i MODELING OF METHANE GENERATION, OXIDATION AND EMISSION IN LANDFILLS by Balasingam Palananthakumar A thesis submitted in partial fulfillment of the requirement for the degree of Master of Engineering Examination Committee Dr. C. Visvanathan (Chairperson) Dr. N.T. Kim Oanh Ir. Karl Iver Dahl Madsen Nationality Sri Lankan Previous Degree B.Sc. Eng. (Civil Engineering) University of Peradeniya Peradeniya, Sri Lanka Scholarship Donor The Government of Japan Asian Institute of Technology School of Environment, Resources and Development Bangkok, Thailand August 1999
Transcript
i
MODELING OF METHANE GENERATION, OXIDATION AND EMISSION IN LANDFILLS
by
Balasingam Palananthakumar A thesis submitted in partial fulfillment of the requirement for the degree of Master of Engineering Examination Committee Dr. C. Visvanathan (Chairperson) Dr. N.T. Kim Oanh Ir. Karl Iver Dahl Madsen Nationality Sri Lankan Previous Degree B.Sc. Eng. (Civil Engineering) University of Peradeniya Peradeniya, Sri Lanka Scholarship Donor The Government of Japan
Asian Institute of Technology School of Environment, Resources and Development
Bangkok, Thailand August 1999
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Acknowledgements First, I wish to express my sincere gratitude to my adviser Dr. C. Visvanathan for his overall supervision of this work, brainstorming guidance whenever I was confused, friendly discussion, providing valuable reference materials and his unreserved willingness to improve the quality of this research throughout. I was really challenged to give my best because of his constant eagerness. I would like to extend my profound gratitude and honest thanks to Ir.Karl Iver Dahl Madson for his supports and encouragement in modeling work from beginning of the study. I would also like to express my profound gratitude and heartfelt thanks to Dr.N.T.Kim who accepted to work as committee member and provided critical comments and suggestions. My sincere gratitude and appreciation is also due to Dr. Patrick Hettiarchi, Associate Professor, University of Calgary for providing reference materials and information for this study. My sincere thanks are due to all Environmental Engineering staff, especially Khun Varin for assisting in the ambient lab, Khun Peter for making the necessary materials available for use and Khun Marasee and Khun Aree for cooperating in official matters. My sincere appreciation is extended to the Government of Japan, which awarded me the scholarship for my entire graduate study. I am indebted to Mr.Dinesh Pokherel who helped me in numerous ways, particularly during the experimental set up. I am also thankful to Ms. Wilai for her sporadic helps in lab works. I can not forget members of thesis group who have helped in one way or another. My sincere appreciation also goes to all my friends who made Asian Institute of Technology as my home. I deeply respect my siblings for their sacrifications, inspirations and supports continuously to uplift me. Finally, I gratefully dedicate this piece of work to my late parents who were the initial source of inspirations and sharing all hardships and upheavals of my soul during the early stage of my life.
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Abstract Methane emission from landfills to the atmosphere, generation in landfills and oxidation in landfill covers are topics of major interest because of methane's role in the greenhouse effect, photochemistry of the troposphere, explosive threats, migration of hazard potential, health and safety issues and energy applications. Applications of modeling techniques in landfills will be useful to find ways to control these hazards associated with methane emissions. A methane inventory model was structured to estimate methane emissions from landfills at the country or regional level. Then, it was used as a policy assessment tool for analyzing possible future emission patterns. It identified the restriction of degradable organic carbon (DOC) in waste by effective solid waste management as the best among other options such as promotion of methane recovery and methane oxidation. As this package contains many educational features, it could also be used as a good guide or professional training tool for people who are interested in landfill gas management. The sensitivity of methane generation rate was analyzed by the methane generation model to assess the effects of variables such as initial conditions, specific growth rate and reaction rate constants. It concluded that hydrolysis rate constant (Kh) and initial organic carbon concentration in waste (C(S)) are the most important parameters affecting the methane generation directly. A methane oxidation model, which was constructed from semi-empirical equations derived from Monod kinetics, inhibitor effects on enzymatic activity and experimental data, confirms that the TCE behaves as a fully competitive inhibitor on the enzymatic activity of methane oxidation. The experimental study conducted to determine the effect of volatile organic compounds (benzene) on methane oxidation shows no effects until the benzene increased to 800 µg/m3. When it was increased to 1400 µg/m3 there was significant effect observed with some interference in measuring actual methane. It indicates the threshold limit of benzene on methane oxidation could be between these two concentrations.
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Table of Contents Chapter Title Page Title Page i Acknowledgment ii Abstract iii Table of Contents iv List of Figures vii List of Tables ix List of Abbreviations x
1 Introduction 1.1 Rationale 01 1.2 Objectives of the Study 03 1.3 Scope of the Study 03
2 Literature Review 04 2.1 Solid Waste Management and Landfills 04 2.2 Global Warming and Landfill Gases 2.2.1 Fundamentals of Greenhouse Gases 06 2.2.2 Impact of Greenhouse Gases 07 2.2.3 Methane contribution of landfills 08 2.3 Landfill Gas Generation 2.3.1 Gas Production 10 2.3.2 Different Phases of Biodegradation 11 2.3.3 Movement of Landfill Gases 12 2.4 Methane Oxidation in Landfills. 2.4.1 Mechanisms of Methane Oxidation 14 2.4.2 Influences of Different Parameters 15 2.5 Landfill Gas Mitigation 2.5.1 Mitigation Options 19 2.5.2 Solid Waste Management Practice 20 2.5.3 Landfill Gas Collections 21 2.5.4 Promoting Methane Oxidation 22
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2.6 Landfill Methane Models 2.6.1 Existing Solutions 24 2.6.2 Models for Inventory of Methane Emissions from
Landfills 25
2.6.3 Numerical Modeling of Gas Generation 26 2.6.4 Modeling of Methane Oxidation 27 3 Methodology 3.1 Work Outline 30 3.2 General Modeling Techniques 30 3.3 Application of Modeling Techniques 3.3.1 Methane Inventory Model 31 3.3.2 Methane Generation Model 32 3.3.3 Methane Oxidation Model 35 3.4 Experimental Study 3.4.1 Experimental Outline 36 3.4.2 Benzene Analysis 37 3.4.3 Calculations 37 4 Results and Discussion 4.1 Outline of Results and Discussion 40 4.2 Methane Emissions from Landfills 4.2.1 Frame Work 40 4.2.2 Components of Package 41 4.2.3 Interface 45 4.2.4 Technical Significance 46 4.3 Model for Methane Generation in Landfills 4.3.1 Basic Considerations 48 4.3.2 Sensitivity Analysis 49 4.3.3 Technical Significance 51 4.4 Methane Oxidation Model 4.4.1 Methodology Development 52 4.4.2 Empirical Formula 55 4.4.3 Simulations and Validations 58 4.4.4 Technical Significance 60
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4.5 Effects of VOC on Methane Oxidation 61 5 Conclusions and Recommendations 65 References 68
Appendices Appendix A Methane Inventory Model : Data and
Calculations Appendix B Methane Generation Model: Data and
Calculations Appendix C Methane Oxidation Model: Data and
Calculations Appendix D Experimental Data and Calculations Appendix E Useful Photographs
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List of Figures Figure Title Page
2.1 Solid Waste Disposal Leading to Global Warming Problem 05 2.2 Global Warming Scenario 06
2.3 Effect of Greenhouse Gases on Environment 08 2.4 Share of Individual Sources (%) of Methane in Estimated Global
Emission 09
2.5 Percentage of Methane Produced Worldwide from Landfills 09 2.6 Schematic Diagram of Pattern of Carbon Flow at Anaerobic
Condition 10
2.7 Generalized Phases in the Generation of Landfill Gase 11 2.8 Amounts of Methane Consumed in 42 Days At 270c by Methane
Oxidizing Bacteria in Media of Different Initial pH Values 17
2.9 Variation of Oxidation Rates with Temperature 17 2.10 Variation of Methane Oxidation Rates with Moisture Content 18 2.11 Methane Balance Diagram 19 2.12 Trends of Waste Generation, Recovery and Disposal in US 20 2.13 Sulfate Reducing Reactor 23 2.14 Model Kinetics Comparison Using First Order Model as Basis for
Optimizing the Constant of Zero Order Model For Period of 35 Years (1957 – 1992)
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2.15 Examples of Model Predictions. 26 2.16 Effect of Hydrolysis Rate on Total Methane Generation 28 2.17 Effect of Acidogenic Decay Rate on Total Methane Generation. 28
3.1 Organic Pathways in Landfill Microbial Ecosystem 30
3.2 Experimental Setup for Column System 30
4.1 Typical Input Sheet for Estimation for a Place 31 4.2 Graphical Output Sheet of Case Study 31
4.3 The Typical Simulation Sheet 32
4.4 Effects of Initial Solid Carbon on Rate of Methane Generation 35 4.5 Effects of Hydrolysis on Rate of Methane Generation 36 4.6 Comparison of Empirical Curve with Experimental Data for Methane
Oxidation Rate 37
4.7 Comparison of Empirical Curve with Experimental Data for Methane Oxidation Rate
37
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4.8 The Model Block Representing Methane Utilization 584.9 The Model Block Representing Methanotroph Population 58410 Validation of Methane Oxidation for Uninhibited Fresh Soils 594.11 Validation of Methane Oxidation for Inhibited Lysimeter Soils 604.12 Observed Methane Oxidation with Different Rate of Methane Supply 624.13 Variations of Methane Oxidation with Different Rate of Methane
Supply 62
4.14 Observed Methane Oxidation with Different Amount of Benzene injection Nominal Values for Initial Concentrations Nominal Values for Initial Concentrations
63
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List of Tables Table Title Page
2.1 Comparison of Properties of Greenhouse Gases 07 2.2 Consumption of Methane with Various Concentration Oxygen and
Nitrogen 15
2.3 Consumption of Methane with Various Concentration Methane and Nitrogen
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4.1 Nominal Values for Initial Concentrations 48 4.2 Nominal Values for Variables 49 4.3 Classification of Inhibitors Based on Properties Various Trials of
Empirical Function for Methane Oxidation Rate in Terms of Temperature
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4.4 Various Trials of Empirical Function for Methane Oxidation Rate in Terms of Temperature
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4.5 Various Trials of Empirical Function for Methane Oxidation Rate in Terms of Moisture Content.
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4.6 Optimum values of Parameters for Fresh Soil 59
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List of Abbreviation
α Total porosity β Retarding factor
µΑ Max. Specific Growth Rate of Acidogenic Biomass µM Max. Specific Growth Rate of Methanogenic Biomass AIT Asian Institute of Technology C Carbon CA Concentration of A ML-3 C(aq) Concentration of Aqueous Carbon ML-3 C(AC) Concentration of Acetate Carbon ML-3 CPMO Constant for potential of methane oxidation C(S) Concentration of Solid Carbon ML-3 CFC Chlorofluorocarbon CH4 Methane CO2 Carbon dioxide COD Chemical Oxygen Demand DOC Degradable Organic Carbon Dz Diffusion Coefficient E Enzyme G Lumped parameter GC Gas Chromatography GHG Greenhouse gases H2S Hydrogen Sulfide ha hector IDRC International Development Research Center IPCC Inter -Governmental Panel on Climate Change K Maximum rate of substrate utilization per unit mass of
microorganism T-1
k Coefficient of permeability LT-1 KdA Death rate constant of Acidogens T-1 KdM Death rate constant of Methanogens T-1 kh First Order Hydrolysis Constant T-1 Ki Inhibitor constant M-1L3 Km Oxidation affinity
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Ks Half saturation constant ML-3 KSA Half saturation constant of Acidogens ML-3 KSM Half saturation constant of Methanogens ML-3 KSS Half saturation constant of Substrates ML-3 I Inhibitor L Litter LFGTEP Landfill gas to energy project MC Moisture contents % mg Milligram min Minutes mL Milliliter MO Methane Oxidation MPB Methane Producing Bacteria MSW Municipal Solid Waste N Nitrogen N2O Nitrous Oxide NH3 Ammonia O2 Oxygen O3 Ozone ORP Oxidation Redox Potential PMO Potential of Methane Oxidation MT-1 ppb Parts per billion ppm Parts per million S Substrate S- Sulfide SO4
2- Sulfate SRB Sulfate Reducing Bacteria sqm Square meter L2 TEI Thailand Environmental Institute UNEP United Nation Environment Prevention USEPA United State Environmental Agency SS Stainless Steel TCE Tri Chloro Ethane Tg 1012 gram UV Ultra violet
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Vmax Maximum oxidation rate MT-1 VOC Volatile Organic Compound Vr Velocity LT-1 Vz Convective velocity LT-1 X Concentration of microorganism ML-3 Y Yield of Methanotroph per Methane Utilized YA Yield of Acidogen per Carbon Utilized YCH4 Yield of Methane per Methanogenic Carbon Utilized yr Year YM Yield of Methanogenic Carbon per Acetate Carbon Utilized
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Chapter 1
Introduction
1.1 Rationale The greenhouse effect has recently been receiving a great deal of scientific and popular attention. Worldwide, many researchers and scientists are engaged in looking for the reasons of heat blanketing that causes the phenomenon called global warming. Methane from landfills has been recognized as one of the importent element of greenhouse gas in addition to carbon dioxide, nitrous oxide and chloroflocarbon. It has twenty-one times of global warming potential of carbon dioxide (Augenstein, 1990). Methane also plays important role in photochemistry of the troposphere. Increases in atmospheric methane reduces the concentration of the hydroxyl radical (OH) and thus increases the methane lifetime, and result in increases in troposheric ozone. It is the only long lived gas that shows chemical feedback effects (Gardner, 1993). In addition, its increased concentration in the atmosphere causes explosion threats in neighborhood environment. Atmospheric concentration of methane has grown from 700 ppbv in pre-industrial times to over to 1700 ppbv today. The global annual input of methane to the atmosphere is estimated to be 535±125 Tg (Smith, 1997), of which about half is considered to be both anthropogenic and originating from biospheric processes, particularly anaerobic bacterial fermentation. Decomposition of refuse in municipal landfills is believed to be one of the major components of this biogenic methane, but past estimates of the emissions from this source have varied greatly, between 20 and 70 Tg per year (Lay, 1998) which is between 6% and 18% of the total methane source (Bingemer, 1998). More reliable estimations are clearly needed and past results show that landfills are notable anthropogenic source of atmosphere methane in many countries, especially in developed countries. This methane source has been targeted in many developed countries as one that is capable of control by recovery and it potentially provides a way of reducing current greenhouse gas emissions and its adverse effects. Recovered methane either is flared or used as a source of energy. However, it is not applicable everywhere, specially small-scale landfills and open dumps in developing countries, because of the lack of methane production and not meeting design regulations for gas collection. In contrast, in developing countries, urban refuse disposal often is in open dumps, which do not result in much methane emission even though they create a range of other environmental problems (TEI, 1996 and Smith, 1997). However, as these dumps will be replaced in the future by covered landfills it is likely that methane production will increase, and in most cases this will not be recovered for use as fuel or flare, but released to the atmosphere.
