with a flow-injection CL device using a Ce(IV)- rhodamine 6G complex, for the determination of humic acid in environmental waters.

This research, which was similar to that of Cheng et al (2007), optimized the reagents

(Ce(IV), Rhodamine, and H2SO4) and parameters (flow rate, ethanol, and time) for the detection of humic acid. The study successfully enriched and quantified humic acid in natural waters, with a detection limit of 0.1 – 35 mg L-1. These studies demonstrate that the use CL-based systems with an acidic Ce(IV)-rhodamine 6G complex has the capacity to measure humic acid with relatively high sensitivity and accuracy.

This study utilized an acidic Ce(IV)-rhodamine 6G complex with batch chemical luminescence to quantify extracted humic acids from the EPS within biosolids. This analysis was done over time, as the involved biosolids were degraded, to understand the relationship between humic acid and time during the stabilization of biosolids. These analyses were done in conjunction with both indicator and pathogenic organism quantification to further understand their relationship with humic acid over time, and, specifically, to determine if there exists an inverse relationship between levels of microbes and humic acid. If this relationship exists, one could expect humic acid to act as a surrogate for determining the degree of biosolids stabilization.

4.2 Methods

4.2.1 Feedstock

Raw cattle manure was collected as substrate from Waterman Dairy Farm at The Ohio

State University. The manure was collected fresh from the feeding lot pads, and diluted with water (1 water:2 manure). The inoculum was obtained from a completely mixed anaerobic digester in Columbus, Ohio that digested both agricultural and residential waste at mesophilic temperatures and was operated by Quasar Energy Group (Columbus,

Ohio, US). Immediately after collection of both manure and inoculum, volatile solid levels (VS) were measured. During these analyses (lasting 24 hours), the manure and inoculum were stored at 4°C. Upon VS determination, a 1:1 VS mixture of manure:inoculum was prepared and distributed into batch digesters.

4.2.2 Experimental Set-up and Procedure

The batch digesters were 150 ml Nalgene screw-cap bottles with a working volume of

100ml. The digesters had one port, to which a stop-cock valve was attached for the release of biogas. After being filled with the 1:1 mixture, the digesters were flushed with

99.9% N2 and then sealed. They were placed in thermally controlled water baths, half at

25°C ± 0.2°C and the other half at 37°C ±0.2°C (Thermo Scientific, Water Bath), during the study period. Samples were taken at, 0, 0.5, 1, 3, 5, 8, 12, 16, 20, 26, and 34 days for both temperatures. All sampling was done in replicate (n=2) for each temperature, with two batch reactors being opened, sampled, and discarded at each time point, for a total of

40 batch digesters.

4.2.3 Chemical Analysis

4.2.3.1 Reagents

Humic acid (salt sodium, technique grade) was purchased from Sigma-Aldrich (St. Louis,

MO), and used without further purification. Cerium (IV) sulfate tetrahydrate (Ce(IV)) and rhodamine 6G (99%) were both purchased from ACROS Organics (Geel, Belgium).

Sulfuric acid, hydrochloric acid, and sodium hydroxide were purchased from Fisher

Scientific (Waltham, MA) and were used to make dilute solutions for subsequent use during the extraction and quantification. The working Ce (IV) solution was prepared daily in sulfuric acid (0.5 N) prior to use (Cheng et al., 2007). The rhodamine 6G was prepared as a stock solution (DI water) and stored at room temperatures (Qu et al., 2012).

Standard solutions of humic acids were prepared daily by further dilution of its stock solution (1 g L-1 [0.1 N NaOH]). The stock solution was stored at 4°C. The dilution series was done in 0.1 N NaOH solutions for use in determining the standard curve.

Ethylenediaminetetraacetic acid ([EDTA] 0.5M) and formaldehyde (37%) were purchased from Fisher BioReagents.

4.2.3.2 Extraction of Humic Acids

Humic acids from the batch-digested biosolids were extracted using four methods, with

PBS alone being the control. Figure 7 details the procedure of each of the extraction processes. The control utilized a phosphate-buffered solution (PBS) as a sole means of extraction. Four grams of biosolids were added to 10 ml of PBS, mixed with a tube rotator (Scientific Equipment Products, MD, USA) for 1 minute, and then centrifuged for

20 minutes at 9500 RPM. The supernatant was filtered through a 0.2µm membrane, and then acidified to a pH of 2 with 1.0N HCL. Precipitation was aided with a shaker (Orbit

Shaker, Lab-Line; IL, USA) for 30 min at 200 RPM. The samples were then centrifuged again for 20 min at 9500 RPM (FisherSci, 225 Centrifuge; Waltham, MA). The supernatant was subsequently decanted off, with the pellet being re-suspended in 10 ml of

0.1N NaOH; this re-suspension constituted the final product (extracted humic acids). The other three procedures utilized either a chemical or a physical extraction method, followed by the same protocol/procedures as the control. The chemical extractants utilized were 10ml of EDTA (2% at 4°C for 1 hour) along with formaldehyde (0.06ml of

37% formaldehyde at 4°C for 1 hour) plus NaOH (10ml of 0.1N at 25°C for 1 hour) (Liu and Fang 2002; D’Abzac et al., 2010). The physical extractant was eleveated

temperature. Four grams of biosolids diluted with 10ml of PBS in centrifuge tubes were placed in a water bath at 80°C for 1 hour. Frølund et al., (1995) recommended 0.5-1 hour for extraction of EPS, noting that cell lysis was of negligible concern at this time interval.

Fang and Jia (1996) likewise demonstrated that EPS extraction was completed in less then 1 hour. Due to the information provided by these and other studies, time was not investigated as a variable (Fang and Jia, 1996; Frølund et al., 1995).

Figure 7: Detailed procedure for each method utilized in the extraction of Humic acids

4.2.3.3 Humic Acid Determination

A Turner 20/20 single tube luminometer (2020-998, Sunnyvale, CA) was utilized in a batch configuration to measure the relative light units of our analytes. Autoclaved microcentrifuge tubes (1.5ml) were utilized as the reagent vessels in the following sequence: 300 µl of commercial or extracted humic acids were added to the reaction vessel, followed by rhodamine 6G (0.0001 mol L-1), and finally freshly prepared acidic

Ce(IV) solution (0.05 mol L-1). The extracted humic acids were diluted (10-1, 10-2, 10-3) prior to analysis, to achieve the appropriate range of detectable humic acids. Of note is that the prepared acidic Ce(IV) solution required 30 minutes of stabilization before use.

