The Pennsylvania State University. The Graduate School. College of Engineering MODELING THE INTERACTION OF TRANSPORT MECHANISMS

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1 The Pennsylvania State University The Graduate School College of Engineering MODELING THE INTERACTION OF TRANSPORT MECHANISMS THROUGH BEDDED MANURE TO EVALUATE THE EFFECTS ON AMMONIA AND GREENHOUSE GAS EMISSIONS A Dissertation in Agricultural and Biological Engineering by Marlyse K. Williams! 2010 Marlyse K. Williams Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2010

2 The dissertation of Marlyse K. Williams was reviewed and approved* by the following: Eileen F. Wheeler Professor of Agricultural and Biological Engineering Dissertation Advisor Chair of Committee Tom L. Richard Associate Professor of Agricultural and Biological Engineering Robert Graves Professor of Agricultural and Biological Engineering Jason Kaye Assistant Professor of Soil Biogeochemistry Paul H. Heinemann Professor of Agricultural and Biological Engineering Head of Department of Agricultural and Biological Engineering *Signatures are on file in the Graduate School ii

3 ABSTRACT According to the Intergovernmental Panel on Climate Change (IPCC), the foremost contributor to global warming is the release of greenhouse gases. Greenhouse gases have been found to warm the Earth by trapping the heat associated with the sun s rays. This causes an increase in the Earth s temperature and affects the well being of the world s population and the environment. Although ammonia is not a greenhouse gas, it has been observed that excess ammonia can be harmful to the environment. Aerial ammonia can travel long distances before being re-deposited on the landscape, where it can leach into the groundwater or be directly carried into bodies of water through surface runoff. Ammonia also contributes to haze, which is a combination of fine particulate matter and can cause respiratory illnesses. It is estimated that 40% of global emissions of ammonia are primarily from livestock manures. Therefore, the focus of this research was to quantify and reduce ammonia and greenhouse gas emissions from heavilybedded farm manure (urine and feces) handling and storage practices. The primary agricultural gases of concern in this study were carbon dioxide (CO 2 ), nitrous oxide (N 2 O), methane (CH 4 ) and ammonia (NH 3 ). Understanding the characteristics of dairy farm manure and the interrelationship between mass transport mechanisms (diffusion and convection) contributing to the release of gaseous emissions is imperative to reducing the gas emissions from these sources. The major goals of this study were to characterize compacted dairy farm manure with various bedding materials (woodchips, sawdust, and hay mix), to measure ammonia and greenhouse gas emissions from the bedded manure while evaluating the effectiveness of compaction as a treatment for reducing emissions, and determine the rates of diffusion responsible for the release of ammonia and greenhouse gases. The results of this study indicate that the permeability values for all bedding types pattern permeabilities between clean gravel and peat, m 2. This result also proved that the sawdust-bedded and hay mix-bedded manure samples followed Darcy s Law of permeability. The woodchip-bedded samples followed a modified Darcy s Law (Dupuit-Forcheimer) because of a lack of water absorbency. In terms of using compaction to reduce the permeability, the results revealed that the permeability was reduced for the sawdust-bedded samples but not for the iii

4 woodchip and hay mix-bedded samples. Using compaction, the air-filled porosities of the bedded samples were significantly reduced for all bedding types. The woodchip-bedded manure had the lowest air-filled porosity of all bedded manures (within a range of m 3 m -3 before compaction and m 3 m -3 after compaction) because of the low water absorbency of the material. The hay mix-bedded manure had the highest air-filled porosities ( m 3 m -3 before compaction and m 3 m -3 after compaction). The effects of compaction were inconclusive as a treatment for reducing ammonia and greenhouse gas emissions and compaction is not recommended as a treatment for reducing ammonia and greenhouse gas emissions. Specifically, the sawdust-bedded samples exhibited a decrease in gas emission rates for NH 3, N 2 O, and CH 4 after compaction, and N 2 O and CH 4 gas emission rates exhibited a greater change from the uncompacted samples when compacted at an applied stress of 6522 N m -2 (75 lbs). Statistically, there was no significant affect of compaction in decreasing NH 3, CO 2, and CH 4 emission rates. However, N 2 O emission rates were significantly reduced by compaction. For the hay mix and woodchip-bedded samples, compaction caused an increase or insignificant change in CH 4, NH 3, and CO 2 production. The rate of diffusion was measured for each bedding type and the results indicated that for all bedding types the range of diffusion coefficients was the same magnitude throughout the trials with a slight trend of the diffusion coefficient decreasing with increased compaction. This trend was not consistent throughout the trials and for all the gases. The hay mix samples had higher overall range of O 2 diffusion coefficients than the sawdust or woodchip-bedded samples because of the higher porosities within the samples before and after compaction. The resulting oxygen diffusion coefficients seem to reflect the theory of passive airflow even at high levels of compaction within the system. The determined effective diffusion coefficients in this study were compared to reference values of the diffusion coefficients of the gases, exposed to air and water. Based on the referenced diffusion coefficients, for all of the gases, the diffusion coefficients calculated through the bedded manure samples decreased with compaction to levels below the diffusion of each gas in air, but remained higher than the levels for diffusion of each gas in water. Lastly, a parameter estimation was performed to determine whether diffusion conditions were truly maintained within the reactors, throughout the study. The results revealed that there was a presence of convection within the hay mix-bedded samples and that the error in the convection iv

5 term was roughly between 10 30%. This study also developed a methodology for determining the rates of non-diffusive processes within bedded manures from experimental data. The larger implications of the results of this study is an increased understanding of the physical properties and mass transport mechanisms of biologically active porous medias such as bedded manures and their impact on ammonia and greenhouse gas emissions. v

6 TABLE OF CONTENTS LIST OF FIGURES...viii LIST OF TABLES...x ACKNOWLEDGMENTS.xii 1. INTRODUCTION LITERATURE REVIEW...4 Introduction Formation and Common Sources of Greenhouse Gases (GHGs) and Ammonia Common GHGs Ammonia as an Aerial Pollutant Formation of GHGs and Ammonia Common Sources of GHGs and Ammonia Measurement Techniques for Detecting Concentrations of GHGs and Ammonia Quantifying Emissions from the Farm Manure Types of Manure Manure Handling and Storage Practices on PA Dairy Farms Physical Characteristics of Manure Transport Mechanisms and Darcy s Law Mass Transport Mechanisms Determining Diffusion Coefficients in Binary Mixtures Determining Diffusion in Porous Media Darcy s Law Modeling Current Models Describing GHG Emissions from Manure Current Models Describing Ammonia Emissions from Manure Current Models of Mass Transport Mechanisms in Porous Media Research Objectives MATERIALS AND METHODS...37 vi

7 Introduction Experimental Design Reactor Design Characterization of Bedded Dairy Manure Manure Collection and Preparation Moisture Content and Organic Matter Determining Permeability Compaction of Manure Samples Temperature Measurement Flux Chamber Design and Validation Gas Flux Measurement Testing the Dominant Mass Transport Mechanism Tracer Gas Selection RESULTS AND ANALYSIS Introduction Characterization of Manure Analysis of Ammonia and Greenhouse Gas Emissions Temperature Data Flux Chamber Validation Ammonia and Greenhouse Gas Emissions Results Estimated Effective Diffusion Coefficients Parameter Estimation and Results Governing Equations Results CONCLUSIONS Implications of this Study Future Work.106 APPENDIX..109 REFERENCES 142 vii

8 LIST OF FIGURES Figure 3.2.1: Overview of Experiment Reactor.39 Figure : Equipment Layout for Permeability Experiments...42 Figure : Schematic of Valve Layout for Permeability Determination...42 Figure : Compaction Experiment Equipment Set-up.44 Figure 3.4.1: Schematic of the Front View of the Flux Chamber..45 Figure 3.4.2: Air Velocity Profile of the Discharge Pipe Using a Hot Wire Anemometer..46 Figure 3.4.3: Schematic of Flux Chamber Validation Equipment.47 Figure 3.4.4: Diagram of Wooden Frame and Sample Spots.48 Figure : Top View Schematic of Gas Concentration Measurement Equipment.49 Figure : Experimental Valve Set-up for Gas Concentration Measurement 50 Figure 3.5.1: Experimental Set-up for N 2 Purge of Reactor...50 Figure 3.5.2: Experimental Set-up for Tracer Gas Input to Reactor..51 Figure 3.5.3: Experimental Set-up for Measuring Diffused Tracer Gas Concentrations from the Reactor..51 Figure 4.1.1: Average Change in Bulk Density of Each Bedding Type After Compaction...56 Figure 4.1.2: Relationship between Compacted Bulk Density and Air-filled Porosity.66 Figure 4.1.3: Air-Filled Porosity Before and After Compaction for Each Bedding Type..68 Figure 4.2.1: Example of the Non-linear Relationship between the Emissions Rate from the Steady state flux chamber and the Non-steady state Flux Chamber...73 Figures : The Effect of Compaction on Ammonia Gas Emission Rates..76 Figures : The Effect of Compaction on Nitrous Oxide Gas Emission Rates...77 Figures : The Effect of Compaction on Methane Gas Emission Rates 78 viii

9 Figures : The Effect of Compaction on Carbon Dioxide Gas Emission Rates79 Figure : Analytical Solution Showing the Effects of the Reaction Coefficient on Tracer Gas Concentrations.100 Figure : Headspace Concentration Profile of Tracer Gas Diffusing Through Bedded Manure to Determine R-value ix

10 LIST OF TABLES Table 2.1: Estimated Annual Greenhouse Gas Emissions Based on Previous Studies Extrapolated for a Model Farm Table 2.2: Estimated Ammonia Emissions Based on Previous Studies.17 Table 2.3: Molecular Weights and Diffusion Volumes of each Gas Component..27 Table 2.4: Calculated Diffusion Coefficients.27 Table 2.5: Gas Composition of Air 28 Table 2.6: Diffusion Coefficient of Mixture..29 Table 3.1: Total Samples Required for the Study..38 Table 3.2: Approximate Times Break-through Points Occurred During Diffusion of Tracer Gas for each Bedding Type...52 Table 4.1: Summary of Moisture Content, Dry Matter Content and Organic Matter Content..55 Table 4.2: Results of Compaction on Bulk Density and Mechanical Strength..57 Table 4.3: Example of Rebound Behavior of Hay Mix Samples...58 Table 4.4: Porosity and Permeabilities for Common Porous Materials.59 Table 4.5: Permeability Data for Each Bedding Type and Trial 60 Table 4.6: Results of the Range of Permeabilities Determined in this Study 62 Table 4.7: Results of Statistical t-test for Permeability.63 Table 4.8: Summary of Air-filled Porosity Data for Each Bedding Type and Trial Table 4.9: Results of Statistical t-test for Air-filled Porosity 67 Table 4.10: Summary of Recorded Temperatures During Emissions Test..70 Table 4.11: Summary of Recorded Temperatures During Diffusion Test...71 Table 4.12: Summary of Measured Airflow Rates...72 Table 4.13: Summary of Non-Linear Equations for Correcting the Flux Chamber Emissions...73 Table 4.14: Summary of Average Ammonia and Nitrous Oxide Emissions 81 Table 4.15: Summary of Average Methane and Carbon Dioxide Emissions...82 x

11 Trial 4.16: Summary of the t-test for Evaluating Effect of Compaction on Gas Emissions...84 Table 4.17: Summary of Annual Greenhouse Gas Emissions for Model Farm Based on Experimental Results Table 4.18: Summary of Annual Ammonia Emissions for Model Farm Based on Experimental Results.87 Table 4.19: Summary of Ranges of Effective SF 6 Diffusion Coefficients for each Trial and Bedding Type Table 4.20: Summary of Effective SF 6 Diffusion Coefficients for each Trial and Bedding Type Compared to Air-filled Porosity After Compaction..89 Table 4.21: Summary of Average Calculated Tortuosities based on SF 6 Mixture Diffusion Coefficient.91 Table 4.22: Summary of Average Diffusion Coefficients of Greenhouse Gases based on Applied Stress 93 Table 4.23: Summary of Average Diffusion Coefficients of Ammonia and Oxygen based on Applied Stress 94 Table 4.24: Summary of Binary Diffusion Coefficients of Oxygen, Ammonia, and Greenhouse Gases in Air, Water, and Bedded Manures...95 Table 4.25: Experimentally Determined Effective SF 6 Diffusion Coefficients Used in Parameter Estimation Analysis Table 4.26: Summary of Reaction Coefficients by Bedding Type.101 xi

12 ACKNOWLEDGMENTS A man s mind, once stretched by a new idea, can never return to its original dimensions. Oliver Wendell Holmes. I am thankful for the adversity and challenges that I ve faced through this experience because it has made me even stronger and more determined. Thank you God. There are so many people who continue to be a support to my life. Even if I don t thank you by name, know that your presence in my life has been appreciated. I would like to thank my advisor, Dr. Eileen Wheeler for just believing in my abilities, for giving me a chance, and for cultivating my creativity. I appreciate the time and energy you have invested in me, and I look forward to making you proud. Thanks to Dr. Tom Richard for supervising the beginnings of this adventure and making me feel so much a part of your lab group. Tom, I thank you for encouraging me to pursue so many international opportunities. Thank you for the great trips, but also thank you for challenging me. Although it was painful at some points, I know it was for my good. To the rest of my committee members, Dr. Jason Kaye and Dr. Robert Graves, thank you so much for the time and effort spent in meeting with me and reading every document that I produced. To my friends and colleagues at University of Delaware and the Philadelphia AMP program who continue to encourage me to live boldly, thank you. To Senior Assistant Dean Michael Vaughan and Mr. Stephen Cox, I m so glad I took your challenge. You were both right your living and dreaming, praying and wishing, it was and never will be in vain. I am your living proof and your working data point and I am so honored to hold that position. Thanks to the Alfred P. Sloan Foundation for the financial support to pursue some really unique opportunities. To Associate Dean Catherine Lyons, thank you for being a mentor, Mom, and friend to me. Also, thank you to the office staff within the Agricultural and Biological Engineering Department for supporting me and helping me find my way through this program. I would also like to thank the Agricultural Engineering Department for financially supporting me through this degree. To my colleagues in Richard Lab and the Olfactometry Lab, thank you so much for your friendship and laughter, critiques and correction over the last few years. To my colleagues in the RISE program Yet still we RISE. The journey is far from over. Man what an adventure! To my family, this is definitely a Family PhD. Thank you for the prayers, encouragement, coffee runs, venting sessions, mathematics help, proofreading, and overall just being there always. It s been a long journey and a long time coming. I hope I ve made you all proud and represented you well. xii

13 Chapter 1 INTRODUCTION A major environmental problem currently plaguing the world is climate change. According to the Intergovernmental Panel on Climate Change (IPCC), the foremost contributor to climate change is the release of greenhouse gases (Houghton et al., 1997). There are several different sources of greenhouse gas emissions; however, the largest source is said to be anthropogenic energy use (EPA, 2006). Greenhouse gases have been found to deplete the ozone layer, which serves as a barrier between the Earth and the sun s rays. Greenhouse gases trap the reflected heat emitted by the sun s rays, which causes an increase in the Earth s temperature and affects the well being of the world s population and the environment. The primary agricultural pollutant gases of concern are carbon dioxide (CO 2 ), nitrous oxide (N 2 O), methane (CH 4 ), and ammonia (NH 3 ). The total amount of ammonia and greenhouse gases released annually worldwide due to anthropogenic activity is estimated to be 540 Mg (FAO, 2002). Of these total anthropogenic releases worldwide, agriculture is said to contribute about 20%. Specifically, for ammonia, over 40% of global emissions are attributed to livestock manure storage and management practices (FAO, 2002). Animal manure, a mixture of urine and feces, is a source of gas emissions released from farms, yet relatively little is known about the quantity of emissions and the impact of manure practices on the mechanisms that lead to gas release. The overall goal of this research was to explore how mass transport mechanisms in farm manure stacks, which include manure and bedding, affect the release of ammonia and greenhouse gases. Based on this information, factors relating to the specific mechanism can be manipulated in an effort to reduce the ammonia and greenhouse gases released. This particular research project focuses on ammonia and greenhouse gas emissions from the dairy farm infrastructures common in Pennsylvania. Pennsylvania is a major exporter of dairy products and has the fourth largest dairy cow population in the U.S.; it is estimated that there were approximately 556,000 milk cows in the state of Pennsylvania in 2006 (NASS, 2006). On average, dairy cows produced 39 kg of manure per kg live animal mass per day (ASABE, 2003). Assuming a typical dairy cow is at least 544 kg, approximately 47 kg of manure will be produced per day and almost 17,237 kg annually by each cow. For the state of Pennsylvania this 1