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Microbial processes mediate many chemical transformations in landfill, and carbon and nitrogen cycle in cover soil. The transformation occurs through refuse decomposition by anaerobic microorganisms (biodegradation) producing methane and other compounds, and oxidation of methane by aerobic methanographs in cover soil. The biodegradation of solid waste depends on the composition of waste and continues for a long time in landfill for final stabilization. Problems associated with biodegradation of solid waste and methane oxidation are the products and by products of these reactions, mainly leachate and landfill gases with trace amount of metals and volatile organic compounds (VOC). The generation of landfill gas exerts pressure to the surrounding area, and landfill gas escapes through the easiest route. The mechanisms of migration are complex and depend on various parameters such as surrounding and top cover soil properties, landfill type, age of landfill, moisture content, pH, temperature, compaction, surrounding pressure. Gas escapes from top cover directly or migrates horizontally to hundreds of meters away from landfill before being released into atmosphere. However, some portions of methane is utilized in landfill cover before released into atmosphere. This phenomenon attracts scientists and researchers to investigate the fate of methane gas in landfill cover soils. First, researchers identified the presence of bacteria responsible for methane utilization. Further studies on these bacteria are followed later with contributions on clear identification of characteristics of bacteria. Methanotrophs, a gram negative, hetrographic, aerobic bacteria present at the top few centimeters of landfill cover oxidizes methane and produces carbon dioxide and water. Microbial methane oxidation by soil mechanism can play a significant role (~10%) in reducing methane concentration in atmosphere (Faso, 1996 and EPA, 1998). These bacteria are also found to use up common methane from the atmosphere at its atmospheric concentration. Information on interaction between factors influencing methane oxidation in soils is not completely understood. However, some of the factors influencing methane oxidation are identified as oxygen concentration, methane concentration, carbon dioxide requirements, oxidation redox potential, pH, temperature, moisture content along with some physical property of soil (Pascal, 1996; Whalen, 1990). Besides them, there is a speculation that toxic components, VOCs, moving with landfill gas affect this microbial activity (Pokhrel, 1998). It is also possible to oxidize methane anaerobically with alternative electron acceptors (Pareek, 1998). However, bacteria that perform this process have never been isolated. Nevertheless, there is strong evidence for anaerobic methane oxidation by sulfate in marine system (Hansen, 1988). Also anaerobic methane oxidation by ions may occur, while very little is known about alternative electron acceptors. However, since anaerobic oxidation is very low portion compare to total methane oxidation (<5%) it will not be considered in this studies Therefore, methane emission from landfill to atmosphere, generation in landfill and oxidation in landfill cover are topics of major interest because of methane role in the greenhouse effect,
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photochemistry on troposphere, explosive threats, migration of hazard potential, health and safety issues and energy applications. Study on effects by soil environment at the rate of methane oxidation is necessary to understand the relationship among them. Applications of modeling techniques in landfills will be useful to find ways to control it. 1.2 Objectives of the Study This study is directed to achieve a multi purposes, namely; 1. Application of modeling techniques and sensitivity analysis
• For methane inventory (macro level) • For methane generation in landfill (micro level) • For methane oxidation in landfill cover (micro level).
2. Experimental study of VOC effects on methane oxidation rate in landfill cover. 1.3 Scope of the Study In the view of stated objectives of this study, the scope of modeling is limited to the following conditions, Excel spread sheet collaborated with Visual Basic is used for modeling of methane
emission from landfills The guidelines established by IPCC in 1995 with some amendments are used to formulate
the model for methane inventory from landfills "Vissim" software is used as a mathematical tool for modeling of methane generation in
landfills The methane generation model is formed using biokinetic model equations describing the
dynamics of the microbial landfill ecosystem The methane oxidation phenomenon is modeled using experiment results from laboratory
scale lysimeter installation filled with mixture of different soil Three different concentrations of benzene are used to analyse the effect of VOC on
methane oxidation in landfill soil
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Chapter 2
Literature Review 2.1 Introduction In 1960, nations began to institute environmental protection measures (Constantinos, 1983). Environmental engineering explored in response of such perceived environmental needs. Insufficient cooperation between environmental protection and waste management still causes pollution phenomena with various human health repercussions. One such case is municipal solid waste (MSW). The objectives of the literature review are to provide a concise relationship between MSW and global warming problem, fundamental idea of global warming potential, details of methane generation and oxidation, and some previous modeling works on it. 2.2 Solid Waste Management (SWM) and Landfills Solid wastes are “waste materials discharged and discarded as unnecessary” from everyday life. These inevitable products of human’s daily activities affect the environment thereby leading to health hazards and non-congenial to the aesthetic point of view. With due consideration to these, which is both detrimental to human and the environment, an effective solid waste management program is necessary and justifiable. Recycle and reuse, incineration, composting and landfilling are some options available for implementation of effective SWM practice. Solid waste management planning requires basic information on the composition and properties of solid waste being generated to determine the appropriate processing technology and recovery options. For metropolitan cities which generates substantial amount of solid waste daily, the need to study the characteristics of the various components making up the city waste is vital to its overall solid waste management. Those are namely;
- Key environmental issues related to solid waste, - Appropriate design conditions, - Financial availability - Community awareness of waste generators
Historically, landfills have been the most economical and agreeable method for the disposal of solid waste throughout the world. Landfills are the physical facilities used for the disposal of residual solid waste in the surface soils of earth. They produce landfill gas (mixture of methane, carbon dioxide and other trace volatile toxic compounds) that threats global environment, mainly global warming. Landfills characteristically have two contrasting microbial ecosystems; often with sharp gradients between them; anaerobic methanogenic zones occur in the upper refuse layer, and methanotrophic zones in aerated cover soils. Rates for both methane production and oxidation can exceed observed rates for other terrestrial ecosystems by large factors. Field flux
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measurements (net emissions) vary over 7 orders of magnitude, from less than 0.0004 g/m2.day methane (Smith, 1997). These net emissions, of course, are the results of methane production, oxidation and gaseous transport processes in the cover soil.
Fig. 2.1 Solid Waste Disposal Leading to Global Warming Problem. Worldwide, 450-500 million-ton waste is generated every year (See Fig.2.1). Consequent of normal solid waste management practice, recycle, reuse, composting and incineration, 320-350 million ton waste goes into landfill disposal and it emits 9-70 Tg/yr methane (Lay et al., 1998) in addition to a huge amount of carbon dioxide. These gasses create the global warming problem.
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2.3 Global Warming and Landfill Gas 2.3.1 Fundamentals of Greenhouse Gases Before the industrial revolution (1750-1800) the amount of CO2 in the atmosphere was 270 ppm and has increased substantially to the present level of 356 ppm. This figure is projected to double by 2100. As the amount of greenhouse gases increase, additional heat will be trapped in the atmosphere causing the phenomenon called " global warming", also known as "greenhouse effect". Greenhouse gasses are found naturally in the atmosphere, in small quantities. They absorb outgoing long wave infra red radiation or heat energy, which the earth and the atmosphere normally radiate back to outer space as illustrated in Fig. 2.2. Greenhouse gas makes the earth warm and inhabitable. The major greenhouse gases are carbon dioxide (CO2), methane (CH4), nitrous oxidize (N2O) and ozone (O3). In addition, there are human produced gases utilized for industrial and economic development that have the capacity to absorb heat, similar to natural greenhouse gasses as well as to destroy the ozone layers, which binds the earth's out atmosphere. These gases are chlorofluoro-carbons (CFCs), hydrochlorofluoro-carbons (HCFCs), hydrofluoro-carbons (HFCs), and perflurinated- carbons (PFCs). The concentration increment of greenhouse gases is shown in Table. 2.1.
Fig. 2.2 Global Warming Scenario. Source: Pokhrel (1998)
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Table. 2.1 Comparison of Properties of Greenhouse Gases
Greenhouse Gases
Greenhouse potential per molecule of
carbondioxide@
Increase in concentration per
year#
%
Contribution to greenhouse
effects*
%
Atmospheric residence
time^
years
Carbondioxide 1 0.50 49 120
Methane 25 1.00 19 10.5
Chloroflorocarbon 12000 5.00 17 55
Nitrous Oxide 320 5.00 5 132
Others 0.25 10 @ Source: TEI (1996) # Source: Oanh (1996) * Source: Foley (1991) ^Source: Peter and Kirks (1993)) Greenhouse gases vary in atmospheric lifetime and in radiative effects, also known as global warming potentials (GWPs) as shown in Table. 2.1. GWP defines warming effects caused by a unity mass (1kg) of a given gas relative to that of carbon dioxide. 2.3.2 Impact of Greenhouse Gases The effect of the greenhouse gases on the global energy system is shown in Fig.2.3. The major effect of greenhouse gases is change in global temperature. Food production on the other hand can be affected in arid and semi arid regions due to low precipitation. Some of the places might become desert where as the cropping system is shifted to upland areas. Sea level rise is predicted based on increase in temperature. Increase in sea level can result the most of the worlds low laying low areas to be waterlogged. Changing temperatures and rainfall patterns will impose the variety of pressures upon plants and animal life of different ecological zones. The most affected ecological system is coral reefs, the earth’s most diverse ecosystem with as many as 3000 species (Pokhrel, 1998). Wildlife habitat and vegetation are also affected resulting in extinction of endangered species. So, once it is disturbed it will be a long run procedure to restore the ecological balance.
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Fig. 2.3 Effect of Greenhouse Gases on Environment Source: UNEP (1993)
2.3.3 Methane Contribution of landfill Methane (CH4) is one of the main sources of greenhouse effects because it has global warming potential greater than other greenhouse gases and its contribution on greenhouse effects is 17% (see Table. 2.1). In 1992, the IPCC/OECD reported that global methane emission from anthropogenic sources was 360 million tones/yr. of which emission from landfill accounted for between 20-70 million tones/yr. This is more than 6% of total methane emission to atmosphere through out the world. Percentagea of various sources of methane emission into atmosphere is shown in Fig.2.4. The proportion of methane produced world wide from landfills is illustrated in Fig. 2.5. The IPCC established guidelines to determine methane emission from MSM landfill in 1995. This simple method, based on the mass balance approach, incorporates no time factor and can be applied to the total waste emanating from the country. The calculation is based on the amount of waste generation and landfill, the fraction of degradable organic carbon (DOC), the fraction of DOC that actually degraded into biogas and the fraction of biogas that was released as methane.
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Fig. 2.4 Share of Individual Sources (%) of Methane in Estimated Global Emission Source: Iserman (1994)
Fig. 2.5 Percentage of Methane Produced Worldwide from Landfill Source: TEI (1996)
Rice paddies22%
Landfills7%
Biomass burning7%
Domesticated ruminants13%
Coal minings5% Unknown
fossil fuels10%
Gas Production andDistribution
5%
CH4 Hydrate1%
Ocean2%
Freshwater 1%
Wetlands19%
Termites 7%
Undomistaked ruminants
1%
Europe25%
Asia22%
South America8%Oceania
2%
North America43%
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2.4 Landfill Gas Generation 2.4.1 Gas Production Solid waste placed in the landfills undergoes a number of simultaneous and interrelated biological, chemical and physical changes. Various types of reactions occur inside the landfill depending upon site conditions, waste characteristics, oxygen, temperature, moisture content, nutrients and other factors. The most important reactions occurring in landfills are those involving the organic MSW that lead to the evaluate of landfill gases and, eventually liquids. Pathways leading to the production of methane and carbon dioxide from anaerobic digestion of organic fraction of solid waste are shown in Fig 2.6 and 2.7.The chemical reaction for the anaerobic decomposition of solid waste can be written as, (bacteria) Organic matter + H2O biodegraded + CH4 + CO2 + other gases organic matter In the anaerobic zone of the mature landfill, the gas phase is represented by methane (50 - 70) % and carbon dioxide (30 - 40)%. Microimpurities, the main components of which are nitrogen, hydrogen, carbon monoxide, hydrogen sulfide, ammonium, may constitute only few percent. (Nozhevnikoa et al., 1993).
Fig. 2.6 Schematic diagram of pattern of carbon flow at anaerobic condition Source: Metcaff and Eddy (1993)
2.4.2 Different Phases of Biodegradation The Landfill gas is the end product in the series of waste decompositions. Normally, the process of waste decomposition is analyzed into five phases. (see Fig.2.7) 1. Initial adjustment: It is the initial phase, in which the organic biodegradable components in waste undergoes microbial decomposition in aerobic condition soon after they are placed. 2. Transition phase: In it, oxygen is depleted and anaerobic conditions begin to develop. 3. Acid phase: In the acid phase, hydrolysis of high mass compound takes place and its products are compounds suitable for microorganism to use as energy or carbon sources. 4. Methane fermentation phase: Methanogenic bacteria converts acetic acid and hydrogen formed in the acid phase to CH4 and CO2. 5. Maturation Phase: It is the final phase where the rate of gas generation is diminished because most of the nutrients have been removed during the previous phases and substrates that remain in landfill are slowly biodegradable.
Fig. 2.7 Generalized Phases in the Generation of Gases.
Source: Tchobanoglous (1993)
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It is impossible to develop a kinetic equation for actual conditions inside a landfill since various unknown parameters are responsible for gas production. The simplest equation used to describe time rate of production of gas is expressed with a first order equation with reaction rate depending on substrate concentration. The modified Monod equation relating rate of substrate utilization both to the concentration of microorganisms in the system and to the concentration of substrate surrounding the organism is expressed as (Tchobanoglous, 1993),
dSdt
KXSKs S
=+
..Equation 2.1
Where, K = Maximum rate of substrate utilization per unit mass of microorganisms (T-1) X = concentration of microorganisms (mass/ volume) Ks = waste concentration at rate of one half of maximum rate of substrate
utilization (m/v) S = concentration of substrate surrounding the microorganism (mass/ volume)
The equation can be approximated as,
dSdt
KX= When S >> Ks ..Equation 2.2
(Zero-order reaction) And
SXKsK
dtdS
= When S << Ks ..Equation 2.3
(First-order reaction) The production rate of gas in landfill is important to determine the landfill gas flux through the landfill cover soil. High rate of production and mature landfill creates more flux and old landfill produces with low flux. This parameter is important for the design of landfill gas recovery and landfill gas management systems. 2.4.3 Movement of Landfill Gas Normally, gases produced in soils are released to the atmosphere by means of molecular diffusion and pressure driving force. Movement of landfill gas is multidirectional and mathematical expression for the movement contains higher order differential expressions. Based on certain assumption a simplified differential equation of landfill gas migrating vertically upward is expressed in one dimensional control volume as follows (Tchobanoglous, 1993);
Where, α = total porosity β = retardation factor accounting for sorption and phase change CA = concentration of compound A, g / cm3 VZ = velocity in vertical direction DZ = effective diffusion coefficient (= D (αgas
10/3/α2), cm2/s G = lumped parameter used for account for all generation terms, gm/cm3.s Z = depth, m. t = time, s
To calculate convective velocity in vertical direction, Darcy’s law can be used to describe the mechanism of gas flow in landfill with the condition of laminar flow (Reynolds’s number less than 1). This condition can be achieved with the mean grain size of the porous medium less than 0.2 cm (Emcon, 1980). Darcy’s law is expressed as:
Vr = - k dhdr
..Equation 2.5
Where, Vr = gas velocity
k = coefficient of permeability
d hd r
= Pressure gradient
The design of cover systems incorporated with gas control requires an understanding of the physical, chemical and biological processes governing gas migration and comprehensive mathematical model to describe gas migration across landfill cover systems described herein. Flow through a landfill cover is essentially multiphase involving a water phase and a gas phase. The gas phase may include such as CH4, CO2, O2, N2 and water vapor. The effect of moisture infiltration into a landfill cover has an impact on gas migration, but considering the time scales, such effects can be neglected. However, the effect of moisture saturation on gas permeability should be taken into account.