After the 30-minute stabilization period, the solution sometimes required minor adjustment, using sulfuric acid, to ensure repeatable analysis from one acidic Ce(IV) batch to the next. Following the introduction of these three reagents (in the specified order) into the reaction vessel, the vessel was immediately placed into the luminometer.

This was replicated (n=3) for each sample, with their relative light units (RLUs) being recorded each time. The same procedure was used for both stock solutions (humic acid), in the generation of standard curves, and for comparing the extracted samples over time.

4.2.3 Microbial Analysis

The indicator organisms measured in this study were Escherichia coli (USEPA, 2002a;

[E. coli]), Enterococcus spp. (USEPA, 2002b) and fecal coliforms (APHA, 2005 ;

USEPA, 1999b). Their levels were assessed using EPA standard methods (USEPA,

2002a; USEPA 2002b; USEPA, 1999b). Following mechanical mixing, the digesters were opened and fresh samples were dispensed into sterile 50ml conical tubes (Fisher

Conical Centrifuge Tubes) and placed on ice. Ten grams from each digester were then diluted with 90ml of sterile PBS, and subsequently mixed. Dilution and mixing occurred within sterile Whirl-Pak bags (Whirl-Pak NASCO, WI, U.S.). The mixed samples were then serially diluted for subsequent membrane filtration using 0.45 µm pore size- nitrocellulose sterile membrane (Fisherbrand; Waltham, MA), and then placing the membrane filter upon its specific media, modified m-TEC for E. coli, m-FC for fecal coliforms, and m-EI for Enterococci (USEPA 2002a and 2002b, USEPA, 1999b). After incubating at specific incubation time and temperature, according to the respective standard methods, the colonies were counted and then colony numbers were calculated after considering dilution rate and inoculum volumes. The results are expressed as log

CFU/dry g.

Salmonella was the sole pathogenic organism investigated in this study. It was quantified using EPA standard method 1682 (USEPA, 2006). Enrichment was done through incubation in tryptic soy broth (TSB). Following incubation in TSB, the samples were spotted from the TSB on selective modified semisolid Rappaport-Vassiliadis (MSRV) medium. Presumptively identified colonies were then isolated on xylose-lysine desoxycholate agar (XLD). Isolated presumptive colonies were then taken for biochemical confirmation with slants of lysine-iron agar (LIA), triple sugar iron agar

(TSI), and urea broth. The final step of confirmation uses serological typing with polyvalent-O-antisera. Calculation for MPN was then done based off the number of

positive tubes and their dilution, as well as the dry weight of the original biosolids sample, and reported as MPN/4g dry solids.

4.2.4. Statistical Analysis

Statistical analysis was conducted using SAS 9.2 software. The replicate treatments were compared using a paired T-Test and a Wilcoxon signed-rank test. Relationships between levels of microbes and levels of humic acid were determined using a Spearman correlation test (proc corr).

4.3 Results

4.3.1 Chemical Luminescence of Commercial Humic Acid

The effectiveness of using batch CL to quantify humic acid with an acidic Ce(IV)- rhodamine 6G complex (Qu et al., 2012) was further demonstrated by this research. The proposed method was successfully applied to the determination of commercial humic acids in the range of 1.0 – 100 mg L-1, with a standard curve that generated an R2 of

0.997. This standard curve was used in the determination of unknown humic acid concentrations within biosolids samples over time.

Figure 8: Standard curve for Humic Acid [1-100mg mg L-1] + Acidic Ce(IV) 0.05 mol L-1 + rhodamine 6G 0.0001 mol L-1, which details an R2 value of 0.997. This standard curve depicts the relationship exhibited by an increase in humic acids concentration and the subsequent increase in RLU.

Almost no signal was observed from acidic Ce(IV), rhodamine 6G, or humic acid individually (Figure 3). The mixture of acidic Ce(IV) and humic acid generated a weak

CL signal of approximately 10 RLU. The mixture of acidic Ce(IV) and rhodamine 6G alone generated a moderate signal of 121.5 RLU. This signal was significantly amplified by the addition of humic acids, with RLU values increasing as the concentration of humic acids increased (Figures 8 and 9). The examination of varying concentrations of hydrogen sulfide, Ce(IV), and rhodamine 6G was also performed (data not shown). The preliminary experiments showed that concentrations of 0.5 N for hydrogen sulfide, 0.05 mol L-1 for Ce(IV) and 0.0001 mol L-1 for rhodamine 6G were optimal for amplifying levels of humic acid in the range of 1 - 100 mg L-1 within this batch system.

4.3.2 EPS extraction methods and resulting Humic Acid levels

Figure 10 illustrates the changing humic acid levels extracted with various extraction methods for the 34 days. The replicated treatments were not statistically different (T-Test and Wilcoxon sign-ranked), indicating that an average of them could be used for our

Spearman correlation test and presentation of the data (Figure 4). Levels of humic acids within EPS from the biosolids samples were dependent upon the extraction method utilized. The control yielded the lowest levels of extracted humic acids, while PBS-heat yielded the second lowest. The chemical extractants produced higher levels of extracted humic acids, with EDTA generating the greater amounts compared to formaldehyde-

NaOH .

Seven of the eight humic acid samples showed increasing levels over time. The only exception was the control at 25°C. Humic acid levels were generally greater for the samples digested at 37°C, though they showed a decrease for the last two time points.

With the exception of PBS-heat (which ended with identical humic acid levels), all 37°C samples ended higher than their 25°C counterparts (Figure 4). Average levels of extracted humic acids are presented in Table 7. The control had the lowest average levels of humic acids, followed by PBS-heat, formaldehyde-NaOH, and finally EDTA.