14 would mean approximately 9.65 Mg of manure annually. In the U.S., there are a total of approximately 9 million milk cows (NASS, 2006), resulting in approximately Mg of manure annually. Therefore, approximately 6% of the total manure resulting from dairy cows raised in the U.S. comes from Pennsylvania. The emissions resulting from this amount of manure could be substantial and garner regulatory attention. Gas release occurs when livestock manure is handled, stored and later land applied as a natural substitute for fertilizer. There are several types of farm manure; the variations depend on the type and amount of bedding material (hay mix, woodchips, sawdust, sand) utilized, as well as the species and management (diet, waste handling, etc) of livestock from which the manure originated. The Department of Energy (DOE) estimates that the largest agricultural contribution of methane gas release is from enteric fermentation (DOE, 2006). In an effort to reduce the impact of gas emissions released by farms, the EPA plans to impose stricter air emissions regulations. Stricter regulations mean increased costs for the farmers to treat and remove their manure, and in turn, the food and livestock industry, as well as the consumer, will spend more money on farm products. Therefore, implementing cost-effective methods to reduce emissions before regulations are imposed would save the food industry and consumers large amounts of money. Thus, the EPA and IPCC are encouraging better manure handling methods. This research would contribute to the EPA and IPCC goals by determining factors related to gas release so that effective methods of controlling these factors can be utilized to reduce gas emissions from manure handling and storage. Gas transport is essential for the preservation and management of microbial communities within porous media. The presence or lack of oxygen determines the type of microbial community and, essentially, the type of gases released through microbial metabolism (Amon et al., 2001). Specifically, the objectives of this proposed research were to characterize heavilybedded dairy manure and describe how mass transfer mechanisms effect NH 3, N 2 O, CO 2, and CH 4 emissions by measuring and monitoring the gas profiles within manure stacks. Very little is understood about how transport mechanisms (such as diffusion and advection), impact gas release from farm manure. Most of the previous research involves composting mixes that sometimes utilize manure as a component in the mixture, but tend to be drier and less dense than manure. Manure addition to soil as a fertilizer to replace nutrients lost during crop production has been a common practice over hundreds of years, completing the circle of nutrient 2

15 recycling. Previous work on composting material has revealed that moisture content, bulking agent ratio, and compaction can affect the permeability and porosity of the compost pile, and therefore impact the oxygen supply (Malinska and Richard, 2006a). The oxygen content in the manure stack has been linked to ammonia and greenhouse gas emissions, with some studies citing increased gas production under anaerobic conditions (Amon et al., 2001). However, there is no standard testing method to measure ammonia and greenhouse gas emissions from manure stacks to confirm which conditions cause more gas release. This research will evaluate the release of gas emissions from manure stacks under several compacted conditions, ranging from predominantly aerobic to predominantly anaerobic conditions. The release of greenhouse gases has affected the world s climate and put the population s well being and the environment in jeopardy. Manure is one source of ammonia and greenhouse gas emissions. Very few controlled studies have attempted to describe environmental and biological conditions within manure stacks. In response to this problem, this study seeks to experimentally link the diffusion of oxygen through dairy farm manure stacks to the release of greenhouse gases and to explore how the transport mechanisms affect gas release. This study will help broaden the field of knowledge about the characteristics of heavily-bedded dairy farm manure and the impact farm manure handling and storage have on the release of ammonia and greenhouse gases. This study also seeks to aid researchers in developing methods to decrease the gas emissions from heavily-bedded farm manure to help farmers meet future emissions regulations. 3

16 Chapter 2 LITERATURE REVIEW Introduction Global climate change is currently a major environmental issue. It was determined that greenhouse gases are the main causes of global climate change (Houghton et al., 1997; Storey, 1997). According to the Department of Energy (DOE), approximately 7,122.1 Mg of greenhouse gases on a carbon dioxide equivalent basis (MMTCO 2 e) were released into the atmosphere in 2004 from the United States (DOE, 2004). Therefore, studies are being conducted to determine methods to properly evaluate the quantity of greenhouse gas emissions released from various major sources and methods to reduce or prevent the release of these gases into the atmosphere. Ammonia is not a greenhouse gas, but it is considered a pollutant because when combined with nitrate or sulfate it becomes an air-borne irritant in the form of haze. Ammonia is a respiratory irritant to humans even at low levels. The Federal Occupational Safety and Health Administration (OSHA) prescribe that workers not be exposed to concentrations of ammonia over 50 ppm in an 8-hour period. This literature review discusses the issues surrounding the effect manure handling and treatment has on the release of greenhouse gases (GHGs). It also explores the relationship between mass transport phenomena in heavily-bedded dairy farm manure and emissions of ammonia and GHGs. Specifically, this study seeks to evaluate how oxygen status and physical characteristics within farm manure effect the gas emissions from manure. This chapter is divided into five major sections: description of the formation and common sources of ammonia and GHGs; types of farm manure and manure handling practices on dairy farms; how these activities contribute to the release of emissions; gas transport mechanisms and factors influencing transport into the atmosphere; and the major project hypotheses and objectives of this study. An overview of the models currently utilized to describe ammonia and GHG emissions from heavily-bedded dairy farm manure are examined, and a summary of research performed within this field is discussed. 4

17 2.1 Formation and Common Sources of Greenhouse Gases and Ammonia This section discusses the common greenhouse gases and describes how ammonia acts as a pollutant to the environment, even though it is a naturally occurring compound. Also, the formation of these gases is investigated and the common sources of the emissions are identified. The overall impact that ammonia and greenhouse gases have on the environment, agriculture, and industry is summarized, and some common methods of quantifying the gas emissions are explored Common GHGs The greenhouse effect is a phenomenon in which the Earth receives energy from the sun, in the form of ultraviolet (UV) light. This sunlight enters the atmosphere and some of it warms the surface of the Earth, while the rest is reflected back into the atmosphere as infrared radiation. It is believed that equilibrium is created between the solar energy received and the amount for heating the Earth and atmosphere (Krupa and Kickert, 1989). Greenhouse gases (GHGs) are gases that act like the glass in a car on a sunny day. On a sunny day, the sunlight beams into a car through the glass windows and no light is reflected back. After a few minutes, heat accumulates in the vehicle steadily increasing the temperature of the car. A similar behavior is observed for the accumulation of greenhouse gases. Specifically these gases allow ultraviolet light to enter the atmosphere and then absorb the reflected infrared radiation, trapping the heat within the Earth s atmosphere and re-emitting it back towards the surface (DOE, 2004). Most gases considered greenhouse gases are naturally occurring and without these gases, the Earth would be too cold for life to survive; however, the overproduction of greenhouse gases from anthropogenic activity enhances the greenhouse effect trapping more heat within the Earth s atmosphere. The greenhouse gases of concern, according to the National Research Council (NRC), are carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), water vapor (H 2 O), ozone (O 3 ) and chlorofluorocarbons (CFCs) (NRC, 2001). This study will focus on CO 2, CH 4, and N 2 O as GHGs of agricultural origin. Each of these gases has the ability to linger in the lower atmosphere, with atmospheric CO 2 having multiple sinks or ways of being stored to achieve removal from the atmosphere (NRC, 2001). The global warming potential (GWP) describes the effect of radiative properties of each gas, the absorption of radiation per kilogram of gas present 5

18 at any instant, as the gas is retained in the atmosphere over a lifetime in comparison to CO 2. Carbon Dioxide has a GWP of 1 and in contrast, CH 4 and N 2 O have GWPs of 23 and 296 over a 100-year period, respectively, before they are destroyed (NRC, 2001; Storey, 1997). This means that the emission of 1 ton of CH 4 is the same as the emission of 23 tons of CO 2 over a 100-year period Ammonia as an Aerial Pollutant Although ammonia is not a greenhouse gas, it has been observed that ammonia can also be harmful to the environment. The two main species of nitrogen that contribute to atmospheric nitrogen deposition are ammonia and ammonium (Gyldenkaerne et al., 2005). Ammonia, when combined with nitric acid in the air, forms aerosol nitrate. Aerosol nitrate intensifies the effects of eutrophication and acidification of soils because aerial ammonia deposits settle quickly on surfaces and is highly water-soluble (Gyldenkaerne et al., 2005). Also, aerial ammonia can travel long distances and leach into the groundwater or be directly carried into bodies of water through runoff (Amon et al., 2001; Gyldenkaerne et al., 2005; Chadwick, 2006). The following sections describe how ammonia and GHGs are formed, and discuss some of the major sources of the release of these chemicals into the atmosphere Formation of GHGs and Ammonia The mechanism of formation of GHGs and ammonia is important to understanding how these gases are released into the atmosphere. In agriculture, the most common source of methane is enteric fermentation from ruminant animals (Richard, 2003). Methane is formed when the microorganisms, methanogens, within the diary cow s stomach convert feed into digestible products and methane is exhaled as a by-product. Methanogens are microorganisms that utilize carbon dioxide and hydrogen or acetate for their metabolic processes (Amon et al., 2001). The chemical formulas below represent two different ways in which methane is formed. CO H 2! CH 4 + 2H 2 O (CO 2 as electron acceptor) CH 3 COOH! CH 4 + CO 2 (acetic acid as electron acceptor) Cellular respiration, organic matter decomposition, and manure storage all contribute to the formation of CO 2 at the farm level. A large portion of atmospheric CO 2 is formed through the 6

19 cellular respiration of organisms that utilize sugars, such as glucose, with oxygen as part of their metabolic processes. The decomposition of organic matter is performed biologically with the organisms using the carbon for food and releasing CO 2 and energy as a byproduct (Richard, 2003). An equation representing this biological process is shown below. C 6 H 12 O 6 + 6O 2! 6CO 2 + 6H 2 O + Energy Ammonia is formed through a multi-step pathway in the decomposition of urine, manure, and dead organisms (NPI, 2005). The urea in urine, when in contact with the urease enzymes in feces, converts the urea to ammonia gas and carbon dioxide. The two-step equation below shows the conversion of urea to ammonia gas.! (NH 2 ) 2 CO + 2H 2 O + Urease! 2NH HCO 3-2NH HCO 3 -! CO 2 + 2NH 3 + OH - Several factors affect the formation of ammonia: temperature, ph, and moisture content. Increased temperature is a catalyst for enzymatic activity and promotes convective mass transfer. The ph affects the amount of ammonia available in the aqueous phase. A higher ph means more ammonia in the solution. A low ph (below 6 7) means more ammonium-n via the equation below. A low ph also means more ammonium-n, which is highly water soluble and not easily volatilized from the manure. NH 4 + " NH 3 + H + High moisture contents result in higher rates of conversion of uric acid to ammonium-n and ammonia is readily produced. From agricultural sources such as manure deposits on barn floors and stored or land applied manure, ammonia is released from the source predominantly through diffusion and volatilization. The concentration difference between the surface of the emitting source, in this case the manure pile, and the surrounding free air causes gas movement. If there is less ammonia in the surrounding air the gas concentrations of ammonia on the surface of the pile are released into the atmosphere until equilibrium is reached between the free air and surface of the pile. Volatilization contributes to ammonia gas release due to wind speed across the 7

20 manure piles. A high exchange rate between the ventilation in a barn for instance and air movement can cause a difference of concentration on the surface of the manure pile causing gas release. Nitrous oxide is formed during aerobic nitrification and anaerobic denitrification. Nitrification is an aerobic process by which ammonium is oxidized to nitrate, NO 3, and nitric acid (NO) and nitrous oxide may be released as an intermediate. The amount produced can be impacted by temperature, ph, moisture, and oxygen availability (Richard, 2003). Increased temperatures effect the growth rate of the bacteria, so that more bacteria are available for nitrate production. However, if temperatures exceed 40 o C (105 o F), microbial activity slows or ceases because the conditions are too warm for this particular type of bacteria. Nitrification produces acid and naturally lowers the ph, but nitrification ceases at a ph of approximately 6 and below. The optimum ph range for nitrification and denitrification to occur is between 7.5 and 8.5. Also, increased oxygen availability promotes and increases nitrification because it is an aerobic process. Increased moisture conditions that limit air-filled pore space decreases nitrification, but increases denitrification. The biological process of nitrification is represented by the equation below. NH O 2 + nitrosomonas! 2H H 2 O + NO 2 2 NO O 2 + nitrobacter! NO 3 2 Denitrifying bacteria, which reduce nitrate to produce nitrogen gas, N 2, and release NO and N 2 O as intermediates, cause denitrification. Denitrification occurs when oxygen levels are low and nitrate becomes the primary oxygen source for the microorganisms. The bacteria dissolve the bond between the N and O molecules to gain O 2. During that process, the nitrate is reduced releasing N 2 O. Denitrification requires a carbon source, which is why bedded manure and fertilizers, with their high organic matter content, are a common farm source of N 2 O. Denitrification typically produces more N 2 O than nitrification (Amon et al., 2001). Researchers have determined a trend between N 2 O and oxygen supply, which stipulates that N 2 O emissions from cattle manure increase as oxygen supply is decreased (Amon et al., 2001). N 2 O may be emitted during storage of farmyard manure because of nitrification of NH 4 -N and from denitrification of nitrate, NO - 3, initially produced by nitrification. These changes may occur 8

21 because of the variation in aeration of stacked farmyard manure (Chadwick, 2006). The process of denitrification is represented below (with methanol, CH 3 OH, representing the carbon source) and the reduction of NO 3 - to N 2 with N 2 O as a byproduct is shown. 6NO CH 3 OH + denitrifiers! 3N 2 + 5CO 2 + 7H 2 O + 6OH - NO 3 -! NO 2 -! NO! N 2 O! N Common Sources of GHGs and Ammonia There are numerous sources of ammonia and greenhouse gases, some of which are anthropogenic while others are naturally occurring. Carbon dioxide (CO 2 ), for instance, is a naturally occurring chemical and is emitted through the carbon cycle. However, anthropogenic activity has increased the rate of release of CO 2 into the atmosphere. Major global human contributions of carbon dioxide are the combustion of fossil fuels and some industrial processes such as mining and the manufacture of cement (EPA, 2006). Land use and deforestation comprise the non-combustible sources of carbon dioxide emission, about 1.5% (105 MMTCO 2 e) of U.S. GHG emissions (DOE, 2004). Methane is also a naturally occurring GHG. It is a major component of natural gas and is formed and released by biological processes in low oxygen conditions such as swampy areas, near the roots of rice plants, and in the stomachs of cows (NRC, 2001). Anthropogenic activity has increased the release of methane through an increase in rice production, cattle raising, coal mining, and the disposal of organic residuals in landfills, all of which emit methane. The four major anthropogenic sources of methane are energy use, agriculture, waste management, and industrial processes (DOE, 2004). Methane represented 9% of the total greenhouse gas emissions released by the U.S. in Anthropogenic methane due to agriculture accounted for 29% of the total methane emissions, MMTCO 2 e, emitted by the U.S in The largest methane contributor from agriculture was enteric fermentation (DOE, 2004). Amon et al. (2001) showed that about 80% of the net methane emissions in a dairy system were attributed to enteric fermentation, while the remaining 20% originated from manure. Ammonia is both naturally occurring and anthropogenic. A major source of ammonia is the decomposition of waste material (NPI, 2005). A large portion of the ammonia present in the atmosphere is due to both nitrogen-containing artificial fertilizers (NRC, 2001) and applied manure. It is estimated that 40% of global emissions of ammonia are from livestock manures 9