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2.5 Methane Oxidation in Landfills 2.5.1 Mechanisms of Methane Oxidation The recent studies says that methane oxidation plays significant role in the rate of methane emission from landfills. Here, rate of oxidation depends on the both biochemical and physical process in the soil ( Hettiarachi et al., 1998). The presence of natural sink of methane was realized based on the discrepancy between theoretical methane production and atmospheric methane concentration, and encouraged scientists and researchers to investigate and identify its existence. The identification was important from two points of view; the reduction of potentially hazardous seeps of methane around landfill sites, and other methanogenic environments; and a reduction in theoretical yield of greenhouse gas from landfills. Generally methane oxidation happens at two conditions, namely, • Aerobic condition: Methane utilizing bacteria are found to be a more diverse group of organism (Whittenbry et al., 1970), with methanotrophs designated for methylotrophic bacteria that can use methane for carbon and energy source (Christopher et al., 1983). The methanotrophs form a specialized group of bacteria which contain the enzyme system responsible for oxidizing methane, in presence of molecular oxygen, to form metabolic products carbon dioxide and water (Barratt, 1995); CH4 + 2 O2 CO2 + 2 H2O + (883 kJ) Theoretically, each mole of CH4 disappears with two moles of O2 to produce one mole of CO2. The actual value doesn’t agree with theoretical value because of carbon assimilation. The maximum carbon conversion efficiency observed is as high as 43 %. The more efficient the bacterium is in assimilation, that is efficient in utilizing energy released by oxidation of CH4 for cell synthesis, the greater the disparity from the theoretical ratio of 1 CH4: 2 O2, with their ratio comes equilibrium once population density reached to its equilibrium condition. Water formed makes favorable environment for methanotrophic activity helping the oxidation process (Pokhrel, 1998). • Anaerobic condition: It is possible thermodynamically, to oxidize methane (organic matter) anaerobically with alternative electron acceptors (Pareek, 1998). Furthermore, anaerobic methane oxidation by sulfate in marine system shows strong evidence (Hansen, 1988). Anyway, bacteria that perform this process have never been isolated. Also anaerobic methane oxidation by ion may occur, while very little is known about alternative electron acceptor. As anaerobic oxidation is very low portion compare to total methane oxidation (<5%) it will not be considered in this study.
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2.5.2 Influences of different Parameters Methane oxidation rate is not constant in all types of soil and all environmental conditions. There are various parameters influencing methane oxidation rate. Although the actual oxidation in soil are not yet completely understood, some identified parameters responsible to control methane oxidation are oxygen, methane and carbon dioxide concentration, oxidation redox potential, pH, temperature, types of soil including compaction status of soil, moisture content, N-amendment, N-turnover, and soil organic matter (Pokhrel, 1998). Some of the influencing parameters and the effects are explained below. 1) Oxygen Concentration: In aerobic methane oxidation process, concentration of oxygen in soil voids plays major role with high rate of oxidation at high concentration of oxygen. High rate of methane oxidation is observed at oxygen concentration between 10 % to 40 % and decrease on either increase or decrease in oxygen concentration as shown in Table 2.2. Table. 2.2 Consumption of Methane with Various Concentration Oxygen and Nitrogen
Partial pressure of Methane consumed per day
Oxygen % Nitrogen % Sample A mL Sample B mL
0 70 0.00 0.00
10 60 1.05 0.94
20 50 0.88 0.88
30 40 1.05 1.05
40 30 - 0.94
60 10 0.35 0.52
70 0 0.23 0.29 Note: Methane consumed per day by methane oxidizing bacteria during 6 days at 320 C
Source: William and Zobell (1989) 2) Methane Concentration: The threshold methane concentration for methane oxidation was found to be 1 nmol CH4 (Roslov, 1992). Atmospheric methane oxidizers have ability to sustain long-term oxidation of sub atomic concentration with high affinity enzyme system and a rather low Vmax (Km= 30-51 nm CH4; Vmax=0.7-3.6 nmol CH4/ hr / g soil) the kinetic properties being sufficient to permit maintenance and cell growth with atmospheric methane as substrate. Increase in methane concentration also decreases the carbon conversion efficiency. Laboratory experiment methane consumption at different headspace concentration indicates the increase in oxidation rate at upto 40% methane concentration. (see Table.2.3).
16
Table. 2.3 Consumption of Methane with Various Concentrations of Methane and Nitrogen
Partial pressure of Methane consumed per day Methane % Nitrogen % Sample A mL Sample B mL
Note: Methane consumed per day by methane oxidizing bacteria during 6 days at 320 C Source: William & Zobell (1949)
3) Carbondioxide: The increase in carbon dioxide in initial mixture was considered to enhance multiplication and oxidation. Increase in CO2 above 20 to 30 % resulted in reduction in methane oxidation. The carbondioxide was required for initial metabolism and no more carbondioxide was required for methanotrophic activities. High concentration of carbondioxide means less oxygen and methane. This results in reduction in methane oxidation rates. 4) Oxidation redox potential: Methane oxidation is high at higher oxidation redox potential with some exceptions. A survey of change in redox potential in the soil profile showed where the layer of methanotrophic bacteria could exist in sufficient numbers, affect methane emission from soil (Pokhrel). 5) pH : There is an optimum pH range for maximum methane oxidation. Methane consumption rate at various pH ranges observed by William and Zobell reported the favorable pH value as 6.6 as shown in Fig. 2.5.1. From the figure it can be seen that, pH values on either side of neutral ranges are not favorable for methane oxidation. 6) Temperature: Effect of temperature on methane oxidation in a fresh soil sample, determined by Pokherl (1998) is shown in Fig.2.8. The figure indicates the high rate of oxidation at temperature between 30 to 360 C with complete inhibition at 460C. Very low oxidation is seen at 50C. Whalen et al.(1990) also has made similar observations. However, the survivability of observation methanotrophs between freezing and thawing cycle is not well understood. ( Pokherl, 1998)
17
Fig. 2.8 Amounts of methane consumed in 42 days at 270C by methane oxidizing bacteria in media of different initial pH values
Source: William & Zobell (1989)
Fig. 2.9 Variation of Oxidation Rates with Temperature (for fresh soil sample, moisture content 16% and initial headspace CH4 = 3.5-4.5%.)
Source: Pokhrel (1998)
7) Moisture Content: Moisture content also plays important role in methane oxidation. (Pokherl, 1998) found optimum moisture that falls within range between 15 to 20% (see Fig.2.10). Decrease in oxidation rate at high moisture content is explained by reduced gas diffusion between soil and gas phase (Pascal & Cleempul, 1996), and presence of diffusion barrier between the bacteria and their substrates, methane and oxygen.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0 10 20 30 40 50
Temperature OC
Met
hane
oxid
atio
n ra
te
(10-6
g C
H4 /
g so
il.h)
18
Fig. 2.10 Variation of Methane Oxidation rates with Moisture content
8) Nutrients : Addition of ammonium ion is found to have inhibition effect with rate of methane oxidation (Whittenbury et al., 1970). In landfill containing low C/N ratio, methane oxidation can be suppressed because of increased turn over ( Smith, 1997). 9) Methane Flow: Conditions and patterns of methane flow too effect soil methanotrophy. Once established, the methanotrophs survive under intermittent flow conditions. The methanotrophic activity is directly related to the detention time of the CH4 in the oxidation layer. Source strength of landfill gas, as well as gas permeability will affect this factor. Although it is possible to decrease the permeability of this layer, CH4 flow rate will be mainly governed by the gas permeability of the hydraulic barrier layer. This is because the effective permeability is determined by the lowest permeability in a layered structure ( Hettiarachi et al., 1998 ). Methane oxidation rate decrease with the increase in depth depending upon soil type and moisture content. The decrease of oxidation is due to absence of aerobic microorganism well below ground level. While sufficient information on factors influencing methane oxidation in soils is available in literature, the interaction among variables are not well known.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20 25 30
Moisture contents (%)
Met
hane
oxid
atio
n ra
te
(*10
-6 g
CH
4 / g
soil.h
)
19
2.6 Landfill Gas Mitigation 2.6.1 Mitigation Options Today, many countries and environmental protection organizations are committed to mitigating methane emission from landfills and large opened dumps by encouraging the program called "control of methane emission from landfills" and developing environmentally and economically developemnt development beneficial landfill gas to energy (LFGTE) projects (Wetherill et al., 1997). The various pathways into which landfill methane is partitioned are shown in Fig.2.11, which illustrates both methanotrophic oxidation and engineered control systems (pumped gas recovery) may reduce emissions.
Fig. 2.11 Methane Balance Diagram. It is hard to control whole migration of landfill gas. However, there are some methods by which it can be controlled to reduce atmospheric emission, minimize surface and subsurface migration, and minimize release of odorous materials, to reduce explosions, and collect for energy production. Those are namely,
- Effective solid waste management (SWM) practice, - Landfill gas collections and - Promoting methane oxidation in landfills.
20
2.6.2 Effective Solid Waste management Practice Solid waste management is a key role in solving environmental problem in many countries. Source separation, recycling, incineration, composting and landfilling are some of the main practice in solid waste management program. The choice of proper option varies from place to place depending upon various factors that have been discussed in section in 2.1. Typical solid waste management practice to provide the way of reducing current greenhouse gas emissions and its adverse effects in USA is shown in Fig. 2.12.
Fig. 2.12 Trends of Waste Generation, Recovery and Disposal in US. As we discussed in section 2.3 organic carbon is the key component in generation of landfill gas. It potentially influences in effective solid waste management to provide the way of reducing current greenhouse gas emissions and its adverse effects. They include,
- Source separation and applications of triple "R" (Reduction, Reuse and Recycle) principle in large extent before go for landfill disposal.
- To encourage composting in case of waste that content high DOC (degradable organic carbon).
- Only low DOC waste to sanitary landfill. In the past, the term sanitary landfill was used to denote a landfill that the waste placed in the landfill was covered at end of the each daily’s operation. Today, Sanitary landfill refers to an engineered facility for the disposal of MSW designed and operated to minimized public health and environmental impacts.
21
2.6.3 Landfill Gas Collections According to USEPA regulations, landfills should have vent pipes for releasing landfill gases. It avoids accumulates landfill gas in bottom. Landfill gas collection system can be categorized into two basic types such as vertical well system and horizontal trench system. The common system for landfill gas collection is the vertical well system. Horizontal wells may be installed to recover landfill gas from active landfill for controlling gas migration (Wetherill et al., 1997). Two methods are in use to collect landfill gas, namely passive control and active control system. • Passive systems:
- They are functional due to the natural pressure gradients (i.e. internal landfill pressure created by landfill gas generation) or concentration gradient to convey the landfill gas to the atmosphere or a control system. This type of control system is applicable when the landfill gases are produced at a higher rate so that it can move in the desired direction. (Tchobanoglous)
• Active control system:
- It is applicable for landfill producing landfill gas not enough to exert pressure for its movement by convection and diffusion. In this system some energy is required for removal of landfill gas.
Generally, recovered methane either flares or uses as source of energy. Although flare convert landfill gas into carbon dioxide and water it is not advisable due to production of smoke and particulate which are considered as air pollutants. Use as source of energy is economical and environmentally friendly method to reduce landfill gas emissions. There are three primary approaches in using landfill gas. They include,
- Direct use of gas locally, - Generation of electricity and distribution through power grid and - Processing and injection into a gas pipeline.
However, use of landfill gas is not practical every where because of
- High impurities: H2S in landfill gas, which causes corrosion in IC combustion engine that converts gas into electric energy.
- Low gas production rate from landfills. - Landfill does not meet regulations to collect gas. - High investment cost. - Lack of skill labour.
22
2.6.4 Promoting Methane Oxidation Landfill cover soils is also a critical component in mitigation of landfill gas in addition to separate buried waste or contaminated materials from the surrounding environment, restrict infiltration of water into waste, and limits release of gas from waste. Since methanotroph bacteria in landfill cover utilize methane it can be controlled to reduce atmospheric emission (Hettiarachi et al., 1998). Mechanism of methane oxidation and factors influencing in methane oxidation are discussed in section in 2.4. Incorporation of soil methonography in landfill cover design promotes methane oxidation in landfill cover soil. Maximum oxidation occurs when the soil is aerobic and there is enough moisture in the soil. The optimum soil moisture content is between 11% and 15% depending on the soil parameter ( Pokhrel, 1998 and Whalen, 1990). To be aerobic, the soil should be more porous to allow oxygen to diffuse deep into the soil. This design should be very precise to minimize the infiltration of excess water, which make leachate production. Leachate recirculation overcomes this problem, meanwhile it helps to maintain desired moisture in the landfill cover soil which enhances methane oxidation and increases landfill compaction. In contrast, it is also favorable to methane generation bacteria in landfills, which increases methane production. In methane control strategy, Pareek et al.(1998) added sulfate in recirculation line, called sulfate reducing reactor (See Fig.2.13). Here, sulfate has been used as electron acceptor and the degradation of organic matter was carried out under sulfate reducing condition by sulfate reducing bacteria (SRB). Methane producing bacteria (MPB) and SRB compete for the utilization (oxidation) of organic matter as follows, Organic matter CH4 + CO2 Organic matter + SO4
2- S2- + CO2 The second reaction becomes predominance as thermodynamically and kinetically, SRB are more favored than MPB in this competition. Henry (1995) has confirmed anaerobic methane oxidation in presence of sulfate. Therefore, this arrangement will be also instrumental in anaerobic oxidation of produced methane. Furthermore, the produced sulfide (S2-), which remained in leachate as dissolved sulfide, was collected and treated to convert sulfide (S2-) to sulfate (SO4
2-) and recycled back at top of landfill reactors as shown in Fig.2.13. Generally, leachate from new landfills has 50- 1000 mg/L sulfate (SO4
2-) (Tchobanoglous,, 1993). When there is high concentration of sulfate (SO4
Hilger's (1998) study in long term laboratory stimulation (under direction of Mortan S.Balarz) showed that addition of NH4
- and N03- had stimulated methane oxidation in soil; grassed soil
had reached high methane uptake than bare soil and lime amended soil had consumed more methane. These results will be also useful to accelerate methane oxidation in landfill cover soil.
Unsaturated zone
Saturated zone 1. Water injection port
2. Thermometer 3. Gas bag 4. Sand layer 5. Solid waste packed layer 6. Control cock of water level 7. Pump 8. Aeration tank 9. Settling
24
2.7 Landfill Methane Models 2.7.1 Existing Solutions A landfill methane model is a tool that can be used to estimate methane generation rate, methane oxidation rate and total methane emission from landfill. This section reviews their features and examines their current predictive abilities. Exiting models are only partially validated. However, their forecast is not very far from real situations and they are widely used. Historically, models have been used in landfill process since 1970. Now, models are used for applications that include the sizing of landfill gas collection system, evaluating the benefits of gas recovery projects and controlling gaseous emission to environment. Augentein (1990) has used a model to estimate total methane emission from US landfills and evaluation of methane net contribution and significance of these emissions to the atmospheric methane build up and green house phenomenon. This shows methane emission from landfills in 'US' increases 1-2% of atmospheric methane build up and it contributes 2-10% impact of green house effects. Zison (1993) has made some efforts to develop landfill gas production curve using various models. In this study, model order or order of kinetics; decay rate constant and mass substrates are considered as major factors in formulating the production curve model. Then, he used first order kinetic model as basis and optimized other model parameters (shown in Fig. 2.14). His results suggest that that order of kinetics are not very important in landfill gas production. Augenstein (1992) and Pacey (1992) have reviewed and examined predictive ability of landfill methane models. This shows that gas generation model uncertainties arise from no of sources such as difficult to trace waste placement, history, location and composition for older landfills and difficult to measure biological parameters, nutrients, temperature and pH and difficult to deduce collection efficiency and difficult to estimate or measure moisture contents. Gas generation profile in landfill where waste placement occurs at slowly changing or constant rate of many years is effected to the greatest extent by the yield rate parameters. The profile assumed for unit batch output tends to become less important as the interval of placement increase. These research findings demonstrate the importance of the modeling of landfill methane is a topic of major interest, because of its role in the greenhouse effect, migration hazard potential, health and safety issues, and energy applications. The modeling process still needs improvements and refinements, I feel that a concerted effort to compile generation data on a range of landfills, including some from different countries to test data against plausible model variations should permit the development of approaches in forecasting methane generation, migration and hence, recovery.
Some models that involve with the objectives of this study are detailed in following sections.