Table 7: Average Humic Acid Levels over the Course of the Study (mg/L)

Extraction 37°C 25°C Difference EDTA 192.51 168.82 23.69 Formadehyde + 141.15 108.67 32.48 NaOH PBS + Heat 98.71 88.95 9.76 Control (PBS) 83.10 33.67 49.43

4.3.3 Fecal Indicators and Pathogenic Organisms

The replicated treatments were not statistically different, indicating that average values could be used for our analyses and for presentation of the data (T-Test and Wilcoxon sign-ranked). The data were log normally distributed, and the microbial results are presented as such. Levels of E. coli and fecal coliforms followed similar trends, with both organisms reaching a non-detectable limit upon day 20 for the 37°C treated biosolids

(Figure 11). However, levels of fecal coliforms at 37°C reached detectable limits on Day

34, with a concentration of 0.98 log (CFU/g). The lower temperature (25°C) resulted in

greater levels of both E. coli and fecal coliforms, with final concentrations at approximately 1.5 log (CFU/g) for both organisms. Enterococci, on the other hand never reached a level less than 4.0 log (CFU/g) over the course of the study. The differences between the two temperatures were smaller for Enterococci; their final concentrations were also similar (4.31 for 37°C and 4.62 log CFU/g for 25°C).

Salmonella demonstrated similar results to those of the indicator organisms, with decreasing levels over time, and with the higher temperature achieving greater reductions of microbes. As demonstrated in Table 8, the 37°C treatment attained an MPN/4g of dry solids below 11 by day 8, and a value of less then 1 (MPN/4g) on day 20. However, levels of Salmonella increased to 5.43 and 6.87 MPN/4g, respectively, at the last two sampling points. The 25°C digested biosolids did not achieve these reductions, and never reached a level below 9 MPN/4g.

Table 8: Salmonella spp. Concentration over the course of the study (average MPN/4g dry solids) 37°C 25°C Time (Days)

0 552.88 552.88 41.92 1 282.73 3 292.09 287.72 5 16.33 83.54 8 8.29 135.31 12 5.61 9.08 16 10.62 64.37 20 0.10 175.95 11.68 26 5.43 34 6.87 18.79

4.4 Discussion

4.4.1 Extraction and Determination of Humic Acids

The extraction and quantification of humic acids from the EPS of biosolids is challenging, due to EPS consisting of various biomacromolecules and microorganisms interacting within a complex environment. The extraction of humic acids can also result in the generation of various byproducts within the extracted product, which can interfere with humic acid quantification. In our study, we tested preliminary methods using NaOH at various times (1-24 hours) and centrifugation as our sole means of extraction

(traditional humic acid extraction [Sumner E., 1999]). The results from NaOH alone produced inconsistent findings, likely as a result of cell lysis. Refinement of our methods was necessary, namely further consideration of our sample matrix, with emphasis upon extracting humic acids while minimizing cell lysis. This ultimately led to the selection of the study’s methods (the three methods and control), as these procedures attempt to minimize cellular lysis while also separating EPS fractions.

The EPS extraction methods showed that EDTA generated the greatest levels of humic acid, followed (in order) by formaldehyde-NaOH , PBS-heat, and the control. The control did not utilize any mechanism to dissociate the EPS, while the heat from PBS-heat attempted to dissociate the EPS by increasing cellular molecular movement (Sheng et al.,

2010). The chemical extractants were thought to be the most effective due to their chemical interaction between both the microorganisms and EPS within this matrix.

EDTA functions by removing divalent cations, which are important in the crosslinking of

charged compounds in the EPS matrix; the removal of these cations results in the dissociation of the matrix (Sheng et al., 2005; Bourven et al., 2012). Formaldehyde prevents cell lysis by fixing the cells, through interaction with the cell membrane

(Alcamo, 1997; Liu et al., 2002). Concurrently, the NaOH increases the pH of the EPS matrix, causing the dissociation of the EPS between acidic groups and the repulsion of negatively charged particles; this allows for optimal EPS extraction (Wingender et al.,1999).

Our extraction and measurement results demonstrate that the use of EDTA and formaldehyde-NaOH (chemical) permitted maximal release of humic acids compared to the other methods. The study’s findings are supported by those of Liu et al. (2002), who investigated the various EPS fractions and their concentrations in assorted sludges. Their results determined that EDTA and formaldehyde-NaOH yielded the highest levels of humic substances amongst the six extraction methods they investigated. Our results, along with those of Liu et al., would suggest that these chemical extractant methods merit utilization in future analyses of sludges and biological residuals.

The effectiveness of an acidic Ce(IV)–rhodamine 6G complex for the quantification of humic acids with CL is demonstrated in Figure 8. The theorized mechanism of the acidic

Ce(IV)-rhodamine 6G complex has been described within recent literature; Qu et al.

(2012) postulate that the mechanism of the CL reaction between acidic Ce(IV) and humic acid is the result of semiquinone radicals being formed during the oxidation of acidic

Ce(IV) solution. These radicals generate a CL signal when excited state semiquinones are converted into quinine. In summary, it is postulated that the mechanism results from the energy transferred from excited-state Ce(III) and semiquinones to rhodamine 6G; this forms an excited-state rhodamine 6G, which returns to its ground state with its characteristic emission around 560 nm (Qu et al., 2012). The result is a strong CL signal.

The importance of reagent order is best exhibited by the mechanisms of the reactions between the three compounds (acidic Ce(IV), rhodamine 6G, and humic acid). The lack of CL signal between humic acid and rhodamine 6G (Figure 9) demonstrates that no reaction occurs between these two, in turn suggesting that rhodamine 6G and humic acid are each oxidized by acidic Ce(IV). Therefore, it is important that the rhodamine 6G and humic acid are added to the reaction vessel before acidic Ce(IV), in order to prevent reaction quenching and ensure reproducible results (RLU).

Our study investigated the levels of humic acid within the EPS of biosolids, using EPS extraction methods in conjunction with classical humic acid extraction methods, followed by CL to quantify the extracted humic acids. It is the first study to use this methodology with biosolids. These modified extraction methods demonstrate the ability of chemical and—to a lesser extent—physical techniques to separate humic acids from EPS. The CL method, using an acidic Ce(IV)-rhodamine 6G complex, further demonstrated a rapid, simple, and sensitive method for studying humic acids within the EPS of biosolids. CL has a multitude of benefits compared to other methods (namely UV-Vis and

fluorescence). These include the low cost of reagents and instrumentation (luminometer), as well as the instrument’s mobility (handheld versions are available); the latter could permit onsite analysis, precluding the need for complex (microbial) laboratory analysis.