22 (FAO, 2002, Duxbury, 1994). This is because the urine of mammals contains urea, which is easily hydrolyzed to ammonium (Webb, 2001). Chadwick (2006) supports this assessment, recording significant ammonia emissions during storage of farmyard manure. The two biggest sources of N 2 O are nitrogen fertilization of soils and animal solid waste application. Globally, agriculture contributes 65 to 80% of nitrous oxide emissions (FAO, 2002) most of which are attributed to land application, with the amount due to animal manure storage still being evaluated. N 2 O emissions from agriculture are due to nitrogen fertilization of soils, and a small percentage is due to release from the solid waste storage of domesticated animals in the United States (DOE, 2004). The following section discusses how ammonia and greenhouse gas concentrations are currently detected and quantified Measurement Techniques for Detecting Concentrations of GHGs and Ammonia Emissions of ammonia and greenhouse gases are influenced by numerous factors, and complex interactions exist between the heavily-bedded dairy manures that emit these gases. Therefore, emissions measurements should be carried out throughout the production cycle with replications because of seasonal variations and changes in weather patterns (Amon et al., 2001; Westerman and Bicudo, 2003). In order to quantify the concentrations of NH 3, N 2 O, and CH 4 from manure, Amon et al. (2001) used high resolution Fourier Transform Infrared (FTIR) spectroscopy. FTIR spectroscopy is based on the concept that individual gases have their own distinct infrared absorption. Therefore, several gases can be analyzed simultaneously using this method. In Amon et al (2001) s research, a high spectral resolution was used to avoid false concentration values. Another technique to measure gases and liquids is gas chromatography, which is used to separate compounds that are volatile. This method involves injecting a known sample into a separation column, where it is transported by an inert gas (typically helium or nitrogen). The compounds are separated based on their partitioning behavior between the mobile and stationary phase of the column (Hao et al., 2004). The Environmental Protection Agency (EPA) has developed an emission hood, in the form of a mobile wind tunnel, to help measure gas emissions. Chadwick (2006) used this method to measure ammonia emissions from manure piles. In this study, the emissions hood was attached 10

23 to a steel trolley so it could easily be rolled across a runner system, to be positioned over the top of the manure piles. A speed-adjustable fan was attached to the emission hood so that air was blown at a known velocity through the hood. Air samples were taken from the inlet and outlet of the hood and passed through absorption flasks of orthophosphoric acid to trap ammonia. The ammonium-nitrogen component of the acid was then analyzed by automated flow-injection into a gas chromatograph (Chadwick, 2006). Although there is currently no standard method for measuring greenhouse gases (Gibbons et al., 2006), a common technique is to somehow capture samples and analyze concentration. Then the concentration must be converted into a mass flux (emission). The sample collection method utilized must measure air exchange rate and/or control surface area. A photoacoustic infrared multi-gas monitor produced by INNOVA AirTech Instruments (Innova model 1412 Photoacoustic Field Gas-Monitor, AirTech Instruments, Ballerup, Denmark) can also be used to analyze the gas concentration (ppm or mg L -1 ). With this method, a gas is irradiated with light at a particular frequency and the irradiated light is modulated causing the temperature and pressure to also be modulated. This results in an acoustic wave or sound, which can be detected by a sound-measuring device. Each gas has different amplitude, which is caused by differences in the size of the gas molecules and the amount of light absorbed. A digital signal results and is converted into concentration readings (INNOVA, 2005). In this study, the photoacoustic gas analyzer was used in conjunction with a flux chamber. A flux chamber is a device used to enclose a known surface area so that gas concentrations can be sampled from the emitting source without altering the gas concentrations at the surface (Hutchinson and Livingston, 1993). There are two types of flux chambers: steady state and nonsteady state. Steady state flux chambers maintain sweep air into and through the chamber at all times and the gas concentration is allowed to reach an equilibrium concentration over a set time period. A non-steady state flux chamber utilizes recirculation of air into the chamber; there is no fresh air replacement into the chamber and the gas concentrations are allowed to accumulate over time. To determine the gas flux rates several models are used to simulate the behavior of the gas concentrations within the flux chamber and depend upon the type of flux chamber used. A nonsteady state flux chamber can promote both linear and non-linear behavior of the gas concentrations. An equation representing a linear gas flux was developed by Hutchinson and 11

24 Mosier (1981) and is shown below. This assumes a constant flux rate over the measurement period. f = V (C t " C i ) At where f = flux, mg m 2 min -1 V = volume of flux chamber (m 3 ) C t = final gas concentration (mg m -3 ), C i = initial gas concentration (mg m -3 ), background concentration A = area covered by flux chamber (m 2 ) t = time (min) ( ) To check for non-linearity, the following relationship is used. C 1 " C i C 2 " C 1 >1 where C 1 = gas concentration at a time t after placing the chamber on the surface C 2 = gas concentration at a time 2t after placing the chamber on the surface If this relationship is true for the collected data, then the non-linear equation also developed by Hutchinson and Mosier (1981) is used to calculate the flux. f = V (C 1 " C i ) 2 At(2C 1 " C 2 " C i ) ln # C " C & 1 i % ( ( ) $ C 2 " C 1 ' The non-linear model assumes that the rate of gas exchange is not uniform over the measurement period, but instead decreases as the gas accumulates inside of the flux chamber. The model used to calculate gas flux rates for steady state flux chambers was also developed by Hutchinson and Mosier (1981) and is shown by Equation f = s A (C out " C in ) ( ) where s = sweep air flow rate C out = gas concentration flowing out of the chamber C in = gas concentration flowing into the chamber 12

25 The advantages of using the flux chamber in conjunction with the photoacoustic gas analyzer are the continuous collection of many measurements in one time period, measurement of gas concentrations from various surfaces, the inexpensive design of the chambers (discussed more in section 3.4), the collection of real-time data without having to store samples for analysis, and the portability of the system. The biggest disadvantage of this system is if incorrectly designed, the chambers could disturb the surface of the emitting source causing temperature, wind, and pressure effects that impact the precision of the gas concentration measurements. Understanding the type of material and surface from which the gases are emitted, as well as the mechanisms influencing the release of the gases into the atmosphere, can improve measurement methods. The following section discusses the amount of GHGs and ammonia emissions released from the dairy farm Quantifying Emissions from the Dairy Farm There are several factors influencing the gas fluxes emitted during studies of the dairy farm, such as the conditions under which the gas concentrations are measured and the measurement techniques used to quantify the gases. Each study examined has its own conditions and biases, which make comparisons difficult. It is for this reason, that several emissions fluxes were collected from various studies in order to evaluate the magnitude of emissions from dairy cow housing, manure storage, and land application of solid manure. The goal of this review was to use emissions data from previous literature to estimate ammonia and greenhouse gas emission fluxes from a specifically described Pennsylvania farm and compare these values to the experimental data. There are several criterion utilized in order to analyze the collected emission flux data: 1. Gases used as tracer gases that are also greenhouse gases, i.e. CO Time period and season in which measurements are taken. 3. Size and type of farm and management practices used in study. 4. Laboratory versus in-situ studies. 5. Measurement techniques. When analyzing CO 2 gas emission fluxes, the information is often scarce because CO 2 is typically used as a tracer gas. In animal housing ventilation experiments for instance, CO 2 is 13

26 commonly used as a tracer gas; however, though the measurements are collected within the study, they are often unreported because the gas was used to determine the ventilation rate of a barn ventilation system. Very few studies report their CO 2 emissions, Jungbluth et al (2001) is one of the exceptions. Jungbluth et al (2001) performed several laboratory trials and reported their emissions values for dairy cow housing and stacked storage of dairy cow manure. The scarcity of reported CO 2 gas emission fluxes is reflected in the comprised table of collected data. The duration and season in which gas concentration measurements are collected from each area of the farm is also an important criterion to consider. Gases such as NH 3 and CH 4 are more frequently emitted with increases in temperature. Therefore the higher gas emissions would be reflected in the summer than during the winter season. Also, weather conditions are significant to quantifying the emissions because windy conditions decrease the concentration of gases in the air above the emitting sources, causing increased diffusion of gases from the source into the atmosphere. The duration over which the gas concentrations are measured is also significant. For instance, in the case of land application of solid manure, would measuring emissions for a week after the manure is applied be sufficient to determine the quantity of gases emitted? Webb et al (2004) stored solid farmyard manure for four months before spreading and measured the gases emitted for 60 days after immediate incorporation and manure left on the surface of the soil. Webb et al (2004) were able to measure the gas concentrations for an extended period of time insitu and observe effects of temperature and weather conditions on the measurements, giving a more comprehensive result. However, several studies claim to capture dependable comprehensive gas emissions data inclusive of temperature and seasonal changes when collecting data for several weeks or a month because of differing measurement techniques. Emissions data calculated from studies that utilized a week s worth of data were considered and compared to the emissions data collected from studies that derived emissions from collecting data over the course of a year. A range of values was considered to help represent seasonal variations when possible. During analysis of various studies, the size and type of farm reflected some of the management practices utilized. For instance, the Kavolelis (2006) study measured ammonia emissions from large tie-stall barns with about 200 cows for each trial and treatment. On a farm that size, manure scraping was done more frequently (twice a day) when most Pennsylvania 14

27 farmers scrape the barns once a day for lactating cows and less frequently for heifers and dry cows (every few days) (Dou et al, 2001). These decisions affect the amount of time the manure and bedding spends in the barn, impacting the surface area for gas emissions. For instance, the less time the urine and feces are in contact with each other in the barn, the less chance there is for the enzyme urease to interact with urea in the urine and in turn release ammonia emissions in the barn. Also, with some farms, cows are given grazing time every day or every few weeks. During these periods, since a large amount of the CO 2 emissions are due to the animals themselves, if the animal and manure are removed at that time, very little CO 2 emissions are recorded within the barn. Therefore, the management practices can strongly impact the magnitude of gas emissions and should be considered when evaluating the recorded data. Whether a study was done in-situ or under laboratory conditions was important when analyzing the emissions fluxes because of the limitations of the study. It is important to note that no particular condition is preferred; however, it is important to consider the conditions in which the measurements were collected. A study of a manure stack in the field, would probably be more long-term, involve a larger sample, and consider the effects of weather and physical conditions more thoroughly than a laboratory study. However, a laboratory study has more control over the variables analyzed when compared to a field study. This is seen in the Jungbluth et al (2001) study compared to the Amon et al (1999) study. The Jungbluth study evaluated methane emissions from dairy cow housing units and sampled the manure from the barn and brought the material into a laboratory, simulating farm conditions, while the Amon study evaluated methane emissions in the barn considering management practices and animal behavior when analyzing their emissions data. The validity of the studies is not questioned, nor is the acceptability of the data scrutinized. However, the sample size, experimental conditions, and range of emissions is noted and compared when evaluating the emissions to consider whether the experimental conditions promoted the emissions falsely. Support for this is seen in the Loyon et al (2008) study where over-aeration of samples caused large amounts of CO 2 emissions and minimal emissions of CH 4. The last criterion when reviewing the literature was measurement techniques. The type of technology and measurement devices used to capture gas concentrations is important when reviewing the gas emissions data. If the equipment used is not sensitive enough to capture low concentrations, important measurements might have been missed throughout the study. Also, 15

28 how close the equipment is able to get to the emitting surface and the frequency of measurements was also scrutinized. For several of the land application studies for instance, measurements of gas concentrations were taken with the measurement equipment several centimeters above the emitting surface at random spots within the experimental plots. This gave the best opportunity for concentrations to be captured, collected, and analyzed. Table A.1 in the Appendix summarizes gas emission rates from animal housing, manure storage, and land application found in literature. Some of the emissions are given in units of mass per livestock unit (LU). For the purposes of this study, a livestock unit is equal to 500 kg of live animal weight. The experimental conditions and duration of each study as well as some of the specific measurement techniques utilized are summarized in the table. All of the research performed on gas emissions from dairy farm manure is interesting but the magnitude of the gas emissions from each source is most informative. Therefore, this analysis compared the magnitude of emissions from animal housing, solid manure storages, and manure spreading. Using a typical Pennsylvania farm, a range of emissions from each of the above sources was highlighted and compared and the conclusions were summarized in Tables 2.1 and 2.2. Dou et al (2001) surveyed 994 dairy farms in south-central Pennsylvania to collect information on their nutrient management practices. Assuming that the average conditions reported in this study reflected a typical Pennsylvania farm, the average farm has about 70 lactating and growing Holstein cows (dry cows, heifers, and calves) with an average weight of about 600 kg (84 LU total) housed in a tie-stall barn with natural ventilation and solid floors with straw bedding. The cows are fed a typical diet of grass, silage, and hay rations. The manure from the barn floor is scraped once a day and stored outside the barn as a solid in a stack that contains both anaerobic and aerobic zones and stored for about 4 months before land application by surface spreading. The pile size is assumed 10 x 8 x 5 m in the shape of a truncated pyramid with rectangular base to hold about a day and a half worth of manure. The manure is largely used on the same farm in which it is generated. There is an average tillable area of 70 ha with the most common crops being corn for grain, corn for silage, and alfalfa (Dou et al, 2001). Now using these conditions as guidelines, an estimate of the annual gas emissions expected from each emitting sector was calculated. Each emission was calculated and then the CO 2 equivalence of each gas, or the amount of CO 2 that would cause the same level of emissions, was also calculated 16

29 based on the relationship that there is 1 CO 2eq per unit of CO 2, 296 CO 2eq per unit N 2 O, and 23 CO 2eq per unit CH 4 (IPCC). Table 2.1. Estimated Annual Greenhouse Gas Emissions Based on Previous Studies Extrapolated for a Model Farm. Estimated Emissions Based on Collected Data: Source N 2 O CH 4 CO 2 Animal Housing Manure Storage kg kg CO 2 eq** kg kg CO 2 eq** kg kg CO 2 eq** 25 7,400 3,770 6,130 86, , , , , , , ,065 Manure Spreading , , , ,000 14,398,000 22,908,000 No data available No data available **Assuming 1 CO 2eq per unit of CO 2, 296 CO 2eq per unit N 2 O, and 23 CO 2eq per unit CH 4 Table 2.2. Estimated Ammonia Emissions Based on Previous Studies. Ammonia emissions from each major farm source are summarized to show the impact of the emissions. Estimated Ammonia Emissions Based on Collected Data: Source Animal Housing Manure Storage Manure Spreading NH 3 (kg) a 90 b 260 c 17,885 38,325 d a Used Kavolelis 2006 study for calculation, b Used Balsari et al, 2007 study, c Used Demmers et al, 1998, d Used Webb et al, 2004 study for calculation. 2.2 Manure Animal manure can be a natural resource or a pollutant to the environment, as alluded to in the previous section. The following section discusses the different types of dairy manure and how manure is classified. It includes an in-depth discussion on manure handling and storage practices on dairy farms and the resulting impact on ammonia and greenhouse gas emissions. Lastly, this section examines some of the physical characteristics of heavily-bedded dairy farm manure and how they relate to the release of gases. 17

30 2.2.1 Types of Manure Manure is classified in various ways such as bedding included, moisture content, and the type of livestock animal the product is from. There are four major types of bedding used to bulk dairy manure: wood chips, straw, sand, and sawdust. The type of bedding material has an effect on the physical and chemical properties of fresh manure (Larney et al., 2006). There are three classifications for dairy farm manure: solid, liquid and slurry. Manure is comprised of urine and feces, with little or no extra water added. Liquid manure usually contains less than 8% solids and often has one or more of the following added: overflow drinking water, wash water or precipitation if storage is uncovered. In comparison, slurries are animal manure with approximately 10% solids. Solid manure contains bedding without the addition of extra water and contains approximately 20% solids. In this study, manure from free-stall dairy cows was used and bedding was added to create solid manure Manure Handling and Storage Practices on Dairy Farms There are several ways in which manure is handled and stored in the dairy farm system. Manure handling is a concern because handling methods have been shown to impact the concentration of ammonia and greenhouse gases released into the atmosphere (Larney et al., 2006). Methods of solid manure handling and storage typically include stockpiling, composting, and direct land application. Stockpiling involves stacking the manure in piles inside covered areas or on outside lots until it can be hauled away and land applied. Composting, which involves processing the waste to increase nutrient retention while decreasing transportation costs, is another method of handling and storing manure (Larney et al., 2006). During composting, ammonium-nitrogen content has been observed to transform into a more stable, organic nitrogen form, so that the compost will release less ammonia emissions (Amon et al., 2001). Larney et al. (2006) compared the effects of direct land applied, stockpiled, and composted manure on ammonia and greenhouse gas emissions. They found that composted manure, which could help meet future phosphorus-based manure regulations, allowed the cost effective transport of twice as much phosphorus as fresh manure. However, more carbon and nitrogen was emitted from composted manure than stockpiled manure. Several studies have shown that composting resulted in higher emissions of ammonia compared with anaerobically stacking the manure (Amon et al., 2001), consistent with Larney et al. s (2006) findings. Also, in Larney et al. s 18