25
Fig. 2.14 Model Kinetics Comparison Using First order Model as Basis for Optimizing the Constant of Zero order Model for 35 years Period (1957 – 1992)
Source: Zison (1993) 2.7.2 Models for Inventory of Methane Emissions from Landfills Methane gas plays an important role in the earth's thermal budget and photochemistry of the troposphere. Atmospheric methane concentration has been increasing in the past three decades at the rate of 1-2% per year. Because of the impact of methane on the environment, it is important to quantify the total methane emissions. Basically inventory methodology contents two main steps such as calculation of municipal solid waste (MSW) landfilled and methane generation rate. MSW landfilled find from total amount of MSW, which is calculated from population and waste disposal per capita, and portion of incineration. Then methane emission to atmosphere is determined from total methane generation and recovery. To determine methane generation rate, Sheldon-Arleta model and Scholl Canyon kinetic models can be used. Even though both of them are first order, Sheldon-Arleta model is two-stage model and Scholl Canyon kinetic model is single stage model (see Fig. 2.15 ). Two-stage model doesn't make any sense since estimation of total methane emission from landfill is time independent. So Scholl Canyon models developed by EPA is preferable to quantity total methane emission from landfill
26
Fig. 2.15 Examples of Model Predictions. Source: Zison (1993)
Scholl Canyon kinetic models is based on characteristics of substrate-limited bacterial growth and It does not take into consideration various parameters which is specific for each landfill. It assumes that the gas production rate is at its peak after a time period of negligible duration during which anaerobic conditions are established and the biomass build up. Thereafter, the gas production rate is assumed to decrease according to that, methane production rate is proportional to remaining methane potential to be produced. The IPCC established guidelines to determine methane emission from MSW landfill in 1995. This simple method, based on the mass balance approach, incorporates no time factor and can be applied to the total waste emanating from the country. The method assumes that an instantaneous release of methane takes place from the refuse during the year it is landfilled. The calculation is based on
- the amount of waste generation and landfill, - the fraction of degradable organic carbon (DOC), - the fraction of DOC that actually degraded into biogas and - the fraction of biogas that was released as methane.
In contrast to landfill, estimations of greenhouse gas from MSW open dumping were less. The IPCC recommended the simplified methodology relating to landfill calculations under the assumption that MSW open dumping conversion of actual DOC to methane was 50% of the MSW landfill conversion.
27
The calculation of greenhouse gas emission from landfill is based on IPCC guide lines as follows, Methane emission (Gg/yr.) = Total MSW (Gg/yr.)
x Fraction MSW landfill. x Fraction DOC in MSW x Fraction actual DOC dissimulated x 0.55 g C as CH4/ g C biogas x Conversion ratio (16/12) - recovered CH4 (Gg/yr).
The calculation of greenhouse gas emission from open dumping is based on the following equation, Methane emission (Gg/yr) = Total MSW (Gg/yr.)
x Fraction MSW open dumped x Fraction DOC in MSW x Fraction actual DOC dissimulated x 0.275 g C as CH4/ g C biogas x Conversion ratio (16/12) - recovered CH4 (Gg/yr
Where Total MSD = population x waste generation rate However, the assumptions made here are questionable. Namely,
- No time factor: It is quite acceptable during steady state. - Instantaneous release of methane: It takes atleast 1 years. - No consideration of natural oxidation of methane: According to researchers,
considerable amount of (~10%) methane oxidize within landfills. 2.7.3 Numerical Modeling of Gas Generation El Fade et al.(1995) has developed a numerical model for gas generation in sanitary landfills. This model highlights the significance of the biochemical and biological parameters that control gas in sanitary landfills. Several simulations have been performed to assess the model sensitivity to its biochemical and biological parameters. These parameters include the pH, the hydrolysis rates of the different waste constituents and biokinetic constants of the two microbial populations of the simplified ecosystem such as acidogens and methanogens. To asses the effects of assumed initial conditions, simulation with varied initial carbon concentrations including aqueous, acetic acid, acidogens and methanogens initial ware conducted. The temperature was held constant during all sensitivity analysis of simulation.
28
Fig. 2.16 Effect of Hydrolysis Rate on Total Methane Generation
Source: El Fade et al.(1995)
Fig. 2.17 Effect of Acidogenic decay Rate on Total Methane Generation Source: El Fade et al.(1995)
29
This system appears reasonably well buffered. The simulation without chemical feed back on the equilibrium shows higher pH values than with chemical feed back, however both well within the optimal pH range for methanogens. That is, the chemical component effects are minimal during applying through its pH feed back. Consequently no further sensitivity analysis was per formed with respect to chemical parameters. The simulation with different hydrolysis rate shows major effect (see Fig. 2.16) on methane generation because hydrolysis of solid organic carbon to aqueous carbon is first step in the process. So a change in the hydrolysis rate generally results in early start of methane generation and higher peaks for all forms of organic carbon. Therefore, the hydrolysis rate is most important parameters that directly affects gas generation in sanitary landfills. Biokinetic parameters come next in order of performance. These parameters include specific growth rate, the half saturation concentration and decay rate of acidogens and methanogens. Model is not very sensitive to acidogen parameters (see Fig. 2.17) because in general, acidogen grow is very faster than methanogens and consequently, concentration of aqueous carbon, acidogens and acetic acid are two or three orders of magnitude higher than methanogens. The simulations indicated that a change in the initial concentration of aqueous carbon does not effect the final steady state values of aqueous and acidogens carbon and the acetic acid and methanogens have a more pronounced effect on gas generation than aqueous and acidogens.
30
Chapter 3
Methodology 3.1 Work Outline Conventional way of standard engineering practice for newly proposed studies consists of three basic steps; first laboratory scale study, then the pilot scale study, finally the full-scale operation. Nowadays, mathematical simulation is in common use before full-scale operation. It minimizes workload of laboratory and pilot scale studies, which are the most time consuming steps. Based on it, the objectives of this studies are to model methane generation, methane oxidation and methane emission in landfills, and to check the affect of VOC on methane oxidation in landfills. Here, the analysis of methane from landfills is aimed on global environmental protection, mainly mitigation of green house gases. It also assessed the potential of energy recovery option from landfill gas. The following sections present the detailed methodological approach adopted in this research. 3.2 General Modeling Techniques Models are necessary to both describe and predict landfill processes in micro and macro level. Current modeling practice provides limited predictive capacity that cannot be achieved easily by monitoring or measuring variables. Generally, the modeling process includes following steps. 1. Selection of model objectives: Initially, model objectives should be well defined to
proceed modeling work. The selection of models for investigating landfills is governed by goals of the study.
2. Problem formation: Based on objectives, a clear statement of the limitation of modeling
formation is necessary because modeling technique are derived from a diverse collection of interdisciplinary methods.
3. Model construction: Construction of model involves with five basic steps, namely;
- Identifying the physical, biological and chemical process, and corresponding physical laws and biological chemical kinetics governing the rates of mass flow between compartment and boxes
- Listing the assumption made, including simplicaton of physical laws - Constructing system of mathematical equations that describe the behavior of the
system. The system of equation is constructed by writing a statement of conservation of mass or energy
- Evaluating boundary conditions - Solving the mathematical equations.
31
4. Model calibration: The calibration data procedure is designed to use field measurement to guide choosing the empirical coefficients in models and to determine if the boundary and initial condition are consistent with interior measurement for the model chosen.
5. Model validation: Validation testing is designed to confirm that the calibrated model is
useful over the range of conditions defined by the calibrations and validation data sets. It is important and time-consuming step in modeling work. Usually it involves with independent data collected from real field or laboratory scale study.
6. Prediction: After making sure the validity, it can be used for prediction. It is the final
stage of modeling work. 3.3 Application of Modeling Techniques 3.3.1 Methane Inventory Model Methane inventory model is a tool used to estimate methane emission from the landfill to atmosphere. The guidelines established by IPCC to estimate methane emission are already discussed in section 2.6.2. It has several uncertainties, namely:
− No time factor − Instantaneous release of methane takes place from the refuse during the year it is
landfilled − No consideration of natural oxidation of methane.
To overcome these uncertainties, following assumptions are made with IPCC guidelines,
− Steady state methane production through out the year − Any changes in waste characteristics will take 1 years to effect methane production − More than 10% of methane oxidize within landfills.
Modified Governing Equation: The calculation of greenhouse gas emission from landfill is based on the following modified equation, Methane emission (Gg/yr) = Total MSW (Gg/yr) x Fraction landfilled in MSW. x Fraction of DOC in landfilled x Fraction actual DOC dissimulated x λg C as CH4/ gC biogas x Conversion ratio (16/12) - oxidized CH4 (10%) - recovered CH4 (Gg/yr). ..Equation 3.1
32
Where, λ = 0.5 for closed landfill and 0.275 for open dump. Total MSW = population x waste generation rate. Since this method is based on the mass balance approach of organic carbon, it is applicable everywhere. Place to place, only following coefficients will be varied.
− Total MSW (ie, population X waste generation rate) − Landfilled fraction. − Fraction of DOC (degradable organic compound) − Methane recovered
Specimen calculation to use this for a particular region (all cities in Thailand) is presented in Appendix-1.2. Data: For case studies, the data (Appendix A) taken from literatures (Rayes and Edwards, 1991 and TEI, 1996) were used. Output: Using this model, following objectives have been achieved, • Prediction of trend of methane emission from landfills to atmosphere according to
prevailing solid waste management practice. • Simulation in rate of methane emission from landfills with different options such as
fraction of DOC, fraction of methane oxidation and fraction methane recovery. • Recommendations to mitigate methane emission from landfills to atmosphere. 3.3.2 Methane Generation Model Here, landfills are considered as solid substrate batch reactor. Methane generation is obtained from the solution of system ordinary differential equations developed from fundamental principle governing the microbial process and biochemical reaction associated with gas generation in anaerobic system. The development of these equations describe the dynamics of the landfill ecosystem including
− the hydrolysis of biogasifiable waste components, − microbial utilization of aqueous carbon by acidogenic micro organism, − growth and decay of acidogenic and methanogenic biomass, − the utilization of acetate produced by acidogens and − ending with consequent generation of methane and carbon dioxide by methanogenic
organism. A simplified pathway leading to methane generation is shown in Fig. 3.1.
33
∑−=i
shdtdC
iis Ck )()(
Fig. 3.1 Organic Pathways in Landfill Microbial Ecosystem
Source: El-Fadel (1994a)
Governing Equations: The application of the mass balance couple with Monod model to landfill ecosystem results in a set of ordinary differential equations as follows; Solid organic carbon utilization rate
..Equation 3.2
C(s)i
C(aq)
C(CH4 )
C(CO2)
C(XM)
C(Ac)
C(XA)
Solid organic carbon
Aqueous organic carbon
Acidogenic biomass carbon
Aceate carbon
Methanogenic biomass carbon
Methane carbon
Carbon dioxide carbon
34
)()( ))(()(
)()(
AaqA
aq
A
A
ii
aq
XCKsC
Yi
shdtdC CCk +−= ∑ µ
)()()(
)()(AAaqA
aqAAXXdCKs
Cdt
dCCK−= +
µ
)()()(
)()(MMAcM
AcMMXXdCKs
Cdt
dCCK−= +
µ
)()( )])([()])()(1[()(
)(
)(
)()(
MAc
Ac
M
M
AAaqA
aq
A
AAc
XCKsMC
YXdCKsC
YAAcdtdC CCKYY ++ −+−= µµ
)()])()(1[()(
)(4
)4(MMAcM
Ac
M
MCHXdCKs
CYMCHdt
dCCKYY +−= +
µ
)(
)(
)])()(1)[(1(
)])()(1)[(1(
)(
)(
4
)(
)()2(
MMAcM
Ac
M
M
AAaqA
aq
A
ACO
XdCKsC
YMCH
XdCKsC
YAAcdtdC
CKYY
CKYY
+−−+
+−−=
+
+
µ
µ
Aqueous organic production rate,
..Equation 3.3 Acidogenic biomass production rate,
..Equation 3.4 Methonogenic biomass production rate,
..Equation 3.5 Acetate production rate,
..Equation 3.6 Methane generation rate,
..Equation 3.7 Carbon dioxide production rate,
..Equation 3.8 Data: Validations data (Appendix B) has been taken from literature (El-Fadel et al., 1994b). Output: • Sensitivity of methane generation rate was analyzed to assess the effects of variables such
as initial conditions, specific growth rate and reaction rate constants. • Options to control methane generation based on the needs.
35
2,2
2
4,4
4
][][
][][ **
OmCHm KOO
KCHCHPMOMO ++=
3.3.3 Methane Oxidation Model Anaerobic methane oxidation is not considered in this study because of its low contribution (less than 5%) in total methane oxidation. As aerobic methane oxidation (MO) required both oxygen and methane, both substrates could be limiting the oxidation process. Monod equation could be applicable. The major influencing parameters in potential of methane oxidation are discussed in section 2.5.2. As data in affect of methane oxidation with temperature and moisture content are available in depth, only both of them were considered in this study. There are two strategies to present predictive relation of MO; firstly, finding empirical relationship between PMO and soil environment variables; then, correlation of MO and PMO using Monod kinetics model. It has leaded a semi-empirical formula for methane oxidation. Governing equation: The following double Monod expression is used as basic governing equation in this study. ..Equation 3.9 Where,
MO = Methane oxidation rate PMO = potential of MO
Km, CH4 = half velocity constant of CH4 Km, O2 = half velocity constant of O2 [CH4] = concentration of O2 [O2] = concentration of CH4 Potential of methane oxidation is given by,
PMO = CPMO*F(t)*F(m) ..Equation 3.10
Where, CPMO = Constant for PMO
F(t) = Functions of temperature. F(m) = Functions of moisture content. Data: To make empirical equations and to validate the model, the experimental data (Appendix C) reported by Pokherl (1998) were used. Output: By using model as a tool, following goals have been achieved; • Empirical expressions for potential of methane oxidation (PMO) related with temperature
and moisture content of the landfill cover soil. • Inhibitor effects on the methane oxidation rate. • Recommendations to improve methane oxidation in landfill cover.
36
3.4 Experimental Study 3.4.1 Experimental Outline In modeling work, validation is important step to make sure the validity of it. Usually it involves with independent data sets that could be generated experimentally. In this study, the models have been validated using data obtained from literatures and the experimental data have been created only to check the affect of VOC (Benzene) on methane oxidation scenario. Experimental set up to simulate methane oxidation with different concentration of Benzes is shown in Fig. 3.2. Only Lysimeters, LYS2 and LYS3 were used for this research work. Benzene feeding unit with feed pump and timer was used to change benzene-feeding concentration periodically. Gas collected from sampling probe and from the top outlet was analyzed directly for methane, carbon dioxide, oxygen and nitrogen concentration using GC (gas chromatography) connected with integrator system, in which helium was used as carrier gas. Since benzene concentration is very low, first it was accumulated in charcoal tube and then measured using GC. Details of measurement of benzene are discussed in following sections.