4.4.2 Relationship between Humic Acids and both Indicator and Pathogenic

Microorganisms

The data suggest the existence of an inverse relationship between levels of humic acid and all indicator and pathogenic organisms measured. This association was supported by a Spearman correlation test, which demonstrated that humic acid levels and levels of microorganisms (both pathogenic and indicator) were negatively related for all extraction combinations (negative r-value and P < 0.05). This indicates that an increase in levels of humic acid is associated with reduced levels of microorganisms.

The suggested inverse relationship between levels of humic acid and levels of the microorganisms studied is further supported by differences between the two temperatures. The 37°C treatment showed substantially greater increases in levels of humic acid, and also greater reductions in levels of microorganisms than did the 25°C treatment. This direct relationship in our data between temperature and levels of humic acid supports the notion that levels of humic acid can be used to predict the level of microbial inactivation within biosolids.

These results also provide data on the stabilization of organic matter and its role in the inactivation of microorganisms. Temperature is widely considered the key variable in microbial inactivation, but our research provides data suggesting that the stabilization of organic matter—as represented in our study by an increase in levels of humic acid—is also an important variable in inactivation. Our results, and those of Smith et al. (2005), would suggest that the mechanism of increased temperatures is the result of increased stabilization of organic matter, which results in microbial reductions due a shortage of labile nutrients.

In addition to the relationship between humic acids and microbes, our results also provide suggestive data on humic acid formation. Our findings that levels of humic acid increased over time during digestion suggest that humic acids are formed as the direct result of decomposition/stabilization of organic matter. Therefore, this research further supports the existing theory that humic acid formation is the result of polymerization reactions during the degradation of organic matter.

4.5 Conclusion

In summary, these results suggest an inverse relationship between levels of humic acid and both pathogenic and indicator organisms. This study also further demonstrated that

EPS extraction methods could be used to separate humic acids from EPS, as well as

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further demonstrating the effectiveness of chemical luminescence and an acidic Ce(IV)- rhodamine 6G complex in quantifying extracted humic acids. The suggested inverse relationship between humic acids and microbes is further supported by comparison of the two temperatures (treatments), with the treatment conducted at 37°C generating reduced levels of microbes and higher levels of humic acids when compared to the treatment conducted at 25°C. Our results also suggest the importance of organic matter stabilization as a key variable in microbial inactivation. Lastly, these results further support the theory that humic acid formation is the result of polymerization reactions during the degradation of organic matter.

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Chapter 5

Conclusion and Recommendations

5.1 Conclusion

This research was intended to assess anaerobic digestion and its efficiency in reducing pathogens and producing biogas at psychrophilic temperatures. This research also intended to investigate key variables (temperature, substrate levels, pH, humic acid, and inoculum-to-substrate ratios) during stabilization practices (anaerobic digestion) to determine if a relationship exists between these variables and microbial levels. The rationale for this research was towards improving the public health (stabilization) and engineering (biogas) aspects of anaerobic digestion and the biosolids produced.

The results from the inoculum-to-substrate ratio study suggest that anaerobic digestion in batch reactors can decrease levels of E. coli and, to a lesser extent, Enterococci spp. at psychrophilic temperatures (Ch. 2, Hypothesis 1). Due to the complexity of variables involved with anaerobic digestion, this could be attributed to a multitude of factors. The factors that are generally thought to have the greatest impact upon inactivation of

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microbes are temperature, levels of specific VFAs, pH, microbial competition, non- ionized organic acids, and substrate limitation (Puchajda and Oleszkiewicz, 2006; Smith et al., 2005). Of these, temperature is widely considered the most important factor in the inactivation of microbes. Our results agree with this theory, but elaborate on what temperature is truly controlling, which is the microbial machinery to utilize substrate for growth and reproduction. Thus, the relationship between levels of substrate and levels of fecal bacteria is an important factor in determining the concentration of certain microbes and their overall carrying capacity within a system. Increased I:S ratio did not improve both biogas production and indicator organism levels, but there was a relationship between increased I:S ratios and lower levels of indicator organisms (Ch.2 Hypothesis 2).

The variable investigated did not demonstrate a digester performance indicator that was predictive of fecal indicators levels within the biosolids (Ch. 2 Hypothesis 3).

Environmental conditions, specifically VFA levels, have also illustrated the potential to be an important variable in specific indicator organism levels. Therefore, designing treatment systems to address both substrate level (reduced loading rates or continued use of inoculum) and environmental conditions (separating hydrolysis and methanogenesis) would be ideal for reducing levels of indicator organisms in psychrophilic anaerobic digestion.

The study evaluating semi-continuous digestion of dairy manure, with a simulated seasonal cycle, produced significant findings. The biosolids generated from all eight digesters met Class B specifications in accordance with USEPA guidelines (< 2x106 CFU fecal coliforms/g dry biosolids), regardless of loading rate or temperature (Ch.3

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Hypothesis 1). Reduced loading rates led to a more stable environment (in terms of pH, levels of VFAs and TIC) as well as lower levels of indicator bacteria, but generated slightly less biogas (Ch.3 Hypothesis 2 and 3). Temperature fluctuations did not appear to influence E. coli and fecal coliform concentrations; however, temperature did appear to have an effect upon levels of Enterococci, illustrating an inverse relationship with the temperature fluctuations (Ch.3 Hypothesis 4). Overall, the results demonstrate the capacity of small-scale digesters to reduce levels of fecal indicator bacteria, and provide important data to improve the performance of small-scale psychrophilic digesters, specifically by reducing loading rates to prevent souring during winter months.