31 (2006) study, stockpiled manure started to partially compost, implying possible mass reductions without further handling, which could lower treatment expenses. The final fate of manure is typically land application. In this case, manure could be directly land applied or applied after composting or stockpiling. The major benefit of land application of manure is the return of nitrogen and phosphorus to the soil as fertilizers typically at a much lower cost than for artificial fertilizers. However, the high ammonia volatilization from surfaceapplied manure results in high atmospheric ammonia releases, reducing the nitrogen fertilizer value of manure; emissions are much lower when manure is incorporated (Sogaard et al., 2002). Also, direct daily land application has the advantages of reduced odors from the farm itself, that composted and stockpiled manure do not; therefore, numerous residents complain about the odors emitted from the manured fields (Larney et al., 2006). A proposed method of manure handling is compaction. Chadwick (2006) proposed that since a decrease in air-filled pore space, which impedes gas flow, resulted in the reduction of ammonia emissions, that compaction could reduce N 2 O by creating anaerobic conditions and inhibiting nitrification. Chadwick (2006) compared the ammonia and greenhouse gas emissions from traditional anaerobic stockpiles of cattle manure to compacted manure, and also evaluated the effects of covering the manure with a plastic sheet. The results from Chadwick (2006) were not consistent or completely conclusive. Compaction and covering seemed to reduce the release of ammonia and some of the greenhouse gases into the atmosphere, but the findings were not reproduced in subsequent trials. This study used compaction to decrease the air-filled porosity within the heavily-bedded dairy manure to create a range of conditions from mostly aerobic to predominately anaerobic conditions to observe the quantity and type of gases released Physical Characteristics of Manure One important characteristic that impacts gas release from a manure stack is temperature. Temperature is vital to microbial activity, and thus important to biological manure treatment processes (Richard, 2003). In their research, Amon et al. (2001) showed that during the summer, as the temperature inside an anaerobic manure heap increased, more methane was emitted. The increased temperature encouraged microbial growth and enzymatic activity; therefore encouraging more methane production. 19

32 Some other important characteristics are the moisture content and bulk density of manure. Moisture content is important because it affects the microbial viability, mechanical strength, bulk density, air-filled porosity, and permeability of materials (Malinska and Richard, 2006a). Also, materials high in moisture content are more susceptible to compaction since they act more plastic (Das and Keener, 1997). A plastic material is one that undergoes permanent damage under a load; it does not retain its shape and is easily malleable. El-Mashad et al. (2005) defined a plastic material as one that under high stress exhibits non-newtonian flow properties and recorded this behavior in cattle slurries that were above 6% total solids content. In the study performed by Das and Keener (1997), under compressive stresses, the manure did not act as a plastic material. Even at moisture contents above 70%, total solids below 30%, the manure remained fibrous and held its shape. The manure behavior was compared to that of biosolids, which does not contain as high a water-holding capacity. However, the total effect of moisture content on the behavior of bedded manure has not been well researched. Researchers agree though, that manure that has been bedded, solid manure, is better for use in systems where airfilled void space is important. This is because excessively wet materials have no air voids so oxygen transport is impaired (Das and Keener, 1997; Malinska and Richard, 2006). Free air space (FAS) is a measure of the air-filled voids within a compost matrix (Su et al., 2006). This factor is critical to aerobic microbial activity because the amount of air-filled voids and pressure gradients determine the amount of oxygen that is supplied to microorganisms and the heat removed to maintain an appropriate composting temperature. Chadwick (2006) proposed that a decrease in air-filled pore spaces could reduce emissions by inhibiting some microbial activity. There are several ways to measure the free air space or air-filled porosity. Su et al. (2006) compared four distinct methods of measuring air space within compost matrices: particle density method (PDM), air pycnometer method, quick method, and the modified air pycnometer method. The PDM requires the determination of wet bulk density, moisture content, and particle density of each sample in order to calculate total air space (TAS). The disadvantage to this process is that it is destructive because the sample is boiled in water to purge any air that might be present in the intra-particle voids. The volume of the sample is determined by measuring the sample mass and the density of water displaced by the sample. A modified version of this PDM was proposed to directly estimate FAS. In the Modified PDM, the air-filled pore volumes were 20

33 calculated from the sample moisture content at saturation and subtracted from the TAS. This is shown by the following equation (Su et al., 2006): where FAS = cm 3 cm -3 TAS = cm 3 cm -3 V a = volume of air in the particle voids V t = total bulk volume of sample FAS = TAS " V a V t ( ) Also, the sample mass is determined in a pycnometer and excess water is removed from the sample by pouring the sample through a screen and removing excess water within the sample with a sponge. This method is questionable because of the crude removal of excess water and possible loss of sample. The air pycnometer method uses the Ideal Gas Law and air pressure changes are measured and used to determine the air volume of a sample. The modified air pycnometer method relies on the same principles, except it is also capable of simulating compressive loads (Su et al., 2006). Test Methods for the Examination of Composting and Compost (TMECC) recommended the quick method as a rough estimate for determining FAS. In this bench-scale method, a 2 L beaker is used and the sample is saturated with water and the excess water is drained. The FAS is estimated based on the mass difference between the drained and undrained sample (Su et al., 2006; TMECC, 2002). The problem with all the above methods is that only one sample can be evaluated at a time which makes replication difficult. For this study, the air-filled porosity was evaluated through measured properties of each sample such as the moisture content, dry matter, organic matter, and density of the sample. Evaluating the air-filled porosity in this study was more efficient and allowed for the analysis of several samples at once, with more chances for reproducibility and precision. The equation used for this analysis is shown below. + %(1# DM) " a =1# - $ wet ' +, & $ water (DM * OM) $ om + DM *(1# OM) $ ash (. * 0 ) ( ) / where " a = air-filled porosity 21

34 # wet = density of wet sample # water = density of water, 1000 kg m -3 # OM = density of organic matter, 1600 kg m -3 # ash = density of ash, 2500 kg m -3 DM = dry matter OM = organic matter Although the size of pore spaces is useful to know about a material, little is known about the size of the air-filled pore spaces in different types of bedded manure, or how much of a size decrease is required to cause reductions in greenhouse gases. This research established other factors, such as transport mechanisms, that may explain why reduced air-filled pore spaces can cause a reduction in the release of ammonia and greenhouse gases. 2.3 Transport Mechanisms and Darcy s Law The previous sections examined how physical characteristics of manure can impact the release of ammonia and some greenhouse gases in different manure management systems. However, few of the previous studies focused directly on the flux (transport) of oxygen and gas emissions through farm manure stacks. The following section considers gas transport mechanisms and the limitations of current efforts to model and manipulate these mechanisms. Also included is a discussion of Darcy s Law, explaining the major concepts of the law and when Darcy s Law can be applied Mass Transport Mechanisms There are two mass transport mechanisms typically discussed, diffusion and convection. Diffusion is described as movement of a substance from a high concentration to a low concentration until a uniform distribution is achieved (Hiemenz and Rajagopalan, 1997). Why do the molecules move from a high concentration to a low concentration and what promotes this movement? It stems from kinetic theory of gases and intermolecular forces acting on the molecules. Kinetic theory states that at temperatures above absolute zero, individual molecules are in constant motion in random directions and are comprised of atoms. The atoms are held together by a positive nucleus and negatively charged electron cloud. These atoms attract and repulse each other as they move in a random path and collide with each other. The theory of intermolecular forces applies to real gases, which move randomly and states that there are forces 22

35 of attraction and repulsion between the atoms. At a long range, there is a weak attractive force between the atoms so they do not have the urge to travel quickly, but they are attracted to each other. However, as they travel in a random path and get closer to each other, at a short range, the electron clouds overlap and they begin to repulse each other. A group of these atoms forms a molecule. It is this random movement and charged nature that causes diffusion because when there is a high concentration of the gas, the molecules continuously collide and repulse each other, so they are attracted to the lower area of concentration and begin to move. Eventually, there is the same amount of molecules on each end of the container with no strong attractions or repulsions and equilibrium is reached. Diffusion is a slow process that can be impeded by overly saturated conditions in porous media. In porous materials, diffusion is caused by capillarity, which refers to small, interconnected pore sizes in the material (Datta, 2002). Hamelers (1993) describes diffusion as an important transport process because of oxygen transport. Oxygen transport is essential for the preservation and management of some microbial communities within porous media. The presence or lack of oxygen also determines the type of microbial community and essentially, the type of gases released through microbial metabolism (Amon et al., 2001). Fick s second law of diffusion is used to describe non-steady systems in which the concentration of gas diffusing through media changes with time. The second law of diffusion states (Fick, 1855): "C 2 "t = D" C " 2 x where C = concentration, mg m -3 t = time, sec x = position, m D = diffusion coefficient, m 2 sec -1 ( ) There are several solutions for Fick s second law equation. In the case of this study, a limited source diffusion solution modeled the expected results at the headspace of the reactor (Kissinger and Heineman, 1996). The limited source solution utilized in this study states: C(x,t) = S "Dt $ 2 #x ' exp & ) ( ) % 4Dt ( 23

36 where S = initial dose of tracer gas, mg m -3 t = time, sec x = position of gas within sample, m D = diffusion coefficient, m 2 sec -1 C = concentration, mg m -3 The boundary conditions for this solution are as follows: C(x,t) t= 0 = 0 0 < x < L C(x,t) x= 0 = C 0 t > 0 "C(x,t) = 0 t > 0 "x x= L Here, L represents the entire length of the stack. By graphically solving Equation using experimental data, the diffusion coefficient can be determined for each sample in the study. Convection is the movement of air or gases caused by pressure differences in a media. The transport may be natural or free convection or force-induced convection. Free convection is caused by differences in buoyancy, which could result from outside wind pressure changes or thermal differences (Henderson et al., 1997). For this study, diffusion conditions were maintained within the reactor, and all precautions were taken to prevent thermal and pressure differences within the media. Currently, models and theoretical equations developed for composting or soil systems are used to describe the mass transfer mechanisms relevant to manure (Hamelers, 1993). A major challenge of this research was determining whether these equations and trends are applicable to the conditions observed in heavily-bedded dairy farm manure. The following section discusses the procedure used in this research to determine the diffusion coefficient in binary mixtures of gases within the manure stacks Determining Diffusion Coefficients in Binary Mixtures To understand how the diffusion coefficients in binary mixtures are defined, estimated, and measured, the concept of the Leonard Jones Potential must be understood. In the previous section on transport mechanisms, it was discussed that intermolecular forces contribute to the reactions that are seen as diffusion. In the description of an atom, which has a positive nucleus and is surrounded by an electron cloud, it is considered to be uncharged because no charges are 24

37 concentrated in any one direction. When the atoms approach each other the electron clouds overlap and undergo deformation, since they cannot diffuse through each other, and a dipole moment is induced. A dipole moment is when the charges of an atom polarize so positive charges form on one end and negative charges on the other end. For a short time, the atoms are attracted to each other and the weak attractive force is called a van der Waals force. So at long ranges, this dipole moment is strong attracting the molecules to each other. The measure of this behavior of attractive and repulsive forces is called the Leonard Jones Potential (LJ) and is described by the following equation: where + $ V = 4" # ' -& ),% r ( 12 * # 6 $ '. & ) 0 ( ) % r ( / V = the Leonard Jones potential (LJ) $ = collision diameter, angstroms (Å) r = separation of atoms (distance from one atom to another), Å " = energy of molecular interaction, J It is important to note that " and $ are also known as Leonard Jones parameters and are typically measured or calculated based on the properties of the gas. In a binary system, both gases are colliding with each other so the rate of diffusion of molecules of gas A exposed to molecules of gas B will be different than the rate of diffusion for the individual gases in air. Diffusion of a binary mixture in a simplistic form is defined by Fick s First Law (Equation ), and is used to describe a change in concentration in gas A with distance, from the point of introduction to gas B. The amount of the gas that flows through a unit area per unit time is defined as the flux. J = "D AB dc A dx ( ) where J = flux, mg cm -2 s -1 D AB = binary diffusion coefficient of gas A through gas B, cm 2 s -1 dc A /dx= the change in concentration of gas A with a change in distance, mg cm -4 25

38 For this research project however, the gas concentration changed with distance and in time as it diffused through the manure stacks. Therefore the application of Fick s Second Law was more relevant to this system. dc A dt = D AB " 2 C A " 2 x ( ) where D AB = binary diffusion coefficient of gas A through gas B, m 2 s -1 dc A /dx= the change in concentration of gas A with a change in distance, mg m -4 dc A /dt= the change in concentration of gas A with a change in time, mg m -3 s -1 The diffusion of many gases in exposure to different substances and gases has been measured by previous researchers and can be referenced. However, there are several instances where the diffusion coefficient desired is not measured for a particular gas in a substance, or at a particular temperature of interest. To combat this issue, numerous equations have been derived using properties of gases such as molecular weight and measured values (i.e. temperature, pressure) to estimate the diffusion coefficient. The focus of this research was multi-gas systems because both anaerobic and aerobic zones exist within heavily-bedded dairy farm manure stacks so various gases were released due to temperature, moisture content, and biological activity. Using a tracer gas in this study, sulfur hexafluoride (SF 6 ), in addition to purging the manure samples with N 2, the gases emitted from the manure stack were binary compounds. The purpose of the tracer gas was to determine the rate of diffusion of the tracer gas through the manure matrix then this value was used to estimate the rate of diffusion of the other gases through the manure based on empirical equations. For this study, the binary diffusion coefficients of interest were: "# $%&!'!()!!! *#!!!$%&!'!()+! )# $%&!'!,-*!!.#!!!$%&!'!,+)! /# $%&!'!012!! &#!!!$%&!'!(-/! There are two ways to determine the diffusion of the binary pairs in this system: extrapolating from experimental data or empirical calculation. For this study, an empirical calculation was used, known as Fuller s correlation (Equation ). The Fuller correlation can be used when the Leonard-Jones parameters are not available and experimental data are limited for the binary system. 26

39 # 1 D AB =10 "3 T 1.75 % + $ M A P )v 1 & ( M B ' [( ) 3 A + ()v) 3 B ] 2 ( ) where D AB = diffusion coefficient of gas A in gas B, m 2 s -1 M = molecular weight, g mol -1 T = temperature ( K) P = pressure (1 atm) %= diffusion volumes of each element in gas, dimensionless Table 2.3: Molecular Weights and Diffusion Volumes of each Gas Component (Adapted from Table 3.1 of Logan, 1999). Compound M (g mol -1 ) Diffusion Volumes SF N CH CO N 2 O NH Air O Table 2.4: Calculated Diffusion Coefficients. Calculated using the Fuller Correlation for the binary compounds within the system. Compound Diffusion Coefficient (m 2 sec -1 x 10-5 ) Diffusion Coefficient (cm 2 sec -1 ) SF 6 -N SF 6 -CO SF 6 -NH SF 6 -N 2 O SF 6 -CH SF 6 -air SF 6 -O Mass transfer of gas mixtures of several components can be described using the diffusion coefficients of binary components in the mixture. Wilke (1950) defined an equation (Equation ) to approximate the mass diffusion of a component in a gas mixture if the binary components are known. Using the diffusion coefficients of the binary compounds determined in 27

40 Table 2.4 and Equation , the diffusion coefficient of the mixture could be determined. For this study, the diffusion of SF 6 in the mixture of N 2, CO 2, NH 3, N 2 O and CH 4 was desired. Knowing the diffusion coefficient of the mixture without porous media gives an initial value for comparison to later experimental findings.! D 1"mixture = y 2 ' ' 1 ' + y 3 + y 4 +!+ y n D 1"2 D 1"3 D 1"4 D 1"n ( ) where D 1-mixture = diffusion of component (tracer) in mixture, m 2 s -1 y i = the mole fraction of each component in air (see Table 2.5) D 1-i = diffusion of binary component, m 2 s -1 ' The composition of air is referenced from CRC Handbook of Chemistry and Physics (1997) at 15 o C and 1 atm and can be used to determine the mole fraction of each component of interest within the system (Table 2.5). Table 2.5: Gas Composition of Air. Mole fraction of each gas component in air at 15 o C and 1 atm. Pure Gas Name Compound Mole fraction Nitrogen N Oxygen O Argon Ar Carbon Dioxide CO Neon Ne Methane CH Helium He Krypton Kr Hydrogen H Xenon Xe Nitrous Oxide N 2 O The temperature range in this study was approximately o C. Therefore, a temperature correction was needed in order to determine the mixture diffusion coefficient within the system. Using the temperature correction equation ( ) proposed by Welty, Wicks, and Wilson (1984) the diffusion coefficient was calculated and shown in Table