Fig. 3.2 Experimental Setup for Column System
37
3.4.2 Benzene Analysis As discussed in previous section, benzene could not be measured directly due to its low concentration. Therefore, the sampling method recommended by the Manual of Analytical Method of U.S National Institute for Occupational Safety and Health (NIOSH) was used. Here, the charcoal tube that contents activated carbon was used to collect the gas sample. The sampling air rate of air check sampler (SKC Model 224-PCXR8) was adjusted at approximately 0.33 L/min. The absorbed benzene in the charcoal tube were desorbed by using 1 mL acetonitrle (CH3CN) and shaken for 30 minutes in ultrasonic bath. A 0.5 µL sample of solvent (benzene dissolved in acetonitrle) was injected into the Shimadzu 14A gas chromatography that uses a flame ionization detector (FID). The column was operated isothermally at 110 oC with nitrogen flow rate of 35 mL/min. Standard solution for benzene 4, 16, 32 ppm were used to calibrate the standard curve of gas chromatography. Peak for benzene was was used to identify benzene concentration in solvent and corresponding area was interpreted in ppm using standard curve. Standard curve is attached in Appendix D-1.1. 3.4.3 Calculations 1. Percentage Methane Oxidation Air flow rate at the top = V1 mL/min Gas flow at the bottom = V2 mL/min Total flow = (V1 + V2) mL/min
= V mL/mim (say)
Percentage of methane inflow = 0.6* V2/V*100% = Y % (say) Percentage of methane outflow = X % (from Gas Chromatography) Total methane consumed in the system =(1-X/Y)*100% = P % (say) (assuming no methane accumulation inside the system) Methane oxidation rate = 0.6*V2*P mL/min. The sample calculation is presented in Appendix D-2.
V1
V2
V
38
Benzene in outflow
2. Concentration of Benzene in Outflow Benzene = Ca µgm-3 Benzene = C ppm Volume = Va m3 Volume = 1 mL Using benzene mass balance,
Ca = C/ Va ..Equation 3.11 Where,
Ca = Actual concentration of benzene (µg/m3) C = Concentration of benzene in solvent (ppm) Va = Actual volume of sampled gas (m3)
Furthermore,
Va = Qa*t/1000 ..Equation 3.12 Where,
Va = Actual volume of sampled gas (m3) Qa = Actual flow rate (L/ min) t = Duration of sampling (min)
Sample calculation is done in Appendix D-1.2.
Absorbed Benzene in charcoal tube
Desorbed Benzene in solvent (CH3CN)
39
CHAPTER 4 40
4.1 Outline of Results and Discussions.................................................. 40 4.2 Methane Inventory Model................................................................ 40
4.2.1 Frame Work ..................................................................................... 40 4.2.2 Details of Package Components ...................................................... 41 4.2.3 Interface ........................................................................................... 46 4.2.4 Technical Significance..................................................................... 46
4.4 Methane Oxidation Model ............................................................... 52 4.4.1 Methodology Development - Effect of Inhibitors on Methane Oxidation
Enzymatic Activity ......................................................................................... 52 4.4.2 Empirical Relationship..................................................................... 55 4.4.3 Simulation and Validation ............................................................... 58 4.4.4 Technical Significance..................................................................... 60 4.4.5 Experimental Study: Effect of VOC on Methane Oxidation Rate... 61
Fig. 4.1 Typical Input Sheet for Estimation for a Place........................................................................42 Fig. 4.2 Graphical Output Sheet of Case Study ....................................................................................43 Fig. 4.3 The Typical Simulation Sheet .................................................................................................45 Fig. 4.4 Effects of Initial Solid Carbon on Rate of Methane Generation..............................................50 Fig. 4.5 Effects of Hydrolysis on Rate of Methane Generation............................................................50 Fig. 4.6 Comparison of Empirical Curve with Experimental Data for Methane Oxidation Rate .........57 Fig. 4.7 Comparison of Empirical Curve with Experimental Data for Methane Oxidation Rate .........57 Fig. 4.8 The Model Block Representing Methane Utilization ..............................................................58 Fig. 4.9 The Model Block Representing Methanotroph Population .....................................................58 Fig. 4.10 Validation of Methane Oxidation for Uninhibited Fresh Soils................................................59 Fig. 4.11 Validation of Methane Oxidation for Inhibited Lysimeter Soils. ............................................60 Fig. 4.12 Observed Methane Oxidation with Different Rate of Methane Supply...................................62 Fig. 4.13 Variations of Methane Oxidation with Different Rate of Methane Supply............................62 Fig. 4.14 Observed Methane Oxidation with Different Amount of Benzene injection ..........................63
Table. 4.1 Nominal Values for Initial Concentrations..............................................................................48 Table. 4.2 Nominal Values for Variables .................................................................................................49 Table. 4.3 Classification of Inhibitors Based on Properties .....................................................................54 Table. 4.4 Various Trials of Empirical Function for Methane Oxidation Rate in Terms of Temperature55 Table. 4.5 Various Trials of Empirical Function for Methane Oxidation Rate in Terms of Moisture
Content. 56 Table. 4.6 Optimum values of Parameters for Fresh Soil.........................................................................59
40
Chapter 4
Results and Discussion 4.1 Outline of Results and Discussions Based on the defined objectives and scope limitations, the results presented herein cover mainly mathematical modeling and experimental studies. Mathematical models investigated the scenario of landfill methane in two different stages namely macro and micro level. The landfill methane inventory model analyzed the estimation of total methane for a particular region or country at a macro level. Meanwhile, methane generation and methane oxidation models minutely examined the sensitivity of involving microbial species. In the case of the methane oxidation model, the methodology was further developed to visualize the effects of inhibitor on the rate of methane oxidation. Meanwhile, experimental study dealt in finding the effect of VOC on methane oxidation. For the present study, benzene was picked as the volatile organic compound. The experimental study was conducted in laboratory scale. 4.2 Methane Inventory Model 4.2.1 Framework As discussed in previous section 2.2, the trend of methane emission from landfills presents a fraction of the Greenhouse Gas Scenario System (G2S2). Therefore, an accounting structure for estimating methane emission is needed in environmental point of view. The methane inventory model was structured to estimate methane emissions from landfills at the country or regional level. Then, it was used as a policy assessment tool for analyzing possible future emission patterns. Emissions projections are based on demographics, policies, and economic and technical assumptions. It provides emissions estimates for base years and 15 years of future. The package also includes general information, slide-show, case study and reference bank (Useful References and Websites). This model incorporates two different parts: static part (example) and the dynamic part (simulation). Additionally, they are equipped with a help file. General information contains rationale and concept of work. Similarly, the slide-show illustrates issues and practical remediations, and they have been arranged in topic-wise. Lastly, an equally useful element of this software package is the reference bank. This part consists of a bibliographic list of a variety of reference materials on solid waste disposal and landfill management, and a list of useful Internet web-sites related to landfill gas control. Methane release from landfills is calculated by estimating the amount of municipal solid waste (MSW) landfilled, the degradable organic carbon content (DOC) of the landfilled
41
waste, and the degradation rate of the DOC to gaseous form. Methane releases from the dissimulated carbon in landfills is estimated based on the fraction of landfill gas that is released as CH4. Finally, methane releases from landfills may be adjusted by estimating the proportion of methane that is recovered and oxidized within the landfill. The prime components of the package are prepared in Microsoft-Office 97 program. General information and reference bank (Useful reference and websites) are prepared in MS-Word 97 program. Slide-show is prepared in PowerPoint and the remaining elements; namely, main menu (Table of contents), examples and explanations and simulation in MS- Excel 97 program utilizing visual basic features. The auxiliary software named ViSsim and Netcapes (or Internet Explorer) will be helpful to glance the additional features such as micro models and websites linked useful documents and information. Details of package components are discussed in the following sections. 4.2.2 Details of Package Components 4.2.2.1 General Information This is a stimulus section, which discusses about rationale, motive and hypothesis of this work. This has been prepared for the following topics:
• Rationale • Solid Waste Management and Landfills • Methane Inventory Model • Governing Equations
This section is also linked with a slide-show case study titled "Solid Waste Disposal Leading to Global Warming Problem". Furthermore, some words in this session are linked with related features to make user understand easily 4.2.2.2 Slide -Show This has been prepared to convey the facts related adverse effect of methane emission and its control measures. It could be viewed in MS-Power point environment. It contents total around 40 slides in following topics;
• Solid Waste Management and Landfills • Greenhouse Gases and Landfill Gas • Methane Generation in Landfills • Methane Oxidation in Landfill covers • Methane Emission from Landfills
Slides subtitled 'methane generation in landfills' and 'methane oxidation landfill covers' are linked with corresponding models that were developed to investigate landfill methane scenario at micro level. To browse those models, technical software named 'ViSsim' is needed.
42
4.2.2.3 Explanation and Examples This section explains the basic hypothesis of estimation and guides step by step to estimate methane emission for a particular place or region. Users, especially beginners in this area, are expected to spend few minutes before going onto the simulation session. It furnishes two examples of estimation for Thailand (cities) and Canada (Ontario). Unlike simulation and case study sheets, example sheets contain both input and output in same sheet and these sheets are static for particular year as they were assigned with fixed input parameters. Moreover, the sheet for new estimation is dynamic and incorporated with help file and comment box (see Fig 4.1 )
Fig. 4.1 Typical Input Sheet for Estimation for a Place 4.2.2.4 Case Study This study predicted the trend of methane emission from landfills at Ontario in Canada based on prevailing solid waste management practice. Then, the methane emission rate was
Replace '?' with appropriate value or word and enter
43
simulated with adjustments in the fraction of DOC, methane oxidation and methane recovery. Finally, it leads to make some recommendations to reduce methane emission in Ontario. The graphical output of methane estimation is shown in Fig. 4.2 for following conditions, - Prevailing condition - Same trend of current practice (1.11% population growth,
DOC=13%, MO=10%, MR=15%). - Reduction of DOC into 10% - Increment in fraction of Methane Oxidation into 15% - Increment of fraction of Methane Recovery into 20%
Fig. 4.2 Graphical Output Sheet of Case Study
The details of calculation could be seen by clicking corresponding calculation buttons (see Fig. 4.2). Estimations of total methane emission for Canada (Ontario) has same order of magnitude of previous estimation using the computer model developed by U.S.EPA. Further, it shows that the reduction of DOC fraction in MSW is the most effective way to reduce the total methane emission to atmosphere from landfills. The detailed method of effective SWM practice to reduce methane emission is explained in Section 2.6.2. Methane recovery is another important option when it is used as a source of energy. Advantages and restrictions of methane recovery are discussed in Section 2.6.3. Natural methane oxidation is also easy,
44
economical and environmental friendly way to reduce the methane emission from landfills. Incorporation of soil methonography in landfill cover designs to promote methane oxidation is discussed in Section 2.6.4. 4.2.2.5 Simulations The previous section gives a confidence from the applicability of this model in Canada (Ontario). As this method is based on the mass balance approach of organic carbon, it is applicable everywhere. Place to place, only following coefficients will be varied,
- Total MSW (ie, population X waste generation rate) - Landfilled fraction. - Fraction of DOC (degradable organic compound) - Methane recovered.
This session provides a route mathematically to simulate methane emission rate with fraction of DOC, fraction of methane oxidation and fraction methane recovery. It will be helpful to find the ways to control methane emission based on needs. Moreover, this simulation session is developed in MS-Excel program incorporating VBA macros as additional tool in feature creation. It includes the following components:
• Control sheets • Help files • Input sheets • Output sheet • Database
In this session, users can access mainly three sections; namely, data input, graphical out put and database. Control sheets are hide and protected to avoid anonymous changes. Additionally, first two-section are equipped with a help file. Input: The user is expected to provide data, which are required in the process of simulation, into the confined blue-shaded cell for that purpose. After finishing all necessary inputs into corresponding cells, the cell-pointer has to be taken out of the cell where input was given last. Now this program automatically makes necessary calculations to estimate net methane emission rate and draws the graph in the sheet named graphical output. Furthermore, the database gives guideline-values for data input. Solid waste data that is used to estimate methane emission are largely unreliable. Due to inconstancies in data recording, definition, collection method and seasonal variation, the data can be considered approximate albeit more accurate than most. For planning purpose, however the data presented in this database sheet is sufficient.
45
Fig. 4.3 The Typical Simulation Sheet Output: After entering the data in the "Data Input" sheet, the output can be viewed in "Graphical Output" Sheet. Simulation by assigning different individual values for fraction of DOC, methane oxidation and methane recovery is expected to provide the user with varieties of scenario related to control of landfill methane emission. 4.2.2.6 Useful References and Websites This database compiles totally more than two hundred reference-materials and websites related to the solid waste disposal and landfills. It is expected that the users would benefit from the information thus compiled in a single sourcebook. The information is categorized into two different groups such as websites and bibliographic-list. Again, websites are arranged into three sub-titles namely landfills, solid waste disposal and organizations. To browse websites, additionally Netscape or Internet Explorer is needed.
46
4.2.3 Interface This package is prepared by using Microsoft-Office 97 application software, with the following programs: MS-Word 97, MS-PowerPoint 97, and MS-Excel 97. Excel 97 with Visual Basic Application (VBA) is used in preparation of simulation games. The hardware requirement for the use of this software is IBM-compatible computer capable of running Windows 95 or latest windows 95. The computer must meet the following minimum hardware requirements to use this software package. 1) IBM-compatible computer capable of running Windows 95, a Pentium 100 or better
processor is highly recommended for performance reasons. 2) Hard disk with 100 MB of free space. 3) A 3.5-inch floppy disk driver. 4) 8 MB or more of RAM-memory 5) Microsoft mouse or other compatible pointing device 6) For additional features, Microsoft Internet Explorer Browser or Netscape. 4.2.4 Technical Significance The intention of this package is originated from a need of structure to estimate methane emission quickly for a region or country. It considers various practical and real-life uses in policy assessment to control landfill methane emission. This package enables the end-users with limited knowledge and technical background in environmental issues to clearly understand various aspects of landfill methane emission scenario. Some important features of this software package are as follows: • User-friendly interface, help files and comment boxes are provided to increase users’
affinity for the operation of the software. • Estimations of total methane emission for Canada (Ontario) shows same order of
magnitude of previous estimation using computer model developed by U.S.EPA. • Through simulation of wide range of input and information, environmental managers,
policy makers and planners will be able to easily visualize varieties of scenario related to control of methane emission from landfills.
• Simulation section could be used to predict the trend of methane emission rate with
different combinations, - With same trend of current practice in solid waste management. - With various adjustment in solid waste management practice.
47
• Simulation results show that the reduction in fraction of DOC is the most effective way to reduce the total methane emission to atmosphere from landfills. This phenomenon could be used to minimize quantity of methane production in landfills. - To do it, segregate waste before landfilling as high-organic substance and low-organic
portion. - Use high-organic substance for composting and send remaining portion for landfilling. - This practice will also give useful product. (ie composted materials could be used as
fertilizer) • Based on simulation results, another easy and economical way to reduce the methane
emission from landfills is enhancing natural methane oxidation. By providing optimum conditions for methonotrophy bacteria, we can simply achieve 15% oxidation in landfill cover soils. Careful design will promote upto 30% (Hilger, 1998).
• Although methane recovery reduces the methane emission, it is not applicable
everywhere, specially in small-scale landfills because of low gas production rate and not meeting design regulations. However, this method will recover the investment when collected gas is used as source of energy.
• This package contains many educational features, for example, detailed information of
solid waste disposal leading to global warming effect, subject-wise slide-show, explanation with examples for beginners in this area, case study, simulation, and bibliographic-listing and related web-site lists.
• This package can also be used as a good guidance or professional training tool for people
who interest in landfill gas management.