The results from our investigation of the relationship between levels of humic acid and microorganisms suggest an inverse relationship (Ch.4, Hypothesis 1). The suggested inverse relationship between humic acids and microbes is further supported by comparison of the two temperatures (treatments), with the treatment conducted at 37°C generating reduced levels of microbes and higher levels of humic acids when compared to the treatment conducted at 25°C. This study also validated the use of EPS extraction methods to separate humic acids from the EPS of biosolids, and further demonstrated the effectiveness of chemical luminescence with an acidic Ce(IV)-rhodamine 6G complex in quantifying extracted humic acids (Ch. 4, Hypothesis 2). Our results also suggest that organic matter stabilization is a key variable in microbial inactivation. Collectively, these results suggests the potential of humic acids to act as a rapid chemical indicator of

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microbial decrease within biosolids, and possibly even a future indicator for biosolids classification.

5.2 Recommendations and Future Research

The quantity of nutrients acts as a surrogate of microbial capacity. Therefore, reduced loading rates and longer retention times have the potential to reduce levels of fecal indicators and pathogens. Higher loading rates can also negatively affect chemical parameters (decrease in pH and TIC, and an increase in VFAs), which can result in an unstable environment. Thus, the incorporation of substrate levels (loading rate) should be considered an equally important variable when using time and temperature guidelines.

The incorporation of loading rate into the time and temperature guidelines holds especially true for SSBTS, since they are much more prone to souring at these reduced temperatures.

Substrate limitation and levels of VFAs have illustrated their importance when considering specific indicator organisms. Therefore, designing systems to extend treatment, simulating substrate-limiting conditions, and incorporating high VFAs might be able to maximize the reduction of these fecal indicator organisms. This could be accomplished through multi-staged digestion (2 or 3), since it separates hydrolyses (pH of ~ 5) and methanogenesis (pH of around 7.8), while also extending treatment times.

Future research on multi-staged digestion could include a study investigating the levels of each indicator organism (E. coli and Enterococci) at each of the phases of digestion (1, 2, and 3), providing further data on the effect of these variables upon specific indicator

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organisms.

The continued use of inoculum-to-substrate ratios following start-up could aid in the digestion of labile organics within a SSBTS. Since these systems lack engineered controls, the use of I:S ratios could help maintain buffering capacity and microbial communities, and extend retention times, while also preventing short circuiting. The identification of an ideal community, whether though biological engineering or cold- adaption could also hold potential to optimizing microbial communities and the conversion of labile organic matter within both small and full-scale digestion processes.

Another recommendation to improve digestion in SSBTS is the incorporation of rudimentary heating or advanced mixing. The incorporation of heating could allow for increased loading rates, leading to increased biogas production, while still maintaining reduced levels microbes. Heating could be improved through insulation, solar heaters, or the use of compost on top of digesters. Mixing is an essential element in the maximization of contact between microbes and labile organic matter, and since most

SSBTS incorporate little to no mixing, the addition could result in significant gains. This could be accomplished though hand cranked propeller mixers, magnetic mixers, or small, appropriately geared windmills.

This dissertation has also demonstrated the potential of humic acid to act as a surrogate for stabilization, potentially presenting a new method for biosolids classification.

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However, further research is needed to see this through. A more rapid extraction method is necessary to minimize the method’s total time, which is currently eight hours. This could be done via a simplified extraction method with EDTA and a syringe filter for on- site extraction. Since handheld Luminometers already exist, the extraction is the key to a rapid on-site assessment. A longitudinal study at a WRRF, utilizing anaerobic digestion, would be needed to apply both the current procedure and several rapid on-site procedures. The proposed longitudinal study could provide valuable information on the viability of a rapid biosolids classification test through the extraction and quantification of humic acids.

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References

“Methane” Science of Methane. Environmental Protection Agency. 4 Sept. 2008

URL: http://www.epa.gov/methane/scientific.html

Abbt-Braun G., Lankes U., Frimmel F.H. 2004 Structural characterization of aquatic humic substanes- the need for a multiple method approach. Aquat. Sci. 66 , 151-

170

Alcamo, I.E., 1997. Fundamentals of Microbiology. Benjamin Cummings, Menlo

Park, CA Chapter 11

Anderson, G.K., Kasapgil, B., Ince, O., 1994. Microbiological Study of Two-

Stage Anaerobic Digestion During Start-Up. Water Res. 28, 2383-2392

APHA. 2005. Standard methods for the examination of water and wastewater. (A.

D. Eaton, L. S. Clesceri, & A. E. Greenberg, Eds.)Test (Vol. 2, pp. 2-59,2-60). American

Public Health Association.

Avella A.C., Gorner T., Donato P.D., 2010. The pitfalls of protein quanification in wastewater treatment studies. Science of the Total Environment 408, 4906-4909

Beecher, N., Crawford, K., Goldstein, N., Kester, G., Lono-Batura, M., Dziezyk,

E. 2007. A National Biosolids Regulation, Quality, End Use & Disposal Survey. North

East Biosolids and Resiudals Association

108

Bevacqua C.E., Rice C.P., Torrents A., Ramirez M. 2009. Steroid hormones in biosolids and poultry litter: A comparison of potential environmental inputs. Science of the Total Environment 409, 2120-2126

Bicudo, J.R. and Goyal, S.M. 2003. Pathogens and Manure Management

Systems: A Review. Environ. Technol. 24, 115-130

Bodik, I., Herdová, B., Drtil, M. 2000. Anaerobic treatment of the municipal wastewater under psychrophilic conditions. Bioprocess Biosyst. Eng. 22. 385-390

Bonjoch X. and Blanch A.R. 2009. Resistance of Faecal Coliforms and

Enterococci Populations in Sludge and Biosolids to Different Hygienisation Treatments.

Microb. Ecol., 57, 3, 478-483

Bourven I., Costa G., Guibaud G. 2012. Qualitative characterization of the protein fraction of exopolymeric substances (EPS) extracted with EDTA from sludge.

Bioresource Technology 104, 486-496

Bowman D. (2009) Manure Pathogens: Manure Managment, Regulations, and

Water Quality Protection. Mcgraw- Hill Pub. NY, NY.

Brooks J.P., Tanner B.D., Gerba C.P., Haas C.N., Pepper I.L. 2005a. Estimation of bioaersol risk of infection to residents adjacent to a land applied biosolids site using an empirically derived transport model. Journal of Applied Microbiology 98, 397-405

Burns, R.T. (2009) Current Status of Manure Anaerobic Digestion in the U.S. and

Beyond. Presentation at the conference. Energy Production From Anaerobic Digestion of

Dairy Manure. Madison, WI.