41 D 1"mixtureT 1,P1 # = P &# 2 % ( T & 1 % ( D 1"mixtureT 2,P 2 $ ' $ ' P 1 T ( ) where D 1-mixtureT1,P1 = diffusion of mixture at T and P of interest, m 2 s -1 D 1-mixtureT2,P2 = diffusion of mixture at known T and P, m 2 s -1 P 1,2 = pressure, atm T 1,2 = temperature, K Table 2.6: Diffusion Coefficient of Mixture. Comparison of the calculated diffusion coefficient from the Mixture Equation to the Fuller Correlation. Mixture Compound Diffusion Coefficient (cm 2 sec -1 ) Equation Used SF 6 - air Wilke Mixture Eq. SF 6 - air Fuller Correlation Eq. The values represented in Table 2.6 represent the diffusion of SF 6 in air within the mixture of gases of interest of the experimental system, in exposure to each other without the impact of the porous media. The next section will describe the procedure for calculating the diffusion coefficient of the mixture components with the impact of porous media impeding gas movement Determining Diffusion in Porous Media The previous section discussed diffusion of mixtures of gases exposed to each other. This study examined the diffusion of ammonia and greenhouse gas through bedded dairy farm manure, which required the determination of an effective diffusivity. Effective diffusion describes the movement through pore spaces of porous media, utilizing the diffusion coefficient of the gases, but considering the tortuosity of the pathways through an area of the media. The tortuosity is not easily described by physical properties of the porous media but is reflective of the way in which the material is packed and the heterogeneity or homogeneity of the mixture. Therefore, the tortuosity will vary between samples even of the same mixture. A basic description of tortuosity as the ratio of the average capillary tube length, Le in m, to the length of the porous media, L in m, in the direction of the diffusive flow path, represented by equation (Moldrup et al., 2001). 29

42 " = Le L ( ) This equation assumes that the capillary tubes are of uniform diameter and are unrestricted. This equation applies more successfully to homogeneous porous media mixtures, and was very difficult to apply to dairy manure that was heterogeneous. From research performed by Moldrup et al. (2001), it was shown that the tortuosity increased with increased water content of the soil sample, especially if the sample had a low porosity value. For solid dairy manure, there is a high moisture content even with added bedding, so a high tortuosity was expected. Also, the capillary tubes within dairy manure are assumed to be restricted because of increased water content, so that equation does not explicitly apply in this case. Therefore, the rate of diffusion becomes important in describing the flow path. There are a few ways to relate the tortuosity to diffusion. For this research, diffusion coefficient of SF 6 in air was known from the mixture equation in the previous section, and the air-filled porosity and diffusion of SF 6 through the media was measured experimentally. Therefore, the diffusion-based tortuosity was determined by the following equation, where the tortuosity factor ranges from zero to greater than or equal to one (Moldrup et al., 2001). " = # * D SF6 $air D SF6 $N 2 $matrix ( ) where D SF6-N2-matrix = diffusion coefficient of SF 6 and N 2 through the bedded manure, m 2 s -1 D SF6-air = diffusion coefficient of SF 6 in air (or O 2, NH 3, CH 4, N 2 O, CO 2 ), m 2 s -1 & = air-filled porosity, dimensionless ' = tortuosity, dimensionless Knowing the tortuosity of each sample helped in the estimation of the rate of diffusion of oxygen, ammonia, and greenhouse gas concentrations through the heavily-bedded dairy manure stacks. The diffusion coefficients of ammonia, oxygen, and greenhouse gases of interest were 30

43 determined using equation , with the knowledge of the tortuosity, air-filled porosity after compaction, and diffusion of SF 6 in each gas determined by the Fuller Correlation Darcy s Law of Permeability Darcy s law describes the flow through porous media and was originally derived for flow through sand (Brown, 2002). Darcy s law considers several key terms, which are important to the physical and geometric properties of porous materials. The Law states that permeability relates the pressure change across a matrix to the distance the airflow travels and velocity at which the flow is moving through the matrix. Permeability is a measure of the ability of fluids and gases to flow through multiphase materials (Malinska and Richard, 2006). Air-filled porosity decreases with increased moisture content because the water fills the porous spaces in the media, blocking airflow. However, some studies indicate an increase in permeability with an increase in moisture content because the moisture promotes clumping of the material and channels are created, allowing increased flow within pore spaces. Darcy s Law is shown by Equation Darcy s law was predominantly used in this study because of the ease of directly calculating the permeability of multiple samples at one time. For this study, the permeability was determined by graphing the velocity of compressed air versus the change in pressure with change in position term (dp dx -1 ). The slope of the line was multiplied by viscosity to determine permeability. where ( = permeability, m 2 µ air = viscosity of air, 1.86 x10-5 kg m -1 sec -1, P = pressure = #*g*h = kg m -1 sec -2 x = distance, m V = superficial velocity, m sec -1 V = " # $ * dp ' & ) ( ) µ air % dx ( In certain conditions, such as high airflow, low permeability, or high saturation, airflow may not be fully laminar. Therefore the Dupuit-Forcheimer equation incorporates Darcy s Law and was used to express the changes in the pressure drop (Equation ). The second term in this equation accounts for the drag force (Richard et al, 2004). 31

44 where # a = density of air, kg m -3 ) = passability, m " dp dx = µ # V + $ a % V 2 ( ) In this study, Equation was used to calculate the permeability when the data graphed with Equation showed a non-linear trend. The same graphical method outlined above was used graphing the dp dx -1 term versus the velocity to determine the permeability. 2.4 Modeling Several models have been developed to describe the emissions of ammonia and greenhouse gases from manure. The majority of these models are dedicated to simulating and predicting ammonia emissions (Jazen et al., 2003). All of these models contain one inherent flaw. They lack the ability to adequately predict the amount of emissions because they lack an effective understanding of how the gases are emitted. The following section will discuss the current models used to describe ammonia and greenhouse gas emissions and the specific limitations of each model. Also, the models used to describe transport mechanisms through manure into the atmosphere are considered Current Models Describing GHG Emissions from Manure There are few models devoted to quantifying and predicting the release of all greenhouse gases. One major difficulty with quantifying emissions is that control of one particular gas may lead to increased emissions of another gas (Amon et al., 2001). Because of this, few models that predict the release of greenhouse gases from manure stacks exist. The majority of models measure and model the greenhouse gas emission from dairy cow houses or the entire manure storage process. Some of these models are discussed in the following section. However, Chianese et al. (2008) created a process-based whole farm model to simulate CH 4 emissions from several sectors of the dairy farm, including slurry manure storage. This model considered the management decisions that affected the methane emissions and the release of other greenhouse gases. 32

45 2.4.2 Current Models Describing Ammonia Emissions from Manure The ability to predict ammonia emissions under various conditions and from multiple sources is important both to the environment and to the farmer who must meet air quality regulations. Sogaard et al. (2002) studied the ammonia loss from field-applied animal manure (ALFAM) model (Sommer et al., 2001) and how well the model predicted ammonia gas emissions from field-applied slurry for various weather, soil, and management conditions. The quality of data used for the model is a major factor in determining its prediction strength because climate and soil composition can vary by region and country. In development of the ALFAM model, seven European countries provided data and used existing knowledge of ammonia volatilization to identify key factors causing ammonia emissions. This model was based on Michaelis-Menten (M-M) equations, and the researchers felt that the data fit the M-M model and adequately predicted the ammonia emissions. However, more weather data from other countries outside of Europe are needed to completely validate the model because of the extreme climate differences between various regions. Another model that traces and quantifies ammonia emissions is the mechanistic model proposed by Monteny et al. (1998). This model looks closely at the loss of nitrogen due to ammonia volatilization within free-stall dairy barns. The model was applied to free-stall barns with slatted floors where feces and urine from cows falls through slats in the floor and into a slurry pit for storage. After urination, which was modeled on a behavioral basis, a small portion of urine remains on the floor, where the urea is converted to ammonia through a reaction catalyzed by urease enzyme. Microorganisms in feces produce urease and its effect is modeled by the M-M equation. This mechanistic model was able to predict emissions from a commercially operated slatted floor research dairy house with less than a 5% error relative to the measured emissions. Despite this excellent result, the model has limitations in accuracy; the model will only work under limited conditions such as slatted floor versus solid floor dairy cow houses. The model was formulated for mostly slatted floor use and several assumptions were made surrounding these conditions. Therefore, before the model can be applied to solid floor dairy cow housing conditions, the validity of several parameters would have to be evaluated. Rotz and Oenema (2006) designed a process-based model to predict ammonia losses from cattle manure in animal housing, during manure storage, following field application, and during grazing based on relationships between ammoniacal N content in manure, ambient temperature, 33

46 manure ph, manure moisture content, and amount of exposed manure surface area. The relationships developed were then integrated into a whole-farm simulation model, which considered manure management and climate effects on farms in order to provide a reasonable annual and long-term emission prediction Current Models of Mass Transport Mechanisms Information on modeling of transport mechanisms through manure is very scarce, as the subject is still unexplored. Most of the models utilize mathematical models in order to estimate the mass transfer coefficients throughout porous media. One method used to estimate oxygen diffusion through porous media is the Lattice Boltzmann method (LBM). Weerts et al. (2001) used the LBM method on unsaturated sandy soil with a low organic matter content to estimate the diffusion coefficient. The LBM uses a grid of lattice points and the points are connected to each other by a bond or link. The particles are assumed to move along the bonds by which they are connected. The advantage to the LBM is that it can trace a population of particles versus just one particle, through a soil sample. The results of the study revealed some agreement between the predicted diffusion coefficient from the LBM and the measured values. There is a major relationship between the connectivity of the pore pathways and the diffusion coefficient. The major drawback of the LBM is the rigid lattice structure used to define the domain of the soil sample. Since the soil pore pathways are not linear, the LBM over-estimates the diffusion values (Weerts et al., 2001). The heavily-bedded dairy manure used for this study is very heterogeneous with high moisture; therefore the pore pathways will not be linear. As a result, using the LBM in this study seems difficult and the results possibly limited and variable. Liu et al. (2006) used the finite element method (FEM) to trace the pore pathways in a soil core and predict the oxygen concentration through the stack, resulting in a relative diffusion coefficient. The relative diffusion coefficient was defined as the ratio of the diffusion coefficient in soil to the diffusion coefficient in free air. Two-dimensional images of several thin sections of soil cores were used for this analysis. The FEM simulated the thin soil sections and pore pathways allowing the shortest path to be found. Then, the oxygen concentrations at several points along the pathway were calculated. Breakthrough curves were produced from the estimated concentration values, and a diffusion coefficient was calculated from the curves. The diffusion coefficient calculated was compared to previously measured values and closely 34

47 predicted the coefficient with little error (Liu et al., 2006). The FEM is an effective method because it can simulate complex geometries in a non-lattice, non-linear way. 2.5 Research Objectives Manure and farm manure handling and storage contribute substantially to ammonia and greenhouse gas emissions. Therefore, research has been implemented to determine various costeffective ways to reduce ammonia and greenhouse gas emissions from manure handling and storage processes. A thorough understanding of the characteristics of heavily-bedded dairy farm manure and the interrelationship between mass transport mechanisms contributing to the release of gaseous emissions is necessary in order to reduce the gas emissions from this source. The overall goal of this research was to explore how mass transport mechanisms in heavily-bedded dairy farm manure stacks affect the release of ammonia and greenhouse gases. The following section describes the hypotheses and objectives utilized by this research to examine how the characteristics and mechanisms of manure affect ammonia and greenhouse gas emissions. Hypothesis 1: The amount of air within the woodchip-bedded samples will decrease the most after compaction as compared to the other amended samples. Objective: Characterize dairy farm manure amended with woodchips, hay mix, or sawdust, under compaction. Determine the bulk density, moisture content, and mechanical strength. Calculate the air-filled porosity of each sample before and after compaction at various compaction levels. Calculate permeabilities for each type of amended manure before compaction and after compaction at various compaction levels. Compare calculated permeabilities to other porous materials, validating bedded manure as a porous material. Hypothesis 2: Decreasing the air-filled porosity and permeability will inhibit gas transport and gas concentrations will decrease substantially (by a factor of 5 or more). 35

48 Objectives: Determine the effect of compaction on air-filled porosity and permeability. Effectively measure the gas concentrations released from amended dairy farm manure. Determine the magnitude of gas emissions released from uncompacted and compacted amended dairy farm manure. Hypothesis 3: Diffusion will occur at low airflow rates throughout the bedded manure stack and the woodchip-amended samples will have the slowest diffusion rates (by an order of magnitude). Objectives: Measure the rates of diffusion through the bedded manure stacks. Determine the range of tortuosities for heavily-bedded manures amended with sawdust, woodchips, or hay mix. Determine the rate of diffusion of ammonia and greenhouse gases through heavily-bedded manure stacks. Determine the rate of diffusion of oxygen through heavily-bedded manure stacks. 36

49 Chapter 3 MATERIALS AND METHODS Introduction The goals of this study were to characterize amended dairy manure under compaction and determine the mass transport mechanism(s) responsible for the release of ammonia and greenhouse gases. In order to effectively meet this goal, several objectives were outlined previously. This section provides an outline of the experiments performed and decisions made to achieve the goals of this study. 3.1 Experimental Design The experimental design for this study allowed for the collection of many datum during a single trial. There were four major parts to this study: physical characterization, gas concentration measurement, determination of mass transfer coefficient, and modeling. To collect the data required for the first three parts of the study required several trials. Each trial required approximately seven days of experiment preparation and data collection. Manure was collected and stored overnight and then the trial began with the preparation of sawdust samples which were mixed and placed in the reactors, physical characteristics were tested, gas concentrations were measured and then the samples were compacted overnight. The next day, physical characteristics were again tested to see the impact of compaction, gas concentrations were measured and then the rate of mass transfer was determined. Fresh manure was collected from the dairy barn and stored overnight in preparation for the woodchip sample assembly the following day. In a similar fashion, a fresh batch of manure was collected and stored overnight to prepare the hay mix samples. The same testing was applied to the woodchip and hay mix samples as was applied to the sawdust samples. Collecting data from the treated hay mix samples ended one trial. Since fresh manure was used for each bedding type a separate control, with replicates, was used for each batch of samples. A total of 4 trials were completed for this study. Table 3.1 shows the total samples required for each trial and the total samples for the entire study. 37

50 Table 3.1: Total Samples Required for the Study Trial Bedding Applied Weights (kg) Replicates Total Samples 1 Sawdust 34, 57, Control w/ bedding No weight 3 3 Woodchips 34, 57, Control w/ bedding No weight 3 3 Hay mix 34, 57, Control w/ bedding No weight 3 3 Total samples Total samples 4 Trials The next section discusses each experiment required to describe the physical characteristics of the heavily-bedded dairy manure used for this study. The resources needed for each experiment and the specific protocol utilized is outlined. 3.2 Reactor Design The reactor designed to measure concentration changes within the system was created with 14 L of sample material positioned between the headspace on the top and a plenum on the bottom of the container (approximately 3 L each in volume). The reactor was comprised of an 18.9 L polyvinyl chloride (PVC) cylindrical container with a 27 cm diameter. A perforated PVC disk with PVC footings and a stainless wire mesh was used on the bottom of the container to create a plenum and prevent material from falling into the bottom airspace. The design of the reactor was based on a compaction-permeability device used by Malinska and Richard (2006) and modified to facilitate all of the experimentation required for this study. PVC " inch (0.6 cm) barbed bulkhead fittings with rubber washers to prevent gas leakage were attached to the sides of the container within the plenum. These fittings facilitated gas introduction for diffusion and directed forced airflow through the reactor for permeability determinations (Figure 3.2.1). For each bedded manure trial, 36 reactors were used. Two 2-way valves were attached with " in (0.6 cm) tubing to the bottom of the container and 3 mm ID Teflon tubing was installed between the valves. This configuration was implemented to allow the outer valves to be closed off from the atmosphere during the tracer gas testing, so the infrared photoacoustic multi-gas analyzer field monitor could sample the gas accumulated in the plenum (Model 1412 Innova AirTech Instruments, Ballerup, Denmark. Detection limits were: CH ppm; CO ppm; NH ppm; N 2 O 0.03 ppm; SF