48
4.3 Methane Generation Model 4.3.1 Basic Considerations The numerical model (the set of Equations 3.2-3.10) used by El-Fadel et al. (1994) represents performance of a unit bioreactor under anaerobic conditions. An actual landfill or landfill cell consists of a number of unit bioreactors. If unit bioreactor performance is known, then landfill performance can be determined by summing biogas generation of all unit bioreactors involving in the landfill. A complete model for a landfill is developed by such summation procedures. It follows then that simulation of landfill biogas generation can be achieved by defining unit bioreactors in landfill, determining their performance and integrating them over whole landfill. However, the performance of a unit bioreactor is good enough for this study because the prime aim of this study is knowing sensitivity of methane generation in view of its control. Based on the general modeling techniques discussed in section 3.2, the first the unit bioreactor or segment in landfill must be defined. Randomly, a unit volume of waste in landfill under anaerobic condition is considered for this study. The set of Equations 3.2 – 3.10 were applied to construct a model using mathematical tools in technical software named ‘ViSsim’. To solve these equations, Rungekutta 2nd order method with step size 0.01 was used. By assigning random values for model parameters, the numerical behaviors of the solution of these equations and overall mass balance (See Appendix B) were checked. Then, the values published in literature (El-Fadel et al., 1994) were used as nominal values for model parameters and initial concentrations for sensitive analysis. Those nominal values used for this study are tabulated in Table 4.1 and 4.2. Table. 4.1 Nominal Values for Initial Concentrations
Variables Initial value
(kg.m-3) Rationale
C(AC) 0.01 Range observed in leachate from actual landfills C(aq) 0.01 Range observed in leachate from actual landfills C(S) 30 Typical range reported from MSW C(XA) 0.00015 Order of magnitude based on laboratory microbial studies C(Xm) 0.00000015 Order of magnitude based on laboratory microbial studies Source: El-Fadel et al., 1994
49
Table. 4.2 Nominal Values for Variables
Variables Nominal value Rationale
µΑ day-1 10 Nominal value reported in anaerobic digestion
µM day-1 0.25 Range reported in anaerobic digestion
KdA day-1 0.05 Range reported in anaerobic digestion
KdM day-1 0.03 Range reported in anaerobic digestion
Kh day-1 0.00002 Range reported in methane fermentation studies where first order kinetics were applied to simulate hydrolysis of cellulosic materials.
KSA kg.m-3 0.05 Range reported in anaerobic digestion
KSM kg.m-3 0.5 Range reported in anaerobic digestion
YA kgVSS.kg-1COD 0.15 Nominal value reported in anaerobic digestion
YCH4 kgVSS.kg-1COD 0.15
YM kgVSS.kg-1COD 0.06 Nominal value reported in anaerobic digestion
Source: El-Fadel et al., 1994 Halvadakis (1983) also used the same set of equations for modeling of methanogenenis in solid-waste landfill bioreactors. He validated the model with field data taken from Mountain View Landfill (California, USA) and reported same range values for those model parameters (See Appendix B-1). Therefore, the need of validation with field data is not important in this study. However, the result of present model was compared with same field data (See Fig. B-10). It does not fit well with this field data because variables are not incorporated with temperature. 4.3.2 Sensitivity Analysis As discussed in section 3.1, laboratory simulation cannot be easily achieved by monitoring or measuring variables to understand landfill process in micro level. Therefore, there is a need of mathematical simulation before going into full-scale operation. The constructed model was used for this purpose. The variables and initial concentrations changed from the nominal value stated in Table 4.1 and 4.2 to check sensitivity of methane generation rate. It showed that hydrolysis rate constant (Kh) and initial organic carbon concentration in waste (C(S)) are important parameters affecting the methane generation directly. Effects of initial solid carbon and hydrolysis rate constant on the are show in Fig.4.4 and 4.5.
50
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rat
e (K
g M
etha
ne c
arbo
n/m
3 was
te) Kh/2
Kh
2Kh
Kh/2
Kh
2Kh
Fig. 4.4 Effects of Initial Solid Carbon on Rate of Methane Generation
Fig. 4.5 Effects of Hydrolysis on Rate of Methane Generation
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rat
e (K
g M
etha
ne c
arbo
n/m
3 was
te) Kh/2
Kh
2Kh
C(s)/2
C(s)2C(s)
51
Except initial solid carbon C(s) and hydrolysis rate (Kh), specific methanogeneius growth rate (µm), methanogeneous half velocity constant (Ks,m) also showed significant difference only in lag period. Other variables did not show much effect on methane generation scenario (See Fig. B-1 and Fig. B-9). 4.3.3 Technical Significance • Sensitivity of methane generation rate analyzed to assess the effects of variables such as
initial conditions, specific growth rate and reaction rate constants shows that hydrolysis rate constant (Kh) and initial organic carbon concentration in waste (C(S)) are important parameters affecting the methane generation directly. This result is useful for landfill managers or designers to practice effective solid waste management in view of landfill gas control.
• Municipal solid waste in many countries contents organic and paper waste more than 60%
of total (Hoornweg et al, 1999). So, the exile of both organic and paper waste results reduction of 60% organic carbon (from 27kg.m-3 to 11kg .m-3) in waste, and according to present model, it leads more than 50% reduction in methane generation rate (from 0.005 to 0.002 kg/m3 waste.day). These phenomena could be used to minimize quantity of methane production in landfill as follows;
− Extraction of organic and paper waste from MSW by source separation or waste processing and composting (Composted materials could be used as fertilizer)
− Landfilling remaining portion. • Based on sensitivity analysis, the reduction of hydrolysis rate constant (Kh) is another
important event in landfill gas mitigation program. Generally, landfill waste reports the hydrolysis rate constant (Kh) between 0.0002 day-1 and 0.001 day-1 (Halvadakis, 1983) (see in Appendix B-2) It ranges the rate of methane generation between 0.005 and 0.02 kg/m3 waste.day . Therefore, this is the most effective way to reduce methane generation in landfill. As hydrolysis rate constant (Kh) depends on waste characteristic and soil environment, this could be implement as follows;
− By avoiding organic substances which have high-hydrolysis rate constant − Proper cover design to avoid water infiltration into the landfills. It will be also
useful to reduce the amount of leachate from landfills. − Draw back of landfilling low-hydrolysis substances is a long after-care period.
• On the other hand, when there is recovery facilities the practice should be vice versa to
collect methane effectively.
52
][**][4,4
4][
][4 XPMOdtCHd
CHmKCHCH+=
12,2
2][
][ ≈+ OmKOO
4.4 Methane Oxidation Model 4.4.1 Methodology Development - Effect of Inhibitors on Methane Oxidation
Enzymatic Activity The aerobic oxidation of methane into CO2 and H2O is described in Section 2.4.1. The rate of utilization of methane is described in Equation 3.9 by using the Monod model (MONOD, 1949). This may generally be appropriate for low substrate concentrations. When substrate concentration is high, some of the components in this equation become nearly constant. In batch experiments, oxygen is not limiting factor. Therefore, Then, Equation 3.9 becomes as follows, . .Equation 4.1 Where,
PMO = potential of methane oxidation rate Km, CH4 = half velocity constant of CH4 Km, O2 = half velocity constant of O2 [CH4] = concentration of CH4 [O2] = concentration of O2 Potential of methane oxidation is given by,
PMO = CPMO*F(t)*F(m) ..Equation 4.2
Where, CPMO = Constant for PMO .
F(t) = Functions of temperature. F(m) = Functions of moisture content. The above equation takes into consideration only the main variable of temperature and moisture content. It has not incorporated with toxic components. Influence of toxic component depends on type of its reaction with enzyme. Various mathematical models have been proposed to describe the effects of inhibition on enzymatic activity. In this study, inhibition occurs due to the presence of another inhibitory substance. This type of inhibition may be divided into two limiting cases:
53
][**][)1(][
][ XKdtSd
iKi
SSKS
S
++=
][**][)1)(]([
][ XKdtSd
iSS K
iKSS
++=
(1) Totally Competitive Inhibition: Here, the binding of the substrate and inhibitor to the enzyme are mutually exclusive. The reaction steps at equilibrium may be represented as:
E + S ES E + I EI ES P + S The substrate utilization rate may be written in terms of the concentration microbial species (X) and free substrate (S) and inhibitor concentrations (i) may be written as: ..Equation 4.3 where,
K - maximum rate of substrate utilization (T-1); KSS - half velocity constant of substrate(ML-3); Ki - inhibition constant (ML-3 ) and [X] - concentration of the microbial species that carries out the rate limiting
transformation (ML-3). (2) Totally Non-Competitive Inhibition: Here, the inhibitor and substrate can simultaneously bind to the enzyme, forming a ternary complex. The simplest model would be one in which binding of either substrate or inhibitor does not influence the affinity of either species to complex with the enzyme. Thus, in addition to above equations, the following reactions are to be considered at equilibrium:
EI + S EIS
ES + I EIS ..Equation 4.4 Between the two limiting cases presented above, several intermediate cases can be considered. A summary of these cases is represented in Table 4.1.
54
][][*][ 4 XKdtCHdYdt
XddM−=
][**][)14
4
(][
][4 XPMOdtCHd
iKi
SMKCH
CH
++=
Table. 4.3 Classification of Inhibitors Based on Properties Type Description 1.a. Fully competitive b. Partially competitive
Inhibitor adsorbs at substrate binding site Inhibitor and substrate combine with different groups; inhibitor binding affects substrate binding
2.a. Noncompetitive b. Noncompetitive
Inhibitor binding does not affect ES affinity, but ternary EIS (enzyme-inhibitor-substrate) complex does not decompose into products Same as 2.a except that EIS decomposes into product at a finite rate different from that of ES
1. Mixed Inhibitor
Combination of above
Source: Gheewala (1995) Classification of inhibitor needs microbial analysis of enzymatic activity. There are significant data on inhibitor effects on methane oxidation by TCE (Pokhrel, 1998) and benzene (presented in next section), but no data on microbial analysis of involved enzymatic activity. This study assumes it as fully competitive inhibitors and develops governing equation of model to validate the data. Then, the proposed equation to assess effect of inhibitors on methane oxidation is as follows ..Equation 4.5 Furthermore, concentration of methanotroph bacteria is represented by following equation,
..Equation 4.6 Where, Y - Yield of methanotroph per methane KdM - Decay coefficient of methanotroph.
55
4.4.2 Empirical Relationship 1. Function of Methane Oxidation Rate in Temperature Effects of temperature on methane oxidation were examined by Pokherel (1998) using incubated fresh soil sample. The perceived rate of methane oxidation at a wide range of temperature (10-50oC) is attached in Appendix C (Table C-1). Various types of curve were tried to fit with those data. Equation and properties of trial curves, and corresponding R2 values are tabulated in Table 4.2. Table. 4.4 Various Trials of Empirical Function for Methane Oxidation Rate in Terms of Temperature
Note: y = rate of methane oxidation (*10-6 CH4/g soil), x = temperature (oC) Based on the R2 values in Table 4.2, logarithmic, power and exponential curve are unfit with experimental data. Even though 4th and 5th order polynomial are fit with data, their unusual shapes and number of peaks did not permit to take into consider because generally, the high activity of microorganism is observed only at a particular range of temperature. 2nd order polynomial obey this rule but R2 value is less than 3rd order polynomial. Therefore, most agreeable one is 3rd order polynomial (see Equation 4.7) at the range of temperature between 20oC - 40oC. Comparison of experimental data with this empirical curve is shown in Fig. 4.4.
2. Function of Methane Oxidation Rate in Moisture Content According to Pokherel's (1998) another investigation, incubated fresh soil sample at 30oC with a range of moisture content (5-25%) yielded distinct rate of methane oxidation (See Table C-1 in Appendix C). Many trials made to fit a curve with these experimental data are tabulated in Table 4.3. Table. 4.5 Various Trials of Empirical Function for Methane Oxidation Rate in Terms of Moisture Content.
Note: y = rate of methane oxidation (*10-6 CH4/g soil), x = moisture content (%) Like previous approach in methane oxidation in terms of temperature, the experimental data shows more deviation from logarithmic, power and exponential curves. Furthermore, 4th and 5th order polynomial were eliminated from consideration due to unusual shape and number of peaks. 2nd and 3rd order polynomial are almost same shape and same number of peaks. However, 3rd order polynomial has R2 value more than 2nd order. Finally, again 3rd order polynomial in Equation 4.8 was become as most agreeable.
y = 214.45x3 - 178.04x2 + 41.709x - 1.889 (at 30oC) ..Equation 4.8 The comparison of experimental data with this empirical curve is shown in Fig. 4.5.
Fig. 4.6 Comparison of Empirical Curve with Experimental Data for Methane Oxidation Rate
(for fresh soil sample with moisture content 16% and initial headspace CH4 = 3 - 4 % )
Fig. 4.7 Comparison of Empirical Curve with Experimental Data for Methane Oxidation Rate
(for fresh soil sample incubated at 30oC with initial headspace CH4 = 3.5 - 4.5 % )
58
4.4.3 Simulation and Optimization The oxidation model was structured from kinetic Equation 4.4 and empirical Equations 4.5 and 4.6 using mathematical tools in 'ViSsim' software. To solve these differential equations, Rungekutta 2nd order method at step size 0.1 was selected. The two main blocks representing methane utilization and methnotroph population are shown in Fig.4.6 and 4.7.
Fig. 4.8 The Model Block Representing Methane Utilization
Fig. 4.9 The Model Block Representing Methanotroph Population After checking the numerical behavior of these equations, it was simulated at temperature 37oC and moisture content 15%. It was matched with experimental data (Table C-2) at the conditions tabulated in Table 4.4. Optimization tool in ‘Vissim’ environment was used to get optimum value of these parameters.
Fig. 4.10 Validation of Methane Oxidation for Uninhibited Fresh Soils (for 10g fresh soil with 15% moisture content incubated at 37oC)
Then, the simulation was proceeded for lysimeter soil. Different values were checked for initial methonotroph concentration until the result of model fits with experimental data in Table C-5 (TCE 0 µg). It fitted with the data when the initial concentration was 101 µg/ g soil (see Fig. 4.9). Finally, the model was simulated for inhibited soil (TCE 1.4 µg). Again, the optimization tool was used to find the inhibitor constant (Ki). The result of the model was fit with data in Table C-5 (TCE 1.4 µg) when Ki was 3.390 g soil/ µg TCE. The validation of the out come of methane oxidation from model with experimental data for lysimeter soil is shown in Fig. 4.9.
60
Fig. 4.11 Validation of Methane Oxidation for Inhibited Lysimeter Soils. (for 10g fresh soil with 15% moisture content incubated at 37oC)
4.4.4 Technical Significance • The assumption made for TCE as fully competitive inhibitor is satisfied with experimental
data. Even though it should be confirmed by microbial analysis, it gives a clue for microbial analysis to find the effect of TCE on enzymatic activity of methane oxidation.
• Validations with the experimental data pronounced basic values of kinetic coefficients for
methanotroph bacteria and inhibitor coefficient for TCE for the particular soil environment. Hereafter, assigning different values for TCE could leads further simulations. Therefore, it is cut down the number of experimental simulations.
• More over, the threshold point of TCE on methane oxidation also could be found by
assigning different values for TCE concentration. It will be useful for planners to find the allowable limit of TCE in methane oxidation enhancing program.