Carneiro T.F., Riau V., Perez M. 2010. Mesophilic anaerobic digestion of sewage

109

sludge to obtain class B biosolids: Microbiological methods development. Biomass and

Bioenergy 34, 1805-1812

Cekmecelioglu D., Demirci A., Graves R. 2004. Optimization of In-Vessel Food

Waste Composting Systems for Inactivation of Pathogenic Microorganisms. Journal of

Food Protection 68, 589 – 596

Chen Y., Cheng J.J., Creamer K.S. 2008. Inhibition of anaerobic digestion process: A review. Bioresource Technology 99, 4044-4064

Chen, T., Fu, B., Wang, Y., Jiang, Q., Liu, H. 2012. Reactor performance and bacterial pathogen removal in response to sludge retention time in a mesophilic anaerobic digester treating sewage sludge. Bioresour. Technol., 106, 20-26

Cheng X., Zhao L., Wang X., Lin J.M., 2007. Sensitive Monitoring of Humic

Acid in Various Aquatic Environments with Acidc Cerium Chemiluminescence

Detection. Analytical Sciences, 23, 1189- 1193

Côté C., Massé D., Quessy S. 2006. Reduction of indicator and pathogenic microorganisms by psychrophilic anaerobic digestion in swine slurries. Bioresour.

Technol., 97. 686-691

D’Abzac P., Bordas F., Van Hullebusch E., Lens P.N.L, Guibaud G. (2010)

Extraction of extracellular polymeric substances (EPS) from anerobic granular sludges: comparison of chemical and physical extraction protocols. Environmental Biotechnology

85, 1589-1599

Evanylo G., Sherony C., Spargo J., Starner D., Brosius M., Haering K. (2008).

110

Soil and water environmental effects of fertilizer-, manure-, and compost-based fertility practices in an organic vegetable cropping system. Appl. Soil Ecol., 127, 50-58

Fang H.H.P., Jia X.S., 1996. Extraction of Extracellular Polymer from Anaerobic

Sludges. Biotechnology Techniques, Vol. 10, No.11, 803-808

Filella M. 2010. Quantifying ‘humics’ in freshwaters: purpose and methods.

Chemistry and Ecology 26, 177-186

Frølund B., Griebe T., Nielsen P.H. 1995., Enzymatic activity in the activated- sludge floc matrix. Appl. Microbiol. Biotechnol. 43. 755-761.

Greenberg A.E., Clesceri L.S., Eaton A.D. “ Standard Methods- For the

Examination of Water and Wastewater” American Public Health Association ,18th

Edition, 1992

Guan T. and Holley R.A. 2003. Pathogen survival in swine manure environments and transmission of human enteric illness- a review. Journal of Environmental Quality

32(2), 383-392

Hang Y.D. 2004. Management and Utilization of Food Processing Wastes.

Journal of Food Science 69 (3), 104 -107

Helmut Kaiser Consultancy, Mar, 2008. Biogas Digesters in China.

Horan N.J., Fletcher L., Betmal S.M., Wilks S.A., Keevil C.W. 2004. Die-off of enteric bacterial pathogens during mesophilic anaerobic digestion. Water Res., 38. 1113-

1120 Html: http://www.cdc.gov/ncidod/eid/vol5no5/mead.htm

111

Hua Cui, Quanlin Zhang, Aung Myint, Xuewu Ge, Lijuan Liu

“Chemiluminescence of cerium (IV) –rhodamine 6G- phenolic compound system”

Jounral of Photochemistry and Photobiology, 2006

Hudson HT. 1971. Solid waste Management in the food processing industry.

Proceedings second National Symposium on Food Processing Wastes. Corvallis, Oreg.

Oregon State University

Jingya Qu, Hui Chen , Chao Lau, Zhihua Wang, Jin-Ming Lin. 2012.On-Line

Solid Phase extraction of Humic acid from Environmental Water and Monitoring with

Flow-Through Chemiluminescence. Analyst, 137, 1824-1830

Jones P., 1980. Health Hazards associated with the handling of animal waste. Vet.

Rec. 5, 4-7

Kameswari K.S.B., Kalyanaraman C., Porselvam S., Thanasekaran K. 2012.

Optimization of inoculum to substrate ration for bio-energy generation in co-digestion of tannery solid wastes. Clean Techn. Environ. Policy. 14, 241-250

Kearney T., Larkin M.J., Levett P.N. 1994. Metabolic Activity of Pathogenic

Bacteria during Semicontiuous Anaerobic Digestion. Appl. Environ. Microbiol., 60 (10).

3647 – 3652.

Kearney T.E., Larkin M.J., Levett P.N. 1993. The effect of slurry storage and anaerobic digestion on survival of pathogenic bacteria. J. App. Bacteriol., 74. 86-93

Khalid A., Arshad M., Anjum M., Mahmood T., Dawson L. 2011. The anaerobic digestion of solid organic waste. Waste Manag., 31, 1737-1744

112

Khuder S., Milz S.A., Bisesi M., Vincent R., McNulty W., Czarjkowski K. 2007.

Health survey of residents living near farm fields permitted to receive biosolids. Arch.

Environ. Occup. Health, 62, 5-11

Kumar R., Gupta M.K., Kanwar S.S. 1999. Fate of bacterial pathogens in cattle dung slurry subjected to anaerobic. World J. Microbiol. Biotechnol., 15, 335-338

Le MS, Mayhew ME, Ratcliff RW. 2000. Enzymic hydrolysis as a means for pathogen control in sludge treatment processes. In: Lowe P, Hudson JA, editors. Proceedings of the

Fifth European Biosolids and Organic Residuals Conference, Wakefield, UK. Aqua

Enviro, Leeds

Lettinga G, Rebac S, and Zeeman G. 2001. Challenge of Psychrophilic Anaerobic

Waste Water Treatment. Trends in Biotechnology 19(9), 363-370

Levett P.N., Larkin M.J., Kearney T.E. 1993. The effect of slurry and anaerobic digestion on survival of pathogenic bacteria. Journal of Applied Bacteriology 74, 86-93.