51 ppm. Sensitivities to water vapor (NH 3, CH 4 ) and carbon dioxide (N 2 O) were compensated. Calibrations were conducted annually per manufacturer instructions by California Analytical Instruments (Orange, CA) at expected gas ranges for manure measurements). Also, this arrangement allows for airflow via mass flow controllers to flow through the reactor during gas flux testing. To prevent pressure differences between the top and bottom airspaces during mass transfer coefficient determination, a 0.6 cm fitting was installed between the bottom fittings within the plenum and pressure release tubing was installed. The tubing can be removed and a rubber stopper can be placed over the fitting to block escaping airflow for permeability determination. A Gamma Seal lid was used to create an airtight seal between the reactor and the atmosphere. PVC barbed bulkhead fittings with rubber washers were attached to the lid and Teflon tubing was installed to give the photoacoustic gas analyzer access to sample the gas diffused to the headspace of the reactor. A small pressure release valve was also installed in the lid of the reactor to ensure the pressure in the headspace did not change relative to the plenum. Figure 3.2.1: Overview of Experiment Reactor 39

52 3.3 Characterization of Bedded Dairy Manure In order to characterize heavily-bedded dairy farm manure amended with woodchips, hay mix, or sawdust, bedded manure was simulated by mixing dairy farm manure with one of the aforementioned bedding materials. An experiment was performed to determine the bulk density, porosity, moisture content, permeability, and mechanical strength of the samples. This information provided basic physical characteristics of the material Manure Collection and Preparation Dairy manure was collected from the feed alley of a sand-bedded freestall heifer barn at The Dairy Research and Teaching Center at the Pennsylvania State University (PSU), located on the University Park Campus (State College, PA). The dairy manure was collected midday and stored overnight in covered 95 L heavy plastic trashcans. The bedding materials used (sawdust, woodchips, and hay-grass mix), were also provided by the University. The sawdust was frequently hauled to a covered storage area near the freestall dairy barns by the Farm Operations Department for use as bedding in several barns. The hay mix samples were often a combination of hay and several different grasses. The hay mix was delivered to the University and stored in a covered storage area for use as bulking material for sand-laden manure to ease transport for field application. The woodchips used in the experiments were from ground up wood pallets delivered to the Organic Materials Processing Educational Center (OMPEC) at PSU. The manure was mixed with the bedding material in a large rectangular plastic trough approximately 20 cm deep. Approximately 20% bedding materials were added to the manure by weight on a wet basis. The weights were measured with a bench scale (Ohaus ES Low Profile TM, Model ES30R, Ohaus Corporation, Pine Brook, NJ) with a capacity of about 30 kg and a resolution of 0.01 kg. The manure and bedding was thoroughly mixed with a gloved hand, allowed to rest for 5 minutes, then mixed again until the sample looked uniform. Enough bedded manure was mixed to fill three 18.9 L reactors at a time. Each bedding trial was comprised of 12 total samples with 3 control samples for each set of bedded manure (see Table 3.1). 40

53 3.3.2 Determining Moisture Content and Organic Matter All bench scale experiments were conducted in the Bioconversion Laboratory in the Department of Agricultural and Biological Engineering at PSU. This lab is located in the Agricultural Engineering Building and is a ventilated area with ample space to conduct several experimental trials at one time. After each sample was thoroughly mixed with bedding and appeared uniform, about g samples were withdrawn in duplicate and filled in the previously tare-weighed crucibles. The 24 crucibles for each bedding type were weighed and then placed in the drying oven for 24 hours at 105 o C (ASTM, 2001). Then, the samples were removed from the oven and cooled to room temperature for about 2 hours in desiccators and then the weight was recorded to determine moisture content on a wet basis. All of the crucible weights were measured with a balance (Ohaus Scout TM Pro Balance, Model SP402, Ohaus Corporation, Pine Brook, NJ) with a capacity of approximately 400 g and a resolution of 0.01 g. To determine organic matter content of the samples, the previously weighed samples were placed in the furnace at 550 o C for 5 hours. After cooling to room temperature in desiccators, the samples were removed and weighed to determine organic matter content (ASTM, 2008) Determining Permeability To determine permeability for each sample in the study by applying Darcy s Law, the pressure difference at specific air velocities through the bedded manure must be measured. To do this, compressed air from a wall unit was metered into the experimental reactor. Two mass flow controllers (Model GFC17A-VADL2-A0A, AALBORG, Orangeburg, NY) were used to control airflow into the reactors. One of the mass flow controllers had a range of flow from 0 10 L min -1 and the other had a range of L min -1. The flow range used in this study was from 2 20 L min -1. A 90-degree valve switch was used to direct the airflow from the compressed air pump to the appropriate mass flow controller then to one of the barbed bulkhead fittings attached to the bottom of each reactor. Flexible tubing (" in) was used to attach the mass flow controllers to the inlet of the reactor and also to attach a differential pressure meter (Testo TM 512, 0 2 hpa, resolution hpa; Testo TM 312, hpa, resolution 0.2 hpa, Sparta, NJ) to the outlet of the reactor. The airflow rates tested on each reactor were 2, 5, 10, 15, and 20 L min -1 (corresponding to velocities of , 0.002, 0.003, 41

54 0.005, m sec -1 ), which filled the plenum volume. The controllers were set to each flow rate for about 1 minute and the steady pressure difference required for the air to force its way through the sample was taken with a differential pressure meter. Figure shows the equipment layout for this experiment. Figure : Equipment Layout for Permeability Experiments This was done for 36 reactors per bedded manure trial. Figure shows a schematic of the valve layout and the variables determined to calculate permeability. Figure : Schematic of Valve Layout for Permeability Determination The permeability of each sample was determined using the measured pressure differences at the determined airflow rates, the area of the reactor (0.055 m 2 ), and Darcy s Law of Permeability or the Dupuit-Forcheimer equation (Equations and ). The airflow 42

55 rates were converted to velocities (m s -1 ) by dividing by the cross-sectional area of the reactor. The permeability was determined by graphing the velocity versus the change in pressure with change in position term (dp dx -1 ). The slope of the line was multiplied by viscosity to determine permeability Compaction of Manure Samples The compaction of the manure samples for this study was done using 11 kg weight plates. Three treatment levels of compaction were used: 34 kg, 57 kg, and 68 kg (75, 125, and 150 lb). Using a random number generator, samples were chosen for each treatment level in triplicate. The weight of each sample was measured using a bench scale (Ohaus ES Low Profile TM, Model ES30R, Ohaus Corporation, Pine Brook, NJ) so that the wet bulk density could be determined. Then, a wire mesh plate and a perforated PVC plate were placed on top of each sample. A hollow PVC cylinder was placed on each sample as centered on the reactor as possible, to support the weight plates and ensure that compaction was as level as possible. The starting height of the sample was marked on the cylinder using a straight edge. Next, the samples were moved against the wall, where supporting poles (for safety to prevent tipping) are affixed to the wall. The first weight plate used had a pole securely attached to it, and was placed on top of the PVC cylinder, then weight plates were lifted onto the pole on each sample corresponding to the treatment levels chosen by the random number generator. The samples were secured to the wall by lowering the support pole onto the weight-bearing pole and locking it in place with an Allen wrench. A marking was made on the PVC cylinder, with a straight edge, to show the deformation of the initial weight on the sample. The samples were compacted overnight. At the end of the test, the weights were removed from each sample, and a marking was made on the PVC cylinder representing the final deformation of the sample. The height difference between the initial height of each sample and the final deformation was recorded and the amount of compaction was determined for each sample. Finally, the PVC plate and wire mesh are removed and each sample is weighed for bulk density. A diagram of this experimental set-up is shown in Figure

56 Figure : Compaction Experiment Equipment Set-up Temperature Temperature is an important factor in quantifying gas emissions from heavily-bedded manure storages and determining mass transfer coefficients. Therefore, it was very important that this study account for temperature variations in the room and within the samples over the course of the experiment without creating large spaces for airflow through the samples. To do this, 5 mm diameter temperature data loggers (ibutton TM, Model DS1921H-F5#, o C, resolution o C, accuracy +/- 1 o C, Embedded Data Systems, Lawrenceburg, KY) were placed directly into the sample reactors and remained in place throughout the experiment. The data loggers were set to record the temperature every ten minutes with two data loggers per reactor (24 total). One data logger was placed on the bottom of the reactor and the other about 2.54 cm (1 inch) from the surface of the sample. The data loggers were started at the same time and placed into the reactor while filling each reactor with sample. To protect the data loggers from moisture, they were each placed in a thin 7.6 x 12.7 cm (3 by 5 ) sterile sample bag (Catalog ID # , VWR International, West Chester, PA) before insertion into the sample. After each set of bedding type experiments, the data loggers were removed from the reactors and data downloaded and cleared. 3.4 Flux Chamber Design and Validation The non-steady flux chamber used in this study was especially fabricated for the experimental conditions. The non-steady state chamber was created from a 2 quart stainless 44

57 steel mixing bowl with a volume of the m 3 and surface area of m 2 and can easily fit inside the reactor to sit on the surface of the manure sample. This ensures that the flux chamber was measuring the gas concentrations from the surface of the sample. A battery was mounted on the top of the chamber to power the DC equipment cooling internal fan (2.7 cfm, 25.5 x 25.5 x 10 mm 12 VDC, Model #: OD HB, Allied Electronics, Fort Worth, TX) on the underside of the chamber, which recirculated the internal air of the chamber. Two barbed bulkhead PVC fittings (0.6 cm) allowed 1 m long 3 mm ID Teflon TM tubing to be connected from the flux chamber to the multi-gas analyzer for the gas concentration readings. An additional fitting was included for pressure release to ensure that the pressure inside the chamber remained equivalent to the pressure in the room. This was important to prevent convection of the gases, causing higher gas concentrations to be released than the manure would normally emit. Figure shows a schematic of the front view of the non-steady state flux chamber. Figure 3.4.1: Schematic of the Front View of the Non-steady State Flux Chamber The ammonia and greenhouse gas concentrations from manure storages are measured in various ways impacting the interpretation of the gas emissions. Therefore it is important to validate the non-steady state flux chamber used in this study to ensure that the gas concentrations measured were representative of the amount of gases emitted by the stored heavily-bedded manure. The flux chamber was validated by comparing the measured emissions from the non-steady state flux chamber to the measured emissions from a mechanically vented metal test chamber (a steady state flux chamber) with a known amount 45

58 of dairy manure. To validate the non-steady state flux chamber used for this study, a mass balance method was utilized. Using a pre-made steady state flux chamber (Figure 3.4.3) of area m 2, the velocity of air flowing through the chamber was determined using the duct transverse technique with a hot wire anemometer (Extech Instruments TM Heavy Duty Hot Wire Thermo-Anemometer, Model , m s -1, resolution 0.1 m s -1, and accuracy within 3%, Extech Instruments Corporation, Waltham, MA). The hot wire anemometer average velocity was compared to readings from a vane anemometer attached to the system. The air velocity of the chamber was created with an air circulation fan and was measured at 80, 100, and 120 volts of the air circulation fan with the hot wire anemometer using a duct transverse technique defined by ASHRAE handbook (ASHRAE, 2001), measuring the velocity along eight different spots (0.16 m, 0.90 m, 1.4 m, 2.7 m, 5 m, 6.3 m, 6.8 m, and 7.5 m) on three different planes (A, B, and C) at one cross-section of the chamber s 8 cm diameter discharge pipe. The duct transverse technique was performed four times during the study, using the hot wire anemometer. Figure shows an example of the air velocity profile from each plane determined in the discharge piping with the hot wire anemometer. Figure 3.4.2: Air Velocity Profile of the Discharge Pipe Using a Hot Wire Anemometer The airflow rate of the steady state flux chamber measured with the hot wire anemometer was determined by multiplying the average velocity values measured from the transverse technique and the area of the 8 cm diameter pipe (0.005 m 2 ). The vane anemometer (Extech Instruments TM Heavy Duty CFM Thermo-Anemometer, m s -1, resolution 0.01 m s -1, accuracy within 2% with low friction, Model , Extech Instruments Corporation, Waltham, MA) was attached to the piping at the end of the chamber 46

59 using duct tape, and the air velocity was measured at 80, 100, and 120 volts at five different times throughout the experiment, approximately every 15 min. Multiplying the area of the vane anemometer ( m 2 ) and measured velocities, the airflow rate was calculated. Figure 3.4.3: Schematic of Flux Chamber Validation Equipment (0.558 m x m x m) Next, a wooden frame was covered with a kitchen-sized plastic garbage bag and 2 kg of fresh manure, collected from the feed alley of the freestall dairy barn at PSU was applied to the frame. The manure was spread flat so that it covered the bottom of the wooden frame. Using the photoacoustic multi-gas analyzer, the concentrations of gases emitted from the manure were measured from four spots using the non-steady state flux chamber. A gas concentration reading was obtained during a measurement cycle which was when the multigas analyzer sample pump sampled for nineteen seconds, flushed the tubing and sample chamber, and then the sample pump stopped for forty-one seconds. During the forty-one seconds, the analyzer measured the gas concentrations pumped into the sample chamber. A measurement cycle lasted a total of one minute. Gas concentrations were measured for a total of five minutes from each spot (5 gas concentration readings). Each spot was measured twice. A diagram of the spots chosen for sampling on the frame is shown in Figure The nonsteady state flux chamber was removed, and the steady state flux chamber was placed on top of the frame and the air velocity at 80 volts was applied and the stable concentrations of gases emitted from the manure were measured from the inlet and then the outlet of the chamber with the multi-gas analyzer for approximately 30 min each. The entire experiment was performed in approximately two hours and was replicated four times. This validation was repeated with fresh manure from the freestall barn used each time, for gas concentrations at air velocities of 100 and 120 volts. 47

60 Figure 3.4.4: Diagram of Wooden Frame and Sample Spots The gas emissions flux rates were calculated using the equations presented in section for the flux chamber. The gas emissions for the non-steady state flux chamber were calculated using equation and the five collected gas concentrations. Using the first gas concentration measurement (background concentration, C i ), the third (C 1 ) and fifth concentration measurements (C 2 ), the area ( m 2 ) and volume ( m 3 ) of the nonsteady state flux chamber, and the time interval between the measurement of C i and C 1 (120 sec), the gas emission rate was determined for each sample in mg m -2 min -1 and then converted to g m -2 d -1. The gas emissions for the steady state flux chamber were calculated using equation , the average input and output concentrations of each gas, the area of the steady state flux chamber ( m 2 ), and the airflow rate of the hot wire anemometer or vane anemometer. The gas emissions of the non-steady state flux chamber were compared to the emissions from the steady state flux chamber by graphing. The resulting relationship was used to calculate the error in the non-steady state flux chamber; this is the adjustment required for analyzing the gas concentrations measured using this particular non-steady state flux chamber Gas Flux Measurement The gas concentrations from the heavily-bedded dairy manure samples were measured before and after compaction. The non-steady state flux chamber was connected to a power source to start the internal fan. It was set on its side so that background gas samples of the room air could be taken for about ten minutes. The flexible tubes on the top of the non-steady state flux chamber were connected to the photoacoustic gas analyzer. The photoacoustic gas analyzer was properly attached to the laptop and set to sample background concentrations 48