• The present model fits with batch experiments. Gas transportation model in soil (Equation
2.4) coupled with this model will represent methane migration and oxidation scenario in lysimeter. Even it could be furnished to visualize this scenario in a real landfill.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0 20 40 60 80 100Time (hrs)
Hea
dpac
e M
etha
ne(m
g)
Model-0.0
Model-1.4
Exp.- 0.0
Exp.- 1.4
µg TCE
µg TCE
µg TCE
µg TCE
61
4.4.5 Experimental Study: Effect of VOC on Methane Oxidation Rate The experimental study was conducted to determine the methane oxidation rate in landfill cover soil at laboratory scale. This experimental study was mainly focused on the effect of volatile organic compounds on methane oxidation. For the present study, benzene was picked as volatile organic compound. The details of experimental set up and the methodology of measuring parameters such as CH4, N2, O2, VOC (benzene), moisture content, temperature are discussed in section 3.4. Schematic diagram of the experimental set-up is shown in Fig. 3.2. Whole experimental run was carried out at ambient temperature (28 to 340 C) in column system. Moisture content of the soil in lysimeter was maintained at 8-9%. Oxidation rate in percentage was determined with injection of benzene concentration. An interrelationship between the behavior of methanotroph bacteria and toxic chemicals was tried to identify from this experiment. First, the experiment was started with low rate of gas supply 2.5 mL/min (60% CH4 and 40% N2). It took 8 days to show the first methane at the column headspace. The time taken to reach top of the column by gas is reasonable. Because according to the volume of lysimeter (34 L) and the gas flow rate mentioned above, gas will take around 10days to reach the headspace of the column. Initially, the headspace methane concentration was high. Later, it stepped down because oxidation of by methanotroph. After it showed 100% oxidation, the gas supply was increased into 3.6 mL/min. There was immediate drastic drop observed in oxidation rate and however then, it developed into 97% oxidation (See Fig.12). It indicates methanotrophs could be more specific with available amount of methane substrate. When the gas supply was increased into 5mL/min the oxidation rate was become stable at 76% oxidation (2.265 mL/min). Totally, it took nearly 60 days for steady state. The approach of steady state was quite slow compare to the previous researcher’s work ( Kightly, 1995 and Pokherel, 1998). The reason could be due to soil type, moisture content and methane supply. Kightly (1995) reported 14 days to take steady state for nutrient amended soil with 40% moisture content. Pokhrel(1998) reported around 40 days with 11% moisture content and 5 mL/min gas supply rate. Therefore, less gas supply rate (2.5 mL/min) and less moisture content (9%) could be caused long time to reach steady state. Notable effects on methane oxidation rates were observed in the column system at different supply rates. (See Fig. 12). Oxidation rate increased when gas supply rate increased, although decrease in percentage oxidation was noticed. At small flow rates between 2.5 and 3.6 mL/min, the oxidation rate was increasing with increase in methane supply rate. The increase in oxidation rate was not proportional to supply rate, rather low increase in oxidation rate was observed. It behaves according to Monod kinetics (Monod, 1949). This is clearly shown in Fig. 13. High rate of methane supply caused only small increase in oxidation rates indicates the condition of constant methane oxidation capacity approaching zero order kinetics (see Equation 2.2). Average oxidation rates at various methane supply rates are shown in Fig. 4.13.
62
Fig. 4.12 Observed Methane Oxidation with Different Rate of Methane Supply
Fig. 4.13 Variations of Methane Oxidation with Different Rate of Methane Supply
0
1
2
3
0 1 2 3 4
Methane suply (mL/min)
Oxi
datio
n ra
te (m
L/m
in)
0
1
2
3
4
0 10 20 30 40 50 60Time (days)
(mL/
min
)
Methane supplyOxidation
63
As shown in Fig. 4.14, when the system was steady state oxidation benzene was started to inject through gas supply line. The detailed arrangement of benzene injection is shown in Fig. 3.2. Water saturated with benzene was used for this purpose. Initially, benzene was fed at low concentration, then it was increased step by step. Effects of different benzene concentration in methane oxidation are shown in Fig. 4.14. First 30 days after started benzene feeding, emission of benzene from lysimeter was maintained around 400-450 µg/m3. From Fig. 4.14, it can be seen that the oxidation was constant through out the period.
Fig. 4.14 Observed Methane Oxidation with Different Amount of Benzene injection Then, the benzene feeding was increased into 750-800 µg/m3. The initial stage of 2nd step showed slight increment on the methane oxidation and later it suddenly increased into extreme level. More over, GC used for the measurement of methane showed 5 additional peaks close to methane peak, which is indication of presence of other gases. (See Appendix D-4). As the GC was calibrated to recognize only CH4, N2, O2 and CO2, the additional peaks were unable to be identified. With this conflict oxidation performance, the experiment was moved to 3rd stage with benzene emission 1400-1450 µg/m3. In this stage, those additional peaks joined together with methane peak and troubled to show the actual area of methane peak. This unusual shape of peak is shown in Appendix D-5. This stage was continued only 20days. Consequence this problem, the benzene feeding was stopped. After the benzene feeding stopped, the additional peaks observed during previous stage were disappeared and methane can be measured. From Fig. 4.14, it can be seen that the oxidation was less to previous stages. It could be because of inhibitor effect by accumulated benzene
0
1
2
3
4
60 70 80 90 100 110 120 130 140Time (days)
(mL/
min
Methane supplyOxidation
Benzene 400-450 µg/m3
Stopped Benzene Feeding
Benzene 1400-1450µg/m3
Benzene 750-800 µg/m3
64
inside the lysimeter. However, the ongoing work will strengthen the reason. Experiment is still on progress. According oxidation results, the benzene did not affect methane oxidation upto 800 µg/m3. When it was increased to 1400 µg/m3 there was significant effects observed with some interference to measure actual methane. It means threshold limit benzene on methane oxidation between these two concentrations. There was a slight increase of oxidation observed in 2nd stage because of high water content with benzene feeding. As discussed in section 3.4, there were two similar columns used in this study; one with methane supply for real purpose and other one with N2 supply for control experiment. The control experiment column was checked periodically. It did not show any significant interference through out the experiment. Therefore, it confirms that all results observed in this system are due to benzene feeding. Effects of TCE on methane oxidation were examined by Pokherel (1998) using incubated lysimeter soil sample. He reported inhibitor effects by TCE at high concentration. This supports to the result of the present work.
65
Chapter 5
Conclusions and Recommendations Based on the work presented in this study, a number of conclusions have been drawn. In turn, certain suggestions are made for further pursuing the present research effort. The following paragraphs present the conclusions and recommendations. The methane inventory model is a structure to estimate methane emission quickly for a region or country. It considers various practical and real-life uses in policy assessment to control landfill methane emission. This package could be handled by the end-users with limited knowledge and technical background in environmental issues to clearly understand various aspects of landfill methane emission scenario. User-friendly interface, help files and comment boxes could provide to increase users’ alliance for the operation of the software. Through simulation of a wide range of input and information, environmental managers, policy makers and planners could easily visualize varieties of scenario related to control of methane emission from landfills. The simulation section could be used to predict the trend of methane emission rate for different combinations; namely, with the same trend of current practice in solid waste management and with various adjustments in solid waste management practice. Simulation results of the methane inventory concluded that the reduction in fraction of DOC is the most effective way to reduce the total methane emission to atmosphere from landfills. This phenomenon could be used to minimize the quantity of methane production in landfills by effective solid waste management. Based on simulation results, another easy and economical way to reduce the methane emission from landfills is enhancing natural methane oxidation. Although methane recovery reduces the methane emission, it is not applicable everywhere, specially in small-scale landfills because of low gas production rate and not meeting design regulations. However, this method will recover the investment when collected gas is used as a source of energy. Moreover, inventory model contains many educational features, for example, the detailed information of solid waste disposal leading to the global warming effect, the subject-wise slide-show, the explanation with examples for beginners in this area, the case study, simulation, and bibliographic-listing and related web-site lists. So, this package could also be used as a good guide or professional training tool for people who are interested in landfill gas management. Sensitivity of methane generation rate was analyzed by the methane generation model to assess the effects of variables such as initial conditions, specific growth rate and reaction rate constants. It concluded that hydrolysis rate constant (Kh) and initial organic carbon concentration in waste (C(S)) are the most important parameters affecting the methane generation directly. This result is useful for landfill managers and designers to implement effective solid waste management in view of landfill gas control.
66
The methane oxidation model has been constructed from semi-empirical equations derived from Monod kinetics, inhibitor effect on enzymatic activity and experimental data, and validated with different sets of experimental data. Therefore, it could be used to find optimums condition in terms of temperature and moisture content for maximum methane oxidation in landfills. Moreover, the model validations with experimental data accept that the inhibitor TCE is fully competitive inhibitor. Although the type of inhibitor should be confirmed by microbial analysis, model result could be a clue for microbial analysis to find the effect of TCE on enzymatic activity of methane oxidation. Also, validations with the experimental data in methane oxidation model pronounced basic values of kinetic coefficients for methanotroph bacteria and inhibitor coefficient for TCE for the particular soil environment. Hereafter, the model could be used to find the threshold point of TCE on methane oxidation. Therefore, it cut down the number of experimental simulations. The experimental study conducted to determine the effect of volatile organic compounds (benzene) on methane oxidation did not show any effects until the benzene increased to 800 µg/m3. When it was increased to 1400 µg/m3 there was significant effect observed with some interference in measuring actual methane. It concluded that the threshold limit of benzene on methane oxidation could be between these two concentrations. To enhance methane oxidation in real landfill, VOC should be maintained less than this limit. Based on the result of the present study, the following suggestions are proposed for further study; • The study on the methane inventory is Western-biased. It should be modified into
developing countries. Moreover, additional work should be done to bring all the components of methane inventory model into the same flat form.
• The review part of the methane generation model concluded that published actual landfill
methanogenesis data are scarce and uncertain. The experimental work on lab-scale bioreactor will fill this important information gap.
• The model formulation on methane generation does not respond to temperature variations
over time. The known sensitivity of methanogenic microbial populations to temperature dictates the inclusion of this factor in the model structure. It is recommended that additional modeling work should be done in this respect
• Empirical formulas for methane oxidation in terms of temperature and moisture were
derived from a only few data points. It is good to confirm these equations with more experimental data.
• The modeling study on methane oxidation fits with batch experiments. This study could
be furnished to visualize this oxidation scenario in a lysimeter or real landfill with incorporation of the gas transportation module in soil.
67
• Some literature reports anaerobic oxidation in presence of SRB (Sulfate Reduction Bacteria). Furthermore, SRB reduces methane generation by competing with MPB (Methane Producing Bacteria) for utilization of organic matters in landfills. . It is recommended that a research work should be done in this respect.
68
References Augenstein, D., and Pacey, J., 1992. Landfill Methane Models. Personal Communication (Hettiaratchi, J.P.A., , The Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada) Augenstein, D., 1990. Greenhouse Effect Contributions of United States Landfill Methane. Personal Communication (Hettiaratchi, J.P.A., , The Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada) Barlaz, M.A., 1990. The Use of Mass Balances for Calculation of the Methane Potential of Fresh and Anaerobically Decompose Refuse. Personal Communication (Hettiaratchi, J.P.A., , The Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada) Baratt, P.A., 1995. Microbial Methane oxidation and its Effective Biological Treatment of Landfill Simulators. Waste Management by Landfill, Rotterdam, ISBN 9054103566. Bingemer, H.G., and Crutzen, P.J., 1994a. Numerical Modeling of Generation and Transport of Gas and Heat in Sanitary Landfills: 1 Model Formulation. Waste Management & Research, 14: 483-504. Christopher, S.L., Larry, N.A., Shujie, Z., and Richard, H.S., 1983. Methylotropic Bactria: Biochemical Diversity and Genetics. Science 221: 1147-1153. Chomsurin, C., 1997. Evaluation of Gas Migration and Methane Oxidation in Domestic Solid Waste Landfill. Masters Thesis, AIT, Thailand, EV-97-12. El-Fadel, M., Findikakis, A.N., and Leckie, J.O., 1994a. Numerical Modeling of Generation and Transport of Gas and Heat in Sanitary Landfills: 1 Model Formulation. Waste Management & Research, 14: 483-504. El-Fadel, M., Findikakis, A.N., and Leckie, J.O., 1994b. Numerical Modeling of Generation and Transport of Gas and Heat in Sanitary Landfills: 1 Model Formulation. Waste Management & Research, 14: 537-552. El-Fadel, M., Findikakis, A.N., and Leckie, J.O., 1997. Numerical Modeling of Generation and Transport of Gas and Heat in Sanitary Landfills: 1 Model Formulation. Waste Management & Research, 15: 103-112 EPA., 1998. Methods for Estimating Greenhouse Gas Emissions from Municipal Waste Disposal, Emission Inventory Improvement Program. Faso, B., 1996. Biological Regulation of Methane Emission in Paddy Fields. http://ss.jircas.affrc.go.jp/newsletter/nl1996/no8/Odayeri.htm Foley, G., 1991. Global Warming: Who is talking Heat?, PANOS Institute, London.
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Gardner, N., Manley, B.J.W., and Pearson, J.M., 1993. Gas Emissions from Landfills and their contributions to Global Warming. Applied Energy, 44: 165-174. Gheewala, S.H., 1995. Biodegradation of Aniline. Masters Thesis, AIT, Thailand, EV-95-35. Halvadakis, C.P., 1983. Methanogenesis in Ssoild Waste Landfill. Doctoral Dissertation, Stanford University, USA, ???. Hansen, L.B., Finster, K., and Fossing, H., 1998. Anaaerobic Methane Oxidation in Sulfate Depleted Sediments. Aquatic Microbial Ecology, 14: 195-204. http://www.int-res.com/journals/ame/14/a014p195.abs.html Hilger, H., 1998. Methane Oxidation in Landfill Cover Soil (Biofilm, Exopolymer). Doctoral Dissertation, North Carolina State University, USA, AAT 99909469. http:// wwwlib.umi.com/dessertations/preview all.html Hettiaratchi, J.P.A., Visvanathan, C., Perera, M.D.N., and Pokhrel, D., 1998. Design of Landfill Cover System Incorporating Soil Methanotrophy for Methane Emission Mitigation. Hoornweg, D., and Laura, T., 1999. What a waste: Solid Waste Management in Asia. Urban Waste management: Working Paper Series, 1. Iserman, K., 1994. Agriculture's Share in Emission of Trace Gases Affecting the Climate and Some Cause Orientated Proposal for Sufficient Reduction this Share. Environmental Pollution, 83(1): 95-111 Kightly, D., Nedwell, D.B., and Cooper, M., 1995. Capacity of Methane Oxidation in Landfill Cover Soils Mesured in Lab scale Soil Microrganism. Applied and Environmental Microbiology, 62(12): 4548-4561. Lay, J.J., Li, Y.Y., and Tatsuya, N., 1998. Mathematical Model for Methane Production from Landfill Bioreactor. Journal for Environmental Enginerring, 19: 730-736. Metcaff and Eddy, Inc., 1993. Waste Water Engineering, New York, Mc Graw-Hill International Education, ISBN 0-07-041690-7. Monod, J., 1949. The Growth of Bactrial Culture. Annual Rev. Microbial, 3: 371-394. Nozhevinikova, A.N., Nekrasava, K.V., and Lebedev, V.S., 1993. Microbiological Process in Landfills. Water Science Technology, 27(2): 243-252 Oanh, N.T.K., 1993. Module 2: Meteorological Aspects of Air Pollution. Victoria 3212, Australia. Victoria University Publication.
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Pareek, S., Matsui, S., and Kim, S.K., 1998. Biodegradation of Lignocellulosic Material Under Sulfidogenic and Methanogenic Conditions in the Landfill Column Reactors. Environmental Technology, 19: 253-261. Pascal, B., Cleemput, O.V., and Villaralvo, I., 1996. Methane Emission from a Landfill and the Methane Oxidizing Capacity of its Soil. Soil Biology and Biochemistry, 28(10/12): 1397-1405. Peter, K., and Granthin, G., 1995. Practical Experience in Landfill Remediation. Waste Management (Proceedings): 14-19 Pokherl, D., 1998. Microbial Methane Oxidation Studies in Laboratory Scale Experiments. Masters Thesis, AIT, Thailand, EV-98-17. Rayes, H.EI, and Edwards, W.C., 1991. Inventory of Methane Emissions from Landfills in Canada. Report, Environment Canada Regulatory Affairs and Program Integration Branch, Canada. Roslev, P., Iversen, N., and Henriksen, K., 1997. Oxidation and Assimilation of Atmospheric Methane by Soil Methane Oxidizers. Applied and Environmental Microbiology, 63(3): 874-880. Segers, R., 1997. Methane Production and Methane consumption: a review of process underlying wetland methane fluxes. Biogeochemistry 41 : 23-51. Smith, K.A. 1997. Measurement and Modeling of Methane Fluxes from Landfills. IGAC Newsletter 10, 1997 September. Tchobanoglous, G., 1993. Integrated Solid Waste Management. New York. McGraw-Hill International. ISBN 0-0-7-0-63237-5. TEI., 1996. Climate Change Logal Solution for Global Problems : 7/1-7/6. UNEP., 1993. Insurance Executives Discuss How to Deal with Consequences of Climate Change. http://www.unep.org/per/ipa/pressrel/r06-0898.001 United Nations Environment Programme., 1990. Environmental Modelling for Developing Countries. Tycooly Publishing, New York 10010, USA, IBN 1-85148-041-2. USEPA., 1997. Climate Action Report: Green House Gas Inventory. http://www.state.gov/www/global/oes/97climate report/ part3.html Ward, R.S., William, and Hills, C.C., 1996. Changes in Major Trace Components of Landfill Gas During Subsurface Migration. Waste Management & Research, 14: 243-261.