Liu H., Fang H.H.P. 2002. Extraction of extracellular polymeric substances (EPS) of sludges. Journal of Biotechnology 95, 249-256.

Logan T.J, Chaney R.L. 1983. Utilization of municipal wastewater and sludge on land-metals. Proceedings of the Workshop on utilization of municipal wastewater and sludge on land. University of California, Riverside CA, 235-326

Lopes W.S., Leite V.D., Prasad S. (2004) Influence of inoculum on performance of anaerobic reactors for treating municipal solids waste. Bioresource Technology 94,

261-266

113

Lossie U. and Putz P. (2008) Targeted control of biogas plants with the help of

FOS / TAC. Hach Lange.

Massé D., Gilbert Y, Topp E. 2011. Pathogen removal in farm-scale psychrophilic anaerobic digesters processing swine manure. Bioresour. Technol., 102, 641-646

Massé D., Masse L., Xia Y., Gilbert Y. 2010. Potential of low-temperature anaerobic digestion to address current environmental concerns on swine production. J

Anim. Sci., 88, E112-E120

Mchugh S., Collins G., Flaherty V. 2006. Long-term, high rate anaerobic biological treatment of whey wastewaters at psychrophilic temperatures. Bioresource

Technology 97(14), 1669-1678

Mead P.S., Slutsker L., Dietz V., McCaig L.F., Bresee J.S., Shapiro C., Griffin

P.M., Tauxe R.V. 1999. Food-Related Illness and Death in the United States. Centers for

Disease Control and Prevention (CDC), Sept. 1999

Müller W, Frommert I, Jörg R. (2004) Standardized methods for anaerobic biodegradability testing. Reviews in Environmental Science and Biotechnology 3(2),

141-158

Nges I.A. and Liu J. 2010. Effects of solids retention time on anaerobic digestion of dewatered-sewage sludge in mesophilic and thermophilic conditions. Renew. Energy,

35, 2200-2206

114

Nielsen-Holm J.B., Seadi T.A., Oleskowicz-Popiel P. 2009. The future of anaerobic digestion and biogas utilization. Bioresource Technology 100, 5478-5484

Niwagaba C., Kulabako R.N., Mugala P., Jonsson H. (2009) Comparing microbial die-off in separately collected faeces with ash and sawdust additives. Waste Management

29, 2214-2219

Oragui J.I. and Curtin T.P. (1987). The removal of excreted bacteria and viruses in deep waste stabilization ponds in northeast brazil. Water Science and Technology, 19,

569-573

Pell A.N. 1997. Manure and microbes: public and animal health problem? Journal of Dairy Science 80(10), 2673-2681

Perlack R.D., Wright L.L., Turhollow A.F., Graham R.J., Stokes B.J., and Erbach

D.C. 2005. Biomass as feedstock for bioenergy and bioproducts industry: The technical feasibility of a billion-ton as annual supply, Html: http://www1.eere.energy.gov/biomass/pdfs/final_billinton_vision_report2.pdf

Puchajada B., Oleszkiewicz J., Sparling R., Reimers R.2006.Low-Temperature

Inactivation of Fecal Coliforms in Sludge Digestion. Water Environ. Res., Vol. 78,

Number 7

Puchajda B., Oleszkiewicz J., Sparling R., Reimers R. 2006b. Low –Temperature

Inactivation of Fecal Coliforms in Sludge Digestion. Water Environment Research 78

(7), 680-685

115

Qu J., Chen H., Lu C., Wang Z., Lin J-M. 2012. On-line solid phase extraction of humic acid from environmental water and monitoring with flow-through chemiluminescence. The Royal Society of Chemistry 137, 1824-1830

Rajendran K., Aslanzadeh S., Taherzadeh M.J. 2012. Household Biogas

Digesters-A Review. Energies, 5, 2911-2942

Rana L., Dhankhar R., Chhikara S. 2010. Soil Characteristics Affected by long term Application of Sewage Wastewater. Int. J. Environ. Res., 4(3). 513-518.

Richards G.P. 2001. Enteric virus contamination of foods through industrial practices: a primer on intervention strategies. Journal of Industrial Microbiology &

Biotechnology 27, 117- 125

Rose WW and co-workers. 1971. Waste from cannery and freezing fruits and vegetables. Proceedings second National Symposium on Food Processing Wastes.

Corvallis, Oreg. Oregon State University

Russ W. and Meyer-Pittroff R. 2004. Utilizing Waste Products from the Food

Production and Processing Industries. Critical Reviews in Food Science and Nutrition,

2004, 44, 57-62

Sabourin L., Beck A., Duenk P.W., Kleywegt S., Lapen D.R., Li H., Metcalfe

C.D., Payne M., Topp E. 2009. Runoff of pharmaceuticals and personal care products following application of dewatered municipal biosolids to an agricultural field. Science of the Total Environment 407, 4596-4604

Sahlstrom L. 2003. A review of survival of pathogenic bacteria in organic waste used in biogas plants. Bioresour. Technol. 87, 161-166

116

Salsali H.R., Parker W.J. , Sattar S.A. 2006. Impact of concentration, temperature, and pH on inactivation of Salmonella spp. by volatile fatty acids in anaerobic digestion.

Can J. Microbiol. 52. 279-286

Shen R., Sheng G-P., Yu H-Q. 2012. Determination of main compoents in the extracellular polymeric substances extracted from activated sludge using spectral probing method. Colloids and Surfaces B:Biointerfaces, 94, 151-156.

Sheng G.-P., Yu G.Q., Yu Z., 2005. Extraction of extracellular polymeric substances from the photosynthetic bacterium Rhodopseudomonas acidophila. Applid

Microbiology and Biotechnology 67 (1), 125-130

Sheng G.P, Yu H.Q., Li X.Y. 2010. Extracellular polymeric substances (EPS) of microbial aggregates in biological wastewater treatment systems: A review.