61 from the non-steady state flux chamber. The mass flow controllers were connected to the bottom of the reactor circulating a constant, low airflow rate of about L min -1 through the bottom plenum, approximately similar to the pumping rate of the photoacoustic gas analyzer across the top plenum, to stabilize the pore matrix and maintain diffusion conditions within the reactor. The non-steady state flux chamber was placed above the reactor to collect a background concentration reading (C i ). Then, the flux chamber was placed into the reactor so that the base of the chamber sat on the surface of the manure. Data were collected from the sample for an additional four minutes with total sampling time for each sample of five minutes (5 gas concentration readings). Then the non-steady state chamber was removed and set on a flat surface, partially open to the room air so that background air could purge the chamber for about two minutes. The bottom tubing of the reactor was then connected to the multi-gas analyzer to measure gas concentrations from the plenum of the reactor for five minutes. The system was then set to collect two minutes of background air to purge the tubing. After the background air cleansed the flux chamber, the next sample was analyzed. After all 12 manure samples in a bedding set were analyzed the same procedure was repeated for two more rounds to account for any temperature changes and aging of the samples. Therefore each reactor was sampled three times for each bedding trial. The three replicate gas concentrations for each reactor were averaged and used to calculate emissions flux rate from each reactor. After compaction, the gas concentrations were measured using the same procedure outlined above to see the effects of compaction on reducing gas emissions. Figure shows the experiment equipment set-up for this test and Figure shows the valve layout of the experiment. Figure : Top View Schematic of Gas Concentration Measurement Equipment 49

62 Figure : Experimental Valve Set-up for Gas Concentration Measurement The gas emissions flux rate was determined by the method described at the end of section 3.4 and then corrected using the relationship determined from the validation of the non-steady flux chamber. 3.5 Testing the Dominant Mass Transport Mechanism To test for the rate of mass transfer in compacted heavily-bedded dairy manure samples, the samples from the compaction test were used for this analysis. After compaction and gas flux measurement, the samples were allowed to stabilize for 30 minutes. Then a Gamma Seal TM lid was securely tightened onto each reactor and 12 reactors were used for each bedding trial. The multi-gas analyzer was used to measure the gas accumulation within the system. The first step was to minimize the effects of microbial activity within the reactor. To accomplish this, each sample was purged with 4 L min -1 of N 2 gas for 7 10 min. The multigas analyzer was connected to the top of the reactor to record background concentrations, while one side of the 90-degree valves on the bottom of the reactor was closed, to force flow through the sample (Figure 3.5.1). Figure 3.5.1: Experimental Set-up for N 2 Purge of Reactor 50

63 Next, the multi-gas analyzer was connected to the bottom of the reactor and all valves were opened to allow flow through the plenum without pressure accumulation. The tracer gas (details in 3.5.1) was pumped into the plenum at a flow rate of 2 L min -1 for 1 min. The photoacoustic gas analyzer sampled the concentration in the plenum after 1 min and this became the input concentration, S (Figure 3.5.2). An average concentration of mg m -3 of tracer gas was recorded in this study. Figure 3.5.2: Experimental Set-up for Tracer Gas Input to Reactor The tracer gas cylinder was disconnected from the reactor and both plenum valves were closed so that the gas diffused through the sample. The gas analyzer was connected to the top of the reactor to record the changes in concentration over time, dc dt -1 (Figure 3.5.3). Figure 3.5.3: Experimental Set-up for Measuring Diffused Tracer Gas Concentrations from the Reactor 51

64 The diffusion coefficient determined from the collected concentrations was the diffusion occurring after the break-through point or point at which an initial concentration of the tracer gas was measured in the headspace of the reactor. This break-through point occurred at different times for each of the bedding types. Table 3.2 shows the approximate times the break-through point occurred for each bedding type and compaction level. Table 3.2: Approximate Times Break-through Points Occurred During Diffusion of Tracer Gas for each Bedding Type. Bedding Type Applied Stress (N m -2 ) Time (min) sawdust woodchips hay mix Data were collected for about min to allow enough tracer gas to accumulate in the headspace to represent the concentration differences between the plenums. The data collection was concluded when the concentrations in the headspace increased more slowly with time or reached a point of equilibrium where the concentrations were approximately the same. At the end of the test, the reactor was open to the atmosphere, the lid was purged with compressed air from a wall unit, and the multi-gas analyzer was set to sample room air to purge the tubing for about 5 min. The next sample was then set and the procedure was repeated. Each of the 12 samples in a bedding trial was tested once Tracer Gas Selection For this study, it was important to select a tracer gas that did not affect the conditions of the system. Therefore, the gas had to be inert, it could not be ammonia, hydrogen sulfide (H 2 S) or any gas emitted by manures, it needed to be detectable at low concentrations, and it could not be highly toxic since there was periodic usage over the course of several weeks on the bedded manure samples. An added factor was the density of the gas used in this particular system. 52

65 The main objective of the mass transfer coefficient testing was to maintain mostly diffusive transfer conditions within the system to determine the rate that air travels through heavilybedded dairy manure. Therefore, a gas that is lighter than air or exhibited buoyancy effects would not be useful because it would travel through the system faster than air. The tracer gas chosen for this study was sulfur hexafluoride (SF 6 ). This tracer gas met all of the criteria outlined in that it is an inert man-made gas so background concentrations in the environment are very low with one downside being it is a potent greenhouse gas. It is detectable at low concentrations with the photoacoustic gas analyzer (0.006 ppm) so very little gas is required for testing. Also, it is not toxic at low concentrations and is odorless so there is little hazard involved in prolonged usage of the gas. Sulfur hexafluoride is heavier than air (146 g mol -1 ), and all other gases under study with a density of 6.27 kg m -3 compared to a density of air of 1.23 kg m -3 at approximately 1 bar and 15 o C. Therefore since SF 6 is denser than air, there should be no natural effects of buoyancy and diffusion should be slow. The SF 6 cylinder used in this study had a certified concentration of 9.95 ppm (59.42 mg m -3 ) with an analytical accuracy of 5% (72 ft 3 cylinder SF 6 10 ppm, Product #: , GTS-Welco, Morrisville, PA). 53

66 Chapter 4 RESULTS AND ANALYSIS Introduction The following sections detail the data collected using the experimental techniques outlined previously. The first section discusses the data collected in order to characterize the physical properties of the heavily-bedded dairy manure used in this study. Several trends in the data are highlighted and the first two hypotheses about the nature and strength of the material are discussed. There is a section of this chapter dedicated to explaining the validation of the flux chamber and the results of the determined ammonia and greenhouse gas emissions. The connections between the physical properties of the material and the resulting gas emissions are also highlighted. The final section details the results of the FEM model created to predict gas emissions from the bedded manure samples. A discussion of the assumptions and parameters used to improve the performance of the model are also discussed. 4.1 Characterization of Manure Data were collected to determine the physical characteristics of the heavily-bedded dairy manure used in this study. Table 4.1 shows an average of the moisture content, dry matter content, and organic matter content for the manure samples based on bedding type from each trial in the study, n = 24 for each bedding type (n=2 replicates for each sample x 12 samples = 24). For the sawdust-bedded dairy manure samples, the range of moisture contents were approximately 70 74% on a wet basis with a dry matter content of 26 29% and organic matter content of approximately 90% on a dry basis. The woodchip-bedded samples had similar moisture contents to the sawdust samples with values of 71 74% on a wet basis, with slightly lower dry matter content and organic matter contents of 25 28% and 81 85% respectively on a dry basis. Finally, the hay mix-bedded samples had more variability in moisture content of all the other bedding types with a range of 68 88% on a wet basis. The mixture of particle size in the hay mix bedding can explain this variability. The hay mix bedding was a mixture of hay and several species of grass clippings cut to various sizes, so when mixed with the manure, various moisture contents resulted. The dry matter content of the hay-mix samples was 13 32% and organic matter content was 83 99% on a dry basis. 54

67 Table 4.1: Summary of Moisture Content, Dry Matter, and Organic Matter Content of Heavily-bedded Dairy Manure (n=24 each bedding type; n=2 for each sample x 12 samples). Moisture Content w.b. (%) Dry Matter w.b. (%) Organic Matter d.b. (%) Bedding Standard Standard Standard Type Trial Average Error Average Error Average Error sawdust woodchips hay mix For all bedding types, trial 3 samples had lower moisture content than the other trials. The moisture content results and the impact of the lower moisture content of trial 3 for all of the bedding types will be scrutinized more heavily when analyzing the permeability data. Also, the organic matter content indicated that there was little contamination from sand bedding from the freestall dairy barn mixed into the manure for all of the trials and all bedding. The average non-organic content did not exceed 20%. A complete list of the moisture content, dry matter content, and organic matter content of each sample is shown in Table A.3 in the appendix. In response to the first hypothesis of this study, compaction was used to determine several other characteristics of bedded dairy manure: air-filled porosity, permeability, bulk density, and mechanical strength. The data for these characteristics are summarized before and after compaction with standard error values. Also represented is the amount of change compaction induced between the control (uncompacted) samples and the compacted samples. Several trends were observed through the bulk density data. Figure shows the average change in the bulk density of each bedding type and trial as a result of compaction (n = 3 for each bar) relative to the uncompacted values and Table 4.2 summarizes the results of compaction on bulk density and mechanical strength for each bedding type. For better 55

68 understanding of the treatments used in this study, a table of treatment codes is shown in the appendix, Table A.2. Figure 4.1.1: Average Change in Bulk Density of Each Bedding Type After Compaction 56

69 Table 4.2: Results of Compaction on Bulk Density (n=3 each) and Mechanical Strength (n=3). Mechanical Strength (N m -2 ) Applied Stress (N m -2 ) Average Uncompacted Bulk Density (kg m -3 ) Compacted Bulk Density (kg m -3 ) Bedding Treatment Trial Average Standard Error Standard Error Average sawdust 1 30,556 4, , ,334 28, , Standard Error woodchips 1 36,111 12, ,111 34, ,444 9, , hay mix 1 50, , ,704 6, ,037 12,

70 The hay mix-bedded samples had the highest percent change in average bulk densities after compaction. This can be explained by the particle size of the materials. Since the hay mix bedding was cut into strips or clippings, there was less surface area for manure to adhere and the bedding material itself was very light in comparison to the woodchip bedding. Therefore it took more bedding to bulk the manure than the other bedding materials causing a lighter, less dense mixture with a high porosity. When compaction was added, the pore spaces were reduced, creating a denser sample. The hay-mix samples also had a high average mechanical strength value in comparison to the other bedding types. A high mechanical strength was assumed to mean that the material resisted compaction more readily than the other bedded manure samples (Table 4.2) and was stronger than the other bedding materials. This result was not surprising again because of the particle size and shape of the hay mix samples. However the hay mix samples were not stronger than the woodchip bedded samples. The long strips of the grass and hay clippings allowed for displacement under a concentrated, sustained applied stress, but overall the displacement on the surface of the material was not sustained. This is shown through laboratory observation of a rebound or change in the displacement after the applied weights were removed from the hay mix samples. The amount of displacement caused by compaction decreased with the top of the manure sample visibly expanding as the applied weights were removed. Table 4.3 shows an example of the height differences observed by the study. Table 4.3: Example of Rebound Behavior of Hay Mix Samples Applied Weight (lbs) Initial Displacement (cm) Final Compacted Height (cm) Trial 1 Hay Mix Samples Compacted Height Difference (cm) Weighted Final Height (cm) Weighted Height Difference (cm) Difference Between Weighted and Compacted Height (cm) These height differences were the difference between the filled height (0.25 m) and the compacted height. For this trial, there was an average 5 mm difference between the recorded deformations while the applied weights were still on the samples versus when the weights 58

71 were removed. This behavior was consistent for all hay mix samples for all trials. The height differences used in calculations were the final height values with weight removed from the samples. Therefore, the woodchip bedded samples were stronger than the hay mix bedded samples. The connection between moisture content and mechanical strength is still unclear and requires more study. It is believed that lower moisture content is more conducive of higher mechanical strength values because moisture weakens the material and makes it more susceptible to compaction. The data from this study are inconclusive about this relationship because for hay mix bedding samples, trial 2 had the highest average moisture content, but the mechanical strength values were not significantly less than the other trials with lower moisture content. This indicates that the particle size and shape of the material may have a greater impact on mechanical strength of heavily-bedded manure than moisture content. When evaluating the sawdust and woodchip-bedded samples, the mechanical strength for the drier trials were not significantly different than the more moist trials. A larger range of moisture contents is required in order to evaluate the relationship between moisture content and mechanical strength for these bedded samples. In order to qualify heavily-bedded dairy manure as a porous media, the calculated permeabilities in this study were compared to the permeabilities measured for other common materials already qualified as porous. Since bedded dairy manure is heterogeneous and has high moisture content when compared to other porous media, there are few common materials available for comparison. Table 4.4 contains porosities and permeabilities of common porous media comparable to manure. Table 4.4: Porosity and Permeabilities for Common Porous Materials Material Porosity (m 3 m -3 ) Permeability (cm 2 ) Permeability (m 2 ) a Limestone x x x x b Sand x x x x c Soil x x x x Clean gravel d g Clean sand e h Peat f i a-f : Adapted from Nield and Bejan (2006); g i: Adapted from Kessler and Greenkorn (1999) The calculated permeability values before and after compaction for each bedding type and trial of this study are summarized in Table

72 Table 4.5: Permeability Data for Each Bedding Type and Trial (n=3 for each) Permeability Before Compaction (m 2 ) After Compaction (m 2 ) Bedding Treatment Trial Applied Stress (N m -2 ) Average Standard Error Average Standard Error % Difference from Control sawdust E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E woodchips E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E hay mix E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E

73 The percent difference for each sample was determined by comparing the stored uncompacted samples (uncompacted samples stored while other samples were compacted) to the compacted samples at each applied stress. When the compacted sample exhibited a lower permeability than the control, a positive percent difference was represented and a negative value denoted a larger permeability than the control sample. A more laminar airflow even under compaction was observed for the sawdust-bedded and hay mix-bedded samples so Darcy s Law was used to determine the permeability values (Equation ). However for the woodchip-bedded samples the Dupuit-Forcheimer equation (Equation ) was used to determine permeability because of the higher saturation forcing non-laminar airflow. After compaction the woodchip-bedded samples showed noticeable amounts of water on the surface of the compacted samples. In an effort to compare the range of permeabilities from this study to those represented in Table 4.4, a summary of the permeability data were represented in Table 4.6. Based on the data, the permeabilities values for all bedding types seem to pattern permeabilities between clean gravel and peat. Overall, the first hypothesis of this study was proven true and the heavily-bedded manure samples followed Darcy s Law of permeability, behaved as common porous material, and the determined permeability values are comparable to other known porous materials. 61

74 Table 4.6: Results of the Range of Permeabilities Determined in this Study (n=3 each code). Permeability Before Compact (m 2 ) After Compact (m 2 ) Treatment Code Applied Stress (N m -2 ) Average Standard Error Range Average Standard Error Range S0_ E E E E E E E E-09 S75_ E E E E E E E E-10 S125_ E E E E E E E E-11 S150_ E E E E E E E E-10 S0_ E E E E E E E E-09 S75_ E E E E E E E E-09 S125_ E E E E E E-09 S150_ E E E E E E E E-10 S0_ E E E E E E E E-09 S75_ E E E E E E E E-09 S125_ E E E E E E E E-10 S150_ E E E E E E E-10 S0_ E E E E E E E-09 S75_ E E E E E E E E-09 S125_ E E E E E E E E-10 S150_ E E E E E E E E-10 W0_ E E E E E E E E-09 W75_ E E E E E E E E-12 W125_ E E E E E E E E-11 W150_ E E E E E E E E-11 W0_ E E E E E E E E-12 W75_ E E E E E E E E-11 W125_ E E E E E E E E-12 W150_ E E E E E E E E-12 W0_ E E E E E E E E-10 W75_ E E E E E E E E-11 W125_ E E E E E E-12 W150_ E E E E E E E E-11 W0_ E E E E E E E E-09 W75_ E E E E E E E E-09 W125_ E E E E E E E-12 W150_ E E E E E E E E-11 H0_ E E E E E E E E-07 H75_ E E E E E E E E-08 H125_ E E E E E E E E-08 H150_ E E E E E E E E-09 H0_ E E E E E E E E-07 H75_ E E E E E E E E-09 H125_ E E E E E E E E-08 H150_ E E E E E E E E-08 H0_ E E E E E E E E-07 H75_ E E E E E E E E-09 H125_ E E E E E E E E-08 H150_ E E E E E E E E-08 H0_ E E E E E E E E-07 H75_ E E E E E E E E-08 H125_ E E E E E E E E-08 H150_ E E E E E E E E-08 62