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Wetherill, D.T., Forbes, D.C., and Kerr, T., 1997. Article: The USEPA's Landfill Methane Outreach Program. International Directory of Solid Waste Management, 1997/1998: 215-217. Whalen, S.C., Reeburgh, W.S., and Sandbeck, K.A., 1990. Rapid Methane Oxidation Cover Soil. Applied and Environmental Microbiology, 56(11): 3405-3411. Whittenbury, R., Phillipse, K.C., and Woilkinson, J.F., 1970. Enrichment Isolation and Some Properties of Methane Utilizing Bactria. Journal of General Microbiology, 24: 205-208. Willam, G.M., and Zobell, C., 1989. The Occurrence and Characteristic of Methane of Oxidizing Bacteria in Marine Sediments 58: 463-473. Young,.A,, Computer Modelling of Landfill. http://info.ox.ac.uk/~ayoung/landfill.html Zison, S.W., 1993. Landfill Gas Production Gas. Personal Communication ( Hettiaratchi, J.P.A., The Deparment of Civil Engineering, University of Calgary, Calgary, Alberta, Canada)
Appendix - A Methane Inventory Model: Data and Calculations
A-2
A-1 Estimation of Methane Emission for Thailand and Ontario (Canada) A-1.1 Published Data Table. A-1 Input Data for Methane Inventory Model
Data Thailand#
(Cities) Canada^ (Ontario)
1. Population 2. Waste generation/Capita 3. Landfill fraction in MSW − Closed landfill − Open dump 4. Fraction DOC in MSW 5. Fraction DOC dissimulated 6. Fraction of C as Methane (λ) − Closed landfill − Open dump 7. Convention ratio 8. Methane Oxidization − Closed landfill − Open dump 9. Methane Recovered − Closed landfill − Open dump
X 1000 Mg/capita.yr % % % %
5882.400
0.321
0.300 0.700
0.120
0.770
0.550 0.275
1.333
10.000 -
- -
12535.900
0.628
0.8000.200
0.130
0.770
0.5500.275
1.333
10.000-
15.000-
#Source: TEI (1996) ^Source: Rayes (1991) A-1.2 Specimen Calculation Considering Thailand data set from Table A1, Waste generated = Population X Waste generaton/Capita = 1888.250 Gg/yr
A-3
Using equation 3.1 for closed landfills, Methane emission = 1888.250 Gg/yr
x 0.3 x 0.12 x 0.77 x 0.55 x 1.33 x 0.9 x 0.9 = 34.546 Gg/yr Similarly for opened dump, Methane emission = 44.782 Gg/yr. Total methane emission = 79.328 Gg/yr Table. A-2 MSW Genartion Rate Used in Ontario Case Study
Year Waste Generation Rate (Kg/day)
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
0.88
0.88
0.88
0.88
0.88
0.97
1.07
1.37
1.75
1.75
1.75 Source: Rayes (1991)
A-4
Table. A-3 Prevailing Trends of Methane Emission from MSW in Ontario from Model
4. Initial Concentrations Aqueous carbon = 0.001 g C/L Acidogens = 0.001 g C/L Acetic acid = 0.0001 g C/L Methanogens = 0.0001 g C/L Note: • Effect of pH on Methanogenic Microbial Growth Rate Constant is maximum when pH
between 6 and 8.The constants become zero when pH< 4.50 or pH> 9.00 • Density of refuse = 600 kg/m3 Source: Havadakis (1983)
B-3
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
µa/2
µa
2µa
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
Ksa/2
Ksa
2Ksa
B-2 Sensitivity Analysis on Methane Generation
Fig. B-1 Effect of Acidogenic Specific Growth Rate on Rate of Methane Generation
Fig. B-2 Effect of Acidogenic Half Saturation Constant on Rate of Methane Generation
B-4
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
Kda/2
Kda
2Kda
Fig. B-3 Effect of Acidogenic Decay Rate on Rate of Methane Generation Fig. B-4 Effect of Methanogenic Specific Growth Rate on Rate of Methane Generation
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
µm/2
µm
2µm
B-5
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
C(aq)/10
C(aq)
10C(aq)
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
Ksm/2
Ksm
2Ksm
Fig. B-5 Effect of Methanogenic Half Saturation Constant on Rate of Methane Generation
Fig. B-6 Effect of Aqueous Initial Carbon on Rate of Methane Generation
B-6
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
C(Xa)/10
C(Xa)
10C(Xa)
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
C(Xm)/10
C(Xm)
10C(Xm
Fig. B-7 Effect of Acidogen Initial Carbon on Rate of Methane Generation
Fig. B-8 Effect of Methonogen Initial Carbon on Rate of Methane Generation
B-7
Fig. B-9 Effect of Acetic Acid Initial Carbon on Rate of Methane Generation
Fig. B-10 Comparison of Model Result with Field Data
0.000
0.005
0.010
0.015
0.020
0.025
0 100 200 300 400 500 600
Time (Days)
Gen
erat
ion
Rate
(Kg
Met
hane
car
bon/
m3 w
aste
) Kh/2
Kh
2Kh
C(ac)/10
C(ac)
10C(ac)
0.00
0.01
0.02
0.03
0.04
0 100 200 300 400 500 600
Time (Days)
Met
hane
Gne
ratio
n R
ate
(Kg
CH
4/ m
3 was
te)
ModelFied Data
B-8
B-3 Overall Carbon Mass Balance of Methane Generation Profile
Note: Simulation of the model for 600 day showed 3.01*10 -14 Kg Carbon/m3 waste loss when all yield coefficients were 1. It is negligible compare to initial carbon 27 kg/ m3 waste.
+++++++
0
Soild Carbon
Aqueous Carbon
Acidogenic Biomass
Acetate Carbon
Methonogenic Biomass
Methane Carbon
Carbon dioxide Carbon
Carbon Balance
B-9
Table. A-1 Corrected, Reduced and Normalized Cumulative Methane gas Data from Mountain View Landfill in California (USA) Source: Havadakis (1983)
Table. C -3 Headspace methane concentration observed in batch experiment with 10 g soil with different moisture contents and incubated at 30OC Time from Moisture Contents Incubation 6% 11% 15% 20%
D-1 Calculation of Methane Oxidation Considering data set dated 05.04.1999 from Table D1, Air flow rate at the top = V1 = 300 mL/min Gas flow at the bottom = V2 = 5 mL/min Total flow = (V1 + V2) mL/min
= V = 305 mL/mim
Percentage of methane inflow = 0.6* V2/V*100 = Y = 0.984 Percentage of methane outflow = X = 0.291 (from Gas Chromatography) Total methane consumed in the system =(1-X/Y)*100 = P = 70.49 (assuming no methane accumulation inside the system) Methane oxidation rate = 0.6*V2*P mL/min. = 2.16 mL/min. Similarly, other data sets in Table D1 were calculated. Table D3 shows the methane oxidation for each sample. D-2 Calculation of Benzene Concentration D-2.1 Standard Curve for Standard Solution:
Fig. D-1 Standard Curve for Benzene
A = 1218.5C - 121.71R2 = 0.9988
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
0 10 20 30 40C (ppm)
A (a
rea)
V1
V2
V
D-3
The 4, 16 and 32 ppm benzene standard-solution were injected in gas chromatography in order to set up the standard curve. The standard curve was plotted by concentration versus area as shown in Figure D1 The equations from the standard curve were performed to calculate the concentration of benzene as follows C = (A + 22,996) /9,918 Where, C = Concentration of benzene in solvent (ppm)
A = Area of benzene peak from chromatogram D-2.2 Specimen Calculation: Considering data set dated 31.05.1999 from Table D2,
A = Benzene Peak area = 110,143 Qa = Actual flow rate = 0.33 L/ min t = Duration of Sampling = 60 min
From standard curve, Benzene concentration in solvent C = 13.424 Using equation 3.11, Actual volume of sampled gas Va = Qa*t/1000 = 0.0198 m3 Using equation 3.12, Actual concentration of Benzene Ca = C/ Va = 13.424/0.0198 = 677.98 µιg/m3 Table D3 shows the results of benzene concentrations with methane oxidation.
Parameters Dimension and Values Soil Core ( Column ) Acrylic material tube - Length 1.20 m - Diameter 19.00 cm Sampling Port Interval 20.00 cm Soil Depth 90.00 cm Soil Type Sandy soil - Sand 85 - Silt / Clay 15 Soil Density 1410 kg / m3
Soil Moisture Content 8-9 Ambient Temperature 28-34 oC Purge Gas 60 Methane + 40 carbondioxide Gas flow 2.5-5.0 mL/min Air flow at top 300 mL / min Amendment with nutrient No
D-12
D-4 Additional Peaks Shown by GC During Benzene Feeding 750-800 µg/m3
D-13
D-5 Strange Methane Peak Shown by GC During Benzene Feeding 1400-1450 µg/m3
Solid Waste Management and LandfillsSolid Waste Management and LandfillsGreenhouse Gases and Landfill GasGreenhouse Gases and Landfill GasMethane Generation in LandfillsMethane Generation in LandfillsMethane Oxidation in Landfill coversMethane Oxidation in Landfill coversMethane Emission from LandfillsMethane Emission from Landfills
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Solid Waste Management and LandfillsSolid Waste Management and Landfills
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Solid Waste Definition
Solid wastes are the wastes arising from human and animal activities that are normally solid and are discarded as useless or unwanted. The term solid wastes is all-inclusive, encompassing the heterogeneous mass of throwaways from the urban community as well as more homogenous accumulation of agricultural, industrial, and mineral wastes.
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Refuse materials by kind, composition, and sourcesKind Composition Sources
Garbage
Rubbish
Ashes
Streetrefuse
Deadanimals
Abandonedvehicles
Industrialwastes
Demolitionwastes
Constructionwastes
Specialwastes
Sewagetreatmentresidue
Waste from preparation, cooking andserving of food; market wastes; wastesfrom handling, storage, and sale ofproduce
a Percentage of waste stream as discarded on a dry mass basis.
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Greenhouse Gases and Landfill GasGreenhouse Gases and Landfill Gas
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GHG and Climate Change
•Human activities increase GHG concentrations and trap more heat.
•The Earth’s climate changes due to the buildup of GHG.-The present associated rate of temperature change is significantly faster than past (observed in 10,000 yrs)
•Climatic changes have adverse effects on ecological system, human health and socio-economic sectors.
•Human health, natural ecological systems and socioeconomic systems are all sensitive to both the magnitude and the rate of climate change•Many physical and ecological system will be simultaneously affected•The ability of natural ecological system to migrate appears to be much slower than the predicted rate of climate change
•“With the growth in atmospheric concentrations of GHG’s, interference with the climate system will grow in magnitude, and the likelihood of adverse impacts from climate that could be judged dangerous will become greater (IPCC, 1995)
•Climate change can add to existing environment stresses.
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GHG Changes Global Climate
Temperature
Precipitation
Sea Level Rise
Heath ImpactsWeather-related Mortality
Infectious Diseases
Air Quality- Respiratory Illness
Agriculture ImpactsCrop yields
Irrigation demands
Forest ImpactsChange in forest compositionForest Health and Productivity
Water Resource ImpactsChange in water supplyWater qualityIncreased Composition for water
Impatcs on Coastal AreasErosion of beaches
Inuadate coastal lands
Cost to defend coastal communities
Species and Natural AreasShift in ecological zonesLoss of habitat and species
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Changing Climate...
Climate has changed over the past century
•Global mean temp increased by 0.5-1.0 0F
•Global sea level risen by 4-10 inches
•Global precipitation over land increased by 1%
“The balance of evidence suggests a discernible human influence on global climate” (IPCC, 1995)
Expected climate changed in future (by 2100)
•Projected temperature increase of 4 0F
•Projected sea level rise of 20 inches
•Likely increase in precipitation
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Trends of Temperature and Precipitation
(1900 to Present)
Temperature Precipitation
Warming
Cooling
Increasing
Decreasing
Note : Cooling in southeast U.S may be due to aerosol influence
Source: Karl et al (1996)
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Increment of GHGs..
Atm concentration of GHGs have increased since industrial revolution.
CO2 -30%; CH4 -100%, N2O -15%
Concentration of GHGs projected to reach double pre-industrial levels by about 2060
Many GHGs remain in atmosphere for long time (decade to centuries)
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The Effect of GH
•There is natural greenhouse effects which keeps the earth warm enough to be habitable.-GHGs like CO2, CH4, and N20 and water vapor trap heat and warm the earth’s surface.•Many researchers proved the basic principles of GH•For a given concentration of GHG, the resulting amount of radioactive forcing (heat trapping energy) can be predicted with precision.•Exactly how the Earth’s climate will respond to enhanced greenhouse gases will also depend on complex interactions between the atmosphere, oceans, land, ice and biosphere.
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Change in GHGs...
Source: IPCC, 1995
CO2 CH4 N20
Pre-Industrial 280ppm v
700ppbv
275ppb v
In 1994 358ppm v
1720ppb v
312ppbv
Rate of change 1.5ppmv/yr
10 ppv/yr
0.8ppbv/yr
Atmosphericlifetime (yr)
50-200
12 120
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1422
17740 37
1826
0
500
1000
1500
2000
CO
2
CH
4
N20
HFC
/PFC
Tota
lSource: EPA(1997)sinks not included
GHG’s Emission from U.S (1995)
MMTCE
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Methane Generation in LandfillsMethane Generation in Landfills
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⊗ The control of landfill gas is recent challenge in landfill technology - environmental protection and energy recovery.
⊗ Understanding landfill gas generation at a process level aids its control efficiency.
⊗ The present session is aimed at advancing the art of landfill gas control by prediction the gas generation performance of a landfill bioreactor.
⊗ Governing Equations: 1st order reaction + Mass balance + Monod kinetics
Methane GenerationMethane Generation
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Simulates successfully.
Cumulative methane generation matches with field data at the MVCLP.
Sensitivity of methane generation with different initial conditions, specific growth rate and reaction rate constants.
Hydrolysis rate constant (Kh) and Initial organic carbon concentration in waste {C(S)} are important parameters in affecting the methane generation directly.
ResultsResults
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18% reduction in methane generation rate (from 0.000017 to 0.000014 kg/m3 waste.day).
To do it, - segregate waste before landfilling as high-organic substance and low-organic portion.- send high-organic substance for composting and send remaining portion for landfilling.- also gives useful product (ie composted materials could be used as fertilizer).
Technical SignificanceTechnical Significance--11
20% reduction (from 30 kg.m-3 to 24kg .m-3 ) in organic carbon
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89% reduction in rate of methane generation.
It could be- the most effective way to reduce methane generation in landfill - Also useful to reduce amount of organic carbon in leachate- Draw back: Long after-care period.
Technical SignificanceTechnical Significance--22
In case of recovery, practice vice versa for effective methane collection.
Technical SignificanceTechnical Significance--33
Reduction of hydrolysis rate constant (Kh) from 0.0002 day-1 to 0.00002 day-1
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Methane Oxidation in Landfill coversMethane Oxidation in Landfill covers
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Methane Emission from LandfillsMethane Emission from Landfills