Biotechnology Advances 28, 882-894

Sinha R.K., Heart S., Bharambe G., Brahambhatt A. 2010. Vermistabilization of sewage sludge (biosolids) by earthworms: converting a potential biohazard destined for landfill disposal into a pathogen-free, nutritive and safe biofertilizer for farms. Waste

Management & Research 28, 872-881.

Smith K. 1994. Health, Energy, and greenhouse gas impacts of biomass combustion in household stoves. Energy for Sustainable Development 1(4), 23-29

Smith S.R., Lang N.L., Cheung K.H.M., Spanoudaki K. 2005. Factors controlling pathogen destruction during anaerobic digestion of biowastes. Waste Manag., 25(4), 417-

425,

117

Sutherland I.W., 2001. The biofilm matrix- an immobilized but dynamic microbial environment. Trends Microbiology. 9, 222-227

Swindal M.G. , Gillespie G.W., Welsh R.J. (2010) Community digester operations and dairy farmer perspectives. Agric. Hum. Values , 27,461-474.

Tanner B.D. , Brooks J.P., Gerba C.P., Haas C.N., Josephson K.L, Pepper I.L.

2008. Estimated Occupational Risk from Bioaersols Generated During Land Application of Class B Biosolids. J.Environ. Qual, 37, 2311-2321

Tanner B.D., Brooks J.P., Haas C.N., Gerba C.P., Pepper I.L. 2005. Bioaerosol

Emission Rate and Plume Characteristics during Land Application of Liquid Class B

Biosolids. Environmental Science and Technology 39, 1584-1590

Thomaz-Soccol, Paulino R.C., Castro E.A., Andreoli C.V. 1997. Helminth eggs viability in sewage and biosolids sludge in Curitiba, Parana, Brazil.” Arquivos De

Biologia E Tecnologia, V.40 issue 4, p. 829-836

U.S. Department of Agriculture (USDA), National Agricultural Statistics Service;

Farms, Land in Farms, and Livestock Operations: 2010 Summary” Feb. 2011. ISSN:

1930-7128

U.S. Environmental Protection Agency 2002b. Method 1600: Enterococci in

Water by Membrane Filtration Using membrane-Enterococcus-Indoxyl--D-Glucoside

Agar (mEI). EPA 821-R-02-022

U.S. Environmental Protection Agency, 1999b. Environmental Regulations and

Technology: Control of Pathogens and Vector Attraction in Sewage Sludge. U.S. EPA

Office of Research and Development, Washington D.C. EPA 625-R-92013

118

U.S. Environmental Protection Agency. 2002a. Method 1103.1 : Escherichia Coli

(E.coli) in Water by Membrane Filtration Using membrane-Thermotolerant Escherichia

Coli Agar (mTEC). EPA-821-R-02-020

U.S. Environmental Protection Agency. 2006. Method 1682 : Salmonella in

Sewage Sludge (Biosolids) by Modified Semisolid Rappaport-Vassiliadis (MSRV)

Medium. EPA-821-R-06-14

UKWIR. 1999. Escherichia coli in UK Mesophilic anaerobically digested sludge.

Report SL-06, UK Water Industry Research Ltd.

US Environmental Protection 1999a. Agency ‘Biosolids Generation, Use, and Disposal in the U.S.’ EPA530-R-99-009,

US Environmental Protection Agency (USEPA). 2007. Anaerobic digesters continue growth in US livestock market. Washington D.C.: US EPA http://ww.epa.gov/agstar/pdf/2007_digester_update.pdf

US Environmental Protection Agency. 1985. Summary of environmental profiles and hazards indices for constituents of municipal sludges. Washington DC.

US Environmental Protection Agency. 1994. A Plain English Guide to the EPA

Part 503 Biosolids Rule. EPA/832/R-93/003

USDA Agricultural Research Service, National Program 206: Manure and

Byproduct Utilization, FY 2005 Annual Report http://www.ars.usda.gov/research/programs/programs.htm?np_code=206&docid=13337

119

Vanotti, M.B., Szogi, A.A., Bernal, M.P., Martinez, J. 2009. Livestock waste treatment systems of the future: A challenge to environmental quality, food safety, and sustainability. OECD Workshop. Bioresource Technology 100, 5371-5373

Viau E. and Peccia J. 2009. Survey of Wastewater Indicators and Human

Pathogen Genomes in Biosolids Produced by Class and Class B Stabilization Treatment.

Applied and Environmental Microbiology 75, 164-174

Waksman S.A. 1952. Soil Microbiology. John Wiley & Sons, Inc. New York

Wang L.K., Shammas N.K., Hung Y.T. Biosolids Treatment Processes. Humana

Press, New Jersey (2007), Vol. 6, Chapter 1: 207-242

Wei J., Jin Y., Sims T., Kniel K.E. 2009. Survival of Human Adenovirus 41 in

Land-Applied Manure and Biosolids. Food Environmental Virology 1, 148-154

Wingender J., Neu T.R., Flemming H.C. 1999. Microbial extracellular polymeric substances: characterization, structure, and function. Springer, Berlin. pgs. 1-15

Wong K., Onan B.M., Xagoraraki I. 2010. Quantification of Enteric Viruses,

Pathogen Indicators, and Salmonella Bacteria in Class B Anaerobically Digested

Biosolids by Culture and Molecular Method. Applied and Environmental Microbiology,

6441 -6448

World Health Organization. 2008. Safe Water, Better Health: Costs, benefits, and sustainability of interventions to protect and promote health. Html: http://whqlibdoc.who.int/publications/2008/9789241596435_eng.pdf

120

Yu L., Yaoqiu K., Ningsheng H., Zhifeng W., Lianzhong X. 2008. Popularizing household- scale biogas digesters for rural sustainable energy development and greenhouse gas mitigation. Renewable Energy 33, 2027-2035

Yu Z., Morrison M., Schanbacher F. 2010. Production and Utilization of Methane

Biogas as Renewable Fuel. Biomass to Biofuels: Strategies for Global industries. Edited by Vertes A., Qureshi N., Blaschek H., and Yukawa H. Chap.20

Ziemba C. and Peccia J. 2011. Net energy production associated with pathogen inactivation during mesophilic and thermophilic anaerobic digestion of sewage sludge.

Water Res., 45, 4758-4768

121