75 As discussed in the section 2.3.4, several studies on permeability also mention a connection between increased permeability with increased moisture content because of clumping of the material creating large pore spaces within the material. For this study, this relationship could not be tested because of a lack of diverse range in moisture contents. However, it would be interesting to see if the same clumping effect occurs within all the materials and if the result is an increase in permeability, or a decrease because of saturation. To determine whether the effect of compaction on the permeability of the samples in this study was significant, a statistical paired t-test (Microsoft Excel, Microsoft Office for Mac 2008, Version , Microsoft Corporation, Redman, WA) was performed testing the hypothesis that the compaction had an effect on the samples, Ho " 0. The null hypothesis is that there is no effect of compaction, or Ho = 0. The results of the paired t-test for permeability are shown in Table 4.7 below. The difference represented in Table 4.7 is the difference between the average of all the permeability values for each bedding and trial before and the average of all the permeability values after compaction. Table 4.7: Results of Statistical t-test for Permeability (p < 0.05 in bold) Average Permeability (m 2 x 10-9 ) Treatment Trial Before Compaction After Compaction Difference t-stat p-value sawdust woodchips hay mix The results indicate that there was a difference between the permeability values of the compacted and uncompacted sawdust-bedded samples (all p-values less than 0.05). These results suggest statistical significance of compaction on altering the permeability of the sawdust-bedded dairy manure samples. However, overall there was no significant difference between the permeability values of the compacted and uncompacted woodchip and hay mix- 63

76 bedded samples (p-values above 0.05). However, for trials 1 and 3 of the woodchip-bedded samples there is an interesting result. Compaction was shown to have an effect on the permeability for those particular trials. Some possible reasons for this result are the lower moisture content of trial 3, allowing more connectivity between the pore spaces than the other, waterlogged trials. One major observation in the laboratory during experimentation to determine permeability was visible free water floating on the top of each of the woodchipbedded samples after compaction. The reduction in permeability in the case of the woodchipbedded samples could be attributed more to the moisture content than compaction. For the hay mix-bedded samples, the permeability was not significantly reduced by compaction, although changes between the uncompacted and compacted samples resulted. This result might be due to the particle size and bulk density of the material. The particle size and high bulk density still allowed for connectivity between the pore spaces resulting in a high permeability. The air-filled porosity was determined before and after compaction and the average values are shown in Table 4.8 (all data in Table A.4). In comparison to other known porous materials, the porosity of the bedded manure was comparable to gravel and peat and again confirmed the first hypothesis that heavily-bedded dairy manure is a porous material. Through the collected data, several trends were observed. Air-filled porosity decreased with increasing applied stress, which was the expected outcome. Also apparent was the small increase in porosity of the uncompacted samples, due to a slight amount of water evaporation from the samples, decreasing the moisture within the pore spaces. The hay mix samples had the highest air-filled porosity values before and after compaction because of the low moisture content, especially in the trial 3 and 4 samples, and also because of the particle size. The particle size and sample packing had a great influence on the air-filled porosity in accordance with other research on porous media. Since the hay mix-bedded samples were cut into clippings and strips, there were more pore spaces between the samples in comparison to the sawdust-bedded and woodchip-bedded samples. This is represented through the relationship between compacted bulk density and porosity after compaction (Figure 4.1.2). 64

77 Table 4.8: Summary of Air-filled Porosity Data for Each Bedding Type and Trial (n=3 for each) Air-filled Porosity (Free Air Space) Applied Stress (N m -2 ) Average Before Compact (m 3 m -3 ) After Compact (m 3 m -3 ) Bedding Treatment Trial Standard Error Average Standard Error sawdust % Decrease from Control woodchip hay

78 As compacted bulk density increased the porosity decreased. This intuitively makes sense because compaction increases the density of the sample and decreases the pore spaces between the particles of the sample. The goal here is to examine the variability of air-filled porosity among the weight treatments and bedding types. Figure 4.1.2: Relationship between Compacted Bulk Density and Air-filled Porosity for Heavily-bedded Dairy Manure The expectation was that the woodchip-bedded samples would have a relatively high air-filled porosity, but these samples had the lowest porosity values. This can be explained by an observation in the laboratory. The woodchips did not retain moisture when mixed with the manure so the pore spaces were waterlogged, reducing air-filled porosity. To determine whether the effect of compaction on the air-filled porosity of the samples in this study was significant, a statistical paired t-test (Microsoft Excel, Microsoft Office for Mac 2008, Version , Microsoft Corporation, Redman, WA) was performed testing the hypothesis that the compaction had an effect on the samples, Ho " 0. The null hypothesis is that there is no effect of compaction, or Ho = 0. Table 4.9 shows a summary of the results of the t-test for air-filled porosity for each trial and bedding type. The difference represented in Table 4.9 is the difference between the average of all the air-filled porosity values before and after compaction of each trial delineated by bedding type. Based on the results of the t-test for 66

79 all bedding types and trials, there was a significant difference between all compacted and uncompacted air-filled porosity values (calculated p-value was below 0.05). Table 4.9: Results of Statistical t-test for Air-filled Porosity (p < 0.05 in bold) Average Air-filled Porosity (m 3 m -3 ) Treatment Trial Before Compaction After Compaction Difference t-stat p-value sawdust woodchips hay mix To determine which compaction level had the greatest effect on reducing the air-filled porosity, the percentage difference between the uncompacted sample of each bedding type and trial was also determined (shown in Table 4.8). Figure shows the changes in airfilled porosity before and after compaction for each bedding type and trial. Based on the calculated percentage difference between the stored control (uncompacted) sample and the compacted samples for all trials of the sawdust-bedded samples, the compaction level that yielded the greatest overall change was an applied stress of 12,573 N m -2 (150 lbs). For the woodchip-bedded samples an applied stress of 10,556 N m -2 (125 lbs) yielded the greatest overall change. Finally, for the hay mix-bedded samples a compaction of 12,573 N m -2 (150 lbs) yielded the greatest overall reduction in air-filled porosity. Of all of the bedded samples, the woodchip-bedded samples had the highest reduction of air-filled porosity, 37% at an applied stress of 12,573 N m -2 during trial 4 of experiments. 67

80 Figure 4.1.3: Air-Filled Porosity Before and After Compaction for Each Bedding Type 68

81 4.2 Analysis of Ammonia and Greenhouse Gas Emissions This section discusses the results of the temperature recordings and validation experiments and the correction factors required in order to evaluate the gas emissions determined in this study. A thorough analysis of the calculated gas emissions before and after compaction is shown. The effectiveness of compaction as a method for reducing gas emission from heavilybedded manure storages is evaluated and discussed Temperature Data Temperature data were recorded throughout the entirety of this study and were most important to evaluating the gas emissions data and the diffusion data. The temperature data were evaluated for major temperature changes that might have caused changes in the emissions data. Table 4.10 shows a summary of the temperatures recorded for each bedding type and trial during the time of the gas concentration measurements to determine the gas emissions flux rates. Each sample had two temperature sensors, one in the top 5 cm of the sample and one on the bottom of the sample, and the temperatures were measured every 10 min for over 15 hours averaged for each sensor. The difference in temperature between the top and bottom temperature probes was also evaluated. Based on the data for all bedding types and trials, the temperatures increased after compaction, which was expected. The standard error values were a good indication of how much the temperature varied during the gas concentration measurement. The hay mix-bedded samples had some of the highest temperatures after compaction, but overall for all bedding types and trials, the temperatures remained below 35 o C. This was important when evaluating the determined methane and nitrous oxide emissions (more detail in 4.2.3). Also, for most of the samples the temperatures varied less than one degree over the course of the testing. A complete list of the recorded temperature data is shown in Table A.9 in the appendix. The recorded temperatures were also examined during the diffusion testing and analysis. In this study in order to maintain diffusion conditions only small temperature changes were allowable. Therefore, the data were evaluated for temperature change above one degree that may cause convective conditions within the reactors. Table 4.11 shows a summary of the temperature data during the diffusion test. 69

82 Table 4.10: Summary of Recorded Temperatures During Emissions Test (n = 3 each) Emissions Test Before Compaction ( o C) After Compaction ( o C) Treatment Code Average Temperature Difference** Standard Error Average Temperature Difference** Standard Error S0_ S75_ S125_ S150_ S0_ S75_ S125_ S150_ S0_ S75_ S125_ S150_ S0_ S75_ S125_ S150_ W0_ W75_ W125_ W150_ W0_ W75_ W125_ W150_ W0_ W75_ W125_ W150_ W0_ W75_ W125_ W150_ H0_ H75_ H125_ H150_ H0_ H75_ H125_ H150_ H0_ H75_ H125_ H150_ H0_ H75_ H125_ H150_ **Temperature Difference represents the difference between the top and bottom temperature sensors in each sample 70

83 Table 4.11: Summary of Recorded Temperatures During Diffusion Test (n = 3 each) Diffusion Test ( o C) Treatment Code Average **Temperature Difference Standard Error S0_ S75_ S125_ S150_ S0_ S75_ S125_ S150_ S0_ S75_ S125_ S150_ S0_ S75_ S125_ S150_ W0_ W75_ W125_ W150_ W0_ W75_ W125_ W150_ W0_ W75_ W125_ W150_ W0_ W75_ W125_ W150_ H0_ H75_ H125_ H150_ H0_ H75_ H125_ H150_ H0_ H75_ H125_ H150_ H0_ H75_ H125_ H150_ **Temperature Difference represents the difference between the top and bottom temperature sensors in each sample 71

84 Based on the data, the temperatures remained fairly stable during the diffusion testing with standard error values below one degree for the majority (140 of 144 samples per Table A.9) of the bedding types and trials. However, the uncompacted hay mix-bedded samples showed a higher temperature difference between the top and bottom sensors, which could indicate the presence of convection within the reactor. The hay mix-bedded samples had higher recorded temperatures than the other bedding types, which could be due to the fact that it easily compacted in comparison to the other bedding types and exhibited the rebounding effect discussed in section 4.1. Based on the overall data, the temperatures remained constant during the diffusion testing indicating that diffusion conditions were maintained for the majority of reactors Flux Chamber Validation To determine the gas emissions emitted from the dairy manure, the airflow rate within the steady state flux chamber was measured with a vane anemometer and also with a hot wire anemometer at three different voltages for the internal fan. Table 4.12 shows a summary of the average measured airflow rates for three voltages. Gas concentrations within the steady state flux chamber were measured for each voltage. Table 4.12: Summary of Measured Airflow Rates (n=1 for 80V; n=2 for100v; n=1 for 120V) Airflow Rate (m 3 min -1 ) Device Voltage (V) Average Standard Error Vane Hot wire Based on the measured airflow rates, the gas emissions rates from the manure in the steady state flux chamber were calculated using the steady state flux chamber equation ( ) presented in section The steady state flux chamber emissions were seen as the real gas emissions so the non-steady state flux chamber emissions were corrected to best fit the steady state flux chamber emission rates. To do this, the calculated gas emission rates from the non- 72

85 steady state flux chamber (determined with equation ) were graphically compared to the calculated emissions of the steady state flux chamber determined with the vane anemometer in order to determine a correction factor for the non-steady state flux chamber for each gas. For all of the gases, a linear equation was determined for calculating the correction factor because the equation defined all of the variables in the gas emission that affect the emission rate. The best fitting relationship was determined by the correlation coefficient values, R 2 value, with the understanding that a higher R 2 value yielded a closer estimate to the real emission values. Figure shows an example of the relationships determined by comparing the steady state flux chamber emissions determined with the vane anemometer airflow rates to the non-steady state flux chamber emissions. Figure 4.2.1: Example of the Relationship between the Emissions Rate from the Steady state and the Non-steady state Flux Chambers The steady state flux chamber emission rates determined with the vane anemometer airflow rates yielded higher R 2 values and fit the data much better than the hot wire anemometer determined emission rates. Table 4.13 shows the linear equations determined for each gas and used to correct the calculated gas emission rates from the non-steady state flux chamber along with the R 2 value that describes how closely the equation modeled the relationship between the data. Table 4.13: Summary of Linear Equations for Correcting the Flux Chamber Emissions Gas Linear Equation R 2 Value NH 3 y = x 0.91 CH 4 y = 25.09x N 2 O y = x 0.34 CO 2 y = 7.709x

86 For each equation, the non-steady state flux chamber emission rate was substituted as the x- value in the equation, g m -2 d -1, and the y-value represented the corrected true emission rate, g m -2 d -1. These equations were used to correct the determined gas emissions for each bedded manure sample in the study Ammonia and Greenhouse Gas Emissions Results The ammonia and greenhouse gas emissions were calculated from the measured concentrations using equation and corrected with the relationships discussed in section The resulting data were analyzed for trends and the effects of compaction. Based on multiple comparison t-test to determine significance between the trials, it was determined that there were differences between the trials; therefore, none of the data were combined. When measuring the gas concentrations before compaction, the expectation was that emissions of N 2 O and CH 4 would be low since manure storage is not indicated as a major source N 2 O, and aerobic conditions were created with the mixing of bedding with the manure. The expectation before compaction was some emissions of NH 3 and CO 2 because of the mixture of feces and urine and the mixture of bedding with the manure as a feed source for microbial activity, fueling microbial respiration. Gas Emission Rates Before Compaction For the sawdust-bedded samples the measured NH 3 gas emissions rate was below 1.0 g m -2 d -1 on average for each of the trials. As expected, before compaction the N 2 O and CH 4 gas emissions rates for the sawdust-bedded samples were low with values at or below 0.7 and 3.0 g m -2 d -1 on average, respectively. The emission rate for CO 2 from the sawdust-bedded manure samples was expected to be higher than all of the other gas emissions because of the introduction of bedding to the manure as a food source for the microorganisms. For most of the sawdust-bedded manure trials, the measured emission rates for CO 2 before compaction were g m -2 d -1 on average. Only trial 1 had recorded emission rates above 1200 g m -2 d -1 before compaction. For the woodchip-bedded manure samples, a similar trend to the sawdust-bedded samples was expected for gas emission rates before compaction. Ammonia emission rates were 74

87 expected to be slightly higher than the rates of sawdust-bedded samples because of the larger particle size of the woodchips allowing more moisture within the samples. The determined gas emission rate for NH 3 supported this theory with values below 4.0 g m -2 d -1 on average for most of the trials, except trial 1 where the highest emission rate of ammonia from woodchipbedded manure was approximately 8.5 g m -2 d -1. Fairly low amounts of N 2 O and CH 4 gas emissions rates were again expected for the woodchip-bedded samples and the determined values were below 0.1 in most cases and 2.0 g m -2 d -1 on average, respectively. Determined CO 2 gas emission rates were on average g m -2 d -1 for all trials. This could be explained by the larger particle size compared to the sawdust-bedded samples. Although a carbon source was available for microbial consumption, the particle size was larger, causing slower breakdown of the material. The gas emission rates for the hay mix-bedded manure samples before compaction were expected to follow a similar trend as the sawdust-bedded manure samples. The determined NH 3 gas emission rate was below 3.0 g m -2 d -1 on average for each of the trials. As expected, before compaction the N 2 O rates for the hay mix-bedded samples were low with values at or below 0.61 g m -2 d -1 on average for all trials. The measured CH 4 gas emissions rate was below 3.7 g m -2 d -1 on average and fairly low as expected. The emission rate for CO 2 from the hay mix-bedded manure samples was relatively higher than expected for the first trial and as expected for the remaining trials with values of g m -2 d -1 and g m -2 d -1, respectively. Gas Emission Rates After Compaction All of the heavily-bedded manure samples were then compacted to various levels and the gas emissions were again evaluated from each sample. Most studies theorize that with increasing compaction level, anaerobic conditions would be created causing an increase in CH 4 and CO 2 emissions (Chadwick, 2006). In this study the hypothesis was that compaction would decrease all of the gas emissions because of restricted porosity and permeability within the material, so that very little gas flow would persist and be emitted from the samples. Figures to show the changes in gas emission rates with compaction at various levels for each gas and each trial. Also Table A.6 in the appendix shows a complete list of the emissions data for each bedding type and trial in this study. 75

88 Figures : The Effect of Compaction on Ammonia Gas Emission Rates 76

89 Figures : The Effect of Compaction on Nitrous Oxide Gas Emission Rates 77

90 Figures : The Effect of Compaction on Methane Gas Emission Rates 78

91 Figures : The Effect of Compaction on Carbon Dioxide Gas Emission Rates 